💖💖作者：计算机毕业设计杰瑞 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常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 深度学校实战项目 计算机毕业设计选题推荐
基于大数据的汽车之家数据分析系统介绍
汽车之家数据分析系统是一套基于大数据技术构建的综合性汽车市场分析平台，采用Hadoop+Spark作为核心大数据处理框架，结合Python开发语言和Django后端框架，前端使用Vue+ElementUI+Echarts技术栈实现数据可视化展示。系统整合了汽车行业的海量数据资源，通过Spark SQL和Pandas、NumPy等数据处理工具，实现对汽车市场的多维度深入分析。平台包含首页展示、个人中心、用户管理、汽车数据信息管理、整体市场行情分析、新能源车市场分析、车辆保值率分析、地区热门车型分析以及系统管理等九大核心功能模块，为用户提供全方位的汽车市场洞察服务。系统运用HDFS进行大数据存储，通过Spark分布式计算引擎处理复杂的数据分析任务，结合MySQL数据库存储结构化数据，实现了从数据采集、处理、分析到可视化展示的完整数据分析流程，为汽车行业决策提供科学的数据支撑。
基于大数据的汽车之家数据分析系统演示视频
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基于大数据的汽车之家数据分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, sum, avg, count, desc, asc, when, isnan, isnull
from pyspark.sql.types import StructType, StructField, StringType, IntegerType, FloatType, DateType
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views.decorators.http import require_http_methods
import json
spark = SparkSession.builder.appName("CarDataAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def market_trend_analysis(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/car_analysis").option("dbtable", "car_sales_data").option("user", "root").option("password", "password").load()
monthly_sales = df.groupBy("sale_month", "brand").agg(sum("sales_volume").alias("total_sales"), avg("average_price").alias("avg_price")).orderBy("sale_month", desc("total_sales"))
brand_market_share = df.groupBy("brand").agg(sum("sales_volume").alias("brand_sales")).withColumn("market_share", col("brand_sales") / df.agg(sum("sales_volume")).collect()[0][0] * 100).orderBy(desc("market_share"))
price_trend = df.groupBy("sale_month").agg(avg("average_price").alias("monthly_avg_price"), count("*").alias("model_count")).orderBy("sale_month")
growth_rate = monthly_sales.withColumn("prev_sales", lag("total_sales", 1).over(Window.partitionBy("brand").orderBy("sale_month"))).withColumn("growth_rate", ((col("total_sales") - col("prev_sales")) / col("prev_sales") * 100)).filter(col("growth_rate").isNotNull())
segment_analysis = df.groupBy("car_segment", "sale_month").agg(sum("sales_volume").alias("segment_sales"), avg("average_price").alias("segment_price")).orderBy("sale_month", "car_segment")
regional_performance = df.groupBy("region", "brand").agg(sum("sales_volume").alias("regional_sales"), avg("customer_satisfaction").alias("satisfaction_score")).orderBy("region", desc("regional_sales"))
top_models = df.groupBy("model_name", "brand").agg(sum("sales_volume").alias("model_sales"), avg("average_price").alias("model_price")).orderBy(desc("model_sales")).limit(20)
seasonal_pattern = df.withColumn("season", when(col("sale_month").isin([12, 1, 2]), "Winter").when(col("sale_month").isin([3, 4, 5]), "Spring").when(col("sale_month").isin([6, 7, 8]), "Summer").otherwise("Autumn")).groupBy("season", "car_segment").agg(sum("sales_volume").alias("seasonal_sales")).orderBy("season", desc("seasonal_sales"))
result_data = {"monthly_trend": monthly_sales.toPandas().to_dict('records'), "market_share": brand_market_share.toPandas().to_dict('records'), "price_trend": price_trend.toPandas().to_dict('records'), "growth_analysis": growth_rate.toPandas().to_dict('records'), "segment_data": segment_analysis.toPandas().to_dict('records'), "regional_data": regional_performance.toPandas().to_dict('records'), "top_models": top_models.toPandas().to_dict('records'), "seasonal_pattern": seasonal_pattern.toPandas().to_dict('records')}
return JsonResponse(result_data)
def new_energy_vehicle_analysis(request):
nev_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/car_analysis").option("dbtable", "new_energy_vehicles").option("user", "root").option("password", "password").load()
fuel_type_comparison = nev_df.groupBy("fuel_type", "sale_month").agg(sum("sales_volume").alias("fuel_sales"), avg("battery_capacity").alias("avg_battery"), avg("driving_range").alias("avg_range")).orderBy("sale_month", "fuel_type")
charging_infrastructure = nev_df.groupBy("region", "charging_type").agg(count("charging_stations").alias("station_count"), avg("charging_time").alias("avg_charging_time")).orderBy("region", desc("station_count"))
battery_technology_trend = nev_df.filter(col("battery_type").isNotNull()).groupBy("battery_type", "sale_month").agg(sum("sales_volume").alias("battery_sales"), avg("battery_life").alias("avg_life"), avg("charging_efficiency").alias("efficiency")).orderBy("sale_month", "battery_type")
price_subsidy_impact = nev_df.groupBy("subsidy_level", "price_range").agg(sum("sales_volume").alias("subsidy_sales"), avg("final_price").alias("actual_price")).withColumn("subsidy_effectiveness", col("subsidy_sales") / col("actual_price")).orderBy(desc("subsidy_effectiveness"))
range_anxiety_analysis = nev_df.filter(col("driving_range") > 0).withColumn("range_category", when(col("driving_range") < 300, "Short Range").when((col("driving_range") >= 300) & (col("driving_range") < 500), "Medium Range").otherwise("Long Range")).groupBy("range_category", "sale_month").agg(sum("sales_volume").alias("range_sales"), avg("customer_satisfaction").alias("satisfaction")).orderBy("sale_month", "range_category")
brand_nev_strategy = nev_df.groupBy("brand", "nev_strategy_type").agg(sum("sales_volume").alias("strategy_sales"), count("model_name").alias("model_count"), avg("research_investment").alias("avg_investment")).orderBy("brand", desc("strategy_sales"))
technology_advancement = nev_df.groupBy("technology_level", "sale_month").agg(sum("sales_volume").alias("tech_sales"), avg("innovation_score").alias("innovation_avg"), avg("market_acceptance").alias("acceptance_rate")).orderBy("sale_month", desc("tech_sales"))
government_policy_impact = nev_df.groupBy("policy_type", "implementation_date").agg(sum("sales_volume").alias("policy_impact_sales"), avg("market_penetration").alias("penetration_rate")).orderBy("implementation_date", desc("policy_impact_sales"))
competition_analysis = nev_df.groupBy("brand", "competitor_brand").agg(sum("sales_volume").alias("brand_sales"), avg("competitive_index").alias("competition_score")).withColumn("market_position", when(col("competition_score") > 80, "Leader").when(col("competition_score") > 60, "Challenger").otherwise("Follower")).orderBy(desc("brand_sales"))
result_data = {"fuel_comparison": fuel_type_comparison.toPandas().to_dict('records'), "charging_analysis": charging_infrastructure.toPandas().to_dict('records'), "battery_trends": battery_technology_trend.toPandas().to_dict('records'), "subsidy_impact": price_subsidy_impact.toPandas().to_dict('records'), "range_analysis": range_anxiety_analysis.toPandas().to_dict('records'), "brand_strategy": brand_nev_strategy.toPandas().to_dict('records'), "technology_trends": technology_advancement.toPandas().to_dict('records'), "policy_impact": government_policy_impact.toPandas().to_dict('records'), "competition_data": competition_analysis.toPandas().to_dict('records')}
return JsonResponse(result_data)
def vehicle_value_retention_analysis(request):
value_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/car_analysis").option("dbtable", "vehicle_value_data").option("user", "root").option("password", "password").load()
age_depreciation_curve = value_df.groupBy("vehicle_age", "brand").agg(avg("current_value").alias("avg_current_value"), avg("original_price").alias("avg_original_price")).withColumn("retention_rate", (col("avg_current_value") / col("avg_original_price")) * 100).orderBy("vehicle_age", "brand")
brand_value_ranking = value_df.groupBy("brand").agg(avg("retention_rate").alias("brand_retention"), count("*").alias("sample_count"), avg("mileage").alias("avg_mileage")).filter(col("sample_count") >= 50).orderBy(desc("brand_retention"))
segment_retention_comparison = value_df.groupBy("car_segment", "vehicle_age").agg(avg("retention_rate").alias("segment_retention"), avg("maintenance_cost").alias("avg_maintenance")).orderBy("car_segment", "vehicle_age")
mileage_impact_analysis = value_df.withColumn("mileage_range", when(col("mileage") < 50000, "Low Mileage").when((col("mileage") >= 50000) & (col("mileage") < 100000), "Medium Mileage").otherwise("High Mileage")).groupBy("mileage_range", "vehicle_age").agg(avg("retention_rate").alias("mileage_retention"), count("*").alias("count")).orderBy("vehicle_age", "mileage_range")
color_preference_impact = value_df.groupBy("exterior_color", "brand").agg(avg("retention_rate").alias("color_retention"), count("*").alias("color_count")).filter(col("color_count") >= 20).orderBy(desc("color_retention"))
transmission_type_analysis = value_df.groupBy("transmission_type", "vehicle_age").agg(avg("retention_rate").alias("transmission_retention"), avg("repair_frequency").alias("repair_rate")).orderBy("vehicle_age", "transmission_type")
market_condition_influence = value_df.groupBy("market_condition", "sale_season").agg(avg("retention_rate").alias("market_retention"), avg("liquidity_index").alias("liquidity")).orderBy("sale_season", "market_condition")
accident_history_impact = value_df.filter(col("accident_history").isNotNull()).groupBy("accident_severity", "vehicle_age").agg(avg("retention_rate").alias("accident_retention"), count("*").alias("accident_count")).orderBy("vehicle_age", "accident_severity")
regional_value_difference = value_df.groupBy("region", "brand").agg(avg("retention_rate").alias("regional_retention"), avg("local_demand_index").alias("demand_index")).orderBy("region", desc("regional_retention"))
maintenance_correlation = value_df.groupBy("maintenance_level", "brand").agg(avg("retention_rate").alias("maintenance_retention"), avg("service_quality_score").alias("service_score")).withColumn("maintenance_premium", col("maintenance_retention") - value_df.agg(avg("retention_rate")).collect()[0][0]).orderBy(desc("maintenance_retention"))
result_data = {"depreciation_curve": age_depreciation_curve.toPandas().to_dict('records'), "brand_ranking": brand_value_ranking.toPandas().to_dict('records'), "segment_comparison": segment_retention_comparison.toPandas().to_dict('records'), "mileage_impact": mileage_impact_analysis.toPandas().to_dict('records'), "color_analysis": color_preference_impact.toPandas().to_dict('records'), "transmission_analysis": transmission_type_analysis.toPandas().to_dict('records'), "market_influence": market_condition_influence.toPandas().to_dict('records'), "accident_impact": accident_history_impact.toPandas().to_dict('records'), "regional_difference": regional_value_difference.toPandas().to_dict('records'), "maintenance_effect": maintenance_correlation.toPandas().to_dict('records')}
return JsonResponse(result_data)
基于大数据的汽车之家数据分析系统文档展示
💖💖作者：计算机毕业设计杰瑞 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常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 深度学校实战项目 计算机毕业设计选题推荐
