| Since time is the important property of things in universe, various kinds of data related to time always run through the whole course of development of various systems. Last century 70 decade, temporal knowledge has been begun to study in fields of database, knowledge storehouse, artificial intelligence and software engineering, etc. The actual applied fields, such as railway transportation, medical science and fault diagnosis, often have some uncertain temporal information or property because of randomness, shortage of relational properties or inaccuracy of information, etc. How to correctly represent, deduce and verify this kind of temporal knowledge is very important. It can offer the strong and expandable describing and proving technologies concerned with the inference of safety, active degree and presetting time etc. And it also can be used in the representation and deduce of temporal knowledge in fields of network agreement, database application, simulated application, natural language understanding, expert system and artificial intelligent application. Furthermore, it is key technology to study temporal relation in multi-media communication synchronism and workflow.Because of the various advantages Petri net, in last few years Petri nets have aroused scholars' great interest in aspects of representation and deduce of temporal knowledge. But in handling of uncertain temporal knowledge, current researching works still have some shortage, as following: Firstly lack some temporal logic; Secondly lack better modeling, analyze and simulation methods facing with huge and complex system; Thirdly inference algorithms has limitation and their applying scope is comparatively narrow, or the efficiency of methods is not high; Finally lack the combination with other theories or technologies such as UML, nerve network and workflow etc.Railway is a huge and complex system, closely concerning with a lot of aspect factors. Train group modeling and analysis, as well as train safety...
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