💖💖作者：计算机毕业设计杰瑞 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学校实战项目 计算机毕业设计选题推荐
基于大数据的谷物农作物数据可视化分析系统介绍
基于Hadoop+Spark的谷物农作物数据可视化系统是一个综合运用大数据技术进行农业数据分析的毕业设计项目。该系统采用Hadoop作为分布式存储框架，利用HDFS对海量谷物农作物数据进行存储管理，通过Spark和Spark SQL实现高效的数据计算和分析处理。系统后端采用Spring Boot框架构建，集成Mybatis进行数据持久化操作，前端运用Vue框架结合ElementUI组件库和Echarts图表库，实现直观美观的数据可视化界面。系统核心功能模块包括系统首页展示、个人信息管理、价格趋势分析、生产与产量分析、灾害影响分析、宏观经济关联分析以及价产效益综合分析等七大模块。通过整合Python的Pandas和NumPy数据处理库，系统能够对谷物农作物的历史数据进行深度挖掘和统计分析，为用户提供多维度的数据洞察和可视化报表，帮助理解农作物市场规律和生产趋势。
基于大数据的谷物农作物数据可视化分析系统演示视频
基于大数据的谷物农作物数据可视化分析系统演示图片
基于大数据的谷物农作物数据可视化分析系统代码展示
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.springframework.stereotype.Service;
import java.util.*;
@Service
public class AgricultureAnalysisService {
private SparkSession spark = SparkSession.builder()
.appName("GrainCropAnalysis")
.config("spark.master", "local[*]")
.config("spark.sql.adaptive.enabled", "true")
.getOrCreate();
public Map<String, Object> analyzePriceTrend(String cropType, String startDate, String endDate) {
Dataset<Row> priceData = spark.read()
.format("jdbc")
.option("url", "jdbc:mysql://localhost:3306/agriculture_db")
.option("dbtable", "crop_price_data")
.load();
priceData.createOrReplaceTempView("price_table");
String sql = "SELECT date_column, crop_name, price, " +
"LAG(price, 1) OVER (PARTITION BY crop_name ORDER BY date_column) as prev_price, " +
"((price - LAG(price, 1) OVER (PARTITION BY crop_name ORDER BY date_column)) / " +
"LAG(price, 1) OVER (PARTITION BY crop_name ORDER BY date_column)) * 100 as price_change_rate " +
"FROM price_table WHERE crop_name = '" + cropType + "' " +
"AND date_column BETWEEN '" + startDate + "' AND '" + endDate + "' " +
"ORDER BY date_column";
Dataset<Row> trendResult = spark.sql(sql);
Dataset<Row> avgPriceResult = spark.sql(
"SELECT AVG(price) as avg_price, MAX(price) as max_price, MIN(price) as min_price, " +
"STDDEV(price) as price_volatility FROM price_table WHERE crop_name = '" + cropType + "'");
List<Row> trendData = trendResult.collectAsList();
Row avgData = avgPriceResult.first();
Map<String, Object> result = new HashMap<>();
result.put("trendData", trendData);
result.put("avgPrice", avgData.getDouble(0));
result.put("maxPrice", avgData.getDouble(1));
result.put("minPrice", avgData.getDouble(2));
result.put("volatility", avgData.getDouble(3));
return result;
}
public Map<String, Object> analyzeProductionYield(String region, String year) {
Dataset<Row> productionData = spark.read()
.format("jdbc")
.option("url", "jdbc:mysql://localhost:3306/agriculture_db")
.option("dbtable", "crop_production_data")
.load();
productionData.createOrReplaceTempView("production_table");
String yieldSql = "SELECT crop_name, SUM(production_amount) as total_production, " +
"AVG(yield_per_hectare) as avg_yield, SUM(planting_area) as total_area, " +
"RANK() OVER (ORDER BY SUM(production_amount) DESC) as production_rank " +
"FROM production_table WHERE region = '" + region + "' AND year = '" + year + "' " +
"GROUP BY crop_name ORDER BY total_production DESC";
Dataset<Row> yieldResult = spark.sql(yieldSql);
Dataset<Row> compareResult = spark.sql(
"SELECT crop_name, production_amount, " +
"LAG(production_amount, 1) OVER (PARTITION BY crop_name ORDER BY year) as prev_year_production, " +
"((production_amount - LAG(production_amount, 1) OVER (PARTITION BY crop_name ORDER BY year)) / " +
"LAG(production_amount, 1) OVER (PARTITION BY crop_name ORDER BY year)) * 100 as growth_rate " +
"FROM production_table WHERE region = '" + region + "' AND year IN ('" + year + "', '" + (Integer.parseInt(year)-1) + "')");
Dataset<Row> efficiencyResult = spark.sql(
"SELECT crop_name, AVG(yield_per_hectare) as efficiency, " +
"COUNT(*) as sample_count FROM production_table " +
"WHERE region = '" + region + "' AND year = '" + year + "' GROUP BY crop_name");
List<Row> yieldData = yieldResult.collectAsList();
List<Row> compareData = compareResult.collectAsList();
List<Row> efficiencyData = efficiencyResult.collectAsList();
Map<String, Object> result = new HashMap<>();
result.put("yieldData", yieldData);
result.put("compareData", compareData);
result.put("efficiencyData", efficiencyData);
return result;
}
public Map<String, Object> analyzeDisasterImpact(String disasterType, String timeRange) {
Dataset<Row> disasterData = spark.read()
.format("jdbc")
.option("url", "jdbc:mysql://localhost:3306/agriculture_db")
.option("dbtable", "disaster_impact_data")
.load();
disasterData.createOrReplaceTempView("disaster_table");
String impactSql = "SELECT region, crop_name, disaster_type, " +
"SUM(affected_area) as total_affected_area, " +
"AVG(yield_loss_rate) as avg_loss_rate, " +
"SUM(economic_loss) as total_economic_loss, " +
"COUNT(*) as disaster_frequency " +
"FROM disaster_table WHERE disaster_type = '" + disasterType + "' " +
"AND disaster_date LIKE '%" + timeRange + "%' " +
"GROUP BY region, crop_name, disaster_type " +
"ORDER BY total_economic_loss DESC";
Dataset<Row> impactResult = spark.sql(impactSql);
Dataset<Row> severityResult = spark.sql(
"SELECT disaster_type, AVG(yield_loss_rate) as avg_severity, " +
"MAX(yield_loss_rate) as max_severity, " +
"CASE WHEN AVG(yield_loss_rate) < 10 THEN 'Light' " +
"WHEN AVG(yield_loss_rate) < 30 THEN 'Moderate' " +
"WHEN AVG(yield_loss_rate) < 50 THEN 'Severe' " +
"ELSE 'Catastrophic' END as severity_level " +
"FROM disaster_table WHERE disaster_type = '" + disasterType + "' GROUP BY disaster_type");
Dataset<Row> recoveryResult = spark.sql(
"SELECT region, crop_name, " +
"AVG(DATEDIFF(recovery_date, disaster_date)) as avg_recovery_days, " +
"COUNT(CASE WHEN recovery_date IS NOT NULL THEN 1 END) as recovered_count, " +
"COUNT(*) as total_disasters " +
"FROM disaster_table WHERE disaster_type = '" + disasterType + "' " +
"GROUP BY region, crop_name");
List<Row> impactData = impactResult.collectAsList();
List<Row> severityData = severityResult.collectAsList();
List<Row> recoveryData = recoveryResult.collectAsList();
Map<String, Object> result = new HashMap<>();
result.put("impactData", impactData);
result.put("severityData", severityData);
result.put("recoveryData", recoveryData);
return result;
}
}
基于大数据的谷物农作物数据可视化分析系统文档展示
💖💖作者：计算机毕业设计杰瑞 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学校实战项目 计算机毕业设计选题推荐
