In the research field of network stability monitoring based on big data, there are some disadvantages. For example, the processing method of the missing value of results influences execution effect, interfere the final filling effect and can only be applied in small data set. Therefore, a missing value filling algorithm for network stability monitoring results based on big data is proposed. The evaluation index of network stability monitoring based on big data is selected, and data missing mode and missing value filling mechanism are analyzed. The filling algorithm for the missing value of results based on big data is designed, the missing value filling parameter framework is constructed, the missing value filling parameters are processed, and the filling of the missing value of network stability detection results is completed. Compared with the traditional algorithm, the experimental results show that the efficiency of the designed missing value filling algorithm is 70.3% higher than that of the traditional filling algorithm, and the accuracy rate is far higher than the traditional method.

Specific emitter identification (SEI) identifies targets mainly by unintentional modulation of the signal. However, due to the high energy of the primary signal, once the primary signal changes, the recognition becomes less effective or even impossible using a feature database that is not updated. In this paper, we propose to use a mutual information improved variable mode decomposition (VMD) algorithm to suppress the primary signal phase of the transmitter. Furthermore, we simulate the feature extraction of the unintentional phase modulation of the transmitter signal and use support vector machine (SVM) for individual identification. The simulation results show that the algorithm improves the recognition rate by about 6% (0 dB) compared to the retained primary signal. The results demonstrate that our proposed phase suppression technique improves the adaptability and accuracy of individual identification of transmitters.