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Voltage violation of the distribution network greatly affects the power supply quality and the use’s power consumption experience. To better improve the voltage quality of the power grid, real-time analysis of voltage violation can helps power grid personnel to handle voltage violation instantly and efficiently though analyzing the attribute indicators on dis-tribution network lines. However, many studies are concerned only with the single voltage violation cause, and ignore the more complicated phenomenon of voltage violations. In this paper, we proposed a joint attributes based neural network multi-classification (JANN) model that take mutual influence between attributes from different nodes in the distribution network into account when voltage violations are detected. Concretely, we construct the set of joint attributes from each node in the distribution network though real-time monitoring of the power grid. Then the joint attribute based neural network model is constructed to analyze the voltage violation phenomenon, and determine the cause multi-classification of voltage violations. Experimental results show that the proposed (JANN) method can reach 95.79% F1-score rate on multi-classification of voltage violation causes.

Late Launch, which is a kind of dynamic measurement technology proposed by both Intel and AMD, offers isolated execution environment for codes needed to be protected. However, since the specifications and documents of Late Launch have hundreds of pages, they are too long and complicated to be fully covered and analyzed. A model based on Horn clauses is presented to solve the problem that there is a lack of realistic models and of automated tools for the verification of security protocols based on Late Launch. A running example is taken to show the execution details of Late Launch. Based on the example, secrecy properties of Late Launch are verified. Whats more, the automatic theorem proving tool ProVerif is used to make the verification more fast and accurate.

In recent years, the urban rail transit network architecture has gradually grown, the contradiction between rail transit passenger flow and transport load has been deepening, and its carrying capacity has also been tested. Passenger flow risk has become the most important source of risk in rail transit. The key to restricting rail transit service quality is how to effectively monitor and manage rail transit passenger flow and provide accurate and convenient early warning to staff. In order to effectively manage the complex passenger flow scene, the premise is to distinguish the real-time state of its moving target. The time series data feature extraction and LSTM data fusion were used to analyze the traffic data sequence in the multilevel rail transit network model. The multilevel rail transit integration of the Internet of things is modeled by the method of data fusion. It can be seen from the experimental data that in the data fusion mode, the network comprehensive evaluation prediction value fitting effect can quickly converge, and the error rate is less than 4%. By comparing the mean square error (MSE) and mean absolute error (MAE) data of the traditional method and the experimental method used in this paper through two different datasets, it was understood that the MAE under the data fusion method was reduced by 8 compared with the traditional method, and the MSE was decreased by 33, indicating that this method can bring better simulation effect to the model. The improvement of the synergistic and complementary functional network and the acceleration of the efficient and convenient flow of elements are the inevitable results of the integration of multilayer rail transit in the metropolitan area.

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