3 results about "Traffic intensity" patented technology

The invention discloses a DQN-based network-on-chip arbitration intelligent optimization method, and belongs to the field of computer system structure network-on-chip interconnection. In an application task execution process, data packet arrival information of an input port of an on-chip network router is collected in real time, time density characteristics and fluctuation characteristics reflecting traffic intensity and time sequence fluctuation characteristics are extracted, and operation state information such as a router cache utilization rate, a port competition degree and an injection rate is combined, so that the data packet arrival information of the input port of the on-chip network router is obtained. And multi-dimensional state description of the router is constructed. A plurality of candidate basic arbitration strategies are preset, a deep Q network reinforcement learning model is adopted, and dynamic selection is carried out among the candidate arbitration strategies according to real-time router state information, so that a better arbitration strategy is selected under the constraint of communication delay and power consumption. Through continuous interactive learning with a network-on-chip operation environment, the method can adaptively match arbitration strategies under different load and flow scenes, effectively reduce communication delay and tail delay, and improve communication efficiency and energy efficiency of the network-on-chip.

This invention discloses a real-time budget scheduling method for an advertising delivery system, relating to the field of dynamic budget management technology. The method includes: collecting real-time delivery traffic data; comparing the current traffic intensity with historical baseline data and time period data to generate a traffic anomaly intensity identifier and recording the corresponding timestamp; when the traffic anomaly intensity identifier meets preset conditions, performing latency detection, packet loss detection, and numerical mutation detection on the delivery feedback data to generate feedback reliability weight parameters; simultaneously obtaining the state update time of the delivery strategy and calculating the difference with the feedback reception time to obtain a lag parameter. This invention, by introducing a traffic anomaly intensity identifier and a multi-dimensional feedback detection mechanism, can achieve dynamic perception and evaluation of the quality and response lag of feedback data; under the premise that a traffic anomaly is triggered, it actively executes feedback detection operations and constructs a lag parameter by combining the strategy state response time difference.

The invention discloses a collaborative defense and attack behavior cognition method for a multi-robot system, and the method comprises the steps: obtaining the suspicious traffic intensity of each robot defense node, and constructing a collaborative filtering architecture; defining an edge / cloud shunting proportion for each defense node, establishing an edge filtering time delay model and an individual filtering cost function, modeling a collaborative filtering process under multi-node resource sharing into a selfish filtering game, and utilizing a load balancing property of edge filtering time delay consistency under balance to obtain a cloud filtering game; designing a distributed iterative algorithm to solve a unique Nash equilibrium shunting strategy in limited iteration; and constructing an optimal model of the attacker budget constraint under the defense balance strategy to solve an optimal attack strategy for performing cognitive prediction and early warning on the attack intensity, the target preference and the attack type. According to the invention, the autonomous decision-making capability of the multi-robot system in a complex confrontation environment can be improved. The method can be widely applied to the technical field of multi-robot cooperative control.