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
- 💕💕文末获取源码联系计算机编程果茶熊
二、系统介绍
- 大数据框架：Hadoop+Spark（Hive需要定制修改）
- 开发语言：Java+Python（两个版本都支持）
- 数据库：MySQL
- 后端框架：SpringBoot(Spring+SpringMVC+Mybatis)+Django（两个版本都支持）
- 前端：Vue+Echarts+HTML+CSS+JavaScript+jQuery
- 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套采用先进大数据技术栈构建的综合性能源数据分析平台，该系统充分利用Hadoop分布式文件系统(HDFS)进行海量能源数据的存储管理，通过Spark大数据计算框架实现高效的数据处理与分析计算，结合Spark SQL提供灵活的数据查询能力，并运用Pandas和NumPy等专业数据科学库进行深度数据挖掘和统计分析。系统采用Python作为核心开发语言，基于Django Web框架构建稳定的后端服务架构，前端采用Vue.js结合ElementUI组件库打造现代化的用户界面，通过Echarts图表库实现丰富的数据可视化效果，底层数据存储采用MySQL关系型数据库确保数据的完整性和一致性。系统功能涵盖完整的业务流程，包括系统首页展示、用户中心管理、用户权限控制、能源消耗数据的增删改查管理、全球能源消耗趋势的时间序列分析、不同国家间能源消耗水平的对比分析、能源可持续性发展指标评估、能源消耗效率的量化分析以及系统运维管理等九大核心功能模块，为用户提供从数据采集、存储、处理到分析、可视化展示的完整大数据解决方案，能够有效支撑全球能源消耗模式的深度洞察与科学决策。
三、基于大数据的全球能源消耗量数据分析与可视化系统-视频解说
还在为毕设选题发愁？这个基于Hadoop+Spark的能源数据分析系统或许是最佳选择
四、基于大数据的全球能源消耗量数据分析与可视化系统-功能展示
五、基于大数据的全球能源消耗量数据分析与可视化系统-代码展示
# 核心功能1：全球能源消耗趋势分析
def analyze_global_energy_trends(request):
# 从Spark SQL查询全球能源消耗历史数据
spark_sql = """
SELECT year, country, energy_type, consumption_value, population, gdp
FROM energy_consumption_table
WHERE year >= 2010 AND year <= 2024
ORDER BY year, country
"""
raw_data = spark.sql(spark_sql).toPandas()
# 数据预处理和清洗
raw_data['consumption_per_capita'] = raw_data['consumption_value'] / raw_data['population']
raw_data['energy_intensity'] = raw_data['consumption_value'] / raw_data['gdp']
raw_data = raw_data.dropna()
# 按年份聚合全球总消耗量
yearly_global = raw_data.groupby('year').agg({
'consumption_value': 'sum',
'consumption_per_capita': 'mean',
'energy_intensity': 'mean'
}).reset_index()
# 计算年增长率
yearly_global['growth_rate'] = yearly_global['consumption_value'].pct_change() * 100
yearly_global['growth_rate'] = yearly_global['growth_rate'].fillna(0)
# 趋势预测使用线性回归
from sklearn.linear_model import LinearRegression
X = yearly_global['year'].values.reshape(-1, 1)
y = yearly_global['consumption_value'].values
model = LinearRegression()
model.fit(X, y)
# 预测未来5年趋势
future_years = np.array([2025, 2026, 2027, 2028, 2029]).reshape(-1, 1)
predictions = model.predict(future_years)
# 构建返回数据结构
trend_data = {
'historical_data': yearly_global.to_dict('records'),
'future_predictions': [{'year': int(year[0]), 'predicted_consumption': float(pred)}
for year, pred in zip(future_years, predictions)],
'trend_analysis': {
'average_growth_rate': float(yearly_global['growth_rate'].mean()),
'peak_year': int(yearly_global.loc[yearly_global['consumption_value'].idxmax(), 'year']),
'correlation_gdp': float(np.corrcoef(yearly_global['consumption_value'], yearly_global['gdp'])[0,1])
}
}
return JsonResponse(trend_data)
# 核心功能2：国家能源消耗对比分析
def compare_country_energy_consumption(request):
# 获取对比参数
countries = request.GET.getlist('countries', ['USA', 'China', 'India', 'Germany', 'Japan'])
comparison_year = request.GET.get('year', '2023')
energy_types = request.GET.getlist('energy_types', ['coal', 'oil', 'natural_gas', 'renewable'])
# Spark SQL多维度查询
countries_str = "'" + "','".join(countries) + "'"
energy_types_str = "'" + "','".join(energy_types) + "'"
comparison_sql = f"""
SELECT country, energy_type, consumption_value, efficiency_ratio,
carbon_emission, renewable_percentage, population, gdp
FROM energy_consumption_table
WHERE country IN ({countries_str})
AND energy_type IN ({energy_types_str})
AND year = {comparison_year}
"""
comparison_data = spark.sql(comparison_sql).toPandas()
# 计算关键指标
country_metrics = {}
for country in countries:
country_data = comparison_data[comparison_data['country'] == country]
if not country_data.empty:
total_consumption = country_data['consumption_value'].sum()
avg_efficiency = country_data['efficiency_ratio'].mean()
total_emission = country_data['carbon_emission'].sum()
renewable_ratio = country_data['renewable_percentage'].mean()
per_capita_consumption = total_consumption / country_data['population'].iloc[0]
energy_intensity = total_consumption / country_data['gdp'].iloc[0]
country_metrics[country] = {
'total_consumption': float(total_consumption),
'per_capita_consumption': float(per_capita_consumption),
'energy_intensity': float(energy_intensity),
'efficiency_score': float(avg_efficiency),
'carbon_emission': float(total_emission),
'renewable_percentage': float(renewable_ratio),
'energy_structure': country_data.groupby('energy_type')['consumption_value'].sum().to_dict()
}
# 排名计算
ranking_data = []
for country, metrics in country_metrics.items():
ranking_data.append({
'country': country,
'efficiency_rank': 0,
'consumption_rank': 0,
'renewable_rank': 0,
'metrics': metrics
})
# 按效率排名
ranking_data.sort(key=lambda x: x['metrics']['efficiency_score'], reverse=True)
for i, item in enumerate(ranking_data):
item['efficiency_rank'] = i + 1
return JsonResponse({'comparison_results': country_metrics, 'rankings': ranking_data})
# 核心功能3：能源可持续性分析
def analyze_energy_sustainability(request):
# 获取可持续性分析参数
analysis_region = request.GET.get('region', 'global')
time_range = request.GET.get('time_range', '2020-2024')
start_year, end_year = map(int, time_range.split('-'))
# 查询可持续性相关数据
sustainability_sql = f"""
SELECT year, country, region, renewable_energy_ratio, carbon_intensity,
energy_efficiency_index, fossil_fuel_dependency, clean_energy_investment,
environmental_impact_score, policy_support_index
FROM sustainability_metrics_table
WHERE year BETWEEN {start_year} AND {end_year}
AND (region = '{analysis_region}' OR '{analysis_region}' = 'global')
"""
sustainability_data = spark.sql(sustainability_sql).toPandas()
# 计算综合可持续性指数
weights = {
'renewable_ratio': 0.25,
'carbon_intensity': -0.20, # 负权重，越低越好
'efficiency_index': 0.20,
'fossil_dependency': -0.15, # 负权重
'clean_investment': 0.15,
'environmental_score': 0.10,
'policy_support': 0.05
}
# 数据标准化处理
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
metrics_columns = ['renewable_energy_ratio', 'carbon_intensity', 'energy_efficiency_index', 'fossil_fuel_dependency', 'clean_energy_investment', 'environmental_impact_score', 'policy_support_index']
sustainability_data[metrics_columns] = scaler.fit_transform(sustainability_data[metrics_columns])
# 计算可持续性综合得分
sustainability_data['sustainability_score'] = (
sustainability_data['renewable_energy_ratio'] * weights['renewable_ratio'] +
sustainability_data['carbon_intensity'] * weights['carbon_intensity'] +
sustainability_data['energy_efficiency_index'] * weights['efficiency_index'] +
sustainability_data['fossil_fuel_dependency'] * weights['fossil_dependency'] +
sustainability_data['clean_energy_investment'] * weights['clean_investment'] +
sustainability_data['environmental_impact_score'] * weights['environmental_score'] +
sustainability_data['policy_support_index'] * weights['policy_support']
)
# 年度趋势分析
yearly_trends = sustainability_data.groupby('year').agg({
'sustainability_score': ['mean', 'std'],
'renewable_energy_ratio': 'mean',
'carbon_intensity': 'mean',
'clean_energy_investment': 'sum'
}).round(4)
# 预警机制 - 识别可持续性风险
risk_threshold = sustainability_data['sustainability_score'].quantile(0.25)
risk_countries = sustainability_data[sustainability_data['sustainability_score'] < risk_threshold]
# 改进建议生成
improvement_suggestions = []
for _, row in risk_countries.iterrows():
suggestions = []
if row['renewable_energy_ratio'] < -0.5:
suggestions.append("增加可再生能源投资比例")
if row['carbon_intensity'] > 0.5:
suggestions.append("实施碳减排技术升级")
if row['energy_efficiency_index'] < -0.3:
suggestions.append("提升能源利用效率")
improvement_suggestions.append({
'country': row['country'],
'year': int(row['year']),
'current_score': float(row['sustainability_score']),
'suggestions': suggestions
})
result_data = {
'sustainability_trends': yearly_trends.to_dict('index'),
'risk_assessment': improvement_suggestions,
'overall_score': float(sustainability_data['sustainability_score'].mean()),
'improvement_rate': float((sustainability_data[sustainability_data['year'] == end_year]['sustainability_score'].mean() -
sustainability_data[sustainability_data['year'] == start_year]['sustainability_score'].mean()) * 100)
}
return JsonResponse(result_data)
六、基于大数据的全球能源消耗量数据分析与可视化系统-文档展示
七、END
💛💛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 💕💕文末获取源码联系计算机编程果茶熊
