1851 results about "Network conditions" patented technology

The invention discloses a cloud edge cooperative control method, which comprises the following steps: establishing a cloud edge cooperative architecture, collecting data by edge nodes, and selecting a corresponding data compression strategy to compress the data; generating an edge-cloud collaborative uploading data stream; the cooperative control layer executes adaptive load balancing based on a cloud-side node state, task characteristics and a network condition which are monitored in real time so as to realize task allocation and scheduling between the cloud and the side; the cloud receives the data stream transmitted by the edge node, decompresses and processes the compressed data according to the metadata information, completes the task and feeds back an execution result; a node performance prediction model is constructed by using system state monitoring information, a task allocation strategy and a resource allocation and data compression strategy are adjusted in real time according to a prediction result, and cloud-side collaborative optimization is realized. Efficient data transmission, flexible task scheduling and self-adaptive resource management are realized, and the performance of the system in a complex and changeable environment is comprehensively improved.

The invention provides a real-time streaming media transmission method and system based on a cloud game and a storage medium, and the method comprises the steps: monitoring network state parameters in real time, and generating network state data; dynamically adjusting the resolution, the frame rate and the bit rate of the video stream based on the network state; a redundant data packet is inserted, and the redundancy proportion is adjusted according to the packet loss rate and the jitter, so that the data reliability is enhanced; dynamically adjusting the transmission rate by adopting a congestion control algorithm; dynamically selecting an optimal server node according to a network condition and a server load; audio and video processing tasks are shared through edge computing nodes, and the load and transmission delay of a main server are reduced; setting a self-adaptive decoding buffer area, dynamically adjusting the size of the buffer area according to a network state, and reducing playing lagging; through real-time monitoring, dynamic adjustment and edge collaboration, smoothness is guaranteed, reliability is improved, delay is reduced, efficient load distribution and user experience optimization are achieved, and the method is suitable for real-time streaming media transmission in a cloud game scene.

The invention discloses a Transform large model training method based on cloud edge collaboration. The method comprises the following steps: S1, constructing a data set; s2, preprocessing the data set; s3, segmenting the preprocessed data set by using a data parallelism strategy, and performing distributed training by combining data parallelism and model parallelism technologies; s4, monitoring computing resources and network conditions of the edge equipment and the cloud server in real time, dynamically adjusting a data distribution strategy, and optimizing computing load distribution; s5, a multi-agent dominant actor-commentator algorithm is combined with a multi-arm tiger machine strategy, and the layered structure of the Transform model is adjusted in real time; s6, performing partial model reasoning tasks on the edge equipment in combination with the edge reasoning capability; s7, protecting data privacy by adopting a federal learning strategy and utilizing a differential privacy technology; and S8, performing hardware acceleration by utilizing heterogeneous computing resources. According to the method, a cloud edge cooperative computing and intelligent scheduling method is adopted, and large model training and reasoning with efficient privacy protection are realized.

The invention relates to the technical field of front-end resources, in particular to an intelligent caching method and device for loading front-end resources on demand, equipment and a medium, and the method comprises the following steps: obtaining user behavior data, predicting an access page, identifying required resources, and constructing a resource dependency graph; calculating resource use frequency and importance, and determining a target resource; selecting a resource format and a compression mode according to the user equipment type; according to the resource dependency relationship graph, loading and caching target resources required by a next page possibly accessed by the user in advance; meanwhile, the caching strategy is dynamically adjusted based on the resource use frequency and the importance index; the method comprises the following steps: monitoring a network condition, dynamically adjusting a resource loading strategy, pre-loading resources before a user requests by using a resource dependency graph and a server pushing technology, and determining a target loading mode when the user accesses. Therefore, the problems that in the prior art, resource loading is low in efficiency, a caching strategy is rigid, environment adaptability is poor, user behavior analysis is insufficient, and performance optimization automation is lack are solved.

The invention discloses a real-time interactive digital human system supporting high concurrency and an implementation method thereof, and relates to the technical field of digital human interaction.The system comprises a model instance pool module used for loading the weight of a deep learning model to a shared memory area through the memory mapping technology; the multi-thread scheduling module is used for scheduling user requests by adopting a lock-free queue and a dynamic priority algorithm; the asynchronous pipeline processing module is composed of decoupled micro-services, and the modules are connected in series through asynchronous message queues; the client SDK is used for dynamically switching a rendering mode according to terminal hardware performance and network conditions; the elastic capacity expansion and contraction module is used for monitoring resource loads in real time and automatically adjusting the number of service instances; and the audio and video synchronization calibration module is used for ensuring that the synchronization error of the audio and video frames is lower than a preset threshold value through a timestamp alignment algorithm. According to the scheme, core challenges in a high-concurrency digital human interaction scene can be systematically solved, and innovative support is provided for large-scale real-time application.

The invention discloses a multi-protocol intelligent interaction heterogeneous device full interconnection method and system, and belongs to the technical field of computers, and the method comprises the steps: S1, collecting data, and deploying edge computing nodes; s2, preprocessing the collected data; s3, performing classification according to task requirements of the data; s4, after classification, determining an optimal communication protocol suitable for the current network condition; s5, realizing data format standardization and communication protocol unification by adopting an OPC UA protocol; s6, on the basis of an OPC UA publishing / subscribing mechanism and a QoS priority mechanism, allowing the equipment to publish or subscribe data in real time and ensuring priority transmission of high-priority data; s7, performing data matching and association, and fusing the data; s8, realizing a global decision; according to the multi-protocol intelligent interaction heterogeneous equipment full interconnection method and system provided by the invention, by introducing protocol unification, intelligent interaction and distributed optimization technologies, the interoperability of multi-protocol equipment and the overall management efficiency of a traffic system can be effectively improved.

A system for the temporal ordering of multi-channel data streams, comprising the following: a multi-channel data reception module configured to receive real-time data streams from multiple sources, extract temporal metadata, and detect discrepancies in timestamps; a time synchronization module configured to align data packets using timestamp normalization, adaptive buffering, and AI-based predictive modeling; an AI-powered predictive modeling module trained to estimate missing or delayed timestamps and to reorder out-of-order data packets based on learned patterns; an adaptive buffer module configured to dynamically adjust buffer sizes based on real-time data arrival rates and network conditions; an ordering and output processing module configured to reconstruct the correct order of the data packets and transmit them to a processing or analysis system; and a module for error detection and correction that is configured to detect inconsistencies in timestamps, transmission delays and missing data points, thus ensuring increased reliability of the ordered data sequence.

The invention relates to the technical field of cloud storage, in particular to an alpine region logging data cloud storage system which comprises a network quality monitoring module, a data grading module, a dynamic bandwidth management module and an intelligent data migration module. According to the invention, by collecting the historical data of the network indexes and calculating the score of the current network state, the network condition is effectively evaluated, the data transmission strategy is optimized, the reliability and efficiency of data transmission are enhanced, priority rating is carried out according to the importance and emergency degree of the data, key data can be transmitted preferentially, the network congestion is effectively reduced, and the data transmission efficiency is improved. The data transmission efficiency is improved, the accuracy and the response speed of data management are optimized, the data access speed is improved by predicting the access frequency of the data and dynamically adjusting the storage medium, the storage cost is reduced, the overall data processing capacity and the cost effectiveness are improved, and the method is suitable for popularization and application. And the security and the quick accessibility of the key data in a severe environment are ensured.

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 relates to the technical field of circuit detection, discloses a power transmission line thermochromic wire clamp heating early warning method and system, effectively solves the problem of data acquisition distortion in a strong electromagnetic environment, and improves the accuracy of state evaluation through multi-source data fusion. The dynamically adjusted early warning model reduces the risk of false alarm and missing alarm caused by equipment aging, the intelligent decision support module shortens the fault handling response time, the data closed-loop mechanism ensures the reliability of the system in the whole life cycle, and the reliability of the system in the whole life cycle is improved through multi-sensor cooperative monitoring and edge calculation processing. And the influence of environmental factors on data acquisition is reduced. The two-channel transmission architecture guarantees the data transmission integrity under different network conditions, the CRC verification mechanism effectively recognizes and corrects transmission errors, a high-quality data basis is provided for a subsequent early warning model, the abnormal data recollection mechanism avoids data missing caused by single collection failure, and continuous and stable operation of the monitoring system is ensured.

The invention provides a network state monitoring and optimization processing method and device and a readable storage medium. The method comprises the steps of obtaining network state parameters through monitoring; determining data transmission parameters according to the network state parameters; determining the transmission priority of the target data according to the importance requirement and the real-time requirement of the target data to be transmitted; and based on the data transmission parameter and the transmission priority, a target path is adopted to carry out data transmission on the target data, and the target path is a preset main path or a standby path with the highest stability when the preset main path is unavailable. According to the method, the parameters can be adjusted in real time according to the network condition, the data transmission process can be optimized according to different service requirements, the capability of flexibly coping with unexpected conditions is achieved, and the data communication quality and the user experience are remarkably improved.

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.

The invention relates to a cloud side-end cooperative processing system based on a network computing technology. A cloud computing platform is used for storage of large-scale data and training of a deep learning model; the edge computing node module is used for receiving the data stream from the end side equipment and processing the data stream in real time; the end side equipment is used for collecting environmental data and primarily processing the environmental data, and the end side equipment comprises one or more of an unmanned aerial vehicle, an unmanned vehicle and an unmanned ship; the cloud edge-end cooperation module is used for efficient communication and calculation cooperation among the cloud, the edge and the end-side equipment; and the task scheduling and resource allocation module is used for scheduling and allocating calculation tasks in real time according to current task requirements, resource states and network conditions. According to the invention, the overall operation efficiency and real-time performance of the cloud side-end cooperative processing system can be improved, and the defects of the existing system in the aspects of processing capability and task scheduling are overcome.

The present invention discloses a method and system for intelligent switching of communication protocols for an omni-channel contact center, the method comprising the steps of: evaluating the performance of each communication protocol according to user needs and the current system status, selecting the optimal communication protocol and opening the corresponding communication service for the user; continuously monitoring the communication quality under the selected current communication protocol, and triggering the reselection of the communication protocol if it is detected that the communication quality is lower than expected; re-evaluating the performance of each communication protocol under the current system state to select the target communication protocol, and switching the current communication service from the current communication protocol to the target communication protocol. The present invention realizes smooth switching between different communication protocols, can intelligently identify and automatically select the optimal communication method, while maintaining the continuity and quality of the call unaffected. In addition, the present invention also supports dynamic adjustment of the communication method according to network conditions, ensuring that users can obtain the best communication experience regardless of the network environment.

The invention relates to the technical field of data transmission, in particular to a meteorological data compression and transmission intelligent control system which comprises a network state evaluation module, a data priority evaluation module, a data compression and encryption module and a data transmission control module. According to the invention, network conditions can be effectively predicted and key data can be transmitted preferentially by evaluating network volatility and data emergencies, the reliability and efficiency of data transmission are improved, delay and packet loss in the data transmission process are reduced through targeted data compression and encryption processing, and the data transmission efficiency is improved. Meanwhile, the safety of data in the transmission process is guaranteed, especially in a bandwidth-limited network environment, quick and accurate transmission of key meteorological information can be guaranteed by adjusting data transmission bandwidth allocation and selecting a proper transmission protocol, resource allocation is optimized, the adaptive capacity of the system to complex network conditions is enhanced, and the service life of the system is prolonged. Therefore, the system can work efficiently under the condition that the network load is large or unstable.

The invention relates to a traffic prediction method based on dynamic graph space-time correlation and adaptive adversarial training. The method comprises the following steps: acquiring a dynamic graph space embedding representation by learning dynamic graph data capture by utilizing an adaptive graph convolutional network; capturing the dynamic graph data according to the time attention network to obtain a dynamic graph time embedding representation; obtaining dynamic graph embedding; after dynamic graph embedding is used as initial embedding and the last preset adversarial node embedding is defined as context node embedding to be used as input of a strategy network, an adversarial reward function and a loss function are set, then the strategy network is trained, and an updated dynamic graph is output according to the trained strategy network. And performing spatial correlation capture and temporal correlation capture on the updated dynamic graph to obtain a traffic prediction result corresponding to the current network equipment. By adopting the method, data pollution and node abnormity can be effectively handled, and traffic prediction can be realized by adapting to continuously changing network conditions.

The invention relates to the technical field of data transmission and updating, in particular to a data transmission and updating system and method of a cloud mobile phone. According to the technical scheme, the system comprises an intelligent network monitoring module, a dynamic bandwidth allocation module, a server load balancing module, an intelligent data synchronization module and a cache management module, and the intelligent network monitoring module monitors network conditions, including network bandwidth, network congestion degree and network stability, between a user terminal and a cloud server in real time. Through the synergistic effect of the intelligent network monitoring module, the dynamic bandwidth allocation module, the server load balancing module, the intelligent data synchronization module, the cache management module and the like, the network condition is accurately monitored, the bandwidth is reasonably allocated, the server load is optimized, the data synchronization and completeness are ensured, and the cache efficiency is improved; stable and reliable network connection, preferential key data transmission and rapid data synchronization and loading are brought to users, and the use experience of the cloud mobile phone and the overall performance of the system are greatly improved.

The invention relates to the technical field of multi-path information processing, and provides a multi-path optimization method, device and system for network routing, and the method comprises the steps: obtaining network node information, obtaining a plurality of available paths based on the network node information, evaluating all available paths, and obtaining network bandwidth data and an initial weight; adjusting the initial weight to obtain an initial path weight and a path priority table, distributing and identifying data packets of network flow by using the path priority table, obtaining a flow data packet with a path identifier, and adjusting the initial path weight after obtaining a flow load log and network delay data based on the flow data packet; and generating a flow scheduling scheme to update the path priority table. Through obtaining network node information and utilizing a bandwidth evaluation algorithm, initial path evaluation reflecting network conditions is generated, and through real-time data feedback of a dynamic monitoring module, a flow scheduling scheme capable of carrying out adaptive adjustment at any time is generated, so that the defects of the prior art in the aspect of dynamic adaptability are improved.

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 discloses a wireless screen projection management system and method based on the Internet of Things technology, and belongs to the technical field of the Internet of Things. In the system, equipment is registered through an Internet of Things platform, the Internet of Things is used for automatic equipment discovery and automatic equipment broadcasting, and a control center identifies and manages newly-added equipment; a user initiates a wireless screen projection request, and the control center authorizes and manages the request based on a preset strategy; a data compression algorithm is adopted to reduce transmission data volume; analyzing the network condition in real time by using a deep Q network algorithm, and dynamically adjusting a data transmission strategy; recording screen projection quality parameters, analyzing historical screen projection data by using an artificial intelligence algorithm, predicting potential problems, providing optimization suggestions, and dynamically allocating and adjusting network bandwidth and equipment resources; collaboration among the equipment is realized by using the Internet of Things; faults occurring in the screen projection process are monitored in real time, a decision tree algorithm is used for analyzing fault causes, and recovery measures are automatically taken.

The invention relates to the technical field of audio pushing optimization, and discloses an audio stream pushing crash prevention method and system, a storage medium and equipment, and the method comprises the steps: dynamically generating a format perception pre-detection thread based on a current audio stream format, and adjusting the priority and sampling frequency of the thread according to the audio bit depth and the sampling rate; multi-level intelligent buffer area management with sound quality priority is implemented, and dynamic adjustment of the size of a buffer area and adaptive switching of the buffer area are realized according to different audio formats and input source types; establishing a cross-brand equipment coordination framework, and setting a watchdog timer with preferential tone quality to perform timed monitoring so as to process response timeout; triggering an elegant degradation strategy of sound quality priority according to the resource availability score, and reducing the audio processing quality step by step to maintain the playing continuity; the key characteristics of each transmission protocol are identified and utilized, the transmission protocols are adaptively switched according to the network condition and the processing capability of brand equipment, and real high-quality and zero-interruption audio experience is brought to users.

The present invention provides systems and methods for cellular network performance monitoring and optimization, enabling SIM-based devices to dynamically adapt to changing network conditions for improved connectivity. The invention introduces a process that includes determining baseline path performance through detailed probing of network metrics, continuously assessing current path performance via real-time monitoring, and instructing the SIM to switch from its current connected mobile network carrier to an alternate carrier when predefined performance thresholds are not met. Switching instructions are securely delivered Over-The-Air (OTA) to the SIM, ensuring seamless transitions to the most efficient and reliable network path. The system leverages both active and passive application layer observations to optimize latency, throughput, and reliability while supporting diverse applications, including IoT devices, industrial systems, and consumer devices.

The invention relates to the technical field of synchronous acquisition, in particular to a multi-camera synchronous data acquisition method and system, and the method comprises the following steps: based on a real-time video stream of a camera, analyzing a video frame, recognizing a moving object in a differentiated video stream, and carrying out the recognition of the moving object in the differentiated video stream; and synchronously recording the occurrence time and position change of the moving object in the differentiated camera to obtain view angle change data. According to the invention, the angle and focal length of the camera are synchronously adjusted to cope with the view angle change of different scenes, and the trigger time of the camera is uniformly configured and is synchronously started through the network, so that the time deviation is reduced, the analysis of the synchronous trigger data set and the timestamp adjustment are synchronously triggered, and the synchronization between the cameras is optimized; the consistency and synchronism of the video streams obtained from different angles are ensured, the data transmission parameters are dynamically adjusted according to the network condition, the efficiency and synchronization precision in the data transmission process are improved, and the influence of network delay and bandwidth change is effectively dealt with.

This disclosure provides systems, methods and apparatus, including computer programs encoded on computer storage media, for network slice feasibility assessment for slice orchestration in a wireless network. Some aspects relate to providing on-demand approval or rejection of a requested network slice at a device associated with service management. The device may select respective resource allocations of the requested network slice for each cell of a set of cells in a wireless network, add the respective predicted resource allocations to respective current resource utilizations at each of the cells, and output a recommendation associated with the requested network slice in accordance with the summations. The device may select the respective resource allocations of the requested network slice in accordance with a service level agreement (SLA) of the requested network slice and, in some implementations, observed network conditions at each of the cells in the wireless network.

The invention discloses a GPRS-based environmental protection data acquisition method and system, and relates to the technical field of environmental protection data acquisition, and the method comprises the steps: collecting environmental data, and dynamically adjusting the sampling frequency according to the change of the environmental data; compressing the collected environment data, and adaptively adjusting the compression ratio according to the change rate of the data; compressed data are transmitted through GPRS, energy is dynamically distributed based on intelligent energy management, and the power consumption of each device is regulated and controlled. According to the method, the bandwidth and the compression ratio are dynamically adjusted, transmission can be optimized according to the real-time network condition, bandwidth resources are fully utilized, and resource waste or overload is avoided. By monitoring the bandwidth in real time and dynamically adjusting the size of the data block, the bandwidth utilization rate is improved, and the problem of bandwidth waste in the prior art is solved.

The invention discloses a dynamic load-oriented data processing task resource optimization method, device and system, and the method comprises the steps: constructing an optimization problem taking the total resource overhead of a system as an optimization target through continuously obtaining network time delay, the flow scale of a data processing task, the distribution of the data processing task in a network topology, and the data size of each client, and carrying out the optimization of the data processing task. Routing paths, aggregation positions and computing power resource allocation are continuously adjusted by solving corresponding optimization problems. According to the method, based on a dynamic load environment, resource configuration of a data processing task is optimized by adopting an intra-network aggregation technology, communication delay is reduced, and the overall performance of a system is improved. According to the dynamic load resource optimization system, the flexibility of the programmable switch is utilized, resources can be dynamically allocated and network flows can be adjusted according to changes of network conditions and data flows, and the utilization efficiency of network resources and the performance of data processing tasks are greatly improved.

The invention discloses a display method and system for a mini-computer host, and relates to the technical field of computer display, and the method comprises the steps: monitoring and analyzing the ambient light intensity and color temperature in real time through a built-in ambient light sensor of the mini-computer host, and collecting ambient light data; the display brightness and the color temperature are intelligently calculated and adjusted through a pre-trained deep learning model by using the ambient light data; when video stream coding parameters are dynamically adjusted, network conditions and video stream complexity are analyzed in real time, and resolution and frame rate are adaptively adjusted; and combining the optimized display parameters with the adjusted video stream and the display. Through ambient light intelligent perception, network path optimization and video stream parameter dynamic adjustment, the problems of complex illumination adaptability and smooth video playing in the prior art are solved, and the user experience is greatly enhanced.

The invention relates to the technical field of network set top boxes, and discloses a firmware segmented loading method and device in the starting process of a network set top box. The method comprises the following steps: performing firmware division on the network set top box to obtain a firmware segment set and an inter-segment dependency relationship matrix; determining a real-time state vector of the starting state based on hardware sensor data of the network set top box; performing starting stage identification on the network set top box according to the real-time state vector to obtain a current starting stage identifier and a time sequence optimization condition parameter; carrying out loading decision and Markov state transition analysis on the firmware segment set based on the current starting stage identifier and the inter-segment dependency relationship matrix to obtain a loading action sequence; and carrying out target path loading on the firmware segment set according to the loading action sequence and the time sequence optimization condition parameters to obtain a loading execution result. According to the method, the firmware segments can be flexibly combined and dynamically scheduled according to actual requirements, and the loading efficiency and stability under different network conditions are ensured.

Various examples are disclosed for a zero-trust authentication model for virtual desktop infrastructure (VDI) sessions. Upon user authentication of a VDI session, security posture assessments can be performed that analyze an ongoing or continuous state of the user, client device, or network conditions. Remedial measures or mitigation procedures can be performed to address potential non-compliance of the VDI session.