Chinese Academy of Sciences

Chengbao Liu

Jie Tan

Liping Wang

Haichao Zhao

Yuwei Ye

The present application discloses paint based on a graphene nano container and a self-repairing coating as well as a preparation method and application thereof. The paint comprises a first component comprising 20&#x2dc;40 parts by weight of epoxy resin; a second component comprising 0.1&#x2dc;2 parts by weight of corrosion inhibitor-loaded graphene nano container, 1 part by weight of diluent, 30&#x2dc;60 parts by weight of epoxy curing agent, 1 part by weight of defoaming agent and 1 part by weight of flatting agent, wherein the corrosion inhibitor-loaded graphene nano container comprises graphene grafted with cyclodextrin and the corrosion inhibitor reversely binding to the cyclodextrin. The paint of the present application is simple in preparation process, green and environmental friendly in raw material, low in price and available, and meanwhile the self-repairing coating formed thereof is excellent in protection performance.

Paint based on graphene nano container and self-repairing coating as well as preparation method and application thereof

Disclosed is an acquisition method for domain rule knowledge of an industrial process. The method comprises the steps of: establishing a domain rule base, establishing a semantic knowledge base, and combining the domain rule base and the semantic knowledge base so as to realize an augmented update of a domain rule knowledge base; describing the domain knowledge of the industrial process by using weighted first-order logic rules so as to form a training sample set of the first-order logic rules; performing a weight learning by applying probability soft logic and the training sample set of the first-order logic rules so as to realize weight to non-weighted rules; performing rule learning through a machine learning algorithm so as to obtain a first-order logic rule on a change in optimization decision-making semantic when multi-source data semantic information changes.

Acquisition method for domain rule knowledge of industrial process

Disclosed is an optimization decision-making method of an industrial process fusing domain knowledge and multi-source data. The method comprises the steps of: acquiring the domain knowledge of the industrial process by using probability soft logic, and building an domain rule knowledge base of the industrial process; fusing multi-source data semantics and multi-source data features to form a new semantic knowledge representation of the industrial process, and constructing a semantic knowledge base of the industrial process; under a posteriori regularization framework, utilizing the domain rule knowledge base of the industrial process and the semantic knowledge base of the industrial process to obtain an optimization decision-making model embedded with the domain rule knowledge and obtain a posteriori distribution model; and migrating knowledge in the optimization decision-making model embedded with the domain rule knowledge into the posteriori distribution model through the knowledge distillation technology.

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Beijing, China

Chengbao Liu