40 results about "Vulnerability model" patented technology

The invention discloses a collapse geological disaster vulnerability model and risk assessment method and system, and relates to the technical field of geological disaster risk assessment, and the method comprises the steps: collecting and preprocessing target rock mass information; collecting monitoring data and long-term rainfall data of the three-axis acceleration sensor; the acceleration fluctuation characteristics are analyzed, and the risk of rock mass instability is recognized in combination with rainfall data; the instability risk of the rock mass is quantified based on a Monte Carlo method; predicting a motion path and a stacking range of the collapsed rockfall by utilizing numerical simulation, and evaluating threats of the collapsed rockfall to surrounding electric power facilities; and performing level-to-level management on the surrounding area of the transformer substation through the risk level model. Real-time monitoring, probability calculation and numerical simulation technologies are adopted, and the problem that in the prior art, power facilities lack dynamic monitoring, accurate evaluation and graded protection is solved. According to the overall scheme, real-time and automatic monitoring and risk grading of collapse geological disasters are achieved, and the safety and reliability of electric power facilities under the disaster condition are effectively improved.

The invention relates to the technical field of power system automation, and discloses a power grid rolling scheduling deep reinforcement learning decision-making method in extreme weather, which comprises the following steps: constructing a tower vulnerability model and a lead vulnerability model to calculate the fault probability of each power transmission line; constructing a running state time sequence transfer model of each power transmission line, and generating a high-risk cascading fault scene set through Monte Carlo sampling; constructing a power grid rolling scheduling mathematical optimization model considering the successive failure of the power transmission line caused by the extreme weather; constructing a Markov decision process model, and training and constructing a strategy network; and outputting the active output adjustment amount and the load shedding amount of the generator by using the strategy network, executing the control strategy, and circulating the steps until the end moment of the extreme weather is reached. The method has the advantages that the problem of intra-day rolling scheduling decision making of the power grid in an uncertain time sequence scene is solved by using a deep reinforcement learning technology, and the safe operation and reliable power supply capability of the power grid under the condition of power transmission line time sequence cascading failure caused by extreme weather is effectively improved.

An integrated system for detecting dam breaches, as an early warning system and for evacuation assistance, consisting of: A geoinformatics analysis module comprising a modeling processor with a graphics processing unit, configured to: analyze spatial, hydrological, and topographic data of a dam and its downstream area; generate hydrological models using probable maximum rainfall (PMP) data to determine probable maximum flood discharge (PMF); generate hydraulic models to simulate flood propagation and determine flood depth, flood extent, flow velocity, and flood arrival time; create flood vulnerability models, vulnerability models, and flood hotspot models using a multi-criteria decision-making (MCDM) framework; and generate evacuation plans for high-risk zones based on the models. A sensor monitoring subsystem includes: a distributed fiber optic sensor (DFOS) configured to monitor the structural load and deformation of a dam, and a radar level gauge configured to measure the water level in the reservoir; a microcontroller unit configured to receive and process sensor data from the sensor monitoring subsystem, to detect dam breaches based on abnormal structural movements detected by the DFOS or sudden drops in water level below predefined thresholds detected by the radar level gauge; a communication module configured to transmit warning messages when dam breaches are detected; a warning system configured to: send SMS notifications to residents in downstream high-risk zones via a GSM communication module, generate real-time alerts via an IoT platform on user devices, and activate siren systems in villages to issue loud alarms and voice announcements; and an evacuation assistance platform configured to provide real-time evacuation assistance in emergencies due to dam breaks, displaying site maps with nearby emergency shelters, evacuation routes, hospitals, government buildings and aid centers, showing hydrological layers including flood extent, water depth, flow velocity and arrival time of the water, and enabling real-time location tracking in relation to safety zones and flood-prone areas.

The invention discloses a power transmission tower wind damage vulnerability analysis method based on long-term marine atmospheric corrosion. The method comprises the following steps: step 1, establishing a wind load numerical simulation model; 2, establishing a corrosion depth prediction model of the steel structure in the marine atmospheric environment; 3, establishing a finite element model of the corroded power transmission tower line system; 4, establishing a wind damage vulnerability model of the power transmission line structure; 5, performing push hover analysis on the power transmission tower structures with different service lives considered; and 6, analyzing the wind damage vulnerability of the power transmission tower structure. According to the power transmission tower wind disaster vulnerability analysis method based on long-term marine atmospheric corrosion, by establishing the wind disaster vulnerability curves under different corrosion degrees, the damage probability of the power transmission tower under the wind load effect can be predicted more accurately, a scientific basis is provided for design, maintenance and operation of the power transmission tower, and the method is suitable for popularization and application. And the stability and the safety of the system in a complex marine atmospheric environment are ensured.

A power distribution network pre-disaster toughness improvement method considering typhoon time sequence and information physics cooperative regulation comprises the following steps: by analyzing the time sequence and uncertainty of a typhoon disaster, constructing a DCPS element vulnerability model under the influence of a strong wind-rainstorm dual disaster-inducing factor; generating a typical DPCS disaster scene set based on a DCPS element vulnerability model in combination with historical typhoon disaster data; the dynamic response of the power distribution network in the typhoon disaster process is incorporated into a pre-disaster toughness improvement framework by considering information physical cooperative regulation; and establishing a pre-disaster toughness improvement model, and performing solution in the generated typical DPCS disaster scene set to obtain an optimal pre-disaster toughness improvement scheme. According to the method, by considering the typhoon disaster time sequence and the system dynamic response, the static hypothesis limitation that the typhoon disaster is simplified into an instantaneous uniform impact event in the traditional research is broken through, and a toughness improvement scheme better meeting the actual requirements of the system is formulated.

The present invention provides an apparatus for analyzing vulnerabilities of smart contract code, the apparatus including: a meta-information extraction unit configured to extract meta-information about input code, which is source code of a smart contract; an intermediate representation language conversion unit configured to convert the input code into an intermediate representation language based on the meta-information extracted by the meta-information extraction unit; a code analysis unit configured to detect whether there is vulnerable code or a rule violation case by analyzing the input code converted into the intermediate representation language by the intermediate representation language conversion unit; and a vulnerability detection unit configured to generate a final vulnerability model by detecting a vulnerability based on the vulnerable code or the rule violation case detected by the code analysis unit, and also provides an analysis method using the apparatus.

The invention discloses a denial of service attack adaptive defense method based on incomplete information random game, and the method comprises the following steps: S1, building a system vulnerability model based on an observability Gramian matrix, and initializing an incomplete information random game framework; s2, respectively constructing complete information sub-game models under different attacker types, and solving corresponding Markov Nash equilibrium; s3, according to the system observation data and the Bayesian rule, the posterior belief for the attacker type is updated; s4, mixing the optimal strategies of different sub games based on posterior belief to form a final decision of a defender; and S5, executing a defense action, recording system state change, and circularly executing strategy updating and optimization. According to the method, intelligent, self-adaptive and resource-efficient defense functions can be realized when the wide-area damping control system is subjected to denial of service attack, and the system security and defense resource consumption are effectively balanced.

The invention discloses a Bayesian network-based cold region tunnel vulnerability dynamic evaluation method and system, and belongs to the technical field of tunnel engineering. According to the method, meteorological data of a cold region is collected, and a tunnel full-life-cycle database is constructed in combination with multi-working-condition numerical simulation and an ANN agent model; calculating vulnerability grades under different working conditions based on a damage index grading system and a vulnerability model; a three-layer dynamic Bayesian network model (environmental factors, response parameters and vulnerability levels) is established, actually measured data are fused, and network parameters are dynamically updated by adopting an MCMC method. The problems that cold region tunnel data are missing and traditional evaluation is high in subjectivity are solved, full-life-cycle vulnerability dynamic accurate evaluation is achieved, and decision support is provided for tunnel healthy service.

The embodiment of the invention provides a CLA-based earthquake vulnerability model construction method and device, equipment and a medium, and the method comprises the steps: analyzing an earthquake intensity parameter in historical engineering dynamic response parameters and a corresponding engineering demand parameter, and determining a first relation between the two parameters; based on the historical engineering dynamic response parameters, probability anti-seismic demand analysis is carried out, and a second relation between the change values of the engineering demand parameters and the change values of the corresponding seismic intensity parameters is determined; and based on the first relationship and the second relationship, determining an earthquake vulnerability function corresponding to the engineering demand parameter as an earthquake vulnerability model. According to the invention, a scientific and rigorous gravity dam earthquake vulnerability model can be constructed.

Techniques for calculating or determining risk scores for certain natural disasters perils based on machine learning model outputs are discussed herein. For example, a machine learning model may weight each of the pixels of a map in accordance with the set of weights associated with a structure, to calculate a risk score for a particular natural disaster peril associated with that structure. A plurality of risk selections may be provided to a user computing device for selection by a user, with those risk selections being associated with that risk score. Advantageously, the computing system facilitates the interaction of datasets with different measurement parameters in a machine learning model. In normalizing datasets before providing the datasets to input nodes of a machine learning model, a computing system may efficiently provide hazard and vulnerability outputs of the machine learning model.

The invention discloses a buried pipeline earthquake vulnerability analysis method based on three-parameter square normal distribution, belongs to the field of civil engineering and earthquake engineering, and solves the problem of failure probability calculation deviation caused by the fact that an existing logarithmic normal distribution model cannot accurately reflect skewness characteristics of structural response data. The method comprises the steps of generating seismic oscillation input data, establishing a buried pipeline finite element model, performing nonlinear time-history analysis, extracting pipeline response data, calculating buried pipeline failure probabilities based on three-parameter square normal distribution under different seismic oscillation intensities, and drawing a vulnerability curve. According to the method, a complete system covering seismic oscillation simulation, buried pipeline response analysis and seismic vulnerability analysis is constructed, and the calculation precision of the buried pipeline seismic failure probability is improved by introducing three-parameter square normal distribution capable of accurately reflecting the structure response data skewness characteristics; therefore, an earthquake vulnerability model which is wider in application range and higher in precision is provided.

The invention discloses a network vulnerability detection method based on Spark multi-source data fusion and feature analysis, which comprises the following steps of: acquiring multi-source data in real time, efficiently processing Spark clusters (including data cleaning, processing and feature correlation calculation), constructing and training a vulnerability model, and finally establishing an early warning mechanism. All links are organically combined to realize omnibearing perception and accurate analysis. According to the method, the Spark and the Pearson correlation coefficient are combined, a filtering type feature selection algorithm is applied, and an early warning mechanism is coordinated with each technology, so that the vulnerability detection accuracy and efficiency are improved together, and the network security is better guaranteed; the serious defects of an existing traditional vulnerability detection method in the aspects of processing capacity, response speed, adaptability to novel attacks and the like are effectively overcome.

The invention discloses a power transmission system online elasticity evaluation and neural network interpretation method and system, and belongs to the technical field of power system elasticity evaluation. The method comprises the following steps: calculating wind speed change based on a typhoon wind field evolution model, and constructing a system vulnerability model; establishing a power transmission system response process in an extreme disaster scene as a Markov decision process; combining a system vulnerability model with a Markov decision process to construct a time-space map attention network estimation elasticity index; key links influencing system elasticity are identified through interpretability analysis; and evaluating the elasticity of the power transmission system on line by using the time-space map attention network and outputting explanatory information. The problems that an existing method is low in calculation efficiency and lacks guidance after evaluation are solved, the efficiency and accuracy of elasticity evaluation in the extreme disaster scene are improved, visual guidance is provided for system elasticity improvement, and safe operation of a power transmission system is guaranteed.

The invention provides an agent collaborative vulnerability reasoning method and system based on logic modeling and structured prompt. The method comprises the following steps: modeling each vulnerability type by adopting a structured prompt language, generating a structured vulnerability expression of each vulnerability type and taking the structured vulnerability expression as a matching rule, and querying and matching in a code warehouse to obtain a matched code snippet; constructing a positive sample and a negative sample by utilizing all the matched code snippets and the structured vulnerability expression to form a training sample set, so as to finely tune the pre-trained large language model and enable the pre-trained large language model to learn a mapping relationship between the code snippets and vulnerability types, thereby obtaining a finely tuned vulnerability model; inputting a to-be-detected program and the corresponding structured vulnerability expression into the fine-tuned vulnerability model, and predicting to obtain candidate vulnerability codes; and inputting the candidate vulnerability codes and the corresponding structured vulnerability expressions into a preset agent, and performing context-aware verification on the candidate vulnerability codes in combination with the atomic operation toolkit to obtain a vulnerability detection result.

A robust pre-disaster reinforcement method for distribution networks considering the temporal nature of disasters and the fault tolerance of fault location includes: establishing a time-varying vulnerability model for distribution network components and generating an uncertain disaster scenario set containing fault states of lines and feeder terminal units across multiple time periods based on the Monte Carlo method; constructing a distribution network segment location model and quantifying the location error under each disaster scenario by comparing the difference between the actual and expected states of nodes; establishing a robust pre-disaster reinforcement model with the goal of minimizing the overall system cost; constructing constraints for the robust pre-disaster reinforcement model and iteratively solving the robust pre-disaster reinforcement model using a column and constraint generation algorithm to output the optimal pre-disaster reinforcement scheme. This method, by meticulously characterizing the time-varying evolution of disasters, incorporates the fault tolerance performance of fault segment location into the pre-disaster reinforcement decision-making framework, seeking the optimal resource allocation scheme under the worst-case disaster scenario, thereby achieving a balance between economy and robustness and improving the resilience of the distribution network to extreme disasters.

The invention discloses a winter wheat drought damage quantitative dynamic risk estimation method, a medium and a program product, and belongs to the technical field of agricultural meteorological disaster risk assessment. The method comprises the following steps: firstly, establishing a vulnerability relation model of a historical cumulative meteorological drought comprehensive index and a winter wheat yield reduction rate; then, current and future drought disaster-inducing factor intensity is calculated by using real-time meteorological data and forecast data; then, winter wheat planting space distribution is extracted based on a satellite remote sensing technology; then, according to the disaster risk principle, the drought disaster-causing risk, the winter wheat exposure degree and the vulnerability model result are fused, the winter wheat yield reduction rates of different time scales in the future are calculated, and risk grades are divided; and finally, respectively generating a current drought influence assessment product and a risk estimation product in a future time period. According to the method, quantitative, dynamic and spatial assessment and estimation of winter wheat drought disaster risks can be realized, and the method can be widely applied to scenes such as agricultural drought disaster monitoring and assessment, disaster reduction scheduling and risk management.

The present invention provides an apparatus for analyzing vulnerabilities of smart contract code, the apparatus including: a meta-information extraction unit configured to extract meta-information about input code, which is source code of a smart contract; an intermediate representation language conversion unit configured to convert the input code into an intermediate representation language based on the meta-information extracted by the meta-information extraction unit; a code analysis unit configured to detect whether there is vulnerable code or a rule violation case by analyzing the input code converted into the intermediate representation language by the intermediate representation language conversion unit; and a vulnerability detection unit configured to generate a final vulnerability model by detecting a vulnerability based on the vulnerable code or the rule violation case detected by the code analysis unit, and also provides an analysis method using the apparatus.

The invention discloses a comprehensive energy system toughness evaluation method considering source-network-load multi-type faults. The method comprises the following steps: constructing a typhoon wind field and rainstorm-ponding equivalent model, a vulnerability model and an available capacity coefficient model; screening to obtain a typical extreme fault scene; according to the optimal load reduction models of the power grid subsystem, the gas grid subsystem and the heat supply network subsystem in the integrated energy system, the optimal load reduction results under the typical extreme fault scenes are calculated based on the available capacity coefficient model; judging whether the coupling equipment can support operation or not, if not, lowering the output upper limit of the coupling equipment to recalculate the optimal load reduction result, and if yes, outputting the current optimal load reduction result, and executing the next step; and constructing a toughness evaluation model to calculate a system toughness index, and completing toughness evaluation.

Techniques for calculating or determining risk scores for certain natural disasters perils based on machine learning model outputs are discussed herein. For example, a machine learning model may weight each of the pixels of a map in accordance with the set of weights associated with a structure, to calculate a risk score for a particular natural disaster peril associated with that structure. A plurality of risk selections may be provided to a user computing device for selection by a user, with those risk selections being associated with that risk score. Advantageously, the computing system facilitates the interaction of datasets with different measurement parameters in a machine learning model. In normalizing datasets before providing the datasets to input nodes of a machine learning model, a computing system may efficiently provide hazard and vulnerability outputs of the machine learning model.

The application discloses a comprehensive energy system resilience evaluation method considering source-network-load multi-type faults, comprising the following steps: constructing a typhoon wind field and a rainstorm-ponding equivalent model, a vulnerability model and an available capacity coefficient model; screening to obtain typical extreme fault scenarios; according to an optimal load reduction model of an electric network, a gas network and a heat network subsystem in the comprehensive energy system, the optimal load reduction results under each typical extreme fault scenario are calculated based on the available capacity coefficient model; it is judged whether the coupling device can support operation, if not, the output upper limit of the coupling device is lowered to recalculate the optimal load reduction result, if yes, the current optimal load reduction result is output for the next step; a resilience evaluation model is constructed to calculate the system resilience index and complete the resilience evaluation.

The present invention proposes a method and system for automatically discovering 5GC signaling security vulnerabilities based on ontology modeling, which is characterized by comprising: ontology modeling based on 5G protocol standards to obtain a signaling interaction model of 5G network elements and network elements, as a 5G virtual platform, and using the 5G virtual platform to generate a 5G signaling instance; ontology modeling based on known 5G signaling security vulnerabilities and defects to obtain a vulnerability ontology model, and characterizing the vulnerability exploitation behavior in the 5G signaling instance based on the vulnerability ontology model; feature extraction of the known 5G signaling security vulnerabilities and defects, and designing security vulnerability and defect inspection rules based on the feature extraction results; based on the inspection rules, discovering the semantic association between the 5G signaling instance and its vulnerability exploitation behavior, so as to automatically reason and discover 5G signaling vulnerabilities and defects. The present invention can automatically derive similar exploitable security vulnerabilities in all signalings of the 5G network based on known signaling security risks.

The present application relates to the technical field of substation risk assessment, in particular to a substation resilience assessment method based on zero-sum game and vulnerability analysis. The method models the substation through network topology analysis, calculates the system function using the reachability matrix thereof; then, a vulnerability model of the element is established, Monte Carlo simulation is performed using the vulnerability function thereof, and the risk of the substation under earthquake disaster is calculated; then, the operation risk and loss of the substation under human attack are modeled, a zero-sum game model of the defender and the attacker is constructed, an effective protection strategy is proposed according to the specific budget limit and the known attack scenario, the influence of the two disasters is comprehensively considered, the substation resilience assessment method considering natural disaster and human attack is established, the safety risk of the substation under extreme events is effectively reduced, and the safe operation ability of the substation is improved.

The invention discloses an intelligent target code vulnerability detection method and device based on cooperation of large and small models, and belongs to the technical field of computer software and artificial intelligence. The implementation method comprises the following steps: 1, mapping static source codes and vulnerability metadata to form a target knowledge base; 2, performing hash value matching on the source code to be detected and a target source code of a target knowledge base; 3, when matching is the same, returning the vulnerability report; 4, when the matching is different, calling an RAG searcher to generate a vulnerability report; 5, performing deep semantic analysis on the vulnerability cue words of the fe-shot learning and thinking chain construction and the to-be-detected source code through the optimized CodeShell parameter level vulnerability model to generate a repair code, and performing differential verification; 6, performing structured JSON (JavaScript Object Notation) and visual HTML (Hypertext Markup Language) output on the vulnerability report, the repair code and the repaired detection source code, and exporting a repair code file; compared with the prior art, the source code vulnerability detection efficiency is improved, and automation of source code vulnerability repair verification is achieved.

The invention provides an automatic mining system and method for an applet API authentication vulnerability based on an IAST technology. The method comprises the steps that a data collection module collects front-end runtime data through an IAST probe and intercepts network traffic through a global agent; the data processing and association module performs fusion processing on the multi-source data and constructs an operation context; the dynamic taint analysis module executes fine-grained data flow tracking and generates an API authentication parameter portrait; the intelligent test engine module automatically generates a targeted test case according to the vulnerability model library; the test execution and replay module realizes automatic replay of the test case through the proxy middleware; and the vulnerability analysis and report module performs multi-dimensional intelligent analysis on the response result and generates a vulnerability report. According to the method, accurate and automatic mining of API authentication vulnerabilities such as applet horizontal unauthorized, vertical unauthorized and Token bypassing is realized, the detection efficiency and accuracy are improved, and the problem of adaptability of a traditional method in an applet complex authentication scene is effectively solved.

The invention relates to the technical field of vulnerability detection, and discloses a vulnerability detection method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a to-be-detected vulnerability and vulnerability information corresponding to the to-be-detected vulnerability; determining whether the to-be-detected vulnerability belongs to a high-value vulnerability or not according to the vulnerability information by utilizing a preset high-value vulnerability model; if the to-be-detected vulnerability does not belong to the high-value vulnerability, identifying the to-be-detected vulnerability by using a preset anomaly detection model, and determining whether the to-be-detected vulnerability belongs to a rare vulnerability; if the to-be-detected vulnerability belongs to the rare vulnerability, analyzing whether the to-be-detected vulnerability belongs to a high-value vulnerability cluster in a clustering model by utilizing a preset clustering model; if the vulnerability belongs to the high-value vulnerability cluster, outputting that the to-be-detected vulnerability is a high-value vulnerability; and if the vulnerability does not belong to the high-value vulnerability cluster, outputting that the to-be-detected vulnerability is a common vulnerability. Therefore, whether the vulnerability is a novel high-value vulnerability or not can be accurately determined, so that the novel high-value vulnerability which is misjudged as a low-value vulnerability can be identified.

The invention discloses a method for evaluating the vulnerability of a transmission tower foundation by considering rolling stone energy and related equipment, and the method comprises the steps: calculating the residual impact resistance energy of the transmission tower foundation after the transmission tower foundation is impacted by rolling stones by analyzing the relationship between the maximum impact resistance energy of the transmission tower foundation and the total lateral shearing force as well as the vertex side displacement when the tower is completely collapsed; a transmission tower vulnerability model is established based on the residual impact resistance energy, and a vulnerability function is obtained; and a vulnerability coefficient is calculated by combining a vulnerability function, and the foundation damage state grade of the transmission tower is determined based on the vulnerability coefficient, so that a basis is provided for evaluating the damage vulnerability of the rolling stone to the transmission tower.

The invention discloses an urban power distribution network anti-seismic toughness evaluation and dynamic risk method considering seismic oscillation intensity spatial variability, and relates to the field of civil engineering disaster prevention and reduction, and the method comprises the steps: determining a seismic oscillation field with the intensity having spatial variability according to historical seismic data and a seismic oscillation attenuation relation; the method comprises the following steps: simplifying a transformer substation into nodes, simplifying a distribution line into edges, determining a topological structure according to network information, constructing a network model based on an improved Space-L method, and reading peak accelerations of each node and a line midpoint of a power network; selecting transformer substation and distribution line vulnerability models, and generating damage samples based on Monte Carlo simulation; according to the method, the system function change condition of the power grid after the earthquake is obtained in combination with the repair model, toughness and risk assessment is carried out, the spatial variability of the seismic field intensity is effectively considered, partition classification dynamic assessment of the power grid earthquake risk is realized, and the system toughness assessment result can provide visual and effective guidance for command decision making.

The present invention relates to the field of low-Earth orbit satellite network security, specifically a method for enhancing the security of low-Earth orbit satellite networks, comprising the following steps: 1) performing a formal analysis of the satellite network to be evaluated and constructing an original model based on a hypernetwork; 2) constructing an edge importance index set, sorting the edges in descending order of importance according to the original model; 3) removing edges from the original model; 4) constructing an edge recovery index set and generating an edge recovery order using a hypernetwork-based recovery algorithm; 5) removing other edges from the protected model to generate a vulnerability model; 6) removing other edges from the protected model to generate a vulnerability model; 7) adding edges to the vulnerability model according to the edge recovery index set to generate a recovery model; and 8) quantitatively evaluating the security of the model, using an algorithm to assess the model's security value. This method improves the overall security of the low-Earth orbit satellite network and effectively mitigates the degree of damage to its functions caused by attacks.

The invention discloses a power distribution network line potential risk early warning method and system. A Batts typhoon model is adopted to establish a mapping relation between a distance and a wind speed; carrying out vulnerability modeling on the power distribution network line, and generating a fault set of the line in the typhoon scene based on the obtained mapping relation between the wind speed and the fault rate of the power distribution line; the generated fault sets of the lines in the typhoon scene are input into a mixed integer programming model of the power distribution system, the load recovery amount is maximized when a multi-line fault occurs, the load flow constraint and the radial topology constraint are met at the same time, and the maximum power supply amount of the power distribution network in each fault set is obtained through statistics; and the maximum power supply quantity of the power distribution network under each fault set is used as data input, line importance ranking is carried out by using a Scioplande method, and an enhancement strategy of the power distribution network line is formed according to a ranking result. Through integration of typhoon disaster modeling and power distribution network reconstruction optimization, dynamic linkage of risk early warning and defense strategies is realized.