当前位置：首页 > news >正文

大数据毕业设计选题推荐-基于大数据的海洋塑料污染数据分析与可视化系统-Hadoop-Spark-数据可视化-BigData

news 2025/9/10 6:50:24

✨作者主页：IT毕设梦工厂✨
个人简介：曾从事计算机专业培训教学，擅长Java、Python、PHP、.NET、Node.js、GO、微信小程序、安卓Android等项目实战。接项目定制开发、代码讲解、答辩教学、文档编写、降重等。
☑文末获取源码☑
精彩专栏推荐⬇⬇⬇
Java项目
Python项目
安卓项目
微信小程序项目

文章目录

一、前言
二、开发环境
三、系统界面展示
四、部分代码设计
五、系统视频
结语

一、前言

系统介绍
本系统是一个基于大数据技术栈的海洋塑料污染数据分析与可视化平台，采用Hadoop+Spark作为大数据处理框架，结合Python/Java语言支持，构建了完整的数据处理分析链路。系统后端基于Django/Spring Boot框架架构，前端采用Vue+ElementUI+Echarts技术栈实现交互式可视化界面。通过HDFS分布式存储海洋污染数据，利用Spark SQL进行大规模数据清洗和预处理，结合Pandas、NumPy等科学计算库实现多维度统计分析。系统具备海洋污染综合分析、塑料来源构成分析、污染区域分布分析、污染事件时空尺度分析等核心功能模块，支持时间序列分析、地理空间聚类、塑料类型分类统计等复杂业务逻辑。通过MySQL数据库存储分析结果和元数据，最终以数据可视化大屏的形式呈现分析成果，为海洋环境保护决策提供数据支撑。系统整体架构支持海量数据的实时处理和批量分析，能够有效处理多源异构的海洋污染监测数据。

选题背景
随着全球工业化进程的加速和塑料制品的广泛应用，海洋塑料污染已成为当今世界面临的重大环境挑战。海洋中的塑料垃圾不仅破坏了海洋生态系统的平衡，还通过食物链对人类健康构成潜在威胁。传统的海洋污染监测主要依靠人工采样和小规模数据分析，难以全面掌握污染分布规律和变化趋势。随着海洋监测设备的普及和传感器技术的发展，海洋环境数据呈现爆发式增长态势，这些数据包含了丰富的时空信息和污染特征。然而，面对如此庞大且复杂的数据集，传统的数据处理方法已无法满足深入分析的需求。大数据技术的兴起为海洋污染数据的深度挖掘提供了新的技术途径，通过分布式计算和机器学习算法，能够从海量数据中提取有价值的污染模式和预测信息。

选题意义
本课题研究具有多重现实意义，从技术角度来看，通过将大数据技术应用于海洋污染分析领域，为传统环境科学研究提供了新的技术思路和方法路径。系统集成了Hadoop、Spark等分布式计算框架，能够高效处理TB级别的海洋监测数据，这为类似的环境大数据应用项目提供了可借鉴的技术方案。从环保实践角度而言，系统通过多维度数据分析和可视化展示，能够帮助环保部门更直观地了解海洋污染现状，为制定针对性的治理措施提供数据依据。虽然作为毕业设计项目，系统在规模和复杂度上相对有限，但其在数据处理流程设计、算法实现和可视化技术融合方面的实践经验，对于培养大数据分析能力和环境信息化素养具有积极作用。同时，项目涉及的跨学科知识整合，包括计算机科学、环境科学和数据科学的结合，体现了当代信息技术在解决实际环境问题中的应用价值，为后续深入研究和技术改进奠定了基础。

二、开发环境

大数据框架：Hadoop+Spark（本次没用Hive，支持定制）
开发语言：Python+Java（两个版本都支持）
后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（两个版本都支持）
前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery
详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy
数据库：MySQL

三、系统界面展示

基于大数据的海洋塑料污染数据分析与可视化系统界面展示：

四、部分代码设计

项目实战-代码参考：

from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
import numpy as np
import pandas as pd
from datetime import datetime
from sklearn.cluster import DBSCAN
import jsonspark = SparkSession.builder.appName("OceanPlasticPollutionAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()def comprehensive_pollution_analysis():df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/ocean_data/ocean_plastic_pollution_data.csv")df = df.withColumn("Date", to_timestamp(col("Date"), "dd/MM/yyyy H:mm"))df = df.withColumn("Year", year(col("Date")))df = df.withColumn("Month", month(col("Date")))df = 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"))yearly_stats = df.groupBy("Year").agg(sum("Plastic_Weight_kg").alias("TotalWeight"), count("*").alias("EventCount"), avg("Plastic_Weight_kg").alias("AvgWeight"))seasonal_stats = df.groupBy("Season").agg(sum("Plastic_Weight_kg").alias("SeasonalWeight"), count("*").alias("SeasonalEvents"))region_analysis = df.groupBy("Region").agg(sum("Plastic_Weight_kg").alias("RegionWeight"), count("*").alias("RegionEvents"), avg("Depth_meters").alias("AvgDepth"))plastic_type_distribution = df.groupBy("Plastic_Type").agg(sum("Plastic_Weight_kg").alias("TypeWeight"), count("*").alias("TypeCount"))depth_analysis = df.withColumn("DepthCategory", when(col("Depth_meters") < 50, "0-50m").when(col("Depth_meters") < 100, "50-100m").otherwise("100m+")).groupBy("DepthCategory").agg(sum("Plastic_Weight_kg").alias("DepthWeight"), avg("Plastic_Weight_kg").alias("AvgDepthWeight"))correlation_data = df.select("Plastic_Weight_kg", "Depth_meters", "Latitude", "Longitude").collect()weight_depth_corr = np.corrcoef([row["Plastic_Weight_kg"] for row in correlation_data], [row["Depth_meters"] for row in correlation_data])[0, 1]monthly_trend = df.groupBy("Year", "Month").agg(sum("Plastic_Weight_kg").alias("MonthlyWeight")).orderBy("Year", "Month")result = {"yearly_analysis": [row.asDict() for row in yearly_stats.collect()], "seasonal_analysis": [row.asDict() for row in seasonal_stats.collect()], "region_analysis": [row.asDict() for row in region_analysis.collect()], "plastic_distribution": [row.asDict() for row in plastic_type_distribution.collect()], "depth_analysis": [row.asDict() for row in depth_analysis.collect()], "weight_depth_correlation": weight_depth_corr, "monthly_trend": [row.asDict() for row in monthly_trend.collect()]}return resultdef plastic_source_composition_analysis():df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/ocean_data/ocean_plastic_pollution_data.csv")total_weight = df.agg(sum("Plastic_Weight_kg")).collect()[0][0]plastic_composition = df.groupBy("Plastic_Type").agg(sum("Plastic_Weight_kg").alias("TypeWeight"), count("*").alias("TypeCount")).withColumn("WeightPercentage", (col("TypeWeight") / total_weight * 100)).orderBy(desc("TypeWeight"))regional_composition = df.groupBy("Region", "Plastic_Type").agg(sum("Plastic_Weight_kg").alias("RegionalTypeWeight")).collect()region_totals = df.groupBy("Region").agg(sum("Plastic_Weight_kg").alias("RegionTotal")).collect()region_total_dict = {row["Region"]: row["RegionTotal"] for row in region_totals}regional_percentages = []for row in regional_composition:region = row["Region"]plastic_type = row["Plastic_Type"]weight = row["RegionalTypeWeight"]percentage = (weight / region_total_dict[region]) * 100 if region_total_dict[region] > 0 else 0regional_percentages.append({"Region": region, "Plastic_Type": plastic_type, "Weight": weight, "Percentage": percentage})time_composition = df.withColumn("Year", year(to_timestamp(col("Date"), "dd/MM/yyyy H:mm"))).groupBy("Year", "Plastic_Type").agg(sum("Plastic_Weight_kg").alias("YearlyTypeWeight")).collect()yearly_totals = df.withColumn("Year", year(to_timestamp(col("Date"), "dd/MM/yyyy H:mm"))).groupBy("Year").agg(sum("Plastic_Weight_kg").alias("YearTotal")).collect()year_total_dict = {row["Year"]: row["YearTotal"] for row in yearly_totals}temporal_trends = []for row in time_composition:year = row["Year"]plastic_type = row["Plastic_Type"]weight = row["YearlyTypeWeight"]percentage = (weight / year_total_dict[year]) * 100 if year_total_dict[year] > 0 else 0temporal_trends.append({"Year": year, "Plastic_Type": plastic_type, "Weight": weight, "Percentage": percentage})plastic_source_mapping = {"Polyethylene Terephthalate (PET)": "饮料瓶类", "Polyethylene (PE)": "购物袋类", "Polystyrene (PS)": "泡沫包装类", "Polypropylene (PP)": "食品容器类", "Polyvinyl Chloride (PVC)": "工业材料类"}source_analysis = []for row in plastic_composition.collect():plastic_type = row["Plastic_Type"]source_category = plastic_source_mapping.get(plastic_type, "其他类型")source_analysis.append({"PlasticType": plastic_type, "SourceCategory": source_category, "Weight": row["TypeWeight"], "Percentage": row["WeightPercentage"], "Count": row["TypeCount"]})result = {"overall_composition": [row.asDict() for row in plastic_composition.collect()], "regional_composition": regional_percentages, "temporal_composition": temporal_trends, "source_analysis": source_analysis, "total_weight": total_weight}return resultdef pollution_distribution_clustering_analysis():df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/ocean_data/ocean_plastic_pollution_data.csv")location_data = df.select("Latitude", "Longitude", "Plastic_Weight_kg", "Region", "Plastic_Type").collect()coordinates = np.array([[row["Latitude"], row["Longitude"]] for row in location_data])weights = np.array([row["Plastic_Weight_kg"] for row in location_data])dbscan = DBSCAN(eps=5.0, min_samples=10)cluster_labels = dbscan.fit_predict(coordinates)clustered_data = []for i, (row, label) in enumerate(zip(location_data, cluster_labels)):clustered_data.append({"Latitude": row["Latitude"], "Longitude": row["Longitude"], "Weight": row["Plastic_Weight_kg"], "Region": row["Region"], "PlasticType": row["Plastic_Type"], "ClusterID": int(label) if label != -1 else -1})cluster_statistics = {}for cluster_id in set(cluster_labels):if cluster_id != -1:cluster_points = [data for data, label in zip(location_data, cluster_labels) if label == cluster_id]total_weight = sum([point["Plastic_Weight_kg"] for point in cluster_points])avg_lat = np.mean([point["Latitude"] for point in cluster_points])avg_lon = np.mean([point["Longitude"] for point in cluster_points])point_count = len(cluster_points)dominant_region = max(set([point["Region"] for point in cluster_points]), key=[point["Region"] for point in cluster_points].count)cluster_statistics[cluster_id] = {"ClusterID": cluster_id, "TotalWeight": total_weight, "PointCount": point_count, "CenterLat": avg_lat, "CenterLon": avg_lon, "DominantRegion": dominant_region, "AvgWeight": total_weight / point_count}regional_density = df.groupBy("Region").agg(sum("Plastic_Weight_kg").alias("RegionWeight"), count("*").alias("EventCount"), avg("Plastic_Weight_kg").alias("AvgWeight")).withColumn("Density", col("RegionWeight") / col("EventCount"))hotspot_analysis = []for region_row in regional_density.collect():region = region_row["Region"]region_data = [row for row in location_data if row["Region"] == region]if len(region_data) > 0:region_weights = [row["Plastic_Weight_kg"] for row in region_data]percentile_90 = np.percentile(region_weights, 90)hotspots = [row for row in region_data if row["Plastic_Weight_kg"] >= percentile_90]hotspot_analysis.append({"Region": region, "HotspotThreshold": percentile_90, "HotspotCount": len(hotspots), "TotalEvents": len(region_data), "HotspotRatio": len(hotspots) / len(region_data)})spatial_autocorrelation = []for i, point1 in enumerate(location_data[:100]):nearby_points = []for j, point2 in enumerate(location_data):if i != j:distance = np.sqrt((point1["Latitude"] - point2["Latitude"])**2 + (point1["Longitude"] - point2["Longitude"])**2)if distance < 2.0:nearby_points.append(point2["Plastic_Weight_kg"])if len(nearby_points) > 0:spatial_correlation = np.corrcoef([point1["Plastic_Weight_kg"]], [np.mean(nearby_points)])[0, 1] if len(nearby_points) > 1 else 0spatial_autocorrelation.append({"PointID": i, "Weight": point1["Plastic_Weight_kg"], "NearbyCount": len(nearby_points), "SpatialCorr": spatial_correlation})result = {"clustered_points": clustered_data, "cluster_statistics": list(cluster_statistics.values()), "regional_density": [row.asDict() for row in regional_density.collect()], "hotspot_analysis": hotspot_analysis, "spatial_autocorrelation": spatial_autocorrelation[:50], "total_clusters": len(cluster_statistics)}return result