With the rapid development of mobile Internet technology and the advent of the 21st century Internet and information age, data has become the most important strategic core resources. With the rapid development of ubiquitous power Internet of things construction, there are still many problems in the data link monitoring and data management: the low effectiveness of data transmission affects the normal operation of the data link; there are few data evaluation models, which lead to the problem of one sidedness and subjectivity in the understanding of the operation status of the data link. Aiming at the problem that the current data link cannot directly show the link quality, this position proposes a comprehensive evaluation method for the entire data link based on the order relationship analysis(G1) method and the entropy weight method. First, establish a complete data link indicator system, and then separately Use the order relationship analysis method to determine the subjective weight, use the entropy weight method to determine the objective weight, and finally combine the two to determine the comprehensive weight, and obtain a comprehensive score that reflects both the expert level and experience and the actual situation related data. The data and transmission conditions of each link are shown, and the comprehensiveness, rationality and scientificity of the method are also demonstrated.

To solve the problems caused by the complex failure mechanisms in hydraulic brake failure systems and the uncertain linkages between fault type and fault symptoms, a method of Bayesian network (BN) is offered for failure diagnosis. A statistical strategy is adopted there in this algorithm on the rule base provided by many experts to discard the weak causal relationship rules while keep the relatively strong ones during the BN structure learning, and a BN-based model of layered architecture is established for fault diagnosis of hydraulic braking failure systems. Experimental data analysis shows that the Bayesian network fault diagnosis model has higher accuracy than fuzzy logic diagnosis method, effectively solving the uncertainties in fault diagnosis.

The Data link security assessment is to ensure the security of data transmission, data storage and data usage in the link. It is an important standard to reflect the safety of data link in time and accurately. We proposed a data link security assessment model based on dynamic weights. The model is evaluated from four aspects: intrusion detection, data content detection, data storage index detection, and data usage permission detection. For each aspect, we used the method of setting dynamic weights to score and evaluate. By setting dynamic weights, we can promptly reflect the potential safety hazards and remind the staff. At present, the security evaluation system for data links is not perfect. The model we proposed can evaluate and score from multiple aspects to reflect the security status of the link.

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