1050 results about "Networking protocol" patented technology

The invention discloses a power grid abnormal flow detection method based on multi-modal data fusion, and the method comprises the steps: collecting the multi-source heterogeneous data of a power grid through an edge calculation node, including the current waveform of an intelligent electric meter, the state variable of an SCADA system, network protocol metadata and an equipment log event; performing space-time alignment preprocessing on the original data, converting an unstructured log into a time sequence by adopting a sliding window mechanism, and eliminating sensor noise through an LSTM auto-encoder; constructing a multi-dimensional feature space which comprises a frequency domain feature, a spatial-temporal feature and a protocol feature, and obtaining a feature vector; the feature vectors are input into a hybrid detection model, the model is composed of an isolated forest algorithm, an improved CNN-LSTM classifier and an information entropy-based rule engine which are connected in parallel, and a dynamic weight fusion strategy is adopted to output an abnormal probability; and when the abnormal probability exceeds a dynamic threshold, triggering a multi-level response mechanism: sending a traffic shaping instruction to the edge device, generating a device fingerprint portrait on the cloud platform, and storing abnormal event features through a block chain.

The invention discloses a question answering method for graph-driven fusion retrieval in the field of computer networks, and relates to technologies such as intention recognition, multi-channel fusion retrieval, hierarchical semantic matching and answer traceable generation. Aiming at the problems of professional term matching, incomplete knowledge, single retrieval and low answer credibility of an existing question-answering system, a dual-channel fusion retrieval and hierarchical semantic matching mechanism based on a knowledge graph is provided, a retrieval strategy is dynamically controlled through intention recognition, accurate matching of structured knowledge is realized by adopting phrase preliminary screening and triple fine matching, and the accuracy of the structured knowledge is improved. In combination with graph subgraph dynamic construction and Prompt generation, a large language model is guided to output high-quality answers, traceable knowledge sources are attached, and the accuracy and credibility of the answers are improved. The method is widely applicable to computer network professional question and answer scenes such as network protocols, configuration management and troubleshooting.

The invention relates to a fuzzy testing method, device and equipment based on a large language model, and the method comprises the steps: analyzing and analyzing RFC standard documents of various network protocols through the large language model, and extracting protocol rules and constraint conditions; according to the protocol rule and the constraint condition, a large language model driving method is adopted to generate a test case conforming to protocol characteristics; and performing a differential test through the test case, and verifying the consistency between the actual software of the network protocol and the RFC standard document. According to the invention, the accuracy and security of implementation of the complex network protocol can be improved, and the test efficiency and coverage rate are optimized.

The invention relates to a heterogeneous network engineering-oriented adaptive protocol conversion gateway system and a control method, and belongs to the technical field of Internet of Things, and the system comprises a protocol identification module which is used for monitoring a data stream in real time and identifying the type of a protocol used in current communication; the adaptive protocol selection module is used for selecting the most suitable protocol according to the network state and the data flow type; the protocol conversion module is used for data format conversion between different protocols and mapping of header information; the network resource optimization module is used for dynamically adjusting the transmission mode of the protocol according to the current network state; and the data caching and traffic scheduling module is used for scheduling the cache and the traffic when different protocols are switched. According to the method, the problem of compatibility among different network protocols is solved, the data transmission efficiency and the utilization of network resources are optimized, and the method can adapt to different application scenes and is widely applied to environments needing cross-protocol communication, such as the Internet of Things, a smart power grid and cloud computing.

The invention discloses a network protocol reverse analysis method based on deep learning and a graph neural network, and the method comprises the steps: basic field detection: carrying out the byte-level feature extraction of a binary data stream through sliding window embedding, bidirectional LSTM coding and knowledge enhanced CRF decoding, and outputting a structured field labeling sequence; performing protocol format clustering based on the sequence: calculating a multi-dimensional similarity through an improved Needleman-Wunsch algorithm, realizing automatic classification of unknown protocols in combination with a dynamic density clustering algorithm optimized by LSH, and outputting a protocol cluster; and performing composite structure analysis based on the protocol cluster: constructing a protocol syntax tree based on a graph neural network, performing multiple rounds of message passing through a graph attention network GAT attention mechanism, identifying a nested structure and performing recursive analysis, and generating a multi-level protocol syntax tree. According to the method, the automation degree and accuracy of complex protocol analysis can be remarkably improved.

The invention relates to the technical field of ground mobile unmanned equipment control, and discloses a ground mobile unmanned equipment autonomous obstacle avoidance control system optimized by artificial intelligence. The system comprises an environment perception layer, a bimodal risk assessment layer, a dynamic decision-making layer, a trajectory optimization layer and a feedback optimization layer. The environment sensing layer adopts a retina fovea centralis imitating mechanism to perform non-uniform sampling on laser radar point cloud data to generate dynamic point cloud partitions; the bimodal risk assessment layer fuses two types of radar data to generate static and dynamic obstacle risk assessment diagrams; the dynamic decision-making layer establishes space-time mapping and generates an obstacle confidence coefficient matrix through a graph neural network; the trajectory optimization layer converts the matrix into a control parameter based on a multi-objective evolutionary algorithm, and issues the control parameter through a time-sensitive network protocol; and the feedback optimization layer monitors environment change, calculates deviation, generates an effectiveness index, and dynamically adjusts a point cloud acquisition strategy until the index is optimal. According to the system, the autonomous obstacle avoidance capability and adaptability of the ground mobile unmanned equipment in a complex environment are enhanced.

The invention provides a software ecological migration method and system based on intelligent analysis. Wherein a compatibility evaluation database is deployed in a target architecture, an instruction set mapping rule is preset, and migration risk data is generated through semantic verification; dynamically allocating resources through a heterogeneous virtualization scheduling engine, calling a hardware acceleration unit translation defect instruction, and generating virtualization equipment simulation parameters based on the migration risk data; deploying protocol conversion middleware, dynamically analyzing and reconstructing a network protocol data packet, and combining instruction execution delay data to adjust analysis granularity; in cross-architecture transmission, instruction conversion defects are injected into control logic, timing sequence errors are corrected through clock offset compensation, and corrected data are transmitted back to update instruction mapping rules. According to the technical scheme provided by the invention, the data leakage risk is reduced, and zero shutdown in the migration process is ensured.

The invention discloses a collaborative unmanned aerial vehicle cluster path planning and scheduling system, and particularly relates to the technical field of unmanned aerial vehicle intelligent control, and the system comprises a multi-mode sensing unit which is composed of a heterogeneous sensor array composed of LiDAR, binocular vision and millimeter wave radar, and an output dynamically updated three-dimensional Gaussian mixture map; the decision control unit is used for implementing double-layer optimization of mixed integer programming task allocation and artificial potential field path planning; the dynamic communication network adopts a hybrid networking protocol of TDMA backbone nodes and 802.11 ax terminal nodes; an energy management module; aiming at the insufficient environment perception and dynamic modeling capability in the prior art, the method achieves the effects that the centimeter-level positioning precision and the dynamic obstacle recognition rate are greater than 92%, the environment model is delayed and compressed to be within 200ms, the response speed is increased by 5 times by setting multi-modal sensor fusion, constructing a dynamically updated 3D Gaussian mixture map and combining an LSTM network to predict the obstacle trajectory in real time, and the dynamic obstacle recognition rate is greater than 92%. And the obstacle avoidance reliability in a complex scene is obviously enhanced.

Systems and methods for managing the distribution of electrical power are disclosed. The system can maintain a status of each account of a plurality of accounts. Each account can correspond to an electrical power generation device, an electrical power consumption device, and an electrical power transmission device. The system can generate a first record object in response to the generation of a unit of power. The system can identify a first path identifying a source device and a destination device to transmit the unit of power. The system can generate a control signal to route the unit of power from the source device to the destination device. The system can update an event tracker to indicate the routing of the unit of power. The system can generate a second record object corresponding to the destination device. The system can update a second account to include the second record object.

The invention relates to the technical field of network communication, provides a dual-stack network adaptive scheduling method and system based on a DNS analysis strategy, and is used for establishing an effective protocol stack feature association mechanism so as to realize cross-protocol-layer intelligent scheduling. The method comprises the steps that a DNS analysis request sent by a client side is received, and the DNS analysis request carries a target domain name and a request protocol type; analyzing the request protocol type to determine a network protocol stack type supported by the client; generating an adaptive scheduling strategy corresponding to the target domain name according to the network protocol stack type and a preset address type priority list; selecting a target IP address type from a pre-configured IP address pool based on the self-adaptive scheduling strategy; and returning an analysis result corresponding to the target IP address type to the client so as to guide the client to establish network connection through the target IP address type.

The invention discloses a video monitoring data storage management system based on big data, and particularly relates to the field of data analysis, comprising multi-source heterogeneous data acquisition, cross-layer feature fusion calculation, comprehensive index fusion, collaborative decision generation and adaptive regulation and control execution. Through multi-level data synchronous acquisition and cross-domain feature fusion, a three-dimensional tensor structure is constructed to eliminate dimensional difference, global state perception of coding, transmission and storage is realized, local optimization limitation caused by a traditional data island is solved, a dynamic weight adjustment model automatically matches an optimal strategy based on a three-order collaborative management coefficient calculated in real time, and a dynamic weight adjustment model is improved. The joint elastic regulation and control of coding parameters and network protocols and the dynamic reconstruction of storage resources are realized, and the defect of response lag of a static threshold mechanism under burst traffic is overcome.

The invention relates to the technical field of network information security, in particular to a novel network information security method and system, and the method comprises the following steps: injecting a dynamic variation engine into the bottom layer of a network protocol stack, and generating a variation protocol fingerprint; generating an equipment identity label with space-time dynamic property; selecting a protocol encapsulation channel based on chaotic encryption or a disguised transmission channel based on bait data injection, wherein the protocol encapsulation channel encrypts or obfuscates an original protocol; a self-adaptive bait generator is embedded in the camouflage transmission channel; extracting attack fingerprints; and inputting the attack fingerprint into the antagonistic defense strategy generator, and synchronously updating the distortion parameter of the dynamic variation engine. According to the invention, the distortion parameter of the dynamic variation engine and the adaptive adjustment mechanism based on the attack fingerprint ensure that the defense strategy always dynamically confronts the attack behavior, and the robustness of the protocol layer protection is improved.

Technologies for creating an optimized and accelerated network pipeline using an emulated network pipeline abstraction layer (NPAL) of an emulated data processing unit (DPU), including an emulated processing device and an emulated acceleration hardware engine, are described. The emulated NPAL supports multiple network protocols and network functions in an emulated network pipeline. The emulated network pipeline includes a set of tables and logic organized in a specific order to be accelerated by the emulated acceleration hardware engine. The emulated acceleration hardware engine can process network traffic data using the emulated network pipeline.

The invention discloses a cross-platform distributed synchronous simulation method adopting RoCEv2, and relates to the technical field of communication simulation, and the method comprises the following steps: configuring parameter information of a switch and nodes, and constructing a heterogeneous distributed network of a RoCEv2 enhanced network protocol; deploying a master node global logic time reference, calibrating a slave node clock, starting a sliding window to continuously monitor offset, dynamically detecting clock offset to trigger adjustment, optimizing a time step length in combination with an ECN mark, and dynamically compensating the clock offset of the heterogeneous platform; sequencing events according to logic timestamps to generate DAG topology, and preloading high-frequency events; event data are transmitted through cross-node RDMA, and the main node is sorted and scheduled according to a logic timestamp, so that cross-platform distributed parallel simulation and consistency verification are realized. The method can improve the processing efficiency and fault-tolerant capability of the communication simulation system on large-scale simulation, and can be widely applied to the technical field of communication simulation.

The invention relates to the technical field of network management, and discloses an intent-driven network management method and system based on a large language model (LLM). The method comprises the following steps: receiving a natural language intention of a network administrator, analyzing the intention by utilizing a pre-trained large language model, extracting a key target and constraint, generating a configuration scheme of a TCP / IP or QUIC protocol based on an analysis result and a network protocol knowledge base, and ensuring the effectiveness and safety of configuration through a network constraint verification module. And the configuration passing the verification is applied to the network system. The system comprises an intention input module, an LLM analysis module, a configuration generation module, a verification module, an application module and the like. According to the invention, through conversion from automation intention to configuration, the network management process is simplified, the management efficiency and flexibility are improved, the method is especially suitable for multi-parameter optimization scenes of protocols such as TCP / IP and QUIC, and intelligent network management is realized.

The present application belongs to the technical field of data processing and relates to a network message processing method and apparatus, and a computer device and a storage medium. The method comprises: on the basis of a received target network message, acquiring a target memory block from a pre-constructed shared memory pool, so as to generate a message memory; on the basis of a network-interface-card driver, performing first identification processing on the target network message, so as to obtain a target network message descriptor; on the basis of the target network message descriptor, performing second identification processing on the message memory, so as to obtain a first socket buffer; and performing parsing processing on the first socket buffer by means of a network protocol stack, and on the basis of a parsing processing result, reading network message data from a virtual memory corresponding to the message memory, so as to complete the processing of the network message. In the present application, on the basis of a constructed shared memory pool, zero-copy network packet reception can be realized during network message processing, thereby avoiding performance loss caused by the memory copy of messages.

A system and methods for integrated data processing and protocol adaptation using dyadic distribution-based compression. The system transforms input data into a dyadic distribution, enabling efficient compression through either variational autoencoders or Huffman encoding. A novel protocol appendix generator creates transformation rules for adapting the compressed data to various network protocols. The system interleaves transformation information with the compressed data, enhancing security and ensuring comprehensive data transmission. An enhanced codeword decoder, employing a hybrid neural network architecture, decodes the data and adapts it to target protocols. The system features a protocol handler using meta-learning techniques for adapting to unfamiliar protocols. Continuous learning mechanisms optimize performance over time. This integrated approach offers significant advantages in data efficiency, security, and protocol flexibility, making it particularly suitable for complex, heterogeneous data environments such as IoT networks, cloud computing, and big data analytics.

Technologies for creating an optimized and accelerated network pipeline using a network pipeline abstraction layer (NPAL) are described. A DPU includes DPU hardware and memory that stores DPU software with the NPAL that supports multiple network protocols and network functions in a network pipeline. The network pipeline includes a set of tables and logic organized in a specific order to be accelerated by an acceleration hardware engine of the DPU. The acceleration hardware engine processes network traffic data using the network pipeline.

The invention provides an automatic network protocol testing method and system based on a large language model. According to the method, through three core technologies of structured protocol specification extraction, hybrid test case generation and retrieval feedback enhanced code generation, full-process automation from a protocol specification document to an executable test code is realized. The method comprises the following steps: firstly, carrying out preprocessing and structured analysis on an RFC document, and extracting protocol field information and state machine description; a complete test case covering normal, abnormal and boundary conditions is automatically generated based on protocol elements; and finally, an initial test code is generated in combination with a test equipment API document retrieval result, and multi-round iterative optimization is performed through running log analysis and an experience knowledge base. Compared with the prior art, according to the scheme, an end-to-end automatic test work chain is achieved, new protocol testing can be adapted without manual protocol modeling, meanwhile, a large language model and expert experience are fused through a feedback mechanism, and the test efficiency and the code quality are remarkably improved.

The invention discloses a network data integration analysis system and method based on a model context protocol MCP, and relates to the technical field of computer networks. The system comprises an MCP-LSP adaptation layer, a network analysis language server, an IDE plug-in, a remote cooperation management module, an MCP analysis engine, an LSP client and an IDE UI component. According to the invention, deep fusion of the network protocol analysis capability and the integrated development environment is realized; by constructing an MCP-LSP adaptation layer, a network analysis language server can convert a semantic analysis result of an MCP analysis engine into an LSP standard message format, and standardized services of functions such as protocol analysis, session state and anomaly detection are realized; the working efficiency of a developer is improved by the IDE plug-in; the remote cooperation management module realizes synchronization and sharing of session states, improves integration, expandability and cooperation efficiency of network analysis, and is suitable for diversified requirements of modern software development teams.

The invention belongs to the technical field of network security vulnerability detection, and relates to a network security vulnerability detection method and system based on artificial intelligence, and the method comprises the steps: extracting structured and unstructured feature fields of a target protocol data packet from network traffic in real time, and carrying out the syntax tree analysis of the structured feature fields to generate a nested hierarchical feature vector; generating a dominant abnormal confidence coefficient through positive and negative sample comparison of the feature vector, performing time sequence state tracking and dynamic qualification verification on the unstructured feature field, generating a hidden abnormal confidence coefficient, judging the existence of the network security vulnerability by integrating the dominant abnormal confidence coefficient and the hidden abnormal confidence coefficient, if the judgment result is yes, identifying vulnerability attributes, and if the judgment result is no, identifying the network security vulnerability. A blocking strategy corresponding to known vulnerabilities or novel vulnerabilities is triggered, accurate identification and efficient response of a network protocol level to security vulnerability threats are realized, and the threats are effectively intercepted at a network communication bottom layer, so that safe and stable operation of a network is guaranteed.

The invention relates to the technical field of copper pipe flaw detection, in particular to a flaw detection ink jet line time sequence synchronous control method, system and equipment, and the method comprises the following steps: flaw detection equipment and an ink jet head establish a master-slave node relationship according to a communication network protocol, the flaw detection equipment is used as a master node, and the ink jet head is used as a slave node; the master node acquires flaw detection signals of the copper pipe in real time, encapsulates the flaw detection signals into a data packet carrying a timestamp, and transmits the data packet to the slave node according to a communication network protocol; after the slave nodes receive the data packets, the timestamps are analyzed, and ink jet trigger compensation time is calculated based on a network transmission delay prediction model; a synchronous ink jet instruction is generated according to the ink jet trigger compensation time, and an ink jet head is controlled to mark the defect position of the copper pipe; and periodically checking clock skew data of the master and slave nodes based on a communication network protocol, and dynamically updating the network transmission delay prediction model. According to the invention, the core problem of mark position offset caused by insufficient clock synchronization precision is effectively solved.

The invention discloses a digital information secure transmission system and method based on network protocol analysis, and relates to the technical field of digital information transmission. In order to solve the problems that an existing secure transmission method mainly focuses on encryption and access control, cannot fully identify security risks possibly caused by protocol vulnerabilities or improper configuration, lacks an active identification and repair mechanism for potential vulnerabilities, is relatively single in processing mode of secure transmission and lacks a flexible real-time response mechanism, the invention provides a secure transmission method based on the protocol vulnerabilities. According to the method, potential security threats and vulnerabilities are detected and identified in real time by deeply analyzing a network protocol, and security risks existing in network communication are dynamically protected in combination with a threat detection and vulnerability repair mechanism, so that higher security is provided in the aspects of data encryption and access control, and the security of the network communication is improved. And potential vulnerabilities and attacks on the protocol level are effectively prevented through deep analysis and behavior pattern recognition of the network protocol.

The invention relates to a network protocol intelligent extraction method and application based on a large language model, and the method comprises the steps: taking a network protocol specification text as an analysis object, and initializing each candidate prompt word; a large language model is constructed, scoring consideration of three indexes including accuracy, format compliance and coverage rate under network protocol specification text extraction application is achieved according to the candidate cue words, cue word variants under a preset cue word variant mechanism are matched under loop iteration, all target cue words are obtained in an optimized mode, and then the target cue words are obtained on the basis of all the target cue words; analysis and extraction of chart description are deepened, efficient and accurate acquisition of a target network protocol text about a protocol message format and a protocol interaction process is achieved by applying a large language model, and compared with a traditional method, the design scheme has obvious advantages in the aspects of accuracy, generalization ability, information utilization, complex protocol processing and the like. And a more efficient and reliable way is provided for extraction of protocol specifications.

The invention provides a network protocol fuzz testing method based on strategy gradient reinforcement learning, and relates to the technical field of fuzz testing, and the method comprises the steps: building a test corpus through obtaining a to-be-tested protocol data packet format; establishing an action space containing a mutation operation set and a state space of a benchmark test data packet feature vector; determining a reward value based on the test result; calculating a strategy gradient by using a reward value, and updating strategy network parameters in combination with an adaptive learning rate; and performing fuzzy testing by using the trained model. According to the invention, the test efficiency is improved, the variation strategy selection is optimized, and the effectiveness of the network protocol security test is enhanced.

The invention provides a low-altitude networking of intelligence multipath data transmission system based on MPQUIC, and belongs to the technical field of novel network protocols, and the system comprises a network state analysis module which is used for obtaining the network state indexes of all communication paths of an unmanned aerial vehicle under different position coordinates; the flow scheduling module is used for predicting the track of the unmanned aerial vehicle, scoring the communication paths by combining the network state indexes of the communication paths, and obtaining the communication paths corresponding to the data flows with different priorities of the unmanned aerial vehicle according to the priorities of the data flows with the scores of the communication paths; and the path selection optimization module is used for dynamically adjusting weight parameters in communication path scores based on deep reinforcement learning by taking network throughput maximization and time delay minimization as targets to obtain optimal communication paths corresponding to data streams with different priorities. According to the invention, the problem that the prior art is difficult to have high reliability and low time delay due to the dynamic change of the network environment caused by the position change of the unmanned aerial vehicle in the low-altitude Internet of Things is solved.

The invention discloses a direct memory access (DMA) communication device for a computing network convergence computing architecture and a working method of the DMA communication device. The DMA communication device comprises a DMA transaction processing module and a protocol conversion module; the DMA transaction processing module is used for analyzing related DMA read-write requests, realizing processing of the DMA read-write requests and receiving of response data of the DMA read requests and completing processing of interrupt requests at the same time, and the protocol conversion module is used for completing conversion of read-write requests between a DMA interface and an AXI interface and mapping of response states. The invention aims to realize efficient protocol conversion between AXI and DMA interfaces, avoid the problem of DMA read request starvation caused by resource competition and ensure the consistency of DMA data access so as to improve the performance of an accelerator in high-performance calculation and artificial intelligence application, reduce transmission delay and improve data transmission bandwidth and energy efficiency.

The invention belongs to the field of underwater acoustic networks, and relates to underwater Internet of Things, underwater environment monitoring, resource development and marine ecological protection. In order to provide an overall architecture of a simulation platform and an implementation technology of a core module of the overall architecture, and further enhance the simulation capability of an underlying protocol and a physical channel on the basis of being compatible with a standard network protocol stack and supporting seamless deployment of an existing application program, the invention provides an underwater Internet of Things semi-physical simulation system based on a container. Comprising a terminal host, a gateway node with underwater sound and wired network communication capabilities, and a plurality of real nodes which can be deployed underwater and have underwater sound communication capabilities, and an MAC simulation module and a channel simulation module are deployed on the terminal host and are used for constructing a simulation environment; and the semi-physical simulation interfaces are simultaneously deployed on the terminal host and the gateway node, so that traffic mapping in a simulation environment of the gateway node and the terminal host is realized. The method is mainly applied to design and manufacturing occasions of an underwater Internet of Things simulation system.

The invention belongs to the technical field of network security, and particularly relates to a network security protection system and method based on an electric power emergency communication environment, and the system comprises a quantum-classical hybrid encryption system which is used for generating a dynamic key in real time; the biological characteristic driven dynamic trust ring is used for equipment identity authentication; the unmanned aerial vehicle relay network protocol is used for data transmission; the secret key after-reading burn-down protocol is used for information secret key after-reading burn-down; a key output by the quantum-classical hybrid encryption system is used for authentication encryption of a biological characteristic driven dynamic trust ring, an unmanned aerial vehicle relay network protocol transmits data encrypted by the quantum key, and a key burn-down protocol is used for reading encrypted data to trigger a key burn-down mechanism. According to the method, the key security is improved, the network attack difficulty is increased, the communication delay in a disaster is reduced, man-in-the-middle capture and attack can be immunized, and the problem of contradiction between the encryption strength and the real-time performance is solved.

The embodiment of the invention provides an air-sea cross-domain communication method and device. The method comprises the following steps: fusing a multi-system communication protocol into a cross-domain communication network protocol through a water surface wireless communication multi-band access technology, and carrying out adaptive networking; the method comprises the following steps: performing dictionary table mapping on original information sent by underwater subsurface buoy sensor equipment to obtain a translated character sequence, constructing a Huffman tree according to the occurrence frequency of each character in the translated character sequence, and generating a Huffman code corresponding to each character according to the Huffman tree; the method comprises the following steps: monitoring channel noise and a channel error rate of an underwater acoustic channel in real time, determining a real-time channel state, determining a channel coding parameter adjustment strategy matched with the real-time channel state through an adaptive algorithm, and dynamically adjusting a coding mode and coding parameters of the channel according to the channel coding parameter adjustment strategy. Uploading the Huffman code to a superior command center through a preset dual channel according to the dynamically adjusted coding mode and coding parameters; according to the invention, the air-sea cross-domain communication efficiency can be effectively improved.