💖💖作者：计算机毕业设计江挽 💙💙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
基于大数据的中国水污染监测数据分析系统统介绍
《中国水污染监测数据分析系统》是一个基于Hadoop+Spark大数据技术架构的综合性环境监测数据处理平台，采用Python作为主要开发语言，结合Django后端框架和Vue+ElementUI前端技术栈构建。系统通过HDFS分布式存储海量水质监测数据，利用Spark SQL和Spark Core进行高效数据处理与分析，配合Pandas、NumPy等科学计算库实现复杂的数据挖掘算法。平台集成了系统首页、用户管理、水污染监测数据管理、污染成因探索分析、水质综合评价分析、核心污染物深度分析、水质时空分布分析等八大核心功能模块，通过Echarts可视化组件实现监测数据的多维度图表展示，为环保部门和科研机构提供了一套完整的水环境质量评估与污染源追溯解决方案，有效提升了大规模水质数据的处理效率和分析精度。
基于大数据的中国水污染监测数据分析系统演示视频
基于大数据的中国水污染监测数据分析系统演示图片
基于大数据的中国水污染监测数据分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, when, avg, sum, max, min, count, date_format, year, month, lag
from pyspark.sql.window import Window
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
spark = SparkSession.builder.appName("WaterPollutionAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def pollution_cause_analysis(monitoring_data_df):
correlation_matrix = monitoring_data_df.select("ph_value", "dissolved_oxygen", "cod", "bod", "ammonia_nitrogen", "total_phosphorus", "suspended_solids").toPandas().corr()
high_correlation_pairs = []
for i in range(len(correlation_matrix.columns)):
for j in range(i+1, len(correlation_matrix.columns)):
corr_value = correlation_matrix.iloc[i, j]
if abs(corr_value) > 0.7:
high_correlation_pairs.append({
'factor1': correlation_matrix.columns[i],
'factor2': correlation_matrix.columns[j],
'correlation': round(corr_value, 4)
})
pollution_threshold = {'cod': 40, 'bod': 6, 'ammonia_nitrogen': 2.0, 'total_phosphorus': 0.4}
exceeding_analysis = monitoring_data_df.select("station_id", "monitor_time", "cod", "bod", "ammonia_nitrogen", "total_phosphorus")
for pollutant, threshold in pollution_threshold.items():
exceeding_analysis = exceeding_analysis.withColumn(
f"{pollutant}_exceeding",
when(col(pollutant) > threshold, col(pollutant) - threshold).otherwise(0)
)
seasonal_pattern = exceeding_analysis.withColumn("month", month(col("monitor_time")))
seasonal_pollution = seasonal_pattern.groupBy("month").agg(
avg("cod_exceeding").alias("avg_cod_exceeding"),
avg("bod_exceeding").alias("avg_bod_exceeding"),
avg("ammonia_nitrogen_exceeding").alias("avg_ammonia_exceeding"),
avg("total_phosphorus_exceeding").alias("avg_phosphorus_exceeding")
).orderBy("month")
station_pollution_rank = exceeding_analysis.groupBy("station_id").agg(
sum("cod_exceeding").alias("total_cod_exceeding"),
sum("bod_exceeding").alias("total_bod_exceeding"),
sum("ammonia_nitrogen_exceeding").alias("total_ammonia_exceeding"),
sum("total_phosphorus_exceeding").alias("total_phosphorus_exceeding"),
count("*").alias("sample_count")
)
station_pollution_rank = station_pollution_rank.withColumn(
"pollution_score",
(col("total_cod_exceeding") * 0.3 + col("total_bod_exceeding") * 0.25 +
col("total_ammonia_exceeding") * 0.25 + col("total_phosphorus_exceeding") * 0.2)
).orderBy(col("pollution_score").desc())
return {
'correlation_analysis': high_correlation_pairs,
'seasonal_pollution': seasonal_pollution.collect(),
'station_ranking': station_pollution_rank.limit(10).collect(),
'pollution_factors': ['cod', 'bod', 'ammonia_nitrogen', 'total_phosphorus']
}
def water_quality_comprehensive_evaluation(monitoring_data_df):
quality_standards = {
'excellent': {'ph_range': (6.5, 8.5), 'dissolved_oxygen': 7.5, 'cod': 15, 'bod': 3, 'ammonia_nitrogen': 0.15, 'total_phosphorus': 0.02},
'good': {'ph_range': (6.0, 9.0), 'dissolved_oxygen': 6.0, 'cod': 20, 'bod': 4, 'ammonia_nitrogen': 0.5, 'total_phosphorus': 0.1},
'moderate': {'ph_range': (5.5, 9.5), 'dissolved_oxygen': 5.0, 'cod': 30, 'bod': 6, 'ammonia_nitrogen': 1.0, 'total_phosphorus': 0.2},
'poor': {'ph_range': (4.0, 10.0), 'dissolved_oxygen': 3.0, 'cod': 40, 'bod': 10, 'ammonia_nitrogen': 1.5, 'total_phosphorus': 0.3}
}
evaluation_df = monitoring_data_df.select("station_id", "monitor_time", "ph_value", "dissolved_oxygen", "cod", "bod", "ammonia_nitrogen", "total_phosphorus")
for level, standards in quality_standards.items():
ph_condition = (col("ph_value") >= standards['ph_range'][0]) & (col("ph_value") <= standards['ph_range'][1])
do_condition = col("dissolved_oxygen") >= standards['dissolved_oxygen']
cod_condition = col("cod") <= standards['cod']
bod_condition = col("bod") <= standards['bod']
ammonia_condition = col("ammonia_nitrogen") <= standards['ammonia_nitrogen']
phosphorus_condition = col("total_phosphorus") <= standards['total_phosphorus']
evaluation_df = evaluation_df.withColumn(
f"meets_{level}",
when(ph_condition & do_condition & cod_condition & bod_condition & ammonia_condition & phosphorus_condition, 1).otherwise(0)
)
evaluation_df = evaluation_df.withColumn(
"quality_level",
when(col("meets_excellent") == 1, "excellent")
.when(col("meets_good") == 1, "good")
.when(col("meets_moderate") == 1, "moderate")
.when(col("meets_poor") == 1, "poor")
.otherwise("severely_polluted")
)
monthly_evaluation = evaluation_df.withColumn("year_month", date_format(col("monitor_time"), "yyyy-MM"))
quality_distribution = monthly_evaluation.groupBy("year_month", "quality_level").count().orderBy("year_month", "quality_level")
station_quality_stats = evaluation_df.groupBy("station_id").agg(
count("*").alias("total_samples"),
sum(when(col("quality_level") == "excellent", 1).otherwise(0)).alias("excellent_count"),
sum(when(col("quality_level") == "good", 1).otherwise(0)).alias("good_count"),
sum(when(col("quality_level") == "moderate", 1).otherwise(0)).alias("moderate_count"),
sum(when(col("quality_level") == "poor", 1).otherwise(0)).alias("poor_count"),
sum(when(col("quality_level") == "severely_polluted", 1).otherwise(0)).alias("severely_polluted_count")
)
station_quality_stats = station_quality_stats.withColumn(
"excellent_rate",
(col("excellent_count") / col("total_samples") * 100).cast("decimal(5,2)")
).withColumn(
"qualified_rate",
((col("excellent_count") + col("good_count") + col("moderate_count")) / col("total_samples") * 100).cast("decimal(5,2)")
)
return {
'quality_distribution': quality_distribution.collect(),
'station_statistics': station_quality_stats.orderBy(col("excellent_rate").desc()).collect(),
'evaluation_standards': quality_standards
}
def core_pollutant_depth_analysis(monitoring_data_df):
key_pollutants = ["cod", "bod", "ammonia_nitrogen", "total_phosphorus", "suspended_solids"]
pollutant_stats = monitoring_data_df.select("station_id", "monitor_time", *key_pollutants)
window_spec = Window.partitionBy("station_id").orderBy("monitor_time")
trend_analysis = pollutant_stats
for pollutant in key_pollutants:
trend_analysis = trend_analysis.withColumn(
f"{pollutant}_previous",
lag(col(pollutant), 1).over(window_spec)
).withColumn(
f"{pollutant}_change",
when(col(f"{pollutant}_previous").isNotNull(),
col(pollutant) - col(f"{pollutant}_previous")).otherwise(0)
).withColumn(
f"{pollutant}_trend",
when(col(f"{pollutant}_change") > 0, "increasing")
.when(col(f"{pollutant}_change") < 0, "decreasing")
.otherwise("stable")
)
pollutant_concentration_levels = {}
concentration_thresholds = {
'cod': [15, 20, 30, 40], 'bod': [3, 4, 6, 10], 'ammonia_nitrogen': [0.15, 0.5, 1.0, 1.5],
'total_phosphorus': [0.02, 0.1, 0.2, 0.3], 'suspended_solids': [10, 15, 25, 30]
}
for pollutant in key_pollutants:
thresholds = concentration_thresholds[pollutant]
concentration_analysis = pollutant_stats.withColumn(
f"{pollutant}_level",
when(col(pollutant) <= thresholds[0], "very_low")
.when(col(pollutant) <= thresholds[1], "low")
.when(col(pollutant) <= thresholds[2], "medium")
.when(col(pollutant) <= thresholds[3], "high")
.otherwise("very_high")
)
level_distribution = concentration_analysis.groupBy(f"{pollutant}_level").count().collect()
pollutant_concentration_levels[pollutant] = level_distribution
seasonal_pollutant_analysis = pollutant_stats.withColumn("season",
when((month(col("monitor_time")) >= 3) & (month(col("monitor_time")) <= 5), "spring")
.when((month(col("monitor_time")) >= 6) & (month(col("monitor_time")) <= 8), "summer")
.when((month(col("monitor_time")) >= 9) & (month(col("monitor_time")) <= 11), "autumn")
.otherwise("winter")
)
seasonal_stats = seasonal_pollutant_analysis.groupBy("season").agg(
avg("cod").alias("avg_cod"),
max("cod").alias("max_cod"),
avg("bod").alias("avg_bod"),
max("bod").alias("max_bod"),
avg("ammonia_nitrogen").alias("avg_ammonia"),
max("ammonia_nitrogen").alias("max_ammonia"),
avg("total_phosphorus").alias("avg_phosphorus"),
max("total_phosphorus").alias("max_phosphorus")
).orderBy("season")
return {
'concentration_levels': pollutant_concentration_levels,
'seasonal_analysis': seasonal_stats.collect(),
'trend_data': trend_analysis.select("station_id", "monitor_time", *[f"{p}_trend" for p in key_pollutants]).collect(),
'key_pollutants': key_pollutants
}
基于大数据的中国水污染监测数据分析系统文档展示
💖💖作者：计算机毕业设计江挽 💙💙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
