312 results about "Vulnerability evaluation" patented technology

The invention provides a network attack risk mapping assessment method and system, and relates to the technical field of network attack risks, and the method comprises the steps: collecting a network flow data packet to extract a multi-dimensional attack feature vector, employing a deep neural network to carry out feature learning to obtain an attack type recognition result and an attack intensity assessment value, and constructing an attack feature standard model; based on the attack type identification result and the attack strength evaluation value, combining an asset weight coefficient, a vulnerability hazard level and a vulnerability utilization difficulty coefficient to construct an asset vulnerability evaluation model; and performing association analysis on the real-time attack feature data and the asset vulnerability assessment score, constructing a risk mapping matrix, calculating a comprehensive risk score, and generating risk early warning information. According to the invention, accurate identification, dynamic assessment and timely early warning of the network attack risk are realized, and the accuracy and real-time performance of risk assessment are improved.

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

The invention relates to the technical field of geological disaster risk evaluation research, in particular to a collapse geological disaster risk evaluation method and system of a transformer substation structure damage model. The collapsed dangerous rock mass is investigated in detail, the stability is evaluated through a stereographic projection method, and the instability probability is calculated through a Monte Carlo method; fLAC3D and PFC3D are coupled to simulate a collapse dynamic process, and risk evaluation is completed in combination with ArcGIS; establishing a transformer substation electrical facility physical model and a structural damage model; establishing a vulnerability evaluation model based on the collapse rockfall impact strength and the electric facility structure resistance; and performing risk evaluation by adopting a matrix analysis method, and estimating economic loss. The invention further comprises corresponding system implementation. According to the method, by combining geological survey, numerical simulation and structural analysis, comprehensive quantitative evaluation of the collapse disaster risk of the transformer substation is achieved, the accuracy, reliability and pertinence of evaluation are improved, and a scientific basis is provided for disaster prevention and reduction of the transformer substation in the mountainous area.

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

The invention relates to a reliability evaluation method and system for a flexible AC / DC power distribution network containing an optical storage system in the field of flexible AC / DC power distribution networks. The method comprises the following steps: collecting photovoltaic data, energy storage data, AC / DC bus data and the like in real time; constructing a multi-time scale dynamic topology model, and establishing a failure probability distribution function; load flow calculation based on an improved forward-backward substitution method; adopting an N-1 + N-1-1 composite fault screening strategy to simulate a typical disturbance scene; establishing a vulnerability evaluation matrix to calculate power failure probability and other indexes; designing an optimal scheduling strategy based on model predictive control; multi-time-scale reliability evaluation is carried out; and outputting an evaluation report and a topology reconstruction suggestion. The system comprises a data acquisition unit, a dynamic modeling unit and the like, and an evaluation and analysis unit is internally provided with an algorithm module and is configured with an FPGA acceleration card. The method breaks through the limitation of traditional steady state evaluation in the technology level, improves the load flow calculation speed in the calculation efficiency level, reduces the operation cost in the application value level, improves the energy storage efficiency, and provides support for reliability evaluation of a novel power system.

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

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

The invention relates to a power distribution network two-stage recovery method based on an electric vehicle and mobile energy storage, and belongs to the technical field of disaster prevention and reduction and emergency recovery of a power system. According to the technical scheme, the method is divided into two stages of recovery: stage 1 pre-disaster pre-configuration and stage 2 post-disaster collaborative recovery; establishing a power distribution network line vulnerability evaluation model, and generating a fault scene in extreme weather based on a wind speed-fault probability curve; constructing a charging station and maintenance station pre-configuration optimization model, and determining the optimal site selection in the traffic network by adopting integer nonlinear programming; establishing a post-disaster recovery mixed integer programming model, coordinating and dispatching the electric vehicle, the mobile energy storage system and the maintenance team, and realizing collaborative optimization of load recovery and line repair; and designing a traffic network and power distribution network coupling mechanism, quantifying a system toughness index and optimizing a resource scheduling strategy. Through multi-resource cooperative scheduling, the post-disaster recovery capability of the power distribution network is remarkably improved, and an innovative solution is provided for improving the toughness of the power distribution network in extreme weather.

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

The invention discloses a virtual power plant and power grid collaborative security risk assessment method and system. The method comprises the steps of determining a virtual power plant and power grid collaborative security risk assessment index; carrying out quantitative analysis on the safety risk assessment indexes, and determining the weight of each safety risk assessment index; according to constraint conditions during cooperative operation of the virtual power plant and the power grid, carrying out assessment calculation on the resource vulnerability of the demand side at a node level; determining a plurality of demand side resource combinations according to a demand side resource vulnerability evaluation result; and setting different scenes when the virtual power plant and the power grid cooperatively operate, calculating the security risk assessment index of each scene, and calculating a corresponding security risk value according to the weight of each security risk assessment index. According to the method, the vulnerability of the demand side resources is considered in the node level, the security risk of cooperation of the virtual power plant and the power grid in various scenes is considered in the global level, and the comprehensiveness and accuracy of risk assessment can be improved.

The invention relates to a low-voltage power distribution network fragile area identification method. The method comprises the steps of obtaining related power data of a low-voltage power distribution network; in combination with a complex network theory, constructing a node importance evaluation index model reflecting the structural vulnerability of the power distribution network, and performing calculation; constructing a state vulnerability evaluation index model reflecting the operation state of the power distribution network and calculating; performing weighting by using a weight analysis method, and performing weighting on the node importance evaluation index and the state vulnerability evaluation index to obtain subjective and objective weights of each index; coordinating subjective and objective weights according to Nash equilibrium of the game theory, and calculating a comprehensive weight; comprehensive vulnerability is calculated by using a TOPSIS algorithm improved by grey relational degree, and vulnerability grades are divided according to Euclidean distance; and constructing a routine and fault determination process, distinguishing routine vulnerability assessment and emergency vulnerability assessment, and outputting node, line and region vulnerability levels. The method can improve the accuracy of low-voltage power distribution network fragile area discrimination, thereby improving the reliability and safety of the power distribution network.

The invention relates to a protection method for a power grid information physical system. The method comprises the steps that attack scenes are determined based on the structure of a power grid information physical system (CPPS) interaction model, different attack scenes correspond to attack targets, and defense targets are different; obtaining a CPPS vulnerability assessment framework based on defense targets of the attack target in different attack scenes; obtaining a CPPS active load loss model based on a Newton-Raphson power flow calculation equation and a preset power constraint condition; based on the CPPS vulnerability assessment framework and the CPPS active loss model, obtaining defense resources allocated to the attack target; and based on the structure of the CPPS interaction model and the defense resources allocated to the attack target, establishing a CPPS attack and defense game model and designing an attack and defense game scene, and based on the game theory, obtaining a CPPS protection scheme. By adopting the method, the structure of the interaction model of the power grid information physical system can be considered, and an optimal defense resource allocation strategy is given.

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

A model generation device of a vulnerability evaluation system acquires each of vulnerability data that has been disclosed from a database and an attack code that has been disclosed, and creates a calculation model for obtaining an exploit probability indicating a probability that the vulnerability is exploited according to an elapsed time from a disclosure time point of each of the vulnerability data that has been acquired, as a distribution of the elapsed time from the disclosure time point of each of the vulnerability data that has been acquired to a disclosure time point of the attack code for exploiting the vulnerability. A model evaluation device of the vulnerability evaluation system receives an input of the elapsed time from the disclosure time point of the vulnerability data to be evaluated, and obtains the exploit probability corresponding to the elapsed time that has been input based on the calculation model.

The invention discloses an order logistics timeliness management system, and relates to the technical field of logistics intelligence, and the system comprises a risk prediction module which collects order logistics full-link data, carries out the preprocessing of the data, calculates the logistics node delay risk probability through a virtual intervention scene and a causal graph model, and obtains a delay risk prediction result; the visualization module is used for performing overlay analysis on the delay risk prediction result and the preprocessed order logistics full-link data to generate a delay risk thermodynamic diagram; the destroy-resistant module is used for carrying out vulnerability evaluation on the delay risk thermodynamic diagram to generate a vulnerability analysis report, and carrying out multi-objective optimization evaluation through a k-shortest path algorithm to generate a destroy-resistant strategy table; the scheduling module is used for carrying out transport capacity resource matching and scheduling scheme selection by utilizing a multi-target resource optimization model based on the survivability strategy table to obtain a scheduling instruction set; according to the invention, causal traceability and probability quantification of logistics delay risks are realized by constructing a collaborative prediction mechanism of a virtual intervention scene and a causal graph model.

The invention discloses a power distribution network equipment vulnerability assessment method in an extreme scene and related equipment, and belongs to the technical field of power equipment disaster influence assessment, and the method comprises the steps: constructing a grid water accumulation model, and calculating the water accumulation height of each grid based on the grid water accumulation model and a rainfall center position; calculating the fault probability of the distribution transformer and the line based on the ponding height and the environmental factor; carrying out fault sampling based on the fault probability of the distribution transformer and the line, and determining which components may have faults; and outputting the fault component list and the fault probability. Through ponding dynamic simulation and equipment failure probability calculation, the accuracy of vulnerability evaluation of the power system is improved.

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

The invention relates to the technical field of software security, in particular to a vulnerability assessment method and device, electronic equipment and a storage medium, which are used for improving the accuracy of vulnerability assessment. The method comprises the following steps: positioning a target vulnerability, extracting a full path of the target vulnerability from a trigger entrance to an outbreak point, and identifying a trigger condition and a trigger step of the target vulnerability according to the full path; based on the triggering condition and the triggering step, the triggering condition complexity and the utilization step number of the target vulnerability are determined, and the step number is used for representing the step number needed by utilization of the target vulnerability; based on the utilization step number, the triggering condition complexity, the risk degree of the target vulnerability, the utilization concealment degree and the utilization success rate, the availability score of the target vulnerability is determined, and the discovering difficulty of the target vulnerability is represented through the utilization concealment degree.

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

The invention relates to the technical field of power system assessment, and discloses a power system vulnerability assessment method and system considering extreme weather influence, and the method comprises the steps: building a line fault probability model of a power system in extreme weather to determine the line time-varying fault probability under dynamic weather influence; generating a typical scene through a time sequence relativity matching generative adversarial network model with gradient penalty and Monte Carlo simulation; taking the generated typical scene as input, simulating cascading failure evolution through an alternating current cascading failure model, and outputting a cascading failure evolution path; and based on a fault evolution result, through a spectrogram theory and a Bayesian network, in combination with severity analysis, structural vulnerability evaluation and causal-probability pre-judgment, multi-level vulnerability quantification of the power system is realized, and the problems that single-level evaluation is insufficient and causal association is ignored in the prior art are solved. And the risk assessment precision and the disaster prevention capability of the power system in extreme weather are improved.

The invention discloses a method for evaluating the vulnerability of a concurrent composite disaster system, and the method comprises the steps: constructing a composite disaster system, and obtaining the vulnerability of the concurrent composite disaster system through the vulnerability of the composite disaster system; the method comprises the following steps: constructing a collapse-landslide vulnerability evaluation index system, performing sensitivity evaluation on collapse disasters and landslide disasters in a concurrent composite disaster system through a deep learning method according to the vulnerability of the concurrent composite disaster system to obtain collapse-landslide sensitivity, and calculating the collapse disasters and the landslide disasters by adopting an entropy weight method to obtain the collapse-landslide sensitivity. The collapse-landslide exposure degree and the collapse-landslide adaptability are obtained; and in combination with the collapse-landslide sensitivity, performing weighted comprehensive evaluation on the collapse-landslide exposure degree and the collapse-landslide adaptability through a TOPSIS method to obtain a vulnerability evaluation result of the concurrent composite disaster system in the collapse-landslide scene. According to the method, the evaluation index weight can be dynamically adjusted to adapt to complex disasters, and the regional evaluation precision and efficiency are improved.

Various embodiments provide methods, systems, computer program products, apparatuses, and / or the like for assessing vulnerability of an IC design to fault injection attacks, such as through a security property-driven vulnerability assessment framework for efficiently evaluating faults with respect to certain security properties associated with the IC design. In one embodiment, a method is provided. The method includes generating, using a fault-injection technique specification, one or more fault models describing attributes of one or more faults. The method further includes selecting, using the fault models and executable security properties associated with a design file of an IC design, a fault list identifying a plurality of possible faults for the IC design. The method further includes identifying, based at least in part on performing a fault simulation on the design file with the fault list, critical locations of the IC design. The method further includes implementing protections at the critical locations.

The invention provides a domain self-adaption-based generative attack and defense confrontation method. Directional interference on a black-box intelligent system is realized by constructing a generative confrontation network framework. The method takes a domain self-adaption theory in transfer learning as a core, builds a dynamic mapping relation between a white box model group and a black box target system, designs a dual-channel generative adversarial network, jointly supervises sample authenticity and cross-model transfer consistency through cross entropy loss and KL divergence constraint, builds a dynamic boundary feedback system, and achieves the dynamic boundary feedback. And a disturbance generation strategy of the generator is adjusted according to the real-time attack effect, and a self-adaptive attack and defense game closed loop is formed. Compared with a traditional black box attack method, the method has the advantages that migration robustness of an adversarial sample in a model heterogeneous scene is remarkably improved, stable attack efficiency can still be maintained when a defense system has protection mechanisms such as adversarial training and input reconstruction, and the method can be applied to the fields such as penetration testing of an intelligent security system and the like. And a technical support is provided for vulnerability evaluation of the intelligent system in a complex environment.

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

The invention relates to the technical field of power grid operation, and discloses a topological power grid operation-based multi-level vulnerability quantitative index construction method, which comprises the following steps of: obtaining topological structure data of a power grid dispatching system to perform hierarchical division, analyzing node data to form a hierarchical matrix, calculating a shortest path length matrix and analyzing betweenness centrality. According to the method, a power grid is divided into a high-voltage level, a medium-voltage level and a low-voltage level according to voltage levels, so that the topological structure of each level is independently analyzed, the modularity increment change of each node moving to an adjacent node is calculated based on a Louvain algorithm, subgroup junctions are gradually optimized by maximizing the modularity increment, and therefore the topological structure of each node is optimized. The hierarchical modularity is combined with the node vulnerability index of the hierarchy to form a more comprehensive vulnerability evaluation model, and the inter-hierarchy coupling problem can be more effectively identified and solved in the power grid optimization process through the comprehensive analysis of the inter-hierarchy and intra-hierarchy vulnerability indexes.

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

A computing platform provides code vulnerability detection and evaluation. The computing platform may use a quantum graph categorizer along with quantum polymorphism execution channels to reduce false positives and provide optimized real-time vulnerability evaluation of code and code segments. The computing platform may use a hybrid approach comprising a mix of static and dynamic vulnerability validation and real-time parallel technique execution.

The invention provides a physical attribute-external environment fused single building flood vulnerability assessment method and device. According to the method, the physical attribute vulnerability characteristics of the single building are automatically and efficiently extracted based on the deep learning model; adopting an entropy weight method to quantify external environment vulnerability indexes of the single building; a four-quadrant coupling analysis model fusing physical attributes of a single building and an external environment is constructed, a multi-dimensional building flood grading system is established, and visualization and accurate positioning of vulnerability grades are realized in combination with a GIS spatial analysis technology. According to the method, the technical level of urban flood vulnerability assessment is improved, the assessment result is more accurate and efficient, and a scientific basis and technical support are provided for urban flood control and disaster reduction decisions.

The invention provides a supply chain risk mitigation strategy evaluation method based on a multi-criterion decision model, and the method comprises the steps: carrying out the cross analysis of a supplier number weight adjustment coefficient and a component technical parameter, obtaining a technical risk coefficient, and carrying out the analysis of component replaceability data, obtaining a technical path dependency degree and supply source limitation, calculating comprehensive risk scores of the key components, and determining a risk priority sequence; formulating a multi-supplier configuration scheme based on a vulnerability assessment result, assessing supplier distribution and component compatibility, calculating a component interchange rate and a supplier coverage range, obtaining a standardized replacement power according to the interchange rate and the coverage range, and if the standardized replacement power is lower than a threshold value, increasing a supplier number weight; and analyzing supply interruption response time and replacement switching efficiency based on the component interchange rate, the supplier coverage range and the quantity weight, monitoring the supplier capacity utilization rate, measuring and calculating the production maintaining time length and the capacity recovery speed, and obtaining risk relief parameters after dynamic adjustment.

The invention provides a gradation-oriented meteorological disaster risk grading intelligent evaluation and pushing method, and relates to the technical field of meteorological disaster early warning, and the method comprises the steps: obtaining meteorological monitoring data of a plurality of monitoring points in a town, employing a space-time attention mechanism and a dynamic weight mechanism to extract and fuse space-time features and geographic element features, and obtaining a gradation-oriented meteorological disaster risk grading intelligent evaluation and pushing result; generating a meteorological disaster risk level through a bidirectional long-short-term memory network; meanwhile, a vulnerability evaluation model is constructed based on functional area distribution information, and risk levels and vulnerability features are dynamically fused to obtain a comprehensive risk index; and finally, constructing a distributed storage network through a hierarchical cascade block chain, and carrying out encryption and fragmentation pushing on the early warning information. According to the invention, accurate assessment and efficient early warning of the meteorological disaster risk of the calmweight can be realized, and the reliability and timeliness of early warning information are improved.