In the market of agricultural products, the price of agricultural products is affected by production cost, market supply and other factors. In order to obtain the market information of agricultural products, the price...In the market of agricultural products, the price of agricultural products is affected by production cost, market supply and other factors. In order to obtain the market information of agricultural products, the price fluctuation can be analyzed and predicted. A distributed big data software platform based on Hadoop, Hive and Spark is proposed to analyze and forecast agricultural price data. Firstly, Hadoop, Hive and Spark big data frameworks were built to store the data information of agricultural products crawled into MYSQL. Secondly, the information of agricultural products crawled from MYSQL was exported to a text file, uploaded to HDFS, and mapped to spark SQL database. The data was cleaned and improved by Holt-Winters (three times exponential smoothing method) model to predict the price of agricultural products in the future. The data cleaned by spark SQL was imported and predicted by improved Holt-Winters into MYSQL database. The technologies of pringMVC, Ajax and Echarts were used to visualize the data.展开更多
To solve the problems in the quality control and improvement of coiled tubing steel strips production, such as scattered and inefficient production data, difficult performance fluctuation factor analysis, complex mult...To solve the problems in the quality control and improvement of coiled tubing steel strips production, such as scattered and inefficient production data, difficult performance fluctuation factor analysis, complex multivariate statistical analysis, and low accuracy and difficulty in mechanical property prediction, an industrial data analysis platform for coiled tubing steel strips production has been preliminarily developed.As the premise and foundation of analysis, industrial data collection, storage, and utilization are realized by using multiple big data technologies.With Django as the agile development framework, data visualization and comprehensive analyses are achieved.The platform has functions including overview survey, stability analysis, comprehensive analysis(such as exploratory data analysis, correlation analysis, and multivariate statistics),precise steel strength prediction, and skin-passing process recommendation.The platform is helpful for production overviewing and prompt responding, laying a foundation for an in-depth understanding of product characteristics and improving product performance stability.展开更多
文摘In the market of agricultural products, the price of agricultural products is affected by production cost, market supply and other factors. In order to obtain the market information of agricultural products, the price fluctuation can be analyzed and predicted. A distributed big data software platform based on Hadoop, Hive and Spark is proposed to analyze and forecast agricultural price data. Firstly, Hadoop, Hive and Spark big data frameworks were built to store the data information of agricultural products crawled into MYSQL. Secondly, the information of agricultural products crawled from MYSQL was exported to a text file, uploaded to HDFS, and mapped to spark SQL database. The data was cleaned and improved by Holt-Winters (three times exponential smoothing method) model to predict the price of agricultural products in the future. The data cleaned by spark SQL was imported and predicted by improved Holt-Winters into MYSQL database. The technologies of pringMVC, Ajax and Echarts were used to visualize the data.
文摘To solve the problems in the quality control and improvement of coiled tubing steel strips production, such as scattered and inefficient production data, difficult performance fluctuation factor analysis, complex multivariate statistical analysis, and low accuracy and difficulty in mechanical property prediction, an industrial data analysis platform for coiled tubing steel strips production has been preliminarily developed.As the premise and foundation of analysis, industrial data collection, storage, and utilization are realized by using multiple big data technologies.With Django as the agile development framework, data visualization and comprehensive analyses are achieved.The platform has functions including overview survey, stability analysis, comprehensive analysis(such as exploratory data analysis, correlation analysis, and multivariate statistics),precise steel strength prediction, and skin-passing process recommendation.The platform is helpful for production overviewing and prompt responding, laying a foundation for an in-depth understanding of product characteristics and improving product performance stability.
