Research And Implementation Of Applying Of Intelligent Closed-loop For Energy Consumption Data Of Industrial Equipment

With the continuous development of industrial Internet technology,big data(BD)and Semantic Web(SW)have been widely used in the analysis of industrial equipment energy consumption data.However,there are still many problems in the data processing of industrial equipment: lack of a multifunctional data analysis platform,high complexity of data and information management,low information sharing,and low degree of intelligence in data analysis.Meanwhile,the Alliance of Industrial Internet(AII)has proposed a "data-information-knowledge-decision" intelligent closed-loop data analysis needs.In response to the above problems and needs,the thesis integrates BD and SW to analyze the energy consumption data of industrial equipment,and implements a "data-information-knowledge-decision" intelligent closed-loop system.Based on the data analysis platform,the system seamlessly connects the information result of energy consumption data with the SW information knowledge module to realize the informationization and information knowledge of equipment energy consumption data.The system provides a wealth of data processing methods,which solves the problems of low sharing of equipment energy consumption information and high management complexity.In the data processing related to energy consumption,the real-time and stability of some BD engines cannot fully meet the actual production requirements,so the thesis optimizes some BD engines.The main research and innovation contents of the thesis are as follows:In view of the AII's demand for "data-information-knowledge-decision" intelligent closed-loop data analysis,the thesis designs the architecture of an intelligent closed-loop system for industrial equipment energy consumption data: the BD informatization module implements the informatization function of industrial equipment energy consumption data;the SW information knowledge module implements the informatization function of industrial equipment energy consumption information.The system connects the two modules to realize the "data-information-knowledge-decision" intelligent closed-loop system.Aiming at the problem of the lack of a multifunctional data analysis platform pointed out by the AII,the thesis builds the data analysis platform that supports multiple data analysis methods.Data analysis methods provided by the data analysis platform: data extraction,conversion,transmission,storage,query,energy consumption report,offlinedata analysis,real-time data analysis,online visual data analysis,Machine Learning,Deep Learning,ontology modeling,knowledge representation,knowledge reasoning,etc.Aiming at the problem of high complexity and low information sharing of industrial equipment energy consumption information management,the data informatization module analyzes and solves the problem of heterogeneous data structure,and the information knowledge module realizes information fusion and equipment energy consumption information interoperability.In order to solve the problem of low real-time and stability of data processing by some BD engines,the thesis optimizes the compression and serialization algorithms of the computing engine.Compression algorithm uses LZ4,LZO,serialization algorithm uses Protobuf,Protostuff,and reconstructs some BD engines.Meanwhile,the thesis implements the high availability and federation mechanisms of HDFS,HBase and other engines.Based on Elastic Search(ES),the HBase secondary index is established.The state mechanism is used to avoid repeated consumption of streaming data.Based on Django,ECharts and other frameworks,a test module for intelligent closed-loop application system of industrial equipment energy consumption data has been developed.The system test results show that: the data analysis platform implements a variety of data analysis methods to meet the needs of multiple data analysis of industrial equipment energy consumption data;the industrial equipment energy consumption information knowledge module has information knowledge representation and reasoning functions;optimized BD Engine compression and serialization algorithms have significantly improved performance compared to the original integrated algorithms.Therefore,the intelligent closed-loop data analysis scheme of industrial equipment energy consumption data proposed in the thesis is reasonable,and the developed applications meet the needs of intelligent closed-loop data analysis of industrial equipment energy consumption data,thereby increasing the degree of intelligence of industrial equipment energy consumption management.