ECharts and Java interface: how to apply statistical analysis in the field of intelligent manufacturing

ECharts and Java interface: How to apply statistical analysis in the field of intelligent manufacturing, specific code examples are needed

Intelligent manufacturing is an important development direction of today's manufacturing industry. Utilize advanced technology and information technology to improve production efficiency, quality and flexibility. Statistical analysis is an indispensable part of intelligent manufacturing, which can help companies monitor and optimize the production process. This article will introduce how to use ECharts and Java interfaces to perform statistical analysis in the field of intelligent manufacturing, and give specific code examples.

ECharts is an open source visualization library based on JavaScript. It provides a wealth of chart types and interactive functions, helping users quickly build a variety of charts. Java is a programming language widely used in enterprise-level application development. It has rich libraries and tools to process data and perform analysis. By combining ECharts and Java interfaces, we can perform various statistical analyzes in the field of intelligent manufacturing to provide enterprises with better decision-making basis.

First, we need to get the data and process it in Java. Suppose we have an intelligent manufacturing system that can collect and store various data in the production process in real time, such as temperature, humidity, pressure, etc. We can use Java's database connection library to connect to the database and write SQL statements to obtain the required data. The following is a sample code for obtaining temperature data:

import java.sql.*;

public class DataAnalysis {
    public static void main(String[] args) {
        try {
            // 连接数据库
            Connection conn = DriverManager.getConnection("jdbc:mysql://localhost:3306/production", "username", "password");
            
            // 执行SQL语句获取温度数据
            Statement stmt = conn.createStatement();
            String sql = "SELECT temperature FROM production_data WHERE production_line = 'A'";
            ResultSet rs = stmt.executeQuery(sql);
            
            // 处理数据
            while (rs.next()) {
                double temperature = rs.getDouble("temperature");
                // 对数据进行统计分析或其他处理
            }
            
            // 关闭数据库连接
            rs.close();
            stmt.close();
            conn.close();
        } catch (SQLException e) {
            e.printStackTrace();
        }
    }
}

Through the above code, we can obtain temperature data from the database and perform further statistical analysis or other processing. Next, we need to convert the data into the format required by ECharts and use ECharts for visual display. The following is a sample code that converts temperature data into the JSON format required by ECharts and displays it in a histogram:

import com.github.abel533.echarts.Option;
import com.github.abel533.echarts.axis.CategoryAxis;
import com.github.abel533.echarts.code.Magic;

public class DataVisualization {
    public static void main(String[] args) {
        // 创建Option对象
        Option option = new Option();
        
        // 创建X轴和Y轴
        CategoryAxis xAxis = new CategoryAxis();
        xAxis.setName("时间");
        xAxis.setData(new String[]{"09:00", "09:10", "09:20", "09:30", "09:40"});
        option.xAxis(xAxis);
        
        com.github.abel533.echarts.axis.ValueAxis yAxis = new com.github.abel533.echarts.axis.ValueAxis();
        yAxis.setName("温度");
        yAxis.setMax(100);
        option.yAxis(yAxis);
        
        // 添加数据
        option.series(Magic.bar, new com.github.abel533.echarts.series.Bar().setData(new int[]{20, 30, 40, 50, 60}));
        
        // 输出JSON格式
        System.out.println(option.toString());
    }
}

Through the above code, we can convert the temperature data into the JSON format required by ECharts and display it in the The console outputs the JSON string. This string can be used directly in the js code of the front-end page, using the ECharts library for chart drawing and interaction.

To sum up, this article introduces how to apply ECharts and Java interfaces to statistical analysis in the field of intelligent manufacturing, and gives specific code examples. Through the combination of ECharts and Java, we can easily process and visualize data, providing better decision-making support for intelligent manufacturing. I hope this article can be helpful to readers in statistical analysis in the field of intelligent manufacturing.

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