263 results about "Real time networks" patented technology

The invention provides a dynamic network security defense method and system oriented to real-time network state adaptation, and the method comprises the steps: building a standardized state vector sequence through collecting key indexes, such as network flow rate, abnormal connection ratio, topology change frequency, protocol distribution deviation degree and the like; based on the sequence, utilizing a time sequence prediction model embedded with a structured attention mechanism to predict attack risk trends of a plurality of time windows in the future in a prospective manner; and in combination with the current state and the risk trend, dynamically selecting an optimal strategy path in a predefined defense action map, mapping the optimal strategy path into a standardized control command which can be executed by equipment, and dispatching, issuing and executing the standardized control command according to the priority. According to the method, closed-loop self-adaptive defense from state perception and risk prediction to strategy generation and execution is realized, and the real-time performance, intelligence and engineering deployability of a network security system are remarkably improved.

The invention provides a network interference positioning method and device based on multi-source data fusion, and relates to the technical field of communication, and the method comprises the steps: extracting a frequency domain feature vector of radio frequency signal data, a spatial feature vector of environment perception data, and a topological feature vector of network topological data, carrying out cross-modal alignment on the frequency domain feature vector and the spatial feature vector; dynamically adjusting a fusion weight based on a real-time network load, and fusing each feature vector based on the fusion weight to obtain a fusion feature tensor; and performing non-line-of-sight propagation correction on the fused feature tensor to obtain a positioning parameter set, and determining the position of the interference source based on the positioning parameter set. According to the method and the device provided by the invention, multi-source data is adopted for positioning, cross-modal alignment is carried out on features of different modals, the fusion weight is dynamically adjusted based on the real-time network load, and through targeted non-line-of-sight propagation correction, the interference source is accurately identified and quickly positioned when the network is in a heavy load state.

The invention discloses a data migration method and device, computer equipment and a storage medium, and realizes a data migration processing mode oriented to an unstable network environment by combining neural network prediction, decision tree anomaly recognition and a distributed cache mechanism. Therefore, the data transmission stability and the migration task completion rate in the cross-network environment are obviously improved. Firstly, a pre-trained neural network is utilized to carry out deep prediction on a real-time network state, and a potential performance reduction trend is identified in advance, so that a migration process can actively adjust a path or a strategy before a risk occurs. And secondly, by performing decision tree identification on a data packet sending sequence, the system can position a potential interruption point based on fine-grained time sequence characteristics, and quantify the risk in an interpretable manner, so that migration control can be accurate to a specific data packet level. Finally, under the condition that the risk is unacceptable, the system automatically activates a distributed cache mechanism, and the durability and the restorability of the data are guaranteed in a redundant copy storage mode.

The invention provides an AI model intelligent training and reasoning integration method and system, and the method comprises the following steps: receiving a model training instruction, and carrying out the preprocessing of original data, and obtaining a training data set; executing model training based on the training data set, monitoring task priorities and resource requirements through a dynamic resource scheduling algorithm, and dynamically adjusting training resource allocation according to a real-time monitoring result; when the model reaches a preset performance index, performing model pruning and quantification to generate an optimization model, and performing parameter fine tuning on the optimization model to obtain a final deployment model; generating reasoning service configuration according to calculation characteristics of the final deployment model, migrating the model and the configuration to a reasoning environment, and starting reasoning service; and dynamically adjusting the number of reasoning nodes according to the real-time network flow of the reasoning service. By implementing the technical scheme provided by the invention, through bidirectional dynamic resource scheduling and model deep optimization, the computing resource utilization rate and the model deployment efficiency are improved, and the high availability of the reasoning service is guaranteed.

A method of scheduling data transmission in a radio access network is provided. The radio access network comprises a scheduler configured to orchestrate communication of data between one or more base stations and a plurality of User Equipments, UEs, according to a set of instructions. The method comprises receiving real-time network usage data. The method further comprises dynamically modifying the set of instructions based on the network usage data. The method further comprises implementing the modified set of instructions so that the scheduler is configured to orchestrate communication of data between the one or more base stations and the plurality of UEs according to the modified set of instructions.

The invention discloses a dual-channel adaptive network stability testing method and system, and belongs to the technical field of computer network testing. The system adopts a dual-channel asynchronous communication architecture in which a control channel and a data channel are physically separated, instructions and state information are transmitted through an independent TCP control channel, and high-speed test data streams are transmitted through a UDP or TCP data channel. The method comprises the following steps: dynamically and adaptively adjusting a timeout parameter based on real-time network delay detection; nanosecond constant interval packet sending control is realized by using a high-precision monotonic clock source and busy waiting compensation mechanism; a packet loss detection, positioning and ACK confirmation mechanism based on the serial number is realized in an application layer; and real-time statistical display and idle detection automatic termination are realized through multi-thread parallel processing. According to the invention, the precision, the stability and the adaptive capability of network testing are remarkably improved, and the method is suitable for long-time stability testing in various scenes such as a high-speed Ethernet, a satellite link, an industrial Internet of Things gateway and the like.

A 3D development engine-oriented adaptive flow loading method relates to the technical field of 3D development engines, and comprises the following steps: collecting environment state data through an environment sensing module; continuously acquiring a network state index through a network monitoring module; establishing a dynamic priority calculation model, taking the environment state data and the network state indexes as input parameters, and generating a quantitative priority score for each resource project; performing dynamic reconstruction and sorting processing on the resource loading task queue based on a priority scoring result, adjusting the execution number of concurrent loading tasks according to a real-time network state index, and selecting a corresponding resource compression strategy; and establishing a resource life cycle management system, and executing allocation optimization and recovery cleaning operation of the memory resources. According to the invention, adaptive flow loading of 3D resources can be realized, and the fluency of a large-scale scene, the network adaptability and the system resource utilization efficiency are improved.

Embodiments here in disclose a method and system for network configuration generation using a network digital twin (NDT) comprising a spatio-temporal graph neural network (STGNN) for performance-based simulation in a communication network system is disclosed. A simulation request is received from a management service (MnS) consumer, including network parameters related to live network nodes. Performance data is retrieved from a management server, and performance is assessed against a performance threshold. Multi-domain telecom data is processed using a spatio-temporal graph neural network (STGNN) by a feature fusion attention mechanism, trained via a hybrid feedback loop with real and synthetic data. Upon meeting performance thresholds, a configuration recommendation is generated by a configuration recommendation engine, including an explanation and confidence value. The configuration recommendation is transmitted to the MnS consumer, enabling network automation without altering the live network directly.

The invention belongs to the technical field of network security management and control, and particularly relates to a data security storage method adaptive to network security prevention and control, which comprises the steps of data acquisition, multi-dimensional feature extraction, optimal prevention and control strategy generation, hierarchical encryption storage, abnormal behavior traceability and dynamic isolation response. According to the system, illegal data injection risks are blocked from an entrance through a source data authentication acquisition module, multi-dimensional effective features of data are extracted through a multi-dimensional feature purification module, the multi-dimensional effective features and real-time network threat situations are fused through a prevention and control strategy adaptation module, and an optimal prevention and control strategy is screened by quantifying the adaptation degree; the hierarchical encryption storage module reasonably distributes storage hierarchies and executes differential encryption and hierarchical backup, the abnormal behavior traceability module accurately identifies abnormal behaviors, and the dynamic isolation response module executes hierarchical isolation and triggers emergency response and strategy optimization, so that full-life-cycle safety management and control of data from collection, storage to access is realized, and the safety of data storage is improved. And the prevention and control accuracy and the system operation efficiency are both considered.

The embodiment of the invention discloses a file fragment uploading method and device, electronic equipment, a computer program and a storage medium. According to the embodiment of the invention, a to-be-uploaded file and file information are obtained, wherein the file information at least comprises a file size; segmenting a to-be-uploaded file according to the initial network speed of the current network to obtain a plurality of initial file fragments; uploading the initial file fragments to the server in sequence, and recording uploading time consumption and uploading rate corresponding to the initial file fragments; adjusting the size of the next initial file fragment according to the uploading time consumption and the uploading rate of the uploaded initial file fragment to obtain a target file fragment; and uploading the target file fragments to the server. Therefore, in the file uploading process, the real-time network state is determined according to the uploading condition of the uploaded fragment by adopting a mode of'dynamic fragment uploading + network self-adaption ', and the size of the fragment is dynamically adjusted according to the real-time network state, so that the file uploading success rate and the user experience are improved, and the method is flexibly applied to various large file uploading scenes.

The invention provides a service debugging method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of service debugging. Initiating a debugging request for a target service instance based on a local agent, and obtaining a short-term security credential by the local agent; wherein the short-term security credential comprises the target service instance identifier; establishing, by the local agent, an encrypted communication channel with a remote agent using the short-term security credential; the remote agent extracts the target service instance identifier from the short-term security credential and obtains a real-time network address of the target service instance; and sending, by the remote agent, the received debugging traffic to the real-time network address of the target service instance. Identity authentication and authorization are realized through a short-term security certificate, and security and credibility of a debugging request are ensured; the security risk in the debugging process is reduced, and the flexibility and efficiency of cross-network environment debugging are enhanced.

The invention discloses a multilayer space dynamic topology adaptive communication network construction method, and belongs to the technical field of communication networks. The method is realized through five steps of constructing a multi-layer fusion architecture of space-based, air-based and ground-based networks, instantiating network slices of power service perception, performing dynamic topology abstraction and resource pre-mapping based on virtual cell division, performing power and bandwidth collaborative allocation based on reinforcement learning, and deploying multi-domain fault diagnosis and predicting a closed loop. According to the invention, power and bandwidth collaborative allocation based on deep reinforcement learning not only can optimize the total capacity of the system, but also can realize dynamic balance of the network load. The system can automatically adjust the power and bandwidth distribution according to the real-time network state in the operation process, and the self-adaptive mechanism enables the network to efficiently cope with the demand changes of different power businesses.

The invention discloses a lightweight SDN (Software Defined Network) attack detection method based on multi-scale iterative attention, relates to the technical field of network security, and solves the problem that an SDN attack detection method based on deep learning in the prior art is insufficient in feature selection static state and spatial modeling and gives consideration to both lightweight and high precision. The method is based on a feature contribution degree evaluation mechanism, the most critical features for attack discrimination are screened out in real time, redundant information is eliminated, and the calculation burden is reduced. Moreover, the attack feature map is generated through normalization, time window overlapping slicing and multi-channel space coding, so that the perception capability of a complex attack mode is improved. Besides, a multi-scale iteration attention mechanism is embedded in a lightweight network architecture, key features are highlighted and redundant information is suppressed through multi-granularity convolution extraction and iteration weight fusion, and both lightweight and high-precision detection are realized, so that the method is suitable for real-time network environment and edge device deployment.

The invention relates to the field of network communication, and discloses a fast message pushing method and device based on converged communication and a medium, and the method comprises the steps: collecting message content data through a multi-source heterogeneous interface, carrying out the normalization processing of the data, and generating standard message metadata; key feature vectors are extracted from the standard message metadata by adopting a deep semantic analysis model, and a message priority evaluation matrix is generated in combination with a user portrait database; based on a multi-channel intelligent routing algorithm, generating an optimal transmission path decision tree according to the priority evaluation matrix and the real-time network state data; and distributing the message to the target communication channel according to the decision tree path through a dynamic load balancing engine, and executing multi-protocol adaptive transmission. According to the method, a whole-process message pushing method from matrix evaluation to intelligent routing and then to multi-protocol adaptive transmission is constructed, a high-reliability and low-delay communication solution is provided for a scene needing rapid message pushing, and the method has remarkable technical breakthrough and commercial application value.

The invention provides an intelligent control method and system for a body-equipped robot based on man-machine interaction data, and relates to the technical field of medical robots, and the method comprises the steps: building a 5G network two-way transmission channel between a control end and a surgical robot end; through a 5G network two-way transmission channel, synchronously transmitting two types of data streams between the control end and the surgical robot end, namely a control instruction data stream generated by the control end and a surgical image data stream collected by the surgical robot end; in the transmission process of the two types of data streams, real-time network time delay is continuously monitored, whether the current network time delay exceeds a preset time delay threshold value or not is judged, and the preset time delay threshold value can be dynamically corrected according to the network state; and when it is judged that the current network delay exceeds the preset delay threshold, dynamically deploying a plurality of network probes on the 5G network two-way transmission channel. According to the invention, the real-time performance, the accuracy and the safety of the remote operation can be ensured.

The invention provides an FTU-DTU cooperative power distribution terminal recording data uploading method and system, and the system comprises a feeder terminal unit which is used for collecting feeder electrical quantity data, generating a recording file when a fault is detected, and transmitting the recording file to a data transmission unit; and the data transmission unit is used for receiving the wave recording file, compressing the wave recording file, adaptively selecting a communication protocol according to a real-time network state, and uploading the compressed wave recording file to a master station system.

The invention belongs to the technical field of computer networks, and discloses a resource-constrained network equipment-oriented double-layer GRU lightweight real-time network anomaly detection method, which comprises the following steps of: constructing an anomaly detection model and training, deploying the trained model to resource-constrained network equipment, the method comprises the following steps: acquiring a system log generated when network flow generated by each application server node flows through in real time, performing data preprocessing on the acquired system log to obtain log semantic vectors, and forming a window sequence by the continuous log semantic vectors to obtain an abnormal probability value; and comparing the abnormal probability value with a set threshold value, and directly outputting an output detection result at an equipment end. According to the method, complexity of entity information is eliminated, time dependence in a time sequence is enhanced, simplification is carried out, a high anomaly detection recall rate is achieved under the condition that low calculation overhead is kept, the risk of missing detection is reduced, and cascade faults caused by the fact that anomalies are not found in time are avoided.

The invention relates to the technical field of integrated control systems, in particular to an integrated control system and method for hollow filter stick forming equipment, and the method comprises the steps: taking an embedded controller for running a real-time operating system as a control decision module, and carrying out the unified execution of all control algorithms; an Ethernet protocol with microsecond-level period and precise clock synchronization is used as a real-time communication module to form a system backbone network; the execution and acquisition layer integrates a motion control module composed of a servo driver and a motor as a network slave station, a signal interaction module composed of a distributed I / O module, and a subsystem integration module formed by bridging a third party subsystem through a communication gateway. According to the system, all key units are deeply integrated into the same real-time network as slave stations, so that centralized management, real-time synchronization and refined closed-loop control of the whole system are realized.

The invention provides a video adaptive transmission and reconstruction method and device based on end-cloud collaboration. The invention provides a camera end and server collaborative low-bandwidth key frame priority transmission and middle frame reconstruction method and device, which realize a collaborative mechanism of end-side semantic key frame intelligent labeling and cloud high-quality middle frame reconstruction, and adaptively switch transmission modes according to a real-time network state. And the video event integrity and the visual coherence in a low-bandwidth environment are effectively ensured.

The invention discloses an intelligent load distribution system and method for a centrifugal compressor unit. The system comprises a data sensing module, an intelligent decision-making module, an instruction execution module and a model updating module. The method comprises the steps that operation parameters of multiple parallel centrifugal compressors are synchronously collected in a high-precision mode through an industrial real-time network supporting hardware timestamps, and time-aligned system state observation data are obtained; constructing a state vector based on the data and a load prediction result, inputting a deep reinforcement learning agent fused with compressor physical characteristic constraints, and generating a load distribution instruction and a unit start-stop suggestion; after safety boundary verification is conducted on the original action, the guide vane opening degree is adjusted through a feedforward speed planning and fuzzy self-adaption PID composite servo control algorithm, and starting and stopping operation is executed; meanwhile, empirical data are collected online, and model parameters are periodically and finely adjusted. According to the invention, efficient, safe, accurate and self-evolution compressor group control is realized.

A device may receive one or more quality of service (QoS) characteristics as a QoS intent for an application associated with a network, and may process the QoS intent, real-time network data, and historical network data, with a machine learning model, to predict application-specific QoS requirements. The device may determine a QoS adjustment based on the QoS intent and the QoS requirements, and may cause the network to implement the QoS adjustment.

The invention relates to a heterogeneous network convergence method and system based on a 5G + TSN multi-modal industrial transmission architecture, and the method comprises the following steps: collecting industrial multi-modal data based on 5G and TSN networks, and carrying out the classification marking of the data to obtain different types of data packets; performing priority judgment on the data packets according to a preset rule to form a priority sorting result, adjusting a transmission sequence according to the priority sorting result, and generating an ordered transmission data packet sequence; in combination with transmission characteristics of 5G and TSN, path planning is carried out on the ordered sequence, and an optimal transmission path is determined; protocol type identification and mapping are carried out on the data packet according to the planned path, protocol conversion and packaging are completed, and an adaptive protocol data packet is generated; and finally, cooperative transmission is carried out through the 5G network and the TSN network, so that the technical problem that dynamic adjustment cannot be carried out according to real-time network states and service requirements because different types of data packets often adopt static or empirical strategies in the aspects of transmission priorities and path selection is solved.

The invention discloses a high-definition video low-delay compression method based on self-adaptive adjustment, and aims to solve the problems of insufficient self-adaptive adjustment capability, too high encoding and decoding delay and low code rate and network bandwidth matching degree in the existing high-definition video compression technology. The method comprises the following steps: firstly, acquiring a high-definition video stream and a real-time network bandwidth parameter, extracting intra-frame texture complexity, inter-frame motion intensity and scene switching frequency characteristics, and constructing a two-dimensional adaptive compression strategy in combination with a real-time bandwidth; low-delay coding is executed by adopting parallel frame fragmentation and simplified adaptive arithmetic coding, adaptive code rate control is realized by combining code rate overflow early warning and intra-frame key region priority bit allocation, and finally, fast inverse transformation, quantization recovery and motion compensation optimization are completed at a decoding end based on strategy parameters synchronously transmitted at a coding end so as to reconstruct a high-definition video frame. According to the invention, the adaptive adjustment capability of video compression is improved, low time delay of coding and decoding is realized, and the method is suitable for real-time high-definition video transmission scenes such as remote monitoring and video conferences.

The invention discloses a power line carrier dual-mode switching method and a power line carrier dual-mode switching device based on weight calculation, relates to the technical field of communication, and aims to solve the problem that mistaken switching and frequent switching are easy to occur when a power line carrier switching strategy is determined based on a channel quality index and a fixed weight. The method comprises the following steps: acquiring multi-dimensional real-time network data and weight data of a power line carrier dual-mode communication network; monitoring first scene feature data in the real-time network data to obtain a first monitoring result, and dynamically adjusting the basic weight according to the first monitoring result to obtain a first target weight; calculating a global decision value; and comparing the global decision value with a decision threshold to obtain a comparison result, and switching a communication link of the power line carrier dual-mode communication network based on the comparison result. The power line carrier dual-mode switching method based on weight calculation provided by the invention is used for improving the accuracy of a communication link of a power line carrier dual-mode communication network by dynamically adjusting the weight.

Systems and methods for content-based routing of network traffic for distributed computing services are disclosed. A load balancing device monitors real-time network traffic data communicated to distributed computing services hosted on computing devices across multiple data centers. The load balancing device receives a user request to add a new distributed computing service, including a content map mapping service parameters to target devices. The load balancing device is configured to transmit incoming traffic requests for the new service to computing devices in either a cloud-based or on-premises data center by adding the content map to its routing configuration. Upon receiving an incoming request, the system determines if it corresponds to the new service, identifies a target device mapped to the request parameters using the content map, and transmits the request to the target device.

The embodiment of the invention discloses an intelligent arbitration method and system based on a multi-protocol vehicle-mounted network, and the method comprises the steps: analyzing messages from different communication protocols, and converting the messages into three-dimensional feature vectors containing data types, real-time features and data scales; acquiring load characteristic parameters of a vehicle-mounted network operation state to form a load characteristic vector, and performing load prediction on a network load trend in a preset time period through a GRU model based on the three-dimensional characteristic vector and the load characteristic vector; performing trigger judgment according to the scheduling trigger judgment parameter and a preset dynamic threshold value; and when the triggering condition is met, performing arbitration adjustment. According to the method provided by the invention, predictive scheduling optimization can be carried out according to the protocol type, the message attribute and the real-time network load state, so that the utilization efficiency of communication resources is improved, and the real-time transmission quality of key messages is guaranteed.

The invention provides a login verification system based on multi-terminal unification, which comprises a data synchronization module, a data verification module and a secure transmission module, and is characterized in that the data synchronization system predicts to-be-synchronized data based on a user historical behavior mode, and selects a synchronization mode for data synchronization according to real-time network quality; the data verification module is used for generating a unique version identifier for the data change and carrying out conflict detection and judgment on the data change; the secure transmission module is used for encrypting data, performing hierarchical control on sensitive data and establishing a secure synchronization channel for data synchronization; according to the invention, through user behavior prediction and a network adaptive strategy, data synchronization can be effectively carried out under different network conditions, and the accuracy and security of data synchronization can be improved through the data verification module and the security transmission module.

This invention provides a method, apparatus, electronic device, and medium for monitoring terminal inspection, applied to an inspection system connected to multiple monitoring terminals to be inspected. The method includes: acquiring historical service traffic information and real-time network stability information of the inspection system; determining inspection configuration information for the multiple monitoring terminals based on the historical service traffic information and the real-time network stability information, the inspection configuration information including inspection concurrency and inspection time period; and performing inspection on the multiple monitoring terminals according to the inspection configuration information to generate monitoring data for the multiple monitoring terminals. This invention achieves automated generation of inspection configuration information during the inspection process, while improving the concurrency performance of the inspection system, increasing the number of concurrent inspections within a period, and improving inspection efficiency.

This invention belongs to the field of industrial network performance optimization technology, specifically providing a DDS-5G network collaborative optimization method based on fuzzy logic. By setting static mapping rules, the QoS Policy parameters of the DDS are mapped to the QoS flow identifiers of the QoS flows in the 5G network, thereby achieving initial DDS-5G collaboration. Simultaneously, by establishing a fuzzy logic inference system, using the QoS Policy parameters and real-time network state parameters as input variables and 5QI as the output variable, and by optimizing and adjusting the QoS Policy parameters of the DDS through DQN and updating the fuzzy logic parameters of the fuzzy logic inference system, further DDS-5G network collaborative optimization is achieved. Through a cross-layer adaptation mechanism, efficient collaboration between the application layer and the 5G network is realized, making it suitable for scenarios with high requirements for low latency and high reliability, such as industrial automation and intelligent manufacturing.

The invention discloses a power grid SCR prediction method and device based on a few-mark data adversarial network and a medium, and the method comprises the steps: collecting control error data of a power grid system from a grid-connected or off-grid moment to a stable moment under different SCRs and different load grid-connected and off-grid working conditions in a historical time period, calculating an evaluation index through the control error data, and constructing a sample set; training and verifying the SCR estimation network by using the obtained sample set to obtain a trained SCR estimation network; the input of the SCR estimation network is each group of data in the sample set, namely ISE, ISTE, IAE, ISAE and load after weight factors are introduced, and the output of the SCR estimation network is a predicted SCR value; a trained SCR estimation network is deployed in simulation, an evaluation index is calculated according to data collected in real time, the evaluation index and a load are input into the trained SCR estimation network for real-time network training, and the purpose of outputting an SCR estimation value at any moment is achieved.