11 results about "Survivability" patented technology

This invention proposes a method for assessing the resilience of heavy platforms based on the DA-PSR model and complex networks. Taking heavy platforms as the object, an objective resilience assessment system is constructed. An assessment index system with causal relationships is established, and a screening method for the assessment index system based on node betweenness values ​​in complex network theory and the k-means algorithm is proposed. This results in a simplified assessment index system where the remaining node betweenness values ​​account for 52.99% of the total betweenness values ​​and the remaining cluster center values ​​account for 90.55% of the total cluster center values. An index weighting method based on mutual information theory and an index calculation method based on extreme damage quantification are proposed. The PSR framework theory is improved through dimensional analysis, and a resilience DA-PSR assessment model is constructed. Considering the performance requirements driven by tasks, a criterion quantification and dynamic grading method based on task requirements is proposed. The assessment shows that a heavy platform with a resilience assessment value of 0.7373 exhibits different assessment levels of "excellent," "medium," and "poor" under different tasks.

The invention relates to the technical field of cloud computing and edge computing, in particular to a multi-cloud cooperative system based on edge computing. Comprising a data acquisition module for acquiring communication characteristic parameters and a current control topology state; the first processing module is used for identifying intermittent communication disturbance and calling a digital twinborn model to predict and control topological entropy increment; the second processing module is used for determining a lagging failure risk value for keeping the current control right affiliation unchanged; the third processing module is used for calculating an entropy increase-risk ratio for measuring the survival stability of the system; and the cooperative control module is used for generating a degradation operation instruction for switching the control node or maintaining the current connection based on a comparison result of the entropy increase-risk ratio and a survival threshold value. According to the method, routing oscillation and control right split brain are effectively avoided, physical collision accidents are prevented, and the survival probability of the system in a severe network environment is improved.

The present technology provides solutions for maintaining communications within a software-defined wide area network (SDWAN) when one or more devices in the SDWAN are isolated from one or more controllers. An example method includes receiving, at a static edge device associated with a static wide area network (WAN) Internet Protocol (IP) address in a data plane of the SDWAN, data from a first device communicating through an edge device of a first Internet service provider (ISP) of the SDWAN, where the first device is associated with a private IP address, associating, by the static edge device, the first device with the private IP address, and communicating, by the static edge device, with the first device at the private IP address. Systems and computer-readable media are also provided.

The invention discloses a cloud platform survivability assessment method, system and device and a medium. Comprising an acquisition module, a first determination module, a second determination module and a third determination module. The acquisition module acquires component information of each component in the cloud platform; the first determination module determines an autonomous controllable degree evaluation result of each component based on the component information; the second determination module determines the limit survival state level of the cloud platform based on the evaluation result; and the third determination module integrates the minimum cloud state of the cloud platform and the limit operation survivability evaluation result based on the level to obtain the survivability evaluation result of the cloud platform. According to the method, the evaluation indexes are extracted from the supply chain and the research and development production dimension, the evaluation range of the survivability of the cloud platform is expanded, the influence of the extreme environment on the cloud platform and the limit survival difficulty are comprehensively revealed, the survival feasibility and the survival quality of the cloud platform in the extreme environment are accurately determined, and the method has higher evaluation accuracy.

The invention discloses an unmanned aerial vehicle autonomous flight control method and system based on multi-vehicle cooperation and dynamic airspace distribution, and relates to the technical field of unmanned aerial vehicle autonomous flight control. According to the invention, a flight control processing strategy of a preset rule is adopted, each unmanned aerial vehicle has an autonomous decision-making capability, a single-point fault problem of a traditional centralized architecture is effectively overcome, even if part of nodes have faults, the system can still maintain stable operation, and the reliability and the survivability are significantly improved; according to the method, an airspace unit model mechanism is introduced, a basic unit of resource allocation is converted from a path line to an airspace unit, essential change of a flight path is realized in space and time dimensions, the mechanism can prevent conflicts fundamentally, and compared with a traditional method, the method has remarkable advantages.

The invention discloses a distributed unmanned cluster cooperative control system, which belongs to the technical field of unmanned system cooperative control, and comprises a heterogeneous cluster composed of various types of unmanned platform nodes, and the cluster comprises an unmanned aerial vehicle node, an unmanned ship node and an unmanned underwater vehicle node; the intelligent control units run on the unmanned platform nodes in a distributed manner and are used for realizing autonomous decision making and local collaboration of the platform; and the dynamic self-organizing communication network is connected with each node in the cluster and is used for realizing information interaction among the nodes. The system provided by the invention has extremely high robustness and survivability, a decentralized architecture eliminates single-point faults, failure or interference of partial nodes does not affect the core function of a cluster, the system is particularly suitable for an adversarial or severe environment, and a distributed task market mechanism enables tasks to be dynamically matched to the most suitable platform, so that the system has high robustness and survivability. The overall operation efficiency and the resource utilization rate are improved, and the cooperative logic of heterogeneous platforms such as air, sea and underwater platforms is effectively unified through a standardized task interaction protocol.

The invention discloses a USV combat strategy optimization method and system based on a dynamic game, and relates to the field of analog simulation. Comprising the steps of S1, constructing a comprehensive USV state space, determining constraint conditions of an action space and designing a reasonable revenue function, S2, comprehensively considering weapon stock and maneuvering characteristic limitation, and determining the USV action space and the constraint conditions of the USV action space, S3, dynamically constructing a particle speed updating formula according to ocean adaptability and communication network performance, and S4, judging whether the USV combat system reaches the hybrid Nash equilibrium or not by comparing income differences under different strategies, providing an effective convergence judgment basis for combat decision, and optimizing the combat strategy. Through the method and the system, the limitation of a traditional method can be broken through, a more scientific and efficient combat strategy is formulated for the USV, and the combat effectiveness and survivability of the USV in the modern sea battle are improved.

This invention provides a method to improve the survivability of domestically produced ruggedized computers in complex electromagnetic environments, relating to the field of electromagnetic protection technology for ruggedized computers. The method includes acquiring the computer's topology and electromagnetic parameters; establishing an electromagnetic energy flow network to identify high-risk areas; designing a multi-layered heterogeneous material non-uniform shielding structure and an energy flow guiding structure; adjusting the physical parameters of the filtering unit; setting up a hybrid grounding network; and configuring protective coordination parameters. This invention improves the survivability and reliability of computers in complex electromagnetic environments through precise electromagnetic energy guidance and multi-layered protection.

The invention belongs to the technical field of cluster unmanned aerial vehicle task allocation. The invention provides a task allocation method based on dynamic dual modes. According to the embodiment of the invention, through group-group-machine three-level role definition, hierarchical management of task coordination is realized, the system complexity is reduced, and the command efficiency is improved. And the group head and the group leader support a dynamic selection mechanism, so that system paralysis caused by failure of the central node is avoided, and the survivability of the system in high-confrontation environments such as a battlefield is improved. After the task is released, a distributed auction algorithm is preferentially adopted to carry out rapid negotiation, so that the real-time performance is ensured; if tasks are not allocated overtime, a centralized particle swarm algorithm is started for global optimization, and efficiency and optimality are both considered. Priorities of tasks are set according to attributes, bidding weights are dynamically adjusted according to the priorities in an auction algorithm, and reasonable resource scheduling is achieved. Task information is stored in a structured mode, the state is updated in real time, multi-task parallel management and cross-group coordination are supported, and the overall task throughput of the system is improved.

The subject of the application is a multi-engine coordinated artificial intelligence system with fractal architecture, precious metal denomination and session survivability protocol.A system for coordinating N commercially available artificial intelligence (AI) models from different vendors into one coordinated product consists of: (a) N AI engines from at least three different vendors operating at the consumer tier, where the cost of a single engine does not exceed $20 per month or is free of charge, (b) a central human node (N+1 engine) acting as a decision router orchestrating the flow of tasks between engines, (c) a complete K_N graph of emergent specialized connections between AI engines, where each pair of engines has a defined functional specialization, and (d) the emergent value of the system (capability / cost ratio) exceeding the sum of the values ​​of the individual components (superlinear scaling), with the total operating cost of the system not exceeding 1 ounce of silver per month at enterprise-grade capability.

The application belongs to the technical field of aircraft terminal countermeasure system design, and particularly relates to an aircraft terminal countermeasure system, which simultaneously covers four links of observation, judgment, decision and execution in an aircraft countermeasure task, and an integrated architecture solves the problem of poor coupling of existing terminal countermeasure sensing and decision technology, shortens the response time of the countermeasure system to external air countermeasure situation changes, and compared with traditional terminal countermeasure technology, is realized based on a deep learning framework, has strong feature extraction and nonlinear mapping capability, and has good generalization capability after training due to a good neural network structure, and is more adaptable to the countermeasure system under complex air countermeasure situation conditions, and greatly improves the survival capability of the aircraft under complex situation conditions.