With the advent of the computer network era, people like to think in deeper ways and methods. In addition, the power information network is facing the problem of information leakage. The research of power information network intrusion detection is helpful to prevent the intrusion and attack of bad factors, ensure the safety of information, and protect state secrets and personal privacy. In this paper, through the NRIDS model and network data analysis method, based on deep learning and cloud computing, the demand analysis of the real-time intrusion detection system for the power information network is carried out. The advantages and disadvantages of this kind of message capture mechanism are compared, and then a high-speed article capture mechanism is designed based on the DPDK research. Since cloud computing and power information networks are the most commonly used tools and ways for us to obtain information in our daily lives, our lives will be difficult to carry out without cloud computing and power information networks, so we must do a good job to ensure the security of network information network intrusion detection and defense measures.
To address the problems of low feature extraction accuracy, large bias of human motion pose recognition and posture recognition error, poor recognition effect, and low recognition rate of traditional human motion posture fast recognition algorithm, we propose a human motion posture fast recognition algorithm using multimodal bioinformation fusion. First, wavelet packet decomposition with sample entropy is used to extract the human motion posture hand features such as kurtosis, time domain feature skewness, and frequency domain feature electromyogram (EMG) integral value and time domain features such as mean, standard deviation, and interquartile distance of leg motion amplitude. Second, after normalizing the two features, the human hand and leg motion feature set is obtained, and finally the feature set is used to construct a human motion posture fast recognition model based on multimodal bioinformation fusion, and the feature set is input into the recognition model, which completes the fusion of human motion posture information by improving the typical correlation analysis method, and the fusion result is used as the input of the minimum distance classifier to achieve human motion posture fast recognition. The results show that the proposed algorithm has high accuracy of feature extraction, small bias of human motion posture recognition, the posture recognition error is -0.21∼0.02, the recognition rate is always above 95%, and the practical application effect is good.
Nowadays, power electronic technology is widely affecting people’s daily work and life. However, there are still many problems in the current power supply research. When the fault information of power transformer is not complete or there is some ambiguity or even the information is lost, it will largely lead to the conclusion and correct conclusion of fault diagnosis. In this case, the fuzzy theory is applied to the fault diagnosis of shunt capacitor, and the fuzzy fault diagnosis system of shunt capacitor is studied. At the same time, a map-based fault diagnosis system is proposed. In this paper, the cloud computing technology is introduced into the deep learning and compared with SVM and DBN algorithm. The research results of this paper show that the accuracy of fuzzy diagnosis results is 94%, 84%, 90%, 80%, 83%, and 70%, respectively, which shows that the model diagnosis reliability is relatively high. Among the three algorithms, MR-DBN overall detection rate is higher and the time-consuming is lower than the other two methods. The diagnostic accuracy and misjudgment rate of DBN are as follows: 96.33% and 3.90%. The diagnosis accuracy and misjudgment rate of SVM are as follows: 96.40% and 3.83%. The diagnostic accuracy and misjudgment rate of MR-DBN are, respectively, 99.52% and 0.57%. Compared with the other two methods, MR-DBN has the highest diagnostic accuracy and the lowest error rate, which to a large extent indicates that MR-DBN algorithm has higher diagnostic accuracy and has greater advantages and reliability in power supply diagnosis and identification. It not only improves the accuracy of power capacitor fault diagnosis and identification but also provides a new method for the application of power capacitor fault research and development.
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