💖💖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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基于大数据的水质数据可视化分析系统介绍
《基于大数据的水质数据可视化分析系统》是一套采用Hadoop+Spark大数据技术架构的综合性水质监测分析平台，系统通过Hadoop分布式文件系统HDFS实现海量水质数据的存储管理，利用Spark分布式计算引擎进行高效的数据处理与分析，结合Spark SQL提供灵活的数据查询能力，同时集成Pandas和NumPy等数据科学库增强数据处理功能。系统后端采用Django或Spring Boot框架构建RESTful API接口，前端基于Vue+ElementUI构建响应式用户界面，通过Echarts图表库实现丰富的数据可视化展示效果。系统核心功能涵盖水质数据分析、综合统计分析、污染物关联分析、水质安全分析、特定污染分析以及高级算法分析等多个专业模块，能够对水质监测数据进行多维度深入挖掘，识别水质变化趋势、污染源分布规律以及各项指标间的关联关系。系统还提供大屏可视化功能，以直观的图表形式展现水质状况的实时监控与历史分析结果，同时具备完善的用户管理、权限控制和系统管理功能，为环保部门、科研院所和相关企业提供专业的水质数据分析解决方案，实现了大数据技术在环境监测领域的创新应用。
基于大数据的水质数据可视化分析系统演示视频
基于大数据的水质数据可视化分析系统演示图片
基于大数据的水质数据可视化分析系统代码展示
spark = SparkSession.builder.appName("WaterQualityAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
sc = spark.sparkContext
def water_quality_analysis(start_date, end_date, station_ids):
water_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/water_quality").option("dbtable", "water_monitoring_data").option("user", "root").option("password", "password").load()
filtered_df = water_df.filter((water_df.monitoring_date >= start_date) & (water_df.monitoring_date <= end_date) & (water_df.station_id.isin(station_ids)))
ph_analysis = filtered_df.groupBy("station_id").agg(avg("ph_value").alias("avg_ph"), max("ph_value").alias("max_ph"), min("ph_value").alias("min_ph"), stddev("ph_value").alias("ph_stddev"))
dissolved_oxygen_analysis = filtered_df.groupBy("station_id").agg(avg("dissolved_oxygen").alias("avg_do"), max("dissolved_oxygen").alias("max_do"), min("dissolved_oxygen").alias("min_do"))
cod_analysis = filtered_df.groupBy("station_id").agg(avg("cod").alias("avg_cod"), max("cod").alias("max_cod"), min("cod").alias("min_cod"))
ammonia_analysis = filtered_df.groupBy("station_id").agg(avg("ammonia_nitrogen").alias("avg_ammonia"), max("ammonia_nitrogen").alias("max_ammonia"), min("ammonia_nitrogen").alias("min_ammonia"))
quality_levels = filtered_df.withColumn("quality_level", when((col("ph_value") >= 6.5) & (col("ph_value") <= 8.5) & (col("dissolved_oxygen") >= 5) & (col("cod") <= 20) & (col("ammonia_nitrogen") <= 1.0), "优").when((col("ph_value") >= 6.0) & (col("ph_value") <= 9.0) & (col("dissolved_oxygen") >= 3) & (col("cod") <= 30) & (col("ammonia_nitrogen") <= 1.5), "良").otherwise("差"))
quality_distribution = quality_levels.groupBy("station_id", "quality_level").count().withColumnRenamed("count", "level_count")
trend_analysis = filtered_df.withColumn("month", date_format(col("monitoring_date"), "yyyy-MM")).groupBy("station_id", "month").agg(avg("ph_value").alias("monthly_avg_ph"), avg("dissolved_oxygen").alias("monthly_avg_do"), avg("cod").alias("monthly_avg_cod"), avg("ammonia_nitrogen").alias("monthly_avg_ammonia"))
exceeding_standards = filtered_df.filter((col("ph_value") < 6.0) | (col("ph_value") > 9.0) | (col("dissolved_oxygen") < 2) | (col("cod") > 40) | (col("ammonia_nitrogen") > 2.0))
exceeding_count = exceeding_standards.groupBy("station_id").count().withColumnRenamed("count", "exceeding_times")
final_result = ph_analysis.join(dissolved_oxygen_analysis, "station_id").join(cod_analysis, "station_id").join(ammonia_analysis, "station_id")
comprehensive_result = {"basic_statistics": final_result.collect(), "quality_distribution": quality_distribution.collect(), "trend_analysis": trend_analysis.collect(), "exceeding_standards": exceeding_count.collect()}
return comprehensive_result
def pollutant_correlation_analysis(start_date, end_date):
water_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/water_quality").option("dbtable", "water_monitoring_data").option("user", "root").option("password", "password").load()
filtered_df = water_df.filter((water_df.monitoring_date >= start_date) & (water_df.monitoring_date <= end_date))
numeric_columns = ["ph_value", "dissolved_oxygen", "cod", "ammonia_nitrogen", "total_phosphorus", "total_nitrogen", "turbidity", "temperature"]
feature_df = filtered_df.select([col(c).cast("double").alias(c) for c in numeric_columns]).na.drop()
assembler = VectorAssembler(inputCols=numeric_columns, outputCol="features")
assembled_df = assembler.transform(feature_df)
correlation_matrix = Correlation.corr(assembled_df, "features", "pearson").head()[0]
correlation_array = correlation_matrix.toArray()
correlation_results = []
for i in range(len(numeric_columns)):
for j in range(i+1, len(numeric_columns)):
correlation_coefficient = float(correlation_array[i][j])
if abs(correlation_coefficient) > 0.3:
correlation_results.append({"parameter1": numeric_columns[i], "parameter2": numeric_columns[j], "correlation": correlation_coefficient, "strength": "强相关" if abs(correlation_coefficient) > 0.7 else "中等相关" if abs(correlation_coefficient) > 0.5 else "弱相关", "relationship": "正相关" if correlation_coefficient > 0 else "负相关"})
pollutant_clustering = feature_df.groupBy().agg(*[avg(col(c)).alias(f"avg_{c}") for c in numeric_columns], *[stddev(col(c)).alias(f"std_{c}") for c in numeric_columns])
high_pollution_conditions = filtered_df.filter((col("cod") > 30) | (col("ammonia_nitrogen") > 1.5) | (col("total_phosphorus") > 0.4))
pollution_patterns = high_pollution_conditions.groupBy("station_id").agg(count("*").alias("pollution_events"), avg("cod").alias("avg_cod_pollution"), avg("ammonia_nitrogen").alias("avg_ammonia_pollution"), avg("total_phosphorus").alias("avg_phosphorus_pollution"))
seasonal_correlation = filtered_df.withColumn("season", when(month(col("monitoring_date")).isin([12, 1, 2]), "冬季").when(month(col("monitoring_date")).isin([3, 4, 5]), "春季").when(month(col("monitoring_date")).isin([6, 7, 8]), "夏季").otherwise("秋季"))
seasonal_pollutant_avg = seasonal_correlation.groupBy("season").agg(*[avg(col(c)).alias(f"avg_{c}") for c in numeric_columns])
return {"correlation_matrix": correlation_results, "clustering_stats": pollutant_clustering.collect(), "pollution_patterns": pollution_patterns.collect(), "seasonal_analysis": seasonal_pollutant_avg.collect()}
def comprehensive_statistical_analysis(time_period):
water_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/water_quality").option("dbtable", "water_monitoring_data").option("user", "root").option("password", "password").load()
station_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/water_quality").option("dbtable", "monitoring_stations").option("user", "root").option("password", "password").load()
joined_df = water_df.join(station_df, water_df.station_id == station_df.id, "left")
if time_period == "recent_month":
filtered_df = joined_df.filter(col("monitoring_date") >= date_sub(current_date(), 30))
elif time_period == "recent_quarter":
filtered_df = joined_df.filter(col("monitoring_date") >= date_sub(current_date(), 90))
else:
filtered_df = joined_df.filter(col("monitoring_date") >= date_sub(current_date(), 365))
overall_statistics = filtered_df.agg(count("*").alias("total_records"), countDistinct("station_id").alias("active_stations"), avg("ph_value").alias("overall_avg_ph"), avg("dissolved_oxygen").alias("overall_avg_do"), avg("cod").alias("overall_avg_cod"), avg("ammonia_nitrogen").alias("overall_avg_ammonia"))
regional_statistics = filtered_df.groupBy("region").agg(count("*").alias("region_records"), avg("ph_value").alias("region_avg_ph"), avg("dissolved_oxygen").alias("region_avg_do"), avg("cod").alias("region_avg_cod"), avg("ammonia_nitrogen").alias("region_avg_ammonia"), max("cod").alias("region_max_cod"), max("ammonia_nitrogen").alias("region_max_ammonia"))
water_type_statistics = filtered_df.groupBy("water_type").agg(count("*").alias("type_records"), avg("ph_value").alias("type_avg_ph"), avg("dissolved_oxygen").alias("type_avg_do"), avg("cod").alias("type_avg_cod"), avg("ammonia_nitrogen").alias("type_avg_ammonia"))
quality_classification = filtered_df.withColumn("water_quality_grade", when((col("dissolved_oxygen") >= 7.5) & (col("cod") <= 15) & (col("ammonia_nitrogen") <= 0.5), "I类").when((col("dissolved_oxygen") >= 6) & (col("cod") <= 20) & (col("ammonia_nitrogen") <= 1.0), "II类").when((col("dissolved_oxygen") >= 5) & (col("cod") <= 20) & (col("ammonia_nitrogen") <= 1.0), "III类").when((col("dissolved_oxygen") >= 3) & (col("cod") <= 30) & (col("ammonia_nitrogen") <= 1.5), "IV类").when((col("dissolved_oxygen") >= 2) & (col("cod") <= 40) & (col("ammonia_nitrogen") <= 2.0), "V类").otherwise("劣V类"))
grade_distribution = quality_classification.groupBy("water_quality_grade").count().withColumnRenamed("count", "grade_count")
monthly_trends = filtered_df.withColumn("year_month", date_format(col("monitoring_date"), "yyyy-MM")).groupBy("year_month").agg(avg("ph_value").alias("monthly_ph"), avg("dissolved_oxygen").alias("monthly_do"), avg("cod").alias("monthly_cod"), avg("ammonia_nitrogen").alias("monthly_ammonia"), count("*").alias("monthly_records"))
station_rankings = filtered_df.groupBy("station_id", "station_name").agg(avg("dissolved_oxygen").alias("avg_do"), avg("cod").alias("avg_cod"), avg("ammonia_nitrogen").alias("avg_ammonia")).withColumn("composite_score", (col("avg_do") * 0.4) - (col("avg_cod") * 0.3) - (col("avg_ammonia") * 0.3)).orderBy(desc("composite_score"))
abnormal_data = filtered_df.filter((col("ph_value") < 4.0) | (col("ph_value") > 10.0) | (col("dissolved_oxygen") < 0.5) | (col("dissolved_oxygen") > 20) | (col("cod") > 100) | (col("ammonia_nitrogen") > 10))
abnormal_count = abnormal_data.count()
return {"overall_stats": overall_statistics.collect(), "regional_stats": regional_statistics.collect(), "water_type_stats": water_type_statistics.collect(), "quality_grades": grade_distribution.collect(), "monthly_trends": monthly_trends.collect(), "station_rankings": station_rankings.collect(), "abnormal_records": abnormal_count}
基于大数据的水质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
