13 results about "Vulnerability model" patented technology

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 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.

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 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 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 invention relates to the technical field of cargo management, in particular to an automatic positioning, bundling and packaging management system for multi-specification cargos, which is characterized in that a surface feature acquisition module is linked with vision and depth sensing equipment, and surface texture images and three-dimensional point cloud data of bamboo boxes to be packaged are accurately captured; the technical limitation that traditional packaging path planning only depends on two-dimensional visual information is broken through, multi-dimensional feature deconstruction is carried out on point cloud data, core feature parameters such as bamboo box surface material hardness distribution, texture fluctuation and local weak areas are comprehensively extracted, and a surface feature data set is generated. On the basis, the mechanical vulnerability value of each pixel point on the surface is calculated point by point based on the data set by means of a vulnerability modeling module, and the abstract material damage resistance of the bamboo box is converted into a visual vulnerability thermodynamic diagram; the method effectively solves the problems that current bamboo box packaging path planning depends on artificial experience, the individual surface feature difference of the bamboo box cannot be accurately adapted, and local stress overload damage is easily caused.

This method provides an earthquake vulnerability analysis method for urban building groups, which efficiently and quickly constructs a numerical building model that takes into account the variability between buildings using a simple multi-degree-of-freedom model based on building attribute parameters, constructs a probabilistic machine learning model that can quantify the intrinsic randomness of building responses, directly provides the probability distribution parameters necessary for a parameterized vulnerability model, and derives a regional earthquake vulnerability model for the building group using a Poisson binomial distribution, starting from the probability of each building in the urban area being unsafe. [Solution] The method includes the steps of: (1) constructing a simple numerical model of the building group; (2) selecting a set of seismic motion records that satisfy site characteristics; (3) obtaining a database of structural and non-structural damage to the building group; (4) establishing a probabilistic machine learning damage prediction model; (5) obtaining the probability of the building group being unsafe; and (6) deriving a regional vulnerability model of the building group.

A method for analyzing the seismic vulnerability of an urban building group based on Poisson binomial distribution includes these steps: (1) establishing a simplified numerical model of the building group; (2) selecting seismic motion records consistent with site features; (3) acquiring a structural and non-structural damage database for the building group; (4) creating a probability machine learning damage prediction model; (5) determining the unsafe probability of the building group; and (6) deducing a regional vulnerability model for the building group. Using simplified MDOF models based on building attribute parameters, numerical models accounting for variability between buildings are efficiently constructed. The probability machine learning model quantifies the intrinsic randomness of building responses and provides probability distribution parameters needed for the parameterized vulnerability model. From the unsafe probability of each building, the regional seismic vulnerability model is derived using Poisson binomial distribution.

The invention provides a classification vulnerability evaluation method for traditional dwellings in Tibet regions. The method comprises the following steps: step 1, historical earthquake damage statistics; 2, deducing an empirical matrix; step 3, generating seismic oscillation; 4, establishing a typical structure nonlinear value; 5, comparing and correcting data; step 6, establishing a combined vulnerability model; and step seven, verification and uncertainty quantification are carried out. According to the method, a bidirectional correction mechanism of an empirical earthquake damage matrix and a numerical vulnerability curve is fused, and Beta distribution probability deduction and Monte Carlo uncertainty propagation are introduced, so that regional and structural accurate evaluation is realized. The result output comprises a damage probability matrix, a vulnerability curve (including a confidence interval) and risk level suggestions. The method is systematized and repeatable, has regional generalization, is suitable for earthquake disaster risk assessment, disaster reduction planning and seismic reinforcement priority division, and has high engineering and application values.

This invention relates to the field of port and coastal engineering safety assessment technology, and discloses a method for evaluating the toughness of guide seawall structures and predicting the probability of damage states. The method includes: collecting and preprocessing multi-source heterogeneous monitoring data; constructing a time-varying Copula damage mode coupling model based on the monitoring sequence, and calculating the correlation coefficient matrix between failure modes; calculating the length of the damage space correlation feature and dynamically generating generalized evaluation units; introducing a historical cumulative dissipated energy index to construct a three-dimensional dynamic vulnerability model, simulating the power-law degradation of structural resistance with cumulative damage, and predicting the instantaneous probability distribution of each damage level. Finally, the system functional potential energy is calculated, and emergency repair resources are converted into a generalized repair potential energy flow, outputting a comprehensive toughness index through weighted integration. This invention comprehensively considers the time-varying coupling characteristics of failure modes and the cumulative damage effect, and incorporates emergency resource scheduling into the evaluation system, achieving dynamic and accurate quantification of the toughness of guide seawall structures.