💖💖作者：计算机毕业设计小途 💙💙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 深度学习实战项目
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基于大数据的谷物农作物数据可视化分析系统介绍
《基于大数据的谷物农作物数据可视化分析系统》是一套运用现代大数据技术深度分析农业数据的综合性平台，该系统采用Hadoop分布式存储框架结合Spark大数据处理引擎作为核心技术架构，通过HDFS实现海量农业数据的可靠存储，利用Spark SQL进行高效的数据查询与计算处理，同时集成Pandas和NumPy等数据科学库增强数据处理能力，后端采用Django框架或Spring Boot框架构建RESTful API服务，前端运用Vue.js响应式框架配合ElementUI组件库打造现代化用户界面，并通过Echarts图表库实现丰富的数据可视化效果，数据持久化采用MySQL关系型数据库确保数据安全性与一致性。系统功能涵盖完整的用户管理模块包括个人信息维护和密码修改，核心业务功能包括大屏可视化展示、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六大分析维度，通过多维度数据挖掘与可视化呈现，为农业决策者提供科学的数据支撑，系统整体架构体现了大数据技术在传统农业领域的创新应用，展现了从数据采集、存储、处理到可视化展示的完整技术链条，具备良好的扩展性和实用性。
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
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from django.http import JsonResponse
import pandas as pd
import numpy as np
spark = SparkSession.builder.appName("GrainCropAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
def price_trend_analysis(request):
crop_type = request.GET.get('crop_type', 'wheat')
start_date = request.GET.get('start_date')
end_date = request.GET.get('end_date')
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/grain_db").option("dbtable", "price_data").option("user", "root").option("password", "password").load()
filtered_df = df.filter((col("crop_type") == crop_type) & (col("price_date").between(start_date, end_date)))
monthly_avg = filtered_df.groupBy(date_format(col("price_date"), "yyyy-MM").alias("month")).agg(avg("price").alias("avg_price"), max("price").alias("max_price"), min("price").alias("min_price"))
trend_analysis = monthly_avg.withColumn("price_change", col("avg_price") - lag("avg_price").over(Window.orderBy("month")))
trend_analysis = trend_analysis.withColumn("change_rate", (col("price_change") / lag("avg_price").over(Window.orderBy("month")) * 100))
volatility = filtered_df.groupBy("month").agg(stddev("price").alias("volatility"))
final_result = trend_analysis.join(volatility, "month").orderBy("month")
price_forecast = final_result.withColumn("forecast_price", col("avg_price") + (col("price_change") * 1.2))
result_data = final_result.select("month", "avg_price", "max_price", "min_price", "change_rate", "volatility", "forecast_price").collect()
response_data = []
for row in result_data:
response_data.append({"month": row["month"], "avg_price": float(row["avg_price"]), "max_price": float(row["max_price"]), "min_price": float(row["min_price"]), "change_rate": float(row["change_rate"]) if row["change_rate"] else 0, "volatility": float(row["volatility"]), "forecast_price": float(row["forecast_price"])})
return JsonResponse({"status": "success", "data": response_data, "total_records": len(response_data)})
def production_yield_analysis(request):
region = request.GET.get('region', 'all')
year = request.GET.get('year', '2023')
crop_types = request.GET.getlist('crop_types[]')
production_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/grain_db").option("dbtable", "production_data").option("user", "root").option("password", "password").load()
yield_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/grain_db").option("dbtable", "yield_data").option("user", "root").option("password", "password").load()
if region != 'all':
production_df = production_df.filter(col("region") == region)
yield_df = yield_df.filter(col("region") == region)
if crop_types:
production_df = production_df.filter(col("crop_type").isin(crop_types))
yield_df = yield_df.filter(col("crop_type").isin(crop_types))
production_df = production_df.filter(col("year") == year)
yield_df = yield_df.filter(col("year") == year)
production_summary = production_df.groupBy("crop_type", "region").agg(sum("production_amount").alias("total_production"), avg("production_amount").alias("avg_production"), sum("planting_area").alias("total_area"))
yield_summary = yield_df.groupBy("crop_type", "region").agg(avg("yield_per_hectare").alias("avg_yield"), max("yield_per_hectare").alias("max_yield"))
combined_data = production_summary.join(yield_summary, ["crop_type", "region"], "inner")
efficiency_analysis = combined_data.withColumn("production_efficiency", col("total_production") / col("total_area"))
efficiency_analysis = efficiency_analysis.withColumn("yield_efficiency_score", when(col("avg_yield") > 5000, "高产").when(col("avg_yield") > 3000, "中产").otherwise("低产"))
regional_ranking = efficiency_analysis.withColumn("region_rank", rank().over(Window.partitionBy("crop_type").orderBy(desc("production_efficiency"))))
trend_comparison = production_df.groupBy("crop_type", "month").agg(sum("production_amount").alias("monthly_production"))
seasonal_pattern = trend_comparison.withColumn("season", when(col("month").isin(["03", "04", "05"]), "春季").when(col("month").isin(["06", "07", "08"]), "夏季").when(col("month").isin(["09", "10", "11"]), "秋季").otherwise("冬季"))
seasonal_summary = seasonal_pattern.groupBy("crop_type", "season").agg(avg("monthly_production").alias("seasonal_avg"))
final_results = regional_ranking.select("crop_type", "region", "total_production", "avg_yield", "production_efficiency", "yield_efficiency_score", "region_rank").collect()
analysis_data = []
for row in final_results:
analysis_data.append({"crop_type": row["crop_type"], "region": row["region"], "total_production": float(row["total_production"]), "avg_yield": float(row["avg_yield"]), "production_efficiency": float(row["production_efficiency"]), "efficiency_level": row["yield_efficiency_score"], "regional_rank": int(row["region_rank"])})
return JsonResponse({"status": "success", "production_analysis": analysis_data, "analysis_year": year, "total_regions": len(set([item["region"] for item in analysis_data]))})
def disaster_impact_analysis(request):
disaster_type = request.GET.get('disaster_type', 'drought')
severity_level = request.GET.get('severity', 'all')
analysis_year = request.GET.get('year', '2023')
disaster_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/grain_db").option("dbtable", "disaster_data").option("user", "root").option("password", "password").load()
production_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/grain_db").option("dbtable", "production_data").option("user", "root").option("password", "password").load()
filtered_disaster = disaster_df.filter((col("disaster_type") == disaster_type) & (col("year") == analysis_year))
if severity_level != 'all':
filtered_disaster = filtered_disaster.filter(col("severity_level") == severity_level)
disaster_regions = filtered_disaster.select("region", "disaster_date", "severity_level", "affected_area").distinct()
affected_production = production_df.join(disaster_regions, "region", "inner").filter(col("year") == analysis_year)
normal_production = production_df.filter((col("year") == analysis_year) & (~col("region").isin([row["region"] for row in disaster_regions.collect()])))
disaster_impact = affected_production.groupBy("crop_type", "severity_level").agg(avg("production_amount").alias("affected_avg_production"), sum("production_amount").alias("affected_total_production"), avg("yield_per_hectare").alias("affected_avg_yield"))
normal_baseline = normal_production.groupBy("crop_type").agg(avg("production_amount").alias("normal_avg_production"), avg("yield_per_hectare").alias("normal_avg_yield"))
impact_comparison = disaster_impact.join(normal_baseline, "crop_type", "left")
impact_metrics = impact_comparison.withColumn("production_loss_rate", ((col("normal_avg_production") - col("affected_avg_production")) / col("normal_avg_production") * 100))
impact_metrics = impact_metrics.withColumn("yield_reduction_rate", ((col("normal_avg_yield") - col("affected_avg_yield")) / col("normal_avg_yield") * 100))
severity_impact = impact_metrics.withColumn("impact_level", when(col("production_loss_rate") > 30, "严重影响").when(col("production_loss_rate") > 15, "中度影响").when(col("production_loss_rate") > 5, "轻微影响").otherwise("影响较小"))
regional_damage = filtered_disaster.groupBy("region").agg(sum("affected_area").alias("total_affected_area"), avg("severity_level").alias("avg_severity"))
economic_loss = impact_metrics.withColumn("estimated_loss", col("affected_total_production") * col("production_loss_rate") / 100 * 2500)
recovery_analysis = impact_metrics.withColumn("recovery_months", when(col("production_loss_rate") > 25, 6).when(col("production_loss_rate") > 10, 3).otherwise(1))
final_impact_data = recovery_analysis.select("crop_type", "severity_level", "production_loss_rate", "yield_reduction_rate", "impact_level", "estimated_loss", "recovery_months").collect()
impact_results = []
for row in final_impact_data:
impact_results.append({"crop_type": row["crop_type"], "severity": row["severity_level"], "production_loss_rate": float(row["production_loss_rate"]) if row["production_loss_rate"] else 0, "yield_reduction_rate": float(row["yield_reduction_rate"]) if row["yield_reduction_rate"] else 0, "impact_level": row["impact_level"], "estimated_economic_loss": float(row["estimated_loss"]) if row["estimated_loss"] else 0, "recovery_period_months": int(row["recovery_months"])})
return JsonResponse({"status": "success", "disaster_analysis": impact_results, "disaster_type": disaster_type, "analysis_year": analysis_year, "total_affected_crops": len(impact_results)})
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：计算机毕业设计小途 💙💙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 深度学习实战项目
