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⚡⚡获取源码主页--> space.bilibili.com/35463818075…
全球能源消耗量数据分析与可视化系统- 简介
基于大数据的全球能源消耗量数据分析与可视化系统是一个集成Hadoop分布式存储、Spark大数据处理引擎、Django后端框架和Vue前端技术的综合性数据分析平台。系统采用HDFS作为底层存储架构，通过Spark SQL对海量能源数据进行高效处理和分析，结合Pandas、NumPy等数据科学库实现复杂的统计计算。平台涵盖全球能源消耗宏观趋势分析、不同国家维度的能源状况横向对比、能源结构与可持续发展专题分析、能源效率与消耗模式分析四大核心维度，提供18个具体功能模块。前端采用Vue+ElementUI框架构建用户界面，通过Echarts图表库实现数据的多维度可视化展示，支持柱状图、折线图、散点图、热力图等多种图表类型。系统能够处理全球200多个国家和地区的历年能源消耗数据，包括总能源消耗量、人均能源使用量、可再生能源占比、化石燃料依赖度、碳排放量、能源价格指数等关键指标，为用户提供直观、准确的能源数据分析结果和可视化图表，助力能源政策制定和学术研究。
全球能源消耗量数据分析与可视化系统-技术 框架
开发语言：Python或Java（两个版本都支持）
大数据框架：Hadoop+Spark（本次没用Hive，支持定制）
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
全球能源消耗量数据分析与可视化系统- 背景
随着全球工业化进程的深入推进和人口数量的持续增长，能源消耗已成为衡量国家经济发展水平和社会进步的重要指标。当前世界各国在能源利用方式、能源结构优化、碳减排目标等方面呈现出显著的差异化特征，传统的统计分析方法已难以满足对海量、多维度能源数据的深度挖掘需求。大数据技术的快速发展为处理全球范围内的能源消耗数据提供了新的技术路径，Hadoop生态系统和Spark计算引擎的成熟应用使得对TB级别的历史能源数据进行实时分析成为可能。与此同时，各国政府和国际组织对能源数据透明度的要求日益提高，迫切需要一个集数据存储、处理、分析、可视化于一体的综合平台。面对气候变化带来的全球性挑战，准确掌握各国能源消耗模式、识别能源效率提升空间、分析可再生能源发展趋势已成为制定科学能源政策的基础工作，这为大数据技术在能源领域的应用创造了广阔空间。
本课题的研究具有多层面的实际价值和应用前景。从技术角度而言，该系统展现了大数据技术在能源数据处理领域的具体应用，验证了Hadoop+Spark架构处理大规模时间序列数据的可行性，为类似的数据分析项目提供了技术参考方案。在学术研究层面，系统通过对全球能源数据的多维度分析，能够为能源经济学、环境科学、可持续发展等相关学科的研究工作提供数据支撑和分析工具。从实用性角度来看，平台的可视化功能有助于政策制定者直观了解各国能源消耗现状和发展趋势，为制定针对性的能源政策提供数据依据。系统的国家对比分析功能可以帮助识别能源利用效率较高的国家，为技术交流和经验借鉴提供参考。虽然作为毕业设计项目在规模和复杂度上存在一定局限性，但其所采用的技术架构和分析方法论在实际的能源数据分析工作中具有一定的借鉴意义，特别是在数据处理流程设计和可视化展示方面积累的经验可以为后续相关项目的开发提供基础。
全球能源消耗量数据分析与可视化系统-视频展示
全球能源消耗量数据分析与可视化系统-图片展示
全球能源消耗量数据分析与可视化系统-代码展示
from pyspark.sql.functions import *
from pyspark.sql.types import *
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views import View
import json
spark = SparkSession.builder.appName("GlobalEnergyAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
class GlobalEnergyTrendAnalysis(View):
def post(self, request):
data = json.loads(request.body)
start_year = data.get('start_year', 2000)
end_year = data.get('end_year', 2023)
analysis_type = data.get('analysis_type', 'total_consumption')
energy_df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/energy_data/global_energy_consumption.csv")
filtered_df = energy_df.filter((col("Year") >= start_year) & (col("Year") <= end_year))
if analysis_type == 'total_consumption':
yearly_stats = filtered_df.groupBy("Year").agg(sum("Total Energy Consumption (TWh)").alias("total_consumption"),avg("Total Energy Consumption (TWh)").alias("avg_consumption"),count("Country").alias("country_count")).orderBy("Year")
elif analysis_type == 'renewable_share':
yearly_stats = filtered_df.groupBy("Year").agg(avg("Renewable Energy Share (%)").alias("avg_renewable_share"),stddev("Renewable Energy Share (%)").alias("renewable_stddev"),min("Renewable Energy Share (%)").alias("min_renewable"),max("Renewable Energy Share (%)").alias("max_renewable")).orderBy("Year")
elif analysis_type == 'carbon_emissions':
yearly_stats = filtered_df.groupBy("Year").agg(sum("Carbon Emissions (Million Tons)").alias("total_emissions"),avg("Carbon Emissions (Million Tons)").alias("avg_emissions"),percentile_approx("Carbon Emissions (Million Tons)", 0.5).alias("median_emissions")).orderBy("Year")
result_data = yearly_stats.collect()
trend_analysis = []
for i in range(len(result_data)):
row_dict = result_data[i].asDict()
if i > 0:
prev_row = result_data[i-1].asDict()
if analysis_type == 'total_consumption':
growth_rate = ((row_dict['total_consumption'] - prev_row['total_consumption']) / prev_row['total_consumption']) * 100
row_dict['growth_rate'] = round(growth_rate, 2)
elif analysis_type == 'renewable_share':
share_change = row_dict['avg_renewable_share'] - prev_row['avg_renewable_share']
row_dict['share_change'] = round(share_change, 2)
elif analysis_type == 'carbon_emissions':
emission_change = ((row_dict['total_emissions'] - prev_row['total_emissions']) / prev_row['total_emissions']) * 100
row_dict['emission_change'] = round(emission_change, 2)
trend_analysis.append(row_dict)
correlation_analysis = {}
if len(result_data) > 1:
years = [row['Year'] for row in result_data]
if analysis_type == 'total_consumption':
values = [row['total_consumption'] for row in result_data]
elif analysis_type == 'renewable_share':
values = [row['avg_renewable_share'] for row in result_data]
elif analysis_type == 'carbon_emissions':
values = [row['total_emissions'] for row in result_data]
correlation_coeff = np.corrcoef(years, values)[0, 1]
correlation_analysis['correlation_with_time'] = round(correlation_coeff, 4)
correlation_analysis['trend_direction'] = 'increasing' if correlation_coeff > 0.1 else 'decreasing' if correlation_coeff < -0.1 else 'stable'
return JsonResponse({'status': 'success','trend_data': trend_analysis,'correlation_analysis': correlation_analysis,'analysis_type': analysis_type,'time_range': f"{start_year}-{end_year}"})
class CountryEnergyRankingAnalysis(View):
def post(self, request):
data = json.loads(request.body)
ranking_type = data.get('ranking_type', 'total_consumption')
target_year = data.get('target_year', 2023)
top_n = data.get('top_n', 20)
energy_df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/energy_data/global_energy_consumption.csv")
latest_year_df = energy_df.filter(col("Year") == target_year)
if ranking_type == 'total_consumption':
ranked_df = latest_year_df.select("Country", "Total Energy Consumption (TWh)", "Population", "GDP").filter(col("Total Energy Consumption (TWh)").isNotNull()).orderBy(col("Total Energy Consumption (TWh)").desc()).limit(top_n)
result_list = []
for row in ranked_df.collect():
country_data = {'country': row['Country'],'total_consumption': round(row['Total Energy Consumption (TWh)'], 2),'population': row['Population'],'gdp': row['GDP']}
if row['Population'] and row['Population'] > 0:
country_data['consumption_per_capita'] = round(row['Total Energy Consumption (TWh)'] * 1000000 / row['Population'], 2)
if row['GDP'] and row['GDP'] > 0:
country_data['consumption_per_gdp'] = round(row['Total Energy Consumption (TWh)'] / row['GDP'] * 1000, 4)
result_list.append(country_data)
elif ranking_type == 'per_capita_consumption':
ranked_df = latest_year_df.select("Country", "Per Capita Energy Use (kWh)", "Total Energy Consumption (TWh)", "Population").filter(col("Per Capita Energy Use (kWh)").isNotNull()).orderBy(col("Per Capita Energy Use (kWh)").desc()).limit(top_n)
result_list = []
for row in ranked_df.collect():
country_data = {'country': row['Country'],'per_capita_consumption': round(row['Per Capita Energy Use (kWh)'], 2),'total_consumption': round(row['Total Energy Consumption (TWh)'], 2) if row['Total Energy Consumption (TWh)'] else None,'population': row['Population']}
result_list.append(country_data)
elif ranking_type == 'renewable_share':
ranked_df = latest_year_df.select("Country", "Renewable Energy Share (%)", "Total Energy Consumption (TWh)", "Carbon Emissions (Million Tons)").filter(col("Renewable Energy Share (%)").isNotNull()).orderBy(col("Renewable Energy Share (%)").desc()).limit(top_n)
result_list = []
for row in ranked_df.collect():
country_data = {'country': row['Country'],'renewable_share': round(row['Renewable Energy Share (%)'], 2),'total_consumption': round(row['Total Energy Consumption (TWh)'], 2) if row['Total Energy Consumption (TWh)'] else None,'carbon_emissions': round(row['Carbon Emissions (Million Tons)'], 2) if row['Carbon Emissions (Million Tons)'] else None}
if row['Total Energy Consumption (TWh)'] and row['Carbon Emissions (Million Tons)'] and row['Total Energy Consumption (TWh)'] > 0:
country_data['carbon_intensity'] = round(row['Carbon Emissions (Million Tons)'] / row['Total Energy Consumption (TWh)'], 4)
result_list.append(country_data)
statistical_summary = self.calculate_ranking_statistics(result_list, ranking_type)
return JsonResponse({'status': 'success','ranking_data': result_list,'ranking_type': ranking_type,'target_year': target_year,'statistical_summary': statistical_summary,'total_countries_analyzed': len(result_list)})
def calculate_ranking_statistics(self, data_list, ranking_type):
if not data_list:
return {}
if ranking_type == 'total_consumption':
values = [item['total_consumption'] for item in data_list if item.get('total_consumption')]
elif ranking_type == 'per_capita_consumption':
values = [item['per_capita_consumption'] for item in data_list if item.get('per_capita_consumption')]
elif ranking_type == 'renewable_share':
values = [item['renewable_share'] for item in data_list if item.get('renewable_share')]
if not values:
return {}
return {'mean': round(np.mean(values), 2),'median': round(np.median(values), 2),'std_deviation': round(np.std(values), 2),'min_value': round(min(values), 2),'max_value': round(max(values), 2),'range': round(max(values) - min(values), 2)}
class EnergyCorrelationAnalysis(View):
def post(self, request):
data = json.loads(request.body)
correlation_type = data.get('correlation_type', 'fossil_carbon_correlation')
analysis_years = data.get('analysis_years', [2020, 2021, 2022, 2023])
energy_df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/energy_data/global_energy_consumption.csv")
filtered_df = energy_df.filter(col("Year").isin(analysis_years))
if correlation_type == 'fossil_carbon_correlation':
correlation_df = filtered_df.select("Country", "Year", "Fossil Fuel Dependency (%)", "Carbon Emissions (Million Tons)", "Total Energy Consumption (TWh)").filter(col("Fossil Fuel Dependency (%)").isNotNull() & col("Carbon Emissions (Million Tons)").isNotNull())
pandas_df = correlation_df.toPandas()
correlation_coeff = pandas_df['Fossil Fuel Dependency (%)'].corr(pandas_df['Carbon Emissions (Million Tons)'])
scatter_data = []
for _, row in pandas_df.iterrows():
scatter_data.append({'country': row['Country'],'year': int(row['Year']),'fossil_dependency': round(row['Fossil Fuel Dependency (%)'], 2),'carbon_emissions': round(row['Carbon Emissions (Million Tons)'], 2),'total_consumption': round(row['Total Energy Consumption (TWh)'], 2) if pd.notna(row['Total Energy Consumption (TWh)']) else None})
high_fossil_high_carbon = pandas_df[(pandas_df['Fossil Fuel Dependency (%)'] > pandas_df['Fossil Fuel Dependency (%)'].quantile(0.75)) & (pandas_df['Carbon Emissions (Million Tons)'] > pandas_df['Carbon Emissions (Million Tons)'].quantile(0.75))]
low_fossil_low_carbon = pandas_df[(pandas_df['Fossil Fuel Dependency (%)'] < pandas_df['Fossil Fuel Dependency (%)'].quantile(0.25)) & (pandas_df['Carbon Emissions (Million Tons)'] < pandas_df['Carbon Emissions (Million Tons)'].quantile(0.25))]
elif correlation_type == 'renewable_carbon_correlation':
correlation_df = filtered_df.select("Country", "Year", "Renewable Energy Share (%)", "Carbon Emissions (Million Tons)", "Total Energy Consumption (TWh)").filter(col("Renewable Energy Share (%)").isNotNull() & col("Carbon Emissions (Million Tons)").isNotNull())
pandas_df = correlation_df.toPandas()
correlation_coeff = pandas_df['Renewable Energy Share (%)'].corr(pandas_df['Carbon Emissions (Million Tons)'])
scatter_data = []
for _, row in pandas_df.iterrows():
scatter_data.append({'country': row['Country'],'year': int(row['Year']),'renewable_share': round(row['Renewable Energy Share (%)'], 2),'carbon_emissions': round(row['Carbon Emissions (Million Tons)'], 2),'total_consumption': round(row['Total Energy Consumption (TWh)'], 2) if pd.notna(row['Total Energy Consumption (TWh)']) else None})
high_renewable_low_carbon = pandas_df[(pandas_df['Renewable Energy Share (%)'] > pandas_df['Renewable Energy Share (%)'].quantile(0.75)) & (pandas_df['Carbon Emissions (Million Tons)'] < pandas_df['Carbon Emissions (Million Tons)'].quantile(0.5))]
low_renewable_high_carbon = pandas_df[(pandas_df['Renewable Energy Share (%)'] < pandas_df['Renewable Energy Share (%)'].quantile(0.25)) & (pandas_df['Carbon Emissions (Million Tons)'] > pandas_df['Carbon Emissions (Million Tons)'].quantile(0.75))]
elif correlation_type == 'price_consumption_correlation':
correlation_df = filtered_df.select("Country", "Year", "Energy Price Index (USD/kWh)", "Per Capita Energy Use (kWh)", "Total Energy Consumption (TWh)").filter(col("Energy Price Index (USD/kWh)").isNotNull() & col("Per Capita Energy Use (kWh)").isNotNull())
pandas_df = correlation_df.toPandas()
correlation_coeff = pandas_df['Energy Price Index (USD/kWh)'].corr(pandas_df['Per Capita Energy Use (kWh)'])
scatter_data = []
for _, row in pandas_df.iterrows():
scatter_data.append({'country': row['Country'],'year': int(row['Year']),'energy_price': round(row['Energy Price Index (USD/kWh)'], 4),'per_capita_use': round(row['Per Capita Energy Use (kWh)'], 2),'total_consumption': round(row['Total Energy Consumption (TWh)'], 2) if pd.notna(row['Total Energy Consumption (TWh)']) else None})
high_price_low_consumption = pandas_df[(pandas_df['Energy Price Index (USD/kWh)'] > pandas_df['Energy Price Index (USD/kWh)'].quantile(0.75)) & (pandas_df['Per Capita Energy Use (kWh)'] < pandas_df['Per Capita Energy Use (kWh)'].quantile(0.5))]
low_price_high_consumption = pandas_df[(pandas_df['Energy Price Index (USD/kWh)'] < pandas_df['Energy Price Index (USD/kWh)'].quantile(0.25)) & (pandas_df['Per Capita Energy Use (kWh)'] > pandas_df['Per Capita Energy Use (kWh)'].quantile(0.75))]
correlation_strength = 'strong' if abs(correlation_coeff) > 0.7 else 'moderate' if abs(correlation_coeff) > 0.4 else 'weak'
correlation_direction = 'positive' if correlation_coeff > 0 else 'negative'
regression_line = self.calculate_regression_line(pandas_df, correlation_type)
return JsonResponse({'status': 'success','correlation_coefficient': round(correlation_coeff, 4),'correlation_strength': correlation_strength,'correlation_direction': correlation_direction,'scatter_data': scatter_data,'regression_line': regression_line,'analysis_years': analysis_years,'correlation_type': correlation_type,'sample_size': len(pandas_df)})
def calculate_regression_line(self, df, correlation_type):
if correlation_type == 'fossil_carbon_correlation':
x_col, y_col = 'Fossil Fuel Dependency (%)', 'Carbon Emissions (Million Tons)'
elif correlation_type == 'renewable_carbon_correlation':
x_col, y_col = 'Renewable Energy Share (%)', 'Carbon Emissions (Million Tons)'
elif correlation_type == 'price_consumption_correlation':
x_col, y_col = 'Energy Price Index (USD/kWh)', 'Per Capita Energy Use (kWh)'
x_values = df[x_col].values
y_values = df[y_col].values
coefficients = np.polyfit(x_values, y_values, 1)
x_min, x_max = x_values.min(), x_values.max()
regression_points = [{'x': x_min, 'y': coefficients[0] * x_min + coefficients[1]},{'x': x_max, 'y': coefficients[0] * x_max + coefficients[1]}]
return {'slope': round(coefficients[0], 4),'intercept': round(coefficients[1], 4),'equation': f"y = {round(coefficients[0], 4)}x + {round(coefficients[1], 4)}",'points': regression_points}
全球能源消耗量数据分析与可视化系统-结语
Hadoop+Spark双引擎：30个国家能源数据分析可视化系统开发全流程
独家揭秘：基于Hadoop的能源数据处理系统核心代码实现
担心计算机毕设太普通？Hadoop+Spark打造的能源数据系统助你脱颖而出
如果遇到具体的技术问题或计算机毕设方面需求，主页上咨询我，我会尽力帮你分析和解决问题所在，支持我记得一键三连，再点个关注，学习不迷路！
⚡⚡获取源码主页--> space.bilibili.com/35463818075…
⚡⚡如果遇到具体的技术问题或计算机毕设方面需求，你也可以在主页上咨询我~~
