146 results about "Vulnerability index" patented technology

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 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.

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external file format support data and an associated source flow record of a digital object; calculating a format outdated risk index, a data integrity attenuation degree index and a source flow chain vulnerability index; calculating the weighted sum of the format outdated risk index, the data integrity attenuation index and the source flow chain vulnerability index to generate a comprehensive vulnerability index; and comparing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

The invention relates to a fault chain searching method and device for a power grid containing a flexible direct access receiving end and a medium, and relates to the field of power grid safety, and the method comprises the steps: S1, constructing an electromagnetic electromechanical joint simulation model; s2, simulation is carried out based on the electromagnetic electromechanical joint simulation model, and comprehensive vulnerability indexes of all alternating-current lines in the receiving-end power grid are obtained through calculation based on simulation data, the comprehensive vulnerability index is obtained by a hybrid multi-infeed short-circuit ratio variable quantity, a DC commutation failure voltage recovery capability metric value, a node voltage offset and a power flow transfer entropy; and S3, sorting all the alternating-current lines according to the comprehensive vulnerability indexes from large to small, selecting the first alternating-current line as a starting point of a fault chain, and searching in combination with an electromagnetic-electromechanical joint simulation model to obtain the fault chain. Compared with the prior art, the method has the advantages of reflecting the response process of the conventional DC control system and the flexible DC control system, improving the fault chain search accuracy and the like.

The invention discloses a multi-energy collaborative optimization energy conservation and emission reduction system and method based on intelligent regulation and control, and the system comprises an intelligent sensing layer which monitors the stress change, carries out the preprocessing of multi-source heterogeneous data, and transmits the data to a dynamic analysis layer; the dynamic analysis layer is used for constructing a digital twinborn model of coupling equipment based on multi-source heterogeneous data, quantifying the interactive influence between the equipment, simulating a chain propagation path of a single fault, and generating an anti-disturbance capability evaluation report in combination with an equipment operation limit value and a vulnerability index; the collaborative decision-making layer is used for generating an initial scheduling scheme, sending the initial scheduling scheme to the toughness interaction layer, triggering a fault response mechanism through a collaborative scheduling model, and synchronously starting standby equipment; and the tough interaction layer responds to auxiliary service requirements of a power grid in real time, receives a user side load regulation instruction, optimizes a green electricity transaction quota in combination with a carbon flow tracking technology, finally connects external energy support with an internal emergency plan through a two-way interaction mechanism, and quickly calls regional standby resources when a fault occurs.

The invention discloses an intelligent power grid fault rapid positioning and self-healing system and method, and relates to the technical field of power grid automation control, and the method comprises the following steps: obtaining environment monitoring data and power grid state data; inputting the obtained data into a formula to obtain geological stress field data and a cable vulnerability index; generating a cable section-level geological stress field data set and a cable section-level vulnerability index data set according to the acquired data based on the power grid state data; fusing the cable section-level geological stress field data set and the vulnerability index data set to generate cable section-level coupling risk index data; when the coupling risk index data exceeds a preset threshold value, generating a fault early warning signal and extracting fault cable section positioning data from the power grid state data; and generating a self-healing scheme according to the acquired data, and triggering the power grid control terminal to execute. And cross-domain coupling modeling is carried out on the geomechanical stress field and the mechanical state of the cable, so that the accuracy of data is improved.

The invention discloses an AC-DC hybrid power grid operation risk assessment method and system based on multiple factors. A new energy output time sequence probability distribution model is constructed; aiming at the new energy output time sequence probability distribution model, constructing a power grid topology vulnerability index, an AC / DC interaction index, an AC / DC hybrid power grid dynamic operation risk index, an economic risk index and an AC / DC receiving end power grid commutation failure risk index; considering a power grid node fault event, an AC line fault event, a voltage out-of-limit event, a power grid economic downlink event and an AC / DC receiving end power grid commutation failure event, weighting and standardizing a risk assessment matrix constructed based on five indexes; dimensionality reduction is carried out on the standardized risk assessment matrix, and a contribution rate vector of the matrix after dimensionality reduction is calculated; and calculating a comprehensive operation risk value based on the contribution rate vector. According to the method, the multi-dimensional indexes can be systematically evaluated, and the comprehensive risk level of the power grid can be accurately quantified.

The present invention relates to a method, device, and medium for searching for fault chains in a receiving-end power grid containing flexible direct current (FDC), and relates to the field of power grid security. The method comprises: step S1: constructing an electromagnetic, electromechanical, and mechanical co-simulation model; step S2: performing simulation based on the electromagnetic, electromechanical, and mechanical co-simulation model, and calculating, based on the simulation data, a comprehensive vulnerability index for each AC line in the receiving-end power grid, wherein the comprehensive vulnerability index is obtained by the change in the hybrid multi-infeed short-circuit ratio, a DC commutation failure voltage recovery capability metric, a node voltage offset, and a power flow transfer entropy; and step S3: sorting all AC lines from largest to smallest according to the comprehensive vulnerability index, selecting the first AC line as the starting point of the fault chain, and searching for the fault chain using the electromagnetic, electromechanical, and mechanical co-simulation model. Compared with the prior art, the present invention has the advantages of reflecting the response process of conventional DC and flexible DC control systems and improving the accuracy of fault chain search.

This invention relates to the field of ischemic injury model construction technology, and particularly to a method for constructing a mouse ischemic injury model for evaluating the protective effect of GDF-15. The method includes acquiring a dataset of mouse physiological parameters to determine physiological feature vectors, thereby determining an individual vulnerability index and performing similarity matching with the vulnerability index of a specific individual. Based on the matching results, a matching sample set is constructed, and then an ischemic injury model is constructed using a deep learning network model. The model is then dynamically simulated using the specific individual vulnerability index, specific intermediate monitoring data, and specific infarct area percentage data to output a simulated protective effect index to determine if the model is qualified. If unqualified, the similarity matching threshold is adjusted based on the degree of deviation, and the matching and model construction are repeated. This invention significantly improves the uniformity and construction success rate of the ischemic injury model through individualized matching and dynamic feedback optimization, providing a reliable model for evaluating the protective effect of GDF-15.

The application discloses a kind of intelligent scheduling and distribution method of protein complex detection task in cloud platform, it is related to big data management technical field;By constructing task feature model;Real-time sequencing is carried out to task using dynamic priority evaluation function containing vulnerability index, and dynamic scheduling sequence is generated;Using improved space-time graph convolutional neural network to construct resource matching prediction model, and generating resource-task matching matrix;Using improved adaptive genetic algorithm, with minimizing completion time and maximizing equipment utilization as target, under the constraint of resource-task matching matrix, the optimal allocation strategy and execution instruction set are generated;Through abnormal hierarchical judgment and dynamic adjustment resource allocation, and generate exception traceability feedback report;Realize biological characteristic perception and physical resource state prediction deep coupling, improve the survival rate of easy-degradable sample and the utilization efficiency of experimental equipment, guarantee the intelligentization and high robustness of cloud platform detection whole process.

The application discloses a kind of legal document quality confrontation test method based on logic game, it is related to legal artificial intelligence technical field, including, collection legal document full-text data, and extract fact triple, evidence citation fragment, law article number and conclusion claim, generate structured argumentation set;Based on the confidence attenuation sequence of multiple rounds of game, in combination with the vulnerability propagation coefficient corresponding to different types of edges in directed heterogenous graph, carry out path vulnerability contribution value calculation, and generate legal document global vulnerability index by stratified accumulation;Based on logic breakpoint list, contradiction conflict list and legal document global vulnerability index, comprehensive evaluation legal document's logic integrity, argumentation consistency and conclusion robustness, generate structured check report.The application provides a complete and feasible implementation path for the automatic check of legal documents combined with natural language processing.

The application relates to the technical field of information asset management and control, and discloses an information asset management system and an evaluation method for the protection strength of the information asset management system. An asset identification module of the method acquires original attribute data of information assets, generates an asset identification code according to asset types and storage locations; a classification mapping module extracts key attributes of the assets, and establishes an association between the asset identification code and the key attributes; a risk evaluation module calculates an asset vulnerability index based on the key attributes, and generates a risk level in combination with external threat data; a dynamic adjustment module updates the key attributes according to the change of the risk level, and recalculates the vulnerability index; and an access control module generates an access permission strategy according to the updated vulnerability index, and limits asset access behavior. The system realizes fine and dynamic management of information assets, balances asset security and management efficiency, and meets the needs of an organization for information asset management.

The invention discloses a high-risk attack identification and dynamic defense method for an electric power information physical system, and relates to the field of safety of the electric power information physical system. The method comprises the following steps: firstly, constructing a weight coloring Petri network seven-tuple model, and describing the cross-domain propagation time sequence characteristics of the continuous load redistribution attack in an information-physical network by introducing a time delay parameter; then optimizing the attack triggering and migration probability based on mixed strategy Nash equilibrium; a top-k sequence mining algorithm is adopted to identify a high-risk attack mode from the attack event sequence, and a vulnerability index is defined to quantify the threat degree of the attack; and finally, modeling defense resource allocation as a combined dobby machine problem, and realizing self-adaptive adjustment of a defense strategy through a weight updating mechanism and sliding window optimization. According to the method, an active defense system of'accurate perception-intelligent decision-closed loop optimization 'can be formed, so that the defense capability of an electric power information physical system facing continuous load redistribution attacks is improved.