Concerning the needs of multi-service and network performance prediction in the Internet of Things (IoT), we propose an asymmetric two-priority polling control system model, and use the neural network algorithm to predict and analyze its performance. Firstly, the mathematical model of the system in the continuous time state is established by using the embedded Markov chain theory and the probability generating function. Meanwhile, the characteristics like the average queue length and average cycle of the system are accurately analyzed, and verified in simulation experiments. Subsequently, a three-layer multi-input single-output backpropagation (BP) network model is constructed to predict the performance of the polling system. The results show that the model can not only distinguish multi-business tasks, but also ensure the system delay. BP neural network prediction algorithm can accurately predict the performance of the system, which has a guiding significance for its performance evaluation, and provides a new method for the research of the polling system.

针对物联网（IoT）中多服务和网络性能预测的需求，我们提出了一种非对称的两优先级轮询控制系统模型，并使用神经网络算法对其性能进行了预测和分析。首先，利用嵌入的马尔可夫链理论和概率生成函数，建立了系统在连续时间状态下的数学模型。同时，对系统的平均队列长度和平均周期等特性进行了精确分析，并在仿真实验中进行了验证。随后，构建了一个三层多输入单输出反向传播（BP）网络模型来预测轮询系统的性能。结果表明，该模型不仅可以区分多种业务任务，而且可以保证系统的时延。