1317 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 Internet can be configured to provide communications to a large number of Internet-of-Things (IoT) devices. Devices can be designed to address the need for network layers, from central servers, through gateways, down to edge devices, to grow unhindered, to discover and make accessible connected resources, and to support the ability to hide and compartmentalize connected resources. Network protocols can be part of the fabric supporting human accessible services that operate regardless of location, time, or space. Innovations can include service delivery and associated infrastructure, such as hardware and software. Services may be provided in accordance with specified Quality of Service (QoS) terms. The use of IoT devices and networks can be included in a heterogeneous network of connectivity including wired and wireless technologies.

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 a collaborative autonomous perception and navigation method in a denial environment, which comprises the following steps: constructing a self-organizing robust communication system to realize dynamic networking and data sharing among multiple unmanned aerial vehicles in the denial environment, and adopting a self-adaptive network protocol to realize automatic configuration of a communication network under network topology change or communication interference; based on a multi-sensor fusion sensing system, environment information and motion information of the unmanned aerial vehicle are processed through a data fusion algorithm; a single unmanned aerial vehicle constructs a reward function based on unmanned aerial vehicle energy consumption, an exploration area, a path overlapping degree and a path danger degree, executes autonomous perception and exploration in an unknown environment and completes mapping; the distributed multiple unmanned aerial vehicles carry out individual communication and game playing in the unmanned aerial vehicle cluster according to map information, respective positions and working states, task distribution is achieved, and path planning and action decision making are completed. According to the invention, autonomy and collaboration of the unmanned aerial vehicle in a denial environment are improved, and an effective solution is provided for navigation in a complex environment.

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 relates to the technical field of flow anomaly identification, and discloses a flow anomaly identification method for a numerical control machine tool protocol, which comprises the following steps of: 1, multi-source data acquisition: acquiring various types of data from a numerical control machine tool and a control system thereof by arranging a data acquisition module, the data sources include network protocol flow data, machine tool sensor data, operation log data and equipment environment data, and the data formats of the data sources are kept uniform; the collected original data is subjected to standardization processing, the problems of data dimension inconsistency, noise interference and missing values are solved, and alignment of the data on a time axis is guaranteed through a synchronization algorithm; through a multi-source data acquisition and high-precision sensor system, the real-time monitoring of the flow of each key fluid system of the numerical control machine tool is realized. And by combining a plurality of feature extraction methods, the traffic anomaly can be effectively identified.

The invention provides a protocol conversion adaptation method and device, an industrial convergence gateway and a data transmission method, and belongs to the technical field of network protocol conversion, and the method comprises the steps: receiving state data periodically sent by an industrial device through an industrial protocol or an industrial protocol data packet returned in response to a query request; extracting key fields in the received industrial protocol data packet, wherein the key fields comprise a source address, a destination address, a function code, a register address and data content; and utilizing a programmable platform and a language to convert the analyzed data into a uniform 5G data packet format which comprises a source address, a target address, a protocol type, a timestamp and a priority field. According to the invention, efficient analysis and forwarding of a mainstream industrial protocol are realized, and data transmission under a 5G network low-delay characteristic is realized; multi-protocol dynamic analysis and expansion are supported, only P4 codes need to be updated when protocols are newly added, hardware does not need to be replaced, and flexibility and adaptation capacity are improved; efficient conversion and packaging are achieved, and the data transmission speed and the overall performance of the system are improved.

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 discloses a network protocol fuzz testing method and device based on state guidance and seed variation, and the method comprises the steps: 1), manually constructing a high-quality basic seed according to an official RFC protocol document, and enabling the seed to be mapped with a corresponding state; 2) inputting the message sequence into a target program for fuzzy testing, interacting with the target program, monitoring a program state, and obtaining feedback; 3) according to the protocol state code and path coverage rate feedback, selecting seeds with high coverage rate to carry out information retention variation, and reserving the state conversion function of the seeds to reduce the number of invalid seeds; and 4) generating a state guide message sequence: selecting a target test state according to a current state and a returned protocol state code, preferentially selecting a state which is not reached or has few arrival times as a target, and then generating a message sequence capable of reaching or approaching the target state according to the basic seed. According to the method provided by the invention, the state information of the protocol is fully utilized, the state conversion capability of the seed is reserved in the seed variation process, and the efficiency and the quality of the network protocol fuzz test are improved through state guidance and targeted message sequence generation.

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 an automatic network protocol fuzz testing system, particularly relates to the field of automatic network protocol testing, is used for solving the problems of protocol vulnerability detection and security verification, and improves the coverage rate and efficiency of protocol testing through an automatic network protocol fuzz testing system. The method comprises the following steps: firstly, capturing and recording complete communication data, generating an FSA of a protocol, mapping a protocol state and a conversion process by utilizing a proxy server, and performing message clustering and state association analysis and simplifying protocol behavior analysis by generating a GMS; secondly, dynamically analyzing the stability of the protocol state and the difference of the message sequence, intelligently selecting a deterministic or non-deterministic test function, and widely covering a protocol behavior scene; in the testing process, input, output and state information is recorded in real time, and the protocol state is automatically reset or returned when abnormity occurs, so that the continuity and traceability of the test are ensured. The potential vulnerabilities can be found comprehensively, the manual test workload is reduced, and the security and robustness of the protocol are ensured.

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.

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.

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 relates to a distributed storage and synchronization system based on a block chain, and belongs to the technical field of distributed file storage. The system comprises a node management module, a data fragmentation and distributed storage module, a consensus mechanism module, a data synchronization module, a cross-chain interaction module, a client interaction module and a decentralized node network. By optimizing a consensus mechanism and introducing a fragmentation technology and a cross-chain technology, the problems of expansibility and consistency of an existing system are solved, and interoperability between different block chain networks is realized. The system has high expandability and flexibility, supports access of various block chain network protocols and intelligent contract standards, and is easy to popularize and apply.

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.