注意：該項(xiàng)目只展示部分功能
1.開發(fā)環(huán)境
發(fā)語(yǔ)言：python
采用技術(shù)：Spark、Hadoop、Django、Vue、Echarts等技術(shù)框架
數(shù)據(jù)庫(kù)：MySQL
開發(fā)環(huán)境：PyCharm
2 系統(tǒng)設(shè)計(jì)
隨著全球工業(yè)化進(jìn)程的不斷推進(jìn)和經(jīng)濟(jì)發(fā)展水平的持續(xù)提升，世界各國(guó)對(duì)能源的需求量呈現(xiàn)出快速增長(zhǎng)的態(tài)勢(shì)。能源作為現(xiàn)代社會(huì)運(yùn)行的基礎(chǔ)動(dòng)力，其消耗模式和結(jié)構(gòu)變化直接影響著各國(guó)的經(jīng)濟(jì)發(fā)展策略和環(huán)境政策制定。當(dāng)前，全球面臨著能源安全、環(huán)境保護(hù)和可持續(xù)發(fā)展的多重挑戰(zhàn)，傳統(tǒng)化石燃料的大量使用導(dǎo)致溫室氣體排放持續(xù)增加，而可再生能源的發(fā)展程度在不同國(guó)家和地區(qū)存在顯著差異。面對(duì)如此龐大且復(fù)雜的全球能源數(shù)據(jù)，傳統(tǒng)的數(shù)據(jù)分析方法已經(jīng)難以滿足深度挖掘和全面分析的需求。大數(shù)據(jù)技術(shù)的興起為處理這些海量、多維度的能源數(shù)據(jù)提供了新的技術(shù)路徑，通過(guò)Spark和Hadoop等分布式計(jì)算框架，可以實(shí)現(xiàn)對(duì)全球能源消耗數(shù)據(jù)的高效處理和深入分析。在這樣的技術(shù)背景和現(xiàn)實(shí)需求下，構(gòu)建一套基于spark的全球能源大數(shù)據(jù)分析與可視化系統(tǒng)具有了實(shí)際的應(yīng)用價(jià)值和技術(shù)探索意義。
基于spark的全球能源大數(shù)據(jù)分析與可視化系統(tǒng)是一套完整的大數(shù)據(jù)分析解決方案，專門針對(duì)全球范圍內(nèi)的能源消耗數(shù)據(jù)進(jìn)行深度挖掘和直觀展示。該系統(tǒng)采用Python作為核心開發(fā)語(yǔ)言，結(jié)合Spark分布式計(jì)算框架和Hadoop生態(tài)系統(tǒng)，能夠高效處理海量的全球能源數(shù)據(jù)，包括各國(guó)歷年總能源消耗量、人均能源使用量、可再生能源占比、化石燃料依賴度、碳排放量等關(guān)鍵指標(biāo)。系統(tǒng)通過(guò)數(shù)據(jù)挖掘和機(jī)器學(xué)習(xí)算法，實(shí)現(xiàn)了四大核心分析維度：全球能源消耗宏觀趨勢(shì)分析、不同國(guó)家維度的能源狀況橫向?qū)Ρ取⒛茉唇Y(jié)構(gòu)與可持續(xù)發(fā)展專題分析、以及能源效率與消耗模式分析。前端采用Vue框架構(gòu)建響應(yīng)式界面，集成Echarts圖表庫(kù)實(shí)現(xiàn)豐富的數(shù)據(jù)可視化效果，包括趨勢(shì)圖、排名對(duì)比圖、相關(guān)性散點(diǎn)圖和聚類分析圖等多種圖表類型，讓復(fù)雜的能源數(shù)據(jù)以直觀、交互式的方式呈現(xiàn)給用戶。后端使用MySQL數(shù)據(jù)庫(kù)存儲(chǔ)處理后的分析結(jié)果，確保數(shù)據(jù)的持久化和快速查詢。整個(gè)系統(tǒng)不僅展示了大數(shù)據(jù)技術(shù)在實(shí)際場(chǎng)景中的應(yīng)用能力，更為全球能源政策制定、可持續(xù)發(fā)展研究和國(guó)際能源合作提供了有價(jià)值的數(shù)據(jù)支撐和決策參考。
3 系統(tǒng)展示
3.1 大屏頁(yè)面
3.2 分析頁(yè)面
3.3 基礎(chǔ)頁(yè)面
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5 部分功能代碼
spark = SparkSession.builder.appName("GlobalEnergyAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def analyze_global_energy_trend():
energy_df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/energy_data/global_energy.csv")
energy_df = energy_df.filter(col("Total Energy Consumption (TWh)").isNotNull() & col("Year").isNotNull())
yearly_trend = energy_df.groupBy("Year").agg(
sum("Total Energy Consumption (TWh)").alias("total_consumption"),
avg("Renewable Energy Share (%)").alias("avg_renewable_share"),
avg("Fossil Fuel Dependency (%)").alias("avg_fossil_dependency"),
sum("Carbon Emissions (Million Tons)").alias("total_emissions"),
avg("Energy Price Index (USD/kWh)").alias("avg_price_index")
).orderBy("Year")
yearly_trend = yearly_trend.withColumn("renewable_growth_rate",
(col("avg_renewable_share") - lag("avg_renewable_share").over(Window.orderBy("Year"))) / lag("avg_renewable_share").over(Window.orderBy("Year")) * 100)
yearly_trend = yearly_trend.withColumn("emission_intensity",
col("total_emissions") / col("total_consumption"))
yearly_trend = yearly_trend.withColumn("energy_efficiency_score",
when(col("emission_intensity") < 0.5, 90)
.when(col("emission_intensity") < 1.0, 70)
.when(col("emission_intensity") < 1.5, 50)
.otherwise(30))
result_df = yearly_trend.select("Year", "total_consumption", "avg_renewable_share", "avg_fossil_dependency", "total_emissions", "renewable_growth_rate", "energy_efficiency_score").toPandas()
connection = mysql.connector.connect(host='localhost', database='energy_analysis', user='root', password='password')
cursor = connection.cursor()
cursor.execute("DELETE FROM global_energy_trends")
for index, row in result_df.iterrows():
insert_query = "INSERT INTO global_energy_trends (year, total_consumption, renewable_share, fossil_dependency, total_emissions, growth_rate, efficiency_score) VALUES (%s, %s, %s, %s, %s, %s, %s)"
cursor.execute(insert_query, tuple(row))
connection.commit()
cursor.close()
connection.close()
return result_df.to_dict('records')
def analyze_country_energy_ranking():
energy_df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/energy_data/global_energy.csv")
latest_year = energy_df.select(max("Year")).collect()[0][0]
latest_data = energy_df.filter(col("Year") == latest_year)
country_rankings = latest_data.select("Country", "Total Energy Consumption (TWh)", "Per Capita Energy Use (kWh)", "Renewable Energy Share (%)", "Carbon Emissions (Million Tons)")
consumption_rank = country_rankings.withColumn("consumption_rank", row_number().over(Window.orderBy(desc("Total Energy Consumption (TWh)"))))
per_capita_rank = country_rankings.withColumn("per_capita_rank", row_number().over(Window.orderBy(desc("Per Capita Energy Use (kWh)"))))
renewable_rank = country_rankings.withColumn("renewable_rank", row_number().over(Window.orderBy(desc("Renewable Energy Share (%)"))))
emission_rank = country_rankings.withColumn("emission_rank", row_number().over(Window.orderBy(desc("Carbon Emissions (Million Tons)"))))
comprehensive_ranking = country_rankings.withColumn("energy_consumption_score",
when(col("Total Energy Consumption (TWh)") > 5000, 100)
.when(col("Total Energy Consumption (TWh)") > 2000, 80)
.when(col("Total Energy Consumption (TWh)") > 1000, 60)
.when(col("Total Energy Consumption (TWh)") > 500, 40)
.otherwise(20))
comprehensive_ranking = comprehensive_ranking.withColumn("sustainability_score",
col("Renewable Energy Share (%)") * 0.7 + (100 - col("Fossil Fuel Dependency (%)")) * 0.3)
comprehensive_ranking = comprehensive_ranking.withColumn("efficiency_ratio",
col("Total Energy Consumption (TWh)") / col("Carbon Emissions (Million Tons)"))
final_ranking = comprehensive_ranking.select("Country", "Total Energy Consumption (TWh)", "Per Capita Energy Use (kWh)", "Renewable Energy Share (%)", "sustainability_score", "efficiency_ratio").orderBy(desc("Total Energy Consumption (TWh)")).limit(50)
result_df = final_ranking.toPandas()
connection = mysql.connector.connect(host='localhost', database='energy_analysis', user='root', password='password')
cursor = connection.cursor()
cursor.execute("DELETE FROM country_energy_rankings")
for index, row in result_df.iterrows():
insert_query = "INSERT INTO country_energy_rankings (country, total_consumption, per_capita_use, renewable_share, sustainability_score, efficiency_ratio, ranking_position) VALUES (%s, %s, %s, %s, %s, %s, %s)"
cursor.execute(insert_query, tuple(row) + (index + 1,))
connection.commit()
cursor.close()
connection.close()
return result_df.to_dict('records')
def analyze_energy_structure_clustering():
energy_df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/energy_data/global_energy.csv")
country_avg_data = energy_df.groupBy("Country").agg(
avg("Renewable Energy Share (%)").alias("avg_renewable"),
avg("Fossil Fuel Dependency (%)").alias("avg_fossil"),
avg("Industrial Energy Use (%)").alias("avg_industrial"),
avg("Household Energy Use (%)").alias("avg_household"),
avg("Per Capita Energy Use (kWh)").alias("avg_per_capita")
).filter(col("avg_renewable").isNotNull() & col("avg_fossil").isNotNull())
assembler = VectorAssembler(inputCols=["avg_renewable", "avg_fossil", "avg_industrial", "avg_household"], outputCol="features")
feature_df = assembler.transform(country_avg_data)
kmeans = KMeans(k=4, seed=42, featuresCol="features", predictionCol="cluster")
model = kmeans.fit(feature_df)
clustered_df = model.transform(feature_df)
cluster_analysis = clustered_df.groupBy("cluster").agg(
count("Country").alias("country_count"),
avg("avg_renewable").alias("cluster_renewable"),
avg("avg_fossil").alias("cluster_fossil"),
avg("avg_per_capita").alias("cluster_per_capita")
)
clustered_with_labels = clustered_df.withColumn("cluster_type",
when(col("cluster") == 0, "Green Energy Leaders")
.when(col("cluster") == 1, "Fossil Fuel Dependent")
.when(col("cluster") == 2, "Balanced Energy Mix")
.otherwise("Transitioning Countries"))
clustered_with_labels = clustered_with_labels.withColumn("energy_transition_score",
col("avg_renewable") * 0.6 + (100 - col("avg_fossil")) * 0.4)
clustered_with_labels = clustered_with_labels.withColumn("development_level",
when(col("avg_per_capita") > 10000, "High")
.when(col("avg_per_capita") > 5000, "Medium")
.otherwise("Low"))
result_df = clustered_with_labels.select("Country", "cluster", "cluster_type", "avg_renewable", "avg_fossil", "energy_transition_score", "development_level").toPandas()
connection = mysql.connector.connect(host='localhost', database='energy_analysis', user='root', password='password')
cursor = connection.cursor()
cursor.execute("DELETE FROM energy_structure_clusters")
for index, row in result_df.iterrows():
insert_query = "INSERT INTO energy_structure_clusters (country, cluster_id, cluster_type, renewable_share, fossil_dependency, transition_score, development_level) VALUES (%s, %s, %s, %s, %s, %s, %s)"
cursor.execute(insert_query, tuple(row))
connection.commit()
cursor.close()
connection.close()
return result_df.to_dict('records')
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