## 1 Introduction  
With the rapid development of Industrial Internet of Things (IIoT) and Knowledge Graph (KG) in recent years, IIoT applications in the industrial field are highly sensitive and critical, such as Industrial Control System (ICS), which integrates hardware and software to monitor and control the industrial environment operation of the system and its related components [1]. A Knowledge Graph (KG) stores facts about the real world as a collection represented by triples. Each triple in the KG is represented as <s, p, o>, where s and o represent the subject and object, and p represents the predicate connecting the subject s and the object. The problem with link prediction is to find the most suitable triples (s, p,?) or (?, p, o) to complete knowledge graphs [2]. Our focus is on the temporal knowledge graph, which adds time information to the triples. As shown in Figure 1, the time series knowledge graph essentially wants to extend the traditional static knowledge graph in the time dimension. The form of the problem with link prediction becomes that it is most likely to be completed under the given time information. That is to say, facts in temporal KG can also have the form of (subject, predicate, object, timestamp) or (subject, predicate, object, time predicate, timestamp), which is used to increase the general triple (s, p, o). For example, facts such as ([PERSON], born, US, 1946) or ([PERSON], President, US, occurs Since 2017-01) express time information about the facts related to [PERSON] [3]. The former expresses the relation type of the predicate that occurs at a specific point in time, whereas the latter uses the time predicate \"occurs Since\" to express the disclosed time period (time range). Recently, a lot of research work has been devoted to the representation learning of TKG. The method of link prediction is generally to embed the subject and predicate of the triple, and then use the scoring function to score. The temporal representation of knowledge graphs remains challenging due to the sparsity and irregularity of dynamic temporal information. The CYGNet model [4] utilizes the historical information of knowledge by designing a special replication module; meanwhile, a generation module is designed to predict the knowledge that appears for the first time. TA-DistMult [5] creates a temporal relationship by treating the characters in r and t as a sequence, and proposes a digit-level LSTM for learning the factual representations that contain temporal information in KGs and can be directly applied to current KGs In the existing scoring function method in the completion task. Through the study of these models, we improved the TransR model and proposed a new TKG representation learning method. The proposed TBRm (Time boundary relationship mapping) represents time information as a single dimension. We map the relationship formed by the TransR model to the time dimension, and by embedding the mapped time predicate into the standard scoring function of knowledge graph completion, Circular neural networks are used to learn the time-aware representation of relationship types. We conduct extensive experiments on four benchmark TKG datasets, and the results show that TBRm can effectively model TKG data through relationships with temporal attributes. In the direction of IIoT, taking cyberattacks on industrial IOT devices as an example, according to statistics from Kaspersky researchers, the number of cyberattacks on IOT devices jumped from 639 million in 2020 to 1.5 billion in 2021 [6]. In addition to the attack itself, there are other factors that affect the damage of network attacks. In this paper, according to the Edge-IIoT dataset, we use the attacker (represented by IP address) as the subject, the sensor and brake (represented by IP address) as the object, and the attack type as the predicate relationship to establish a static knowledge map.