## 1 Introduction
[12] addressed the difficulty of comprehensively describing the system operating state of the power system under large disturbances, established a multi-state Markov model based on the model, and carried out a control strategy design based on the model constructed, which improved the transient stability of the AC/DC hybrid system. [PERSON]. [13] utilized the Markov model to describe the transfer process between different fault states and proposed a method for evaluating the probability of the smooth operation state of DC circuit breakers of different topologies based on the characteristic parameters of the availability rate and fault rate. The Markov process is widely used in the reliability modeling and analysis of systems and plays a significant role in studying the reliability index of equipment in different operating environments. In recent years, artificial intelligence algorithms have been rapidly developed that do not need to obtain the product degradation distribution and life distribution and thus have been more applied in the field of switchgear and electrical appliances. The reliability modeling method based on AI algorithms is a method to use the degradation data containing failure information monitored in real time during experiments as a test or training data set and input it into the corresponding AI model for training, analysis, and determination [14; 15; 16]. Data-driven modeling methods based on artificial intelligence mainly include fuzzy comprehensive judgment method, gray theory, support vector machines, artificial neural networks, convolutional neural networks, and chaos theory methods [17; 18; 19; 20; 21; 22; 23]. The authors of [24] applied fuzzy theory to vacuum circuit breakers to establish parameters such as first-open-phase arc ignition time, back-open-phase arc ignition time, and first-open-phase distribution uniformity as a set of factors for judging the object and used the secondary judgment model to evaluate the state of circuit breakers, realizing the comprehensive judgment of the opening and breaking life of vacuum circuit breakers. Ref. [25] takes the motion speed of the moving contact of the high-voltage circuit breaker in the closing process as the characteristic quantity to characterize the mechanical failure and uses the improved generalized radial basis artificial neural network to identify the circuit breaker fault type, realizing the rapid fault diagnosis of a high-voltage circuit breaker. In references [26; 27], the arc feature set of each break was established based on the real-time monitoring of voltage and current signals and the conditional density estimation was used to train the data and establish a prediction model for the relative residual electric life; shortly thereafter, the current waveform data of an AC contactor in AC-4 experimental conditions were used as the training and testing set and a prediction model was established by using a convolutional neural network to predict the resistance performance of an AC contactor. The prediction of electrical performance was realized by using a convolutional neural network to build a prediction model. Artificial-intelligence-based methods are characterized by strong adaptive ability, flexible modeling, and fewer assumptions; however, it is often difficult to obtain a comprehensive reliability assessment for products with multiple failure forms and small amounts of data. The safety margin method is a method of reliability assessment using performance thresholds or margins as the characteristic covariates; different scholars have different meanings for its concept, sometimes it denotes the reliability, sometimes it is used to evaluate the riskiness of the system, etc. Ref. [28] proposed to utilize the static safety distance as a way to describe the safety margins of the grid as well as the branches. Ref. [29] used the remaining active power transfer capacity on the line as the safety margin of the line. Ref.