对分布式网络数据包优先级传输模型进行优化，可以提高分布式网络中资源调度和信息传输性能。传统方法采用时频耦合尺度分解算法，在大量的冗余数据干扰下，降低了数据的优先级识别精度和传输性能。建立一种基于自适应加权量化特征分解和冗余数据滤除的分布式网络数据包优先级传输模型。首先构建分布式网络数据包优先级传输的信道结构模型，采用级联滤波算法对数据包中冗余数据进行滤波预处理，对数据库中的信息传输流进行自适应加权量化特征分解后，通过特征提取实现优先级的自适应识别，实现传输模型改进。仿真实验结果表明，采用改进模型进行分布式网络数据包优先级传输，数据传输的吞吐性能较好，执行时间较短，展示了较好的应用性能。%Optimize the transmission model of distributed network packet priority, can improve the resource scheduling in distributed network and information transmission performance. The traditional method using time and frequency coupling scale decomposition algorithm, under a lot of redundant data interference, reduce the priority identification precision of the data and the transmission performance. Establish a based on adaptive weighted quantitative feature decomposition and redundant data transmission model of distributed network packet filtering priority. First build distributed network packets priority transmission channel structure model, using cascade filtering algorithm for redundant data in the packet filtering pre-processing, transmit information to the database after the flow characteristics of adaptive weighted quantitative decomposition, through feature extraction to realize adaptive priority recognition, realization of transmission model is improved. The simulation results show that the improved model for distributed network packet transmission priority, data transmission throughput performance is good, the execution time is shorter, shows a good application performance.
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