8results about How to "High reliability" patented technology

The application discloses a kind of based on cross-chain and DID's high-efficiency trusted identity authentication method of Internet of Vehicles, comprising: in key generation stage, identity management entity generates decentralized identity based on the registration request of target vehicle and issues verifiable identity credential, generates the digest or index mark corresponding to verifiable identity credential;In cross-domain identity authentication stage, when target vehicle v enters second trust domain, cross-domain authentication request is sent to second roadside unit, and cross-domain authentication request at least includes: the digest or index mark of verifiable identity credential, session-related temporary identification or pseudonym identification and the privacy proof information used to prove that target vehicle meets cross-domain access condition;Second roadside unit obtains the certificate state corresponding to digest or index mark;And when certificate state is valid, privacy proof information is verified, and cross-domain authentication is completed accordingly.The application realizes high-efficiency trusted authentication without revealing real identity and credential.

The application discloses a tunneling target positioning method based on complex loose body multi-modal data survey, relates to the technical field of underground space artificial intelligence survey, and comprises the following steps: synchronously collecting multi-modal sensing data and tunneling working condition data in a tunneling process and performing adaptive noise reduction processing; extracting an energy envelope feature sequence of the multi-modal sensing data and performing depth mapping alignment; inputting the aligned multi-modal data features into a double-branch feature coding network, introducing a semantic prior vector as a context constraint, distributing fusion weights of the multi-modal data features through a cross attention mechanism, and outputting a high-dimensional fusion feature tensor; and constructing a physical information constraint neural network, taking electromagnetic and acoustic physical equations and auxiliary sensor data as constraint terms of a loss function for calculation, and outputting three-dimensional positioning coordinates of a tunneling target in a tunnel global coordinate system. The method eliminates time-depth conversion errors caused by complex loose body multi-variation.

The invention relates to a method for detecting changes of nucleotide sequences by using an isothermal nucleic acid amplification technology, a primer set for detecting selected genetic variants and components of a reaction mixture.

The application relates to a complex simulation system-oriented verification cascade failure early warning method and device. The method comprises the following steps: constructing a multilayer verification network according to multiple verification tasks of a complex simulation system. According to the multilayer verification network, each verification task is divided into a plurality of continuous time slices, each time slice corresponds to a complete complex simulation system running and index collection process. The state transition probability between nodes of the multilayer verification network is calculated, so that the failure probability of the verification node state in the multilayer verification network is obtained by adopting a Bayesian network. The joint state between the verification nodes on the key propagation path is evaluated for failure, and the joint failure probability is obtained. According to the failure probability and the joint failure probability, a three-level early warning mechanism is constructed, so that the early warning state sample iterative learning is carried out through an optimization evaluation algorithm, and a cascade failure early warning scheme is generated. The method can improve the verification efficiency and adaptability of the complex simulation system.

The invention relates to the technical field of thermal protection health management of high-speed aircrafts, and particularly discloses a thermal environment rapid prediction method and system based on pre-training. The method comprises the following steps: establishing a high-fidelity engineering algorithm by using a modified Newton theory and a Fay-Riddle formula, and generating a source domain heat flow data set; constructing a deep convolutional neural network composed of a geometric feature extraction module, a working condition feature extraction module and an overall heat flow calculation module; a pre-training-fine tuning framework is adopted, a deep convolutional neural network is trained through multi-working-condition source domain data, a pre-training model is obtained, and freezing-fine tuning is carried out on a small amount of sensor measuring point data of a target working condition; further fusing local sensor data into the network to form a sensor data fusion network; and finally, rapid prediction of the thermal environment is completed within millisecond-level time. According to the method, the prediction precision is remarkably improved, and the application requirement under the condition of finite computing power is met.

The application discloses a time service link intrusion detection system for power dispatching, and relates to the technical field of power dispatching. The system comprises an information acquisition module, which acquires information interfaces of time service links to obtain target interfaces, and the target interfaces are detection points. The application independently executes a double-layer detection process of rapid detection and deep detection for each segment, and realizes comprehensive judgment of the whole link through segment superposition, so that the accuracy and robustness of detection are improved. The output information of each segment is compared through the introduction of a third node storage to form an independent and tamper-proof evidence chain, the trustworthiness and auditability are enhanced, the impact of single-point failure and attack on the clock continuity and consistency of the system is reduced, the segment superposition double-layer detection mechanism can improve the timeliness and accuracy of detection, improve the coverage ability of complex and distributed attacks, enhance the consistency and traceability across segments, and realize the non-repudiation of evidence under the resistance attack through decentralization comparison.

The application relates to a collaborative optimization method based on a reputation-driven layered consensus and adaptive sharding, and belongs to the field of blockchains. Firstly, a node reputation evaluation model based on multiple-dimensional indexes such as response performance is constructed. In view of the characteristics of heterogeneous and dynamic changes of nodes of a consortium chain, relevant features such as network topology, business semantics and organizational trust are extracted, spectrum clustering and a greedy optimization architecture are adopted to dynamically divide sharding, layered consensus driven by reputation is taken as a collaborative auxiliary, and a consortium chain performance optimization framework with deep fusion of consensus and sharding is constructed. Through multi-dimensional feature fusion and adaptive adjustment of nodes, efficient consensus and load balancing of a large-scale consortium chain network are realized. The collaborative optimization method proposed by the application has important significance for improving the throughput and expansibility of the consortium chain, and the constructed framework achieves higher transaction throughput and lower cross-sharding communication overhead than a baseline model in a dynamic network environment.

The invention relates to the technical field of artificial intelligence and official document processing, in particular to an official document intelligent writing method and system based on cooperation of a large language model and multiple agents. Through thinking chain driving outline generation, fine-grained evidence injection, structured negative feedback correction and AST mapping national standard typesetting, automatic official document generation with logic coherence, accurate facts and compliant formats is realized.