In the manufacturing process

environmental factors such as tasks

natural conditions

power levels

etc.

restrict the state change of objects and their relationships. Intelligent manufacturing units need to adaptively understand and judge events and complex situations under different context constraints

and a adaptive situation identification method based on complex event processing is proposed naturally

to make real-time optimization decision reasonable. In view of the phenomenon that the influence of context constraints on event discrimination is neglected

a context-aware hierarchical event model is built

and new operators of events such as contemporaneity

context and collaboration are given

while manufacturing situation model and aggregation process are proposed. Aiming at the shortage of generating method on situation model in knowledge base

the mapping association between an object and environment data is established firstly

and such sensed information is transformed into context based events. Integrating the distance calculation of ordinal

nominal variable and adaptive entropy weight method

a improved mixed clustering method is put forward to deal with the diversity and relevance of complex event instance attributes

providing service support for real-time situation identification. Finally

4 real data sets and 1 simulation data set of are employed for manufacturing process. Experiment results have verified the validity and adaption of the proposed model and method in large-scale problem

and expounded that context factors can significantly improve the accuracy of event judgment and situation recognition in complex manufacturing applications.