Modeling of big production data storage of fully mechanized mining equipment based on workflow-driven deep coupling network

As the main equipment in coal production, the Fully Mechanized Mining Equipment is a typical multi-stage and multi components electromechanical system. Any component's failure will lead to the unsafe production environment and low productive efficiency. With the flash development of information technology, the production data can be collected is growing exponentially. For the multi-stage and multi-components electromechanical system, how to effectively manage and use the production data which reflects the nature of the complex production system has become a difficult issue due to the massive and unorganized product data, and the complicated interactive effects among different stages and components. Aiming at this issue, a structural data storage and application framework is proposed based on complex network theory. According to the lifecycle of production data, the data management framework is divided into seven layers, including hardware layer, model layer, discrete data layer, relevancy data layer, application layer, decision support layer and feedback control layer. The seven layer framework reflects the process of data modeling, configuring, generating, relating and utilizing. The objective of the framework is to support the data management of an equipment manage and maintain decision-making software in the multi-components Fully Mechanized Mining Equipment system.