764 results about "Real time networks" patented technology

The present invention relates to the technical field of data transmission. Disclosed is an application-driven three-dimensional spatial data transmission method. The method comprises: determining system key performance indicators (KPIs) by means of qualitative and quantitative analysis; constructing an AI-driven adaptive three-dimensional data transmission mechanism, and dynamically adjusting a transmission strategy on the basis of a real-time network state, a device capability, an application scenario and the KPIs; developing an adaptive compression algorithm set oriented to three-dimensional data, so as to meet differentiated compression requirements of different application scenarios; performing loop execution of a test, and adjusting and optimizing the data transmission mechanism and the compression algorithm set on the basis of a test feedback result and the real-time network state; and deploying an optimized transmission method to a production environment, and collecting field data to optimize the system performance and verify the achievement of the KPIs. By constructing a qualitative and quantitative analysis framework based on machine learning, the present invention quantifies differentiated transmission requirements of different application scenarios and formulates transmission strategies meeting the scenario requirements.

The invention provides a security encryption communication method and system based on quantum key management. The method comprises the following steps: acquiring system state data, and generating node state data, network connection diagram data and a system state matrix; receiving a communication request data packet, and generating a request feature vector, a service weight coefficient and a service demand matrix; the method comprises the following steps: collecting real-time network state data, constructing an enhanced Bayesian network model, and generating a network state scoring matrix; acquiring key pool state data, establishing a key consumption prediction model, and generating a key resource distribution matrix; constructing a multi-objective optimization model, and generating a scheduling strategy matrix; and executing the scheduling strategy and performing performance evaluation. According to the method, the quantum state de-coherence effect and the channel noise influence are considered, efficient management and safe distribution of quantum keys are achieved by establishing a complete evaluation and prediction system, and the performance and reliability of a communication system are improved.

The invention relates to the technical field of information synchronization, in particular to a cross-platform electronic communication synchronization system, which comprises a network construction and path optimization module for collecting basic network parameters of communication equipment of a plurality of platform networks, obtaining cross-platform network data according to the basic network parameters, selecting an optimal transmission path according to the cross-platform network data, and transmitting the optimal transmission path to a server; and generating real-time optimized path information. In the invention, the basic network parameters of the communication equipment of the plurality of platform networks are collected, the cross-platform network data are acquired, the edge weight is dynamically updated according to the delay and the bandwidth, and the optimal transmission path is selected based on the network state, so that the effect of efficiently utilizing network resources is achieved, and the real-time performance and the accuracy of synchronous data transmission are remarkably improved. The to-be-synchronized data is classified and subjected to priority setting, and the data processing sequence is adjusted in combination with the real-time network data, so that accurate scheduling of the synchronization task is realized, the delay and conflict problems in data transmission are reduced, and the efficiency and reliability of data synchronization are improved.

The invention relates to the technical field of communication networks, and discloses a service channel time slot allocation method, which comprises the following steps of: 1, acquiring network data in real time from network equipment, the network data comprising bandwidth utilization rate, delay, packet loss rate and service flow type; 2, predicting future network flow fluctuation through a long-short-term memory network model based on historical flow data and real-time network data; and step 3, dynamically adjusting the allocation of time slot resources according to the network flow fluctuation prediction result, and preferentially allocating the time slot resources for high-priority services. According to the method, the dynamic adjustment of the time slot resources is realized by combining the flow prediction of the LSTM model and the Q-learning reinforcement learning algorithm, so that the system can flexibly allocate the resources according to the actual network load condition, the resource waste is reduced, the utilization efficiency of the bandwidth is improved, and the utilization rate of the overall network resources is remarkably improved especially under the condition of high network load.

The invention relates to the field of communication, in particular to a method for protocol optimization and dynamic routing for a network communication system. According to the invention, based on deep learning and big data analysis technologies, protocols in network communication are intelligently optimized. Through analysis of historical communication data, information such as a flow mode and a congestion condition in network communication is learned and predicted, so that communication protocol parameters are dynamically adjusted, and the efficiency and the stability of the network communication are improved. And meanwhile, a multi-objective optimization algorithm and a real-time network monitoring technology are adopted to realize dynamic routing selection. By monitoring network states such as bandwidth, delay, packet loss rate and the like in real time, an optimal routing path is calculated in combination with an objective function, and a data packet is transmitted along the path. By optimizing a data packet structure and a transmission mechanism of a protocol, redundant data is reduced, and the data transmission efficiency is improved. The method can effectively improve the transmission efficiency of network communication, reduce the time delay and packet loss rate, and improve the network stability and reliability.

The invention discloses a communication transmission control cabinet based on big data, and relates to the technical field of communication transmission, and the control cabinet comprises the steps: introducing a real-time network state perception and prediction module, collecting the data of a link through an embedded sensor, carrying out the prediction through combining with an LSTM time sequence model, carrying out the estimation of a future link state, introducing a multipath transmission mechanism, and carrying out the real-time network state perception and prediction. When tasks are distributed, bandwidth, delay and load conditions of paths are synthesized, batch uploading is performed through caching and compression strategies of edge computing nodes, occupation of links is effectively reduced, when link performance is reduced, a path switching mechanism can select alternative paths with optimal performance in real time, influences of link interruption on communication tasks are avoided, and meanwhile, the communication efficiency is improved. According to the high-real-time task priority strategy, an optimal path is adopted, delay of key tasks is reduced, non-real-time tasks are compressed and processed in batches at edge nodes, resources are not competed with real-time tasks, interference is avoided, and the requirements for ship navigation and real-time monitoring tasks are met.

The invention provides a monitoring video transmission method and system based on wireless communication, and relates to the technical field of monitoring video transmission, and the method comprises the steps: obtaining and preprocessing network state data, the network state data comprising bandwidth, time delay and packet loss rate; establishing a network state prediction model based on the LSTM neural network, and predicting a future network state according to the network state prediction model; based on the future network state, a multi-objective optimization algorithm is adopted to perform transmission path selection; and for the selected transmission path, video coding parameters are adjusted, the coding parameters comprise a code rate, a resolution ratio and a frame rate, and a self-adaptive transmission protocol is adopted to dynamically adjust the transmission rate according to a real-time network state. According to the method, different paths can be dynamically selected, network congestion or degradation is avoided, and the video transmission quality and rate are adaptively and flexibly adjusted according to the current network state, so that stable transmission of the video quality under different network conditions is ensured.

The invention discloses a multi-source heterogeneous data synchronization system, and relates to the field of computer information management application. The system fuses key technologies such as flow dynamic regulation and control, self-adaptive load balancing and intelligent semantic mapping. Through a real-time network state monitoring and self-adaptive flow regulation and control algorithm, the data transmission rate is dynamically adjusted according to the server resource utilization rate, the data priority and the network bandwidth, data backlog and system overload are avoided, and the robustness and the real-time performance of a synchronization task are remarkably improved. The intelligent field mapping engine is based on semantic analysis, word vector representation and a machine learning model, automatically recognizes field semantic consistency of heterogeneous data sources, generates an optimal mapping rule and dynamically optimizes a mapping result, the manual configuration cost is reduced, and the mapping accuracy rate reaches 98% or above. The system has high expandability and self-learning ability, is suitable for a large-scale multi-source heterogeneous data integration scene, and provides an efficient, intelligent and self-adaptive full-stack solution for a data synchronization task.

Provided is a generative sequence processing model that is specifically finetuned for cybersecurity applications. This specialized generative sequence processing model can be finetuned on a comprehensive range of cybersecurity data and associated finetuning tasks, providing the model with rich capabilities for analyzing, understanding, describing, and taking action with respect to the real-time cybersecurity data generated by one or more cybersecurity operations tools. Thus, the creation and use of the generative sequence processing model represents a solution to the technical challenge of usefully analyzing and acting upon a large volume of data generated by a number of disparate cybersecurity operations tools deployed by an organization.

The invention discloses a remote centralized control distributed data processing and analysis method based on a JMS, and the method comprises the steps: achieving the message routing and resource scheduling through the real-time monitoring of the state of a system, reinforcement of learning decision, deep learning optimization and multi-dimensional message distribution; dynamically adjusting message flow rate and queue length based on real-time network conditions, node load and flow fluctuation; through edge node intelligent task scheduling, distributed collaboration of calculation and storage is realized; adaptive compression algorithm selection is carried out by using deep learning, and the consumption of message transmission bandwidth is reduced; non-tampering storage of messages is realized through a block chain technology; based on a container arrangement technology, dynamic expansion and contraction of computing resources are realized; and dynamic resource scheduling and optimization decision are realized by utilizing reinforcement learning and multi-dimensional monitoring data. According to the invention, the bottleneck problem of the traditional message transmission system is overcome, and the system can efficiently and reliably process mass data in a high-concurrency and high-throughput scene.

The embodiment of the invention provides a dynamic allocation method and device for AI reasoning tasks, and the method comprises the steps: monitoring the workload, memory availability, network delay, bandwidth and energy consumption of each computing node in a cloud edge cooperation system in real time, the computing node comprising a local edge device, an edge server and a cloud platform; according to the delay requirement, the calculation requirement and the data privacy requirement of the task and the real-time state of each calculation node, dynamically distributing the task to different calculation nodes; adjusting the complexity of an AI model used for executing the task according to the resource use condition of the computing node; and according to the historical performance data and the real-time network condition in the task execution process, optimizing a task allocation strategy.

Methods, systems, and computer-readable storage media for receiving, from probes, probe data indicative of vulnerabilities of one or more devices to cybersecurity threats. The devices are connected over a network. Aggregated probe data is generated by mapping the probe data using data relationships obtained from a relational database, The data relationships define vulnerability types of cybersecurity threats. A cybersecurity compliance score of each of the one or more devices is determined using a correlation of the aggregated probe data to cybersecurity status scenarios defining consequences related to the cybersecurity threats. A cybersecurity assessment report including the compliance score of the one or more devices and an action plan preventing the consequences related to the cybersecurity threats are provided.

The invention relates to an Internet of Things equipment intelligent management platform based on edge computing, and aims to realize efficient and intelligent management of Internet of Things equipment through an edge computing technology. According to the platform, the edge nodes are deployed near the data source, so that local processing and storage of the data are realized, the data transmission delay is remarkably reduced, and the response speed of the system is improved. And the platform is provided with an intelligent management module, so that the equipment state can be monitored in real time, faults can be predicted, repair operation can be automatically executed, and the stability and reliability of the system are enhanced. And a self-adaptive data stream processing algorithm is adopted, and the data stream is dynamically adjusted according to a real-time network state and equipment requirements, so that the high efficiency of data processing is ensured. The secure transmission module protects the data transmission process through an end-to-end encryption technology, and prevents data leakage and tampering. The cloud-side collaborative resource scheduling system realizes optimal allocation of resources, and a user-defined rule engine provides high flexibility and personalized management strategies to meet different user requirements.

The invention discloses a generative AI endogenous communication network architecture and a hierarchical collaborative scheduling method, and relates to the technical field of communication networks, the communication network architecture comprises an intention extraction layer, a mapping adaptation layer and a network execution layer, the intention extraction layer processes multi-modal data by using a generative AI model to obtain a semantic demand representation of a user, and the mapping adaptation layer carries out mapping on the semantic demand representation of the user; the mapping adaptation layer maps the semantic demand representation into a service quality index by using a generative AI model, maps a real-time network state of a heterogeneous network in the network execution layer into a network performance index, and generates a transmission scheduling strategy based on the service quality index and the network performance index, and the network execution layer works based on the transmission scheduling strategy, so that the network performance of the heterogeneous network is improved. End-to-end deterministic transmission scheduling of data packets of a service is implemented. According to the invention, efficient deterministic transmission scheduling of intelligent computing services in a 6G network can be realized.

The invention provides a heterogeneous network resource virtualization modeling and intelligent arrangement method and system based on a knowledge graph, and relates to unified modeling, dynamic retrieval and intelligent resource arrangement of heterogeneous network equipment. The method specifically comprises: 1, a unified modeling method based on a knowledge graph: integrating protocol attributes, dynamic states and topological relationships of heterogeneous devices such as a 5G base station, an SDN switch, a router, an Internet of Things gateway and the like into a structured knowledge graph, breaking the barrier of a manufacturer private data model, and realizing semantic-level collaborative scheduling of cross-domain resources; 2, designing a semantic retrieval engine: querying dynamic conditional reasoning through a natural language, replacing traditional manual rule definition, and improving retrieval response speed and accuracy; and 3, developing a graph-driven intelligent arrangement framework: combining a graph neural network (GNN) and reinforcement learning (RL), automatically generating a resource allocation strategy according to a real-time network state, reducing manual intervention and improving the resource utilization rate.

The invention provides a QoS routing optimization method and system, a computer and a readable storage medium, and the method comprises the steps: S1, obtaining a real-time network performance data set which comprises link bandwidth, delay and packet loss rate; s2, modeling a network topology into a graph structure based on the real-time network performance data set, and generating a network state feature matrix; s3, constructing a state space and an action space according to the network state feature matrix, wherein the action space comprises a plurality of candidate paths from a source node to a target node; s4, selecting a candidate path from the action space through a main network, evaluating the performance value of the candidate path by adopting a target network, updating the main network according to the performance value, and generating an empirical data set; and S5, optimizing a path selection strategy according to the empirical data set, and updating a routing table. According to the method and the device, the optimal path meeting the QoS requirement can be found for the data flow in different service environments in the dynamic network environment.

The invention discloses a weak network environment video compression method based on deep learning, and relates to the technical field of video compression, and the method comprises the following steps: 1, carrying out the feature extraction of video content through a deep learning model; 2, dynamically adjusting video compression parameters according to the extracted features and real-time network conditions; step 3, compressing the video by using a self-adaptive coding strategy; according to the video compression method based on deep learning, the video compression efficiency and adaptability in a weak network environment are remarkably improved, the video content is subjected to feature extraction by using the deep learning model, key information in the video can be intelligently identified, such as a motion vector, texture complexity and scene change, and the video compression efficiency and adaptability are improved. Therefore, a more accurate compression strategy is realized, the compression ratio of the video data is optimized, and the smoothness and the stability of video playing are ensured to be kept under variable network conditions.

The invention relates to the technical field of communication networks, in particular to a real-time network flow prediction and resource optimization distribution method and system based on a space-time multi-mode generation model. Multi-modal feature extraction and fusion are carried out; model prediction is generated; reinforcement learning scheduling optimization; self-supervision and feedback closed loop are realized; executing resource allocation; the method has the advantages that multiple data sources such as spatio-temporal data, user behaviors and application types are fused through the multi-modal large model, future network traffic distribution is generated through the generation model, and the accuracy and timeliness of traffic prediction are improved. The network resources of different areas and time periods are intelligently scheduled through the reinforcement learning model, real-time and automatic resource allocation is realized, and the network quality and user experience of high-demand areas are ensured.

The invention relates to the field of rail transit control systems, and discloses a rail transit intelligent control system and method based on the Internet of Things, and the system comprises a sensing layer which is integrated with a visual sensor, a thermal imaging sensor, a sound sensor, a pressure sensor, a temperature and humidity sensor, a CO2 sensor and other sensors, the sensing layer collects various data of the rail transit system in real time, and the sensing layer performs joint optimization processing on sensor data by adopting deep learning and reinforcement learning algorithms through an adaptive data fusion method so as to realize high-precision real-time monitoring, data quality screening and anomaly detection; and the network layer comprises a wireless communication module and adopts an intelligent routing mechanism based on real-time network load prediction. A self-adaptive data fusion method is adopted by the sensing layer, and the sensing capability of the rail transit system is greatly improved through intelligent data processing, so that the system can realize real-time and accurate response to a complex traffic environment.

The invention relates to the technical field of data exchange, in particular to a TCP-IP protocol-based application program data interaction method, which comprises the following steps of: collecting a signal quality index of application program data in a physical layer, capturing signal quality data of each node through real-time network monitoring, and performing time sequence analysis; and extracting a signal quality trend and an abnormal point, and generating an initial signal analysis result. According to the invention, through real-time analysis and trend prediction, potential problems in the network path can be effectively predicted, network path optimization is realized, the data are analyzed through the reinforcement learning model, the network path is predicted and optimized in real time, the adaptive adjustment capability of the network is enhanced, and the data transmission efficiency is greatly improved. Through simulation testing and real-time monitoring, delay reduction of data transmission and bandwidth utilization maximization are ensured, and then through dynamic adjustment of the size of a TCP window, adjustment is performed according to real-time network congestion conditions, and the overall performance of the network is greatly optimized.

The invention relates to the technical field of mobile data communication services, and provides a power dual-mode carrier network communication quality improvement system and method based on an optimization algorithm, and the method comprises the steps that a network state monitoring module monitors the condition in a power dual-mode carrier network in real time, and transmits monitoring data to a channel quality evaluation and prediction module; the channel quality evaluation and prediction module evaluates the current channel quality by adopting a machine learning model based on the data provided by the network state monitoring module, and predicts the future channel quality change; the resource allocation optimization module is used for dynamically allocating carrier resources of a reverse link by adopting an optimization algorithm based on a channel quality prediction result and real-time network load information; and the cross-modal carrier fusion module provides an allocation scheme according to the resource allocation optimization module. According to the method, the resource utilization efficiency, the communication stability and the overall performance of the power dual-mode carrier network can be remarkably improved, and main problems existing in the prior art are solved.

The invention discloses a network resource allocation method and device, equipment, a storage medium and a program product, and relates to the technical field of wireless, and the method comprises the steps: determining an initial game strategy of a game theory model according to a real-time network state data set of a communication network, and allocating initial network resources for each game participant of the game theory model, the game participants perform dynamic adjustment based on the initial network resources to generate an optimized resource allocation strategy; and based on the network state change data, performing network scheduling on the optimized resource allocation strategy through an intelligent scheduling algorithm to generate a resource allocation scheme. Since the initial network resources are allocated to each game participant through the game theory model and each game participant can dynamically adjust the initial network resources, the resource allocation scheme can be generated by flexibly adapting to the network state change data finally. The situation that existing 5G resource allocation and scheduling ignore multi-dimensional resource requirements is avoided, so that the overall network resource utilization efficiency is improved.

The invention discloses a hydrogen fuel engine electronic thermostat heating assembly control method based on the Internet of Things, particularly relates to the technical field of hydrogen fuel engine control, and is used for solving the problems of cooling response lag and action conflict caused by disconnection of control logic and a real-time network state of an existing temperature control system in a dynamic network environment. Electronic thermostat temperature data and network state data are collected in real time to generate a dynamic synchronization coefficient, network topology toughness evaluation and causal influence analysis are combined to predict instruction conflict areas and divide priorities, and gradient correction is performed on heating power and valve opening based on a target deviation compensation value. And a correction instruction is issued through the distributed control unit and a communication reliability weight is synchronously updated, so that dynamic coordination of a network state and a temperature control parameter is realized, and efficient and stable operation of the hydrogen fuel engine under a complex working condition is ensured.

The invention discloses a cross-regional network fault prevention and repair method, system and device and a medium, and the method specifically comprises the steps: collecting the multi-modal data of a cross-regional network according to distributed monitoring nodes, and generating a multi-modal AI model through the training of the multi-modal data based on a quantum computing platform; deploying the multi-modal AI model to each edge node of a cross-regional network, and performing fault detection on each edge node according to the multi-modal AI model to obtain a fault detection result; according to the fault detection result and the real-time network state of the cross-regional network, dynamically adjusting a traffic scheduling strategy of the cross-regional network through a reinforcement learning algorithm; and determining a fault self-healing strategy according to the fault detection result, and executing a fault self-healing operation on the cross-regional network through the fault self-healing strategy. According to the method, the technical bottleneck faced by a cross-regional network fault prediction and self-healing system is effectively solved, and the real-time performance, the accuracy and the self-adaptive capability of the system are improved.

The invention relates to the technical field of satellite telemetering and remote control, and discloses a port task processing method for a satellite measurement, operation and control system, which comprises the following steps of: 1, detecting all available network interfaces through a port scanner, and establishing a multi-path communication channel comprising a public network, a private network and a satellite link; step 2, acquiring packet loss rate, delay, bandwidth and jitter parameters of each path in the multi-path communication channel in real time, and updating the parameters based on a preset period; and step 3, generating a comprehensive score of each path according to the packet loss rate, the delay, the bandwidth and the jitter parameters. According to the method and the device, the technical scheme of dynamic scoring and switching of the multi-path communication channel is adopted, and the technical effect of automatically selecting the optimal path according to the real-time network performance is achieved; the problems of high transmission delay, large packet loss rate and incapability of adaptive adjustment caused by network fluctuation are solved.

The invention discloses a data transmission method and device, equipment and a storage medium, and relates to the technical field of data transmission. The method comprises the following steps: preprocessing collected initial data to obtain data to be transmitted, and monitoring network quality in a data transmission process in real time to obtain a real-time network condition; compressing the to-be-transmitted data according to the data characteristics of the to-be-transmitted data and the real-time network condition to obtain candidate data; performing feature extraction on the candidate data according to data features to obtain target feature data; and determining a data transmission strategy according to the network topology structure, the data characteristics and the real-time network condition of the edge device, and transmitting the target characteristic data to the target device based on the data transmission strategy. According to the technical scheme, data compression, feature extraction and transmission strategy determination are dynamically carried out according to the real-time network condition, the data transmission problem caused by the unstable network environment can be reduced, and the data transmission efficiency and reliability are effectively improved.

The invention provides a power distribution station house wireless communication method based on a cooperative coding algorithm, which comprises the following steps: data processing of dynamic environment adaptation: according to the real-time requirement of power distribution station house equipment data, segmenting original data into data blocks carrying priority labels, and dynamically generating a redundant coding strategy based on a real-time network state; network-aware cooperative transmission: transmitting data blocks and redundant information through multi-node cooperation, wherein a transmission path and a redundancy distribution proportion are dynamically adjusted according to network signal quality, node load and fault state; and fault-tolerant recovery of closed-loop feedback: the receiving end jointly decodes the data block and the redundant information, triggers a local retransmission instruction according to a decoding result, and feeds back the local retransmission instruction to the network architecture to optimize a subsequent transmission path.

The invention relates to the technical field of artificial intelligence, can be applied to the fields of finance, medical treatment and the like, and provides a network fault processing method based on artificial intelligence, and the method comprises the following steps: carrying out the trend prediction and anomaly detection through a machine learning model based on real-time network performance indexes and historical alarm data, and obtaining a network fault detection result; generating an alarm set containing fault type inference; performing association analysis on the alarm set and network topology and change records, constructing a fault propagation map, and outputting a root cause node list sorted according to probability; generating an equipment parameterized repair script according to the root cause node list; executing the repair script and monitoring an execution state in real time to obtain a repair result; and verifying the repair result. Compared with the prior art, the method has the advantages that the end-to-end fault processing period is remarkably shortened, the manual intervention requirement is reduced, and the service continuity is guaranteed in a complex network environment.

The invention provides a reliability-aware dependent task unloading method based on topology reconstruction in industrial internet edge computing, which comprises the following steps of: constructing a system model covering an edge cloud network platform, an industrial cloud platform and internet of things equipment, and establishing a transmission delay model and a reliability model; constructing a task unloading mathematical model with the maximum reliability level; the method comprises the following steps: modeling a micro-service dependency relationship into a weighted directed acyclic graph based on a network flow theory, and carrying out topology reconstruction on micro-services applied to the Internet of Things through a Ford-Fulkerson approximation algorithm to obtain a micro-service grouping structure formed by minimum cut division; the micro-service grouping structure serves as priori knowledge to be input into the deep Q network, the deep Q network is used for solving a task unloading mathematical model, a task unloading strategy is dynamically adjusted, resource allocation is optimized, and an optimal calculation unloading scheme in the industrial internet edge calculation environment is obtained. The micro-service deployment is optimized, the communication overhead is reduced, and the system reliability and the resource utilization rate are improved through real-time network state dynamic decision making.

The invention discloses an AI-based automatic connection and handshake protocol for a trusted data space connector. The AI-based automatic connection and handshake protocol comprises a data supply end connector, a data demand end connector and a platform operation end, the data supply end connector is used for collecting and preprocessing data; the demand end connector is used for receiving and processing data; the platform operation end is used for connection management, authority distribution and security auditing; the method has the beneficial effects that through automatic connection and an intelligent handshake protocol, manual intervention is reduced, and the data circulation cost is reduced; multi-factor authentication, dynamic encryption and privacy protection technologies are adopted to ensure the safety of the whole life cycle of the data; aI and machine learning technologies are introduced, so that the system can intelligently select an optimal strategy according to real-time network conditions and data features; interoperation and data sharing among different data spaces are realized, and circulation and value mining of data elements are promoted.