一、个人简介

💖💖作者：计算机编程果茶熊
💙💙个人简介：曾长期从事计算机专业培训教学，担任过编程老师，同时本人也热爱上课教学，擅长Java、微信小程序、Python、Golang、安卓Android等多个IT方向。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
计算机毕业设计选题
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二、系统介绍

大数据框架：Hadoop+Spark（本次没用Hive，支持定制）
开发语言：Python
后端框架：Django
前端：Vue
详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy
数据库：MySQL

基于大数据的各省碳排放数据分析与可视化系统是一个面向环境监测与能源管理领域的数据分析平台，该系统依托Hadoop分布式存储架构与Spark计算引擎，能够对全国各省份的碳排放数据进行海量存储和快速处理。在技术实现层面，系统后端采用Django框架搭建服务接口，前端则通过Vue框架构建交互界面，数据持久化方面选用MySQL数据库来管理用户信息及配置数据，而大规模的碳排放原始数据则存储在HDFS分布式文件系统当中。系统核心功能涵盖了多维特征关联分析模块，该模块能够挖掘不同碳排放指标之间的关联性；排放来源结构分析模块可以解析各行业对总排放量的贡献占比；地理空间分布分析模块通过地图可视化展示各省份的排放强度差异；排放趋势演变分析模块则追踪历史数据变化规律。除了这些分析功能外，平台还配备了用户中心用于权限管理，公告管理模块负责发布政策信息，数据可视化大屏则以图表形式直观呈现分析结果，通过Spark SQL与Pandas、NumPy等工具的配合使用，系统在处理TB级数据时依然保持着比较好的响应速度，为环保部门及研究机构提供了数据支撑。

三、视频解说

基于大数据的各省碳排放数据分析与可视化系统 – 演示视频

四、部分功能展示

五、部分代码展示

from pyspark.sql import SparkSession
from pyspark.sql.functions import col,sum,avg,count,year,month,desc,rank,dense_rank
from pyspark.sql.window import Window
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views import View
import json

spark = SparkSession.builder.appName("CarbonEmissionAnalysis").config("spark.sql.warehouse.dir","/user/hive/warehouse").config("spark.executor.memory","4g").config("spark.driver.memory","2g").getOrCreate()

class MultiDimensionalCorrelationAnalysis(View):
    def post(self,request):
        params = json.loads(request.body)
        start_year = params.get('start_year')
        end_year = params.get('end_year')
        province_list = params.get('provinces',[])
        hdfs_path = "hdfs://localhost:9000/carbon_data/emission_records.csv"
        df = spark.read.csv(hdfs_path,header=True,inferSchema=True)
        filtered_df = df.filter((col("year")>=start_year)&(col("year")<=end_year))
        if province_list:
            filtered_df = filtered_df.filter(col("province").isin(province_list))
        aggregated_df = filtered_df.groupBy("province","year").agg(sum("total_emission").alias("total_emission"),sum("industrial_emission").alias("industrial_emission"),sum("transportation_emission").alias("transportation_emission"),sum("residential_emission").alias("residential_emission"),avg("gdp").alias("avg_gdp"),avg("population").alias("avg_population"))
        pandas_df = aggregated_df.toPandas()
        correlation_matrix = pandas_df[['total_emission','industrial_emission','transportation_emission','residential_emission','avg_gdp','avg_population']].corr()
        correlation_dict = correlation_matrix.to_dict()
        feature_importance = {}
        for feature in ['industrial_emission','transportation_emission','residential_emission','avg_gdp','avg_population']:
            corr_value = abs(correlation_matrix.loc['total_emission',feature])
            feature_importance[feature] = round(corr_value,4)
        sorted_features = sorted(feature_importance.items(),key=lambda x:x[1],reverse=True)
        emission_per_capita = pandas_df['total_emission']/pandas_df['avg_population']
        emission_per_gdp = pandas_df['total_emission']/pandas_df['avg_gdp']
        pandas_df['emission_per_capita'] = emission_per_capita
        pandas_df['emission_per_gdp'] = emission_per_gdp
        intensity_stats = {'avg_per_capita':round(emission_per_capita.mean(),2),'max_per_capita':round(emission_per_capita.max(),2),'min_per_capita':round(emission_per_capita.min(),2),'avg_per_gdp':round(emission_per_gdp.mean(),4),'max_per_gdp':round(emission_per_gdp.max(),4),'min_per_gdp':round(emission_per_gdp.min(),4)}
        result_data = {'correlation_matrix':correlation_dict,'feature_importance':dict(sorted_features),'intensity_statistics':intensity_stats,'sample_records':pandas_df.head(20).to_dict('records')}
        return JsonResponse({'code':200,'message':'多维特征关联分析完成','data':result_data})

class EmissionSourceStructureAnalysis(View):
    def post(self,request):
        params = json.loads(request.body)
        target_year = params.get('year')
        target_province = params.get('province',None)
        hdfs_path = "hdfs://localhost:9000/carbon_data/emission_records.csv"
        df = spark.read.csv(hdfs_path,header=True,inferSchema=True)
        filtered_df = df.filter(col("year")==target_year)
        if target_province:
            filtered_df = filtered_df.filter(col("province")==target_province)
        source_columns = ['industrial_emission','transportation_emission','residential_emission','agricultural_emission','energy_emission']
        aggregated_data = filtered_df.agg(*[sum(col(c)).alias(c) for c in source_columns]).collect()[0]
        total_sum = sum([aggregated_data[c] for c in source_columns])
        structure_dict = {}
        for source in source_columns:
            emission_value = aggregated_data[source]
            percentage = round((emission_value/total_sum)*100,2) if total_sum>0 else 0
            structure_dict[source] = {'value':round(emission_value,2),'percentage':percentage}
        sorted_sources = sorted(structure_dict.items(),key=lambda x:x[1]['value'],reverse=True)
        top_source = sorted_sources[0][0] if sorted_sources else None
        province_comparison = filtered_df.groupBy("province").agg(*[sum(col(c)).alias(c) for c in source_columns])
        province_pandas = province_comparison.toPandas()
        province_pandas['total'] = province_pandas[source_columns].sum(axis=1)
        for source in source_columns:
            province_pandas[f'{source}_pct'] = (province_pandas[source]/province_pandas['total'])*100
        province_ranking = province_pandas.sort_values(by='total',ascending=False).head(10)
        ranking_records = province_ranking[['province','total']+[f'{s}_pct' for s in source_columns]].to_dict('records')
        industry_detail = filtered_df.groupBy("province","industry_type").agg(sum("industrial_emission").alias("industry_emission")).orderBy(desc("industry_emission"))
        industry_pandas = industry_detail.toPandas()
        industry_top10 = industry_pandas.head(10).to_dict('records')
        result

六、部分文档展示

七、END

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