7 results about "Link time" patented technology

The invention relates to a dynamic routing optimization method and device, computer equipment, a readable storage medium and a program product. The method comprises the following steps: acquiring a topological space feature vector and a predicted time sequence feature according to a node load and a link time delay of a network topology, fusing the topological space feature vector and the predicted time sequence feature to obtain a target fusion feature, and according to the target fusion feature, obtaining a target fusion feature; determining a target routing action from all routing actions in the routing action space, executing a traffic forwarding operation corresponding to the target routing action, obtaining a network performance parameter after the traffic forwarding operation is executed, obtaining a target fusion reward value based on the network performance parameter and the state prediction parameter, and sending the target fusion reward value to the routing action space; and updating model parameters of the decision network model according to the target fusion reward value, and performing dynamic routing optimization based on the updated decision network model. By adopting the method, the communication reliability can be improved.

The invention discloses an unknown threat detection cooperative processing method, system and device, and the method comprises the steps: firstly building a cloud-side-end three-layer distributed architecture, defining the functions of each layer, configuring an adaptive communication protocol, and achieving the flow collection, real-time detection and global management of an industrial protocol; a front-end real-time visualization module is constructed based on a preset technology stack, a threat board is designed, and a data updating mechanism is optimized; then, a back-end collaborative scheduling system is built, micro-services are split, and dynamic load balancing and multi-priority task scheduling are achieved; and finally, a full-link time sequence cooperation mechanism is established, data time sequence consistency is guaranteed through time synchronization, delay compensation and real-time monitoring, unknown threat detection full-link cooperation is integrally achieved, the problems of insufficient real-time performance, high-concurrency processing bottleneck, time sequence dislocation and visualization fuzziness in the prior art are solved, and the energy industry Internet security protection requirements are met.

The invention discloses a traffic scheduling method and system, relates to the technical field of network communication, and solves the problem that high-dynamic and sudden traffic scheduling requirements are difficult to meet in the prior art. According to the embodiment of the invention, through the multi-objective optimization function, the variance of the link load can be effectively inhibited, the link time delay and packet loss rate are reduced, and the network quality is improved; by introducing an optimization algorithm combining a Lagrangian multiplier method and a real-time gradient descent method, a flow distribution scheme can be quickly adjusted based on network change conditions, and high-dynamic and sudden flow scheduling requirements can be better met.

The application belongs to the technical field of database fault tolerance, and discloses a database system data protection and disaster recovery intelligent operation and maintenance platform. The platform is equipped with a distributed non-intrusive self-healing data collection probe, which can stably collect full-link time sequence data such as database kernel, business SQL, storage IO and network bandwidth. Through a Prophet, LSTM and attention mechanism fusion model, the business load fluctuation is accurately predicted, a multi-objective optimization system is constructed in combination with data grading, RTO / RPO compliance requirements, and differentiated strategies such as real-time synchronization, adaptive snapshot and full backup are customized for core, general and non-core businesses. During the load peak, the backup task is automatically degraded, and during the low peak, the protection is strengthened, and the resource scheduling is linked to dynamically allocate computing power. The platform realizes transaction-level incremental capture based on the database kernel redo / undo log native interface, accurately filters uncommitted and invalid transactions, and controls the synchronization delay within 10ms, thereby reducing the performance loss.

The invention discloses a toy remote interaction delay alignment method and system based on a software development kit, and relates to the technical field of remote interaction control, and the method comprises the steps: building an inter-user media session between a plurality of control terminals, and building a control session between the control terminals and a toy; the software development kit collects a control instruction sending timestamp and a toy execution timestamp to calculate a control link time delay, and obtains a media link time delay of a media session between users; calculating a compensation amount according to the delay difference value, establishing a virtual time axis and a control instruction buffer queue, and scheduling and sending according to the generation time and the compensation amount; and in a multi-user scene, constructing a collaborative time track containing predicted effective time of each control behavior, and giving a recommended collaborative control time window. Fine-grained alignment of a control link and a media link is realized, time delay fluctuation is inhibited, and synchronism and immersion of the multi-user remote interactive toy are enhanced.

The invention relates to the technical field of computer networks, provides a link resource scheduling model training method in a network high-dynamic change scene, and aims to solve the problem that network link resources cannot be efficiently and flexibly distributed in a dynamic network in the prior art for concurrence of multiple types of services. And the network traffic scheduling accuracy and the resource utilization efficiency are improved. The scheme comprises the following steps: extracting traffic time sequence features based on a network traffic sample set to obtain network state, time and external information feature embedding; capturing multi-scale time sequence characteristics; through a multi-scale time sequence prediction model, based on feature embedding, the future link traffic is accurately predicted; then, constructing a scheduling model based on deep reinforcement learning, and forming a state space by the predicted flow and multi-dimensional network state information such as link time delay, packet loss rate, queue length and the like; and finally, dynamically generating a scheduling weight of the weighted fair queue through a reinforcement learning agent, and realizing self-adaptive optimal distribution of network resources.