6 results about "Traffic intensity" patented technology

The invention discloses a multi-level interconnection network based on real-time flow monitoring and dynamic priority adjustment. A multi-dimensional traffic sensing model is constructed through traffic intensity, queue depth, congestion signals and static priority dynamic indexes. A dynamic urgency value is generated by adopting a configurable weighting strategy, and a three-mode closed-loop optimization mechanism is designed. A layered interconnection structure is adopted, crossbar switches are used in computing clusters, and the computing clusters are connected through a low-diameter routing network. In a cluster gateway, a dynamic arbitration unit monitors the flow intensity in real time, calculates the urgency degree in combination with a queue state and a global congestion signal, and dynamically selects a fixed priority and a weighted polling or polling arbitration strategy. The problem that the resource utilization rate of a traditional static arbitration mechanism is reduced due to task switching is effectively solved, the risk of fairness imbalance in a mixed key data flow scene is effectively solved, and the communication adaptation capacity of a reconfigurable architecture to diversified computing loads is improved.

The present application relates to the technical field of traffic big data, and discloses a truck traffic intensity evaluation method, wherein the method comprises: obtaining traffic data corresponding to each vehicle in a target road network in a preset time period; based on license plate numbers and a public security traffic management motor vehicle database, target traffic data corresponding to a truck is selected from the traffic data; a traffic intensity comprehensive index of the truck in the target road network is determined based on a plurality of traffic intensity indexes calculated from the target traffic data; and the truck traffic intensity of the target road network is evaluated based on the traffic intensity comprehensive index and a preset interval. The present application provides basic data with strong continuity, wide coverage and high authenticity, and the basic data is analyzed in multiple dimensions, so that the truck traffic intensity obtained by the present embodiment is more accurate and has more reference value.

The application discloses a kind of urban agglomeration emission reduction methods and systems based on carbon emission space overflow under traffic influence, the method is: collection target urban agglomeration carbon emission data and social, economic, traffic intensity and other multiple index data are pretreated;Target urban agglomeration is divided into multiple traffic types based on traffic intensity data index;Carbon emission space correlation network between each city in target urban agglomeration is constructed, and the overflow of carbon emission of target urban agglomeration is analyzed, then the carbon emission overflow diagnosis result is output in depth analysis;Based on the diagnosis result and the constructed city emission reduction strategy library, intelligent optimization algorithm is used, and individual carbon reduction scheme design is carried out for high-carbon city from the angle of carbon emission overflow.The application analyzes carbon emission space overflow under the influence of traffic transportation, proposes urban agglomeration collaborative carbon reduction method and optimization system, has the advantages of accurate control, considers comprehensively, strong universality, policy support, and can be applied to each urban agglomeration.

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.