💖💖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
全国降水分析可视化系统介绍
全国降水分析可视化系统是一个基于大数据技术栈的综合性气象数据分析平台，采用Hadoop+Spark分布式计算框架作为核心处理引擎，结合Python语言的强大数据处理能力和Django Web框架的稳定性，构建了完整的降水数据采集、存储、分析和可视化解决方案。系统前端采用Vue.js+ElementUI+Echarts技术栈，实现了响应式的用户界面和丰富的图表展示效果，后端通过Django框架提供RESTful API服务，数据持久化采用MySQL数据库。系统核心功能包括全国各地区降水数据的实时采集与存储、基于历史数据的降水趋势分析、机器学习算法驱动的降水预测模型、多维度的数据可视化展示以及完善的系统管理功能。整个系统架构遵循分层设计原则，数据处理层利用Spark SQL进行大规模数据查询和统计分析，业务逻辑层通过Django的MVC模式实现各种业务功能，展示层通过Echarts图表库将复杂的气象数据转化为直观的可视化图表，为用户提供了一个功能完善、性能稳定的降水数据分析工具。
全国降水分析可视化系统演示视频
全国降水分析可视化系统演示图片
全国降水分析可视化系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import LinearRegression
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
spark = SparkSession.builder.appName("RainfallAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
@csrf_exempt
def process_rainfall_data(request):
rainfall_df = spark.read.option("header", "true").csv("hdfs://localhost:9000/rainfall_data/*.csv")
rainfall_df = rainfall_df.withColumn("rainfall_amount", col("rainfall_amount").cast("double"))
rainfall_df = rainfall_df.withColumn("year", year(col("date")))
rainfall_df = rainfall_df.withColumn("month", month(col("date")))
rainfall_df = rainfall_df.filter(col("rainfall_amount").isNotNull())
monthly_stats = rainfall_df.groupBy("province", "year", "month").agg(
avg("rainfall_amount").alias("avg_rainfall"),
sum("rainfall_amount").alias("total_rainfall"),
count("rainfall_amount").alias("day_count"),
max("rainfall_amount").alias("max_rainfall")
)
regional_comparison = rainfall_df.groupBy("province").agg(
avg("rainfall_amount").alias("province_avg"),
stddev("rainfall_amount").alias("province_std")
)
seasonal_pattern = rainfall_df.withColumn("season",
when(col("month").isin([12, 1, 2]), "winter")
.when(col("month").isin([3, 4, 5]), "spring")
.when(col("month").isin([6, 7, 8]), "summer")
.otherwise("autumn")
).groupBy("province", "season").agg(avg("rainfall_amount").alias("seasonal_avg"))
result_data = {
"monthly_stats": monthly_stats.collect(),
"regional_comparison": regional_comparison.collect(),
"seasonal_pattern": seasonal_pattern.collect()
}
return JsonResponse(result_data, safe=False)
@csrf_exempt
def predict_rainfall(request):
historical_df = spark.read.option("header", "true").csv("hdfs://localhost:9000/rainfall_data/*.csv")
historical_df = historical_df.withColumn("rainfall_amount", col("rainfall_amount").cast("double"))
historical_df = historical_df.withColumn("temperature", col("temperature").cast("double"))
historical_df = historical_df.withColumn("humidity", col("humidity").cast("double"))
historical_df = historical_df.withColumn("pressure", col("pressure").cast("double"))
historical_df = historical_df.filter(col("rainfall_amount").isNotNull())
feature_df = historical_df.withColumn("month", month(col("date")))
feature_df = feature_df.withColumn("day_of_year", dayofyear(col("date")))
feature_df = feature_df.withColumn("is_summer", when(col("month").isin([6, 7, 8]), 1).otherwise(0))
assembler = VectorAssembler(
inputCols=["temperature", "humidity", "pressure", "month", "day_of_year", "is_summer"],
outputCol="features"
)
feature_df = assembler.transform(feature_df)
train_data, test_data = feature_df.randomSplit([0.8, 0.2], seed=42)
lr = LinearRegression(featuresCol="features", labelCol="rainfall_amount")
lr_model = lr.fit(train_data)
predictions = lr_model.transform(test_data)
rmse = lr_model.summary.rootMeanSquaredError
r2 = lr_model.summary.r2
request_data = json.loads(request.body)
predict_province = request_data.get("province")
future_features = spark.createDataFrame([
(request_data.get("temperature"), request_data.get("humidity"),
request_data.get("pressure"), request_data.get("month"),
request_data.get("day_of_year"), 1 if request_data.get("month") in [6,7,8] else 0)
], ["temperature", "humidity", "pressure", "month", "day_of_year", "is_summer"])
future_features = assembler.transform(future_features)
future_prediction = lr_model.transform(future_features)
predicted_rainfall = future_prediction.select("prediction").collect()[0]["prediction"]
return JsonResponse({
"predicted_rainfall": round(predicted_rainfall, 2),
"model_rmse": round(rmse, 3),
"model_r2": round(r2, 3),
"province": predict_province
})
@csrf_exempt
def generate_visualization_data(request):
rainfall_df = spark.read.option("header", "true").csv("hdfs://localhost:9000/rainfall_data/*.csv")
rainfall_df = rainfall_df.withColumn("rainfall_amount", col("rainfall_amount").cast("double"))
rainfall_df = rainfall_df.withColumn("year", year(col("date")))
rainfall_df = rainfall_df.withColumn("month", month(col("date")))
china_map_data = rainfall_df.groupBy("province").agg(
avg("rainfall_amount").alias("avg_rainfall")
).orderBy(desc("avg_rainfall"))
time_series_data = rainfall_df.groupBy("year", "month").agg(
avg("rainfall_amount").alias("monthly_avg")
).orderBy("year", "month")
province_ranking = rainfall_df.groupBy("province").agg(
sum("rainfall_amount").alias("total_rainfall"),
avg("rainfall_amount").alias("avg_rainfall"),
count("rainfall_amount").alias("record_count")
).orderBy(desc("total_rainfall"))
extreme_weather = rainfall_df.filter(col("rainfall_amount") > 50).groupBy("province").agg(
count("rainfall_amount").alias("extreme_days"),
max("rainfall_amount").alias("max_rainfall")
)
drought_analysis = rainfall_df.filter(col("rainfall_amount") < 5).groupBy("province", "year").agg(
count("rainfall_amount").alias("drought_days")
).filter(col("drought_days") > 30)
seasonal_heatmap = rainfall_df.withColumn("season",
when(col("month").isin([12, 1, 2]), "winter")
.when(col("month").isin([3, 4, 5]), "spring")
.when(col("month").isin([6, 7, 8]), "summer")
.otherwise("autumn")
).groupBy("province", "season").agg(avg("rainfall_amount").alias("seasonal_rainfall"))
trend_analysis = rainfall_df.groupBy("province", "year").agg(
avg("rainfall_amount").alias("yearly_avg")
).withColumn("trend_indicator",
when(col("yearly_avg") > lag("yearly_avg").over(Window.partitionBy("province").orderBy("year")), "increase")
.when(col("yearly_avg") < lag("yearly_avg").over(Window.partitionBy("province").orderBy("year")), "decrease")
.otherwise("stable")
)
visualization_result = {
"china_map": china_map_data.collect(),
"time_series": time_series_data.collect(),
"province_ranking": province_ranking.collect(),
"extreme_weather": extreme_weather.collect(),
"drought_analysis": drought_analysis.collect(),
"seasonal_heatmap": seasonal_heatmap.collect(),
"trend_analysis": trend_analysis.collect()
}
return JsonResponse(visualization_result, safe=False)
全国降水分析可视化系统文档展示
💖💖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
