💖💖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
基于大数据的网约车平台运营数据分析系统介绍
网约车平台运营数据分析系统是一个基于大数据技术的综合性数据处理与分析平台，专门针对网约车行业的运营特点和数据特征设计开发。该系统采用Hadoop+Spark大数据处理框架作为核心技术架构，通过Python编程语言结合Django后端框架构建稳定的服务层，前端使用Vue+ElementUI+Echarts技术栈实现直观的数据可视化界面。系统涵盖网约车信息管理、驾驶员行为分析、地理维度分析、运营效率分析和时间维度分析等七大功能模块，能够对海量的网约车运营数据进行高效采集、存储、处理和分析。通过Spark SQL进行复杂的数据查询和统计分析，利用Pandas和NumPy进行数据清洗和数学计算，最终通过Echarts图表组件将分析结果以直观的可视化形式呈现给用户，为网约车平台的运营决策提供有力的数据支撑和分析依据。
基于大数据的网约车平台运营数据分析系统演示视频
基于大数据的网约车平台运营数据分析系统演示图片
基于大数据的网约车平台运营数据分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, sum, max, min, when, desc, hour, date_format
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views import View
import json
from datetime import datetime, timedelta
spark = SparkSession.builder.appName("NetworkCarAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
def driver_behavior_analysis(request):
driver_data = spark.sql("SELECT driver_id, order_count, total_distance, total_revenue, avg_rating, complaint_count FROM driver_statistics")
driver_pandas = driver_data.toPandas()
driver_pandas['efficiency_score'] = (driver_pandas['order_count'] * 0.3 + driver_pandas['total_distance'] * 0.2 + driver_pandas['total_revenue'] * 0.3 + driver_pandas['avg_rating'] * 0.2) / driver_pandas['complaint_count'].replace(0, 1)
high_performance_drivers = driver_pandas[driver_pandas['efficiency_score'] > driver_pandas['efficiency_score'].quantile(0.8)]
risk_drivers = driver_pandas[(driver_pandas['complaint_count'] > 3) | (driver_pandas['avg_rating'] < 4.0)]
behavior_analysis = spark.sql("SELECT driver_id, COUNT(*) as violation_count, AVG(speed_limit_exceeded) as avg_speed_violation, SUM(harsh_braking) as harsh_braking_count, SUM(harsh_acceleration) as harsh_acceleration_count FROM driver_behavior_logs WHERE created_date >= date_sub(current_date(), 30) GROUP BY driver_id")
behavior_pandas = behavior_analysis.toPandas()
behavior_pandas['risk_score'] = behavior_pandas['avg_speed_violation'] * 0.4 + behavior_pandas['harsh_braking_count'] * 0.3 + behavior_pandas['harsh_acceleration_count'] * 0.3
final_analysis = pd.merge(driver_pandas, behavior_pandas, on='driver_id', how='left')
final_analysis['overall_rating'] = np.where(final_analysis['risk_score'] > 5, 'High Risk', np.where(final_analysis['efficiency_score'] > final_analysis['efficiency_score'].median(), 'Excellent', 'Average'))
top_drivers = final_analysis.nlargest(10, 'efficiency_score')[['driver_id', 'efficiency_score', 'overall_rating']].to_dict('records')
risk_drivers_list = final_analysis[final_analysis['overall_rating'] == 'High Risk'][['driver_id', 'risk_score', 'complaint_count']].to_dict('records')
monthly_trend = spark.sql("SELECT DATE_FORMAT(order_date, 'yyyy-MM') as month, AVG(avg_rating) as monthly_avg_rating, COUNT(*) as monthly_orders FROM driver_monthly_stats GROUP BY DATE_FORMAT(order_date, 'yyyy-MM') ORDER BY month DESC LIMIT 12")
trend_data = monthly_trend.toPandas().to_dict('records')
return JsonResponse({'top_drivers': top_drivers, 'risk_drivers': risk_drivers_list, 'monthly_trend': trend_data, 'total_drivers': len(final_analysis)})
def geographic_dimension_analysis(request):
geographic_data = spark.sql("SELECT pickup_district, dropoff_district, COUNT(*) as order_count, AVG(distance) as avg_distance, AVG(fare_amount) as avg_fare, AVG(duration) as avg_duration FROM order_details WHERE order_date >= date_sub(current_date(), 30) GROUP BY pickup_district, dropoff_district")
geo_pandas = geographic_data.toPandas()
district_summary = geo_pandas.groupby('pickup_district').agg({'order_count': 'sum', 'avg_distance': 'mean', 'avg_fare': 'mean', 'avg_duration': 'mean'}).reset_index()
district_summary['demand_intensity'] = district_summary['order_count'] / district_summary['order_count'].max() * 100
hotspot_analysis = spark.sql("SELECT pickup_latitude, pickup_longitude, COUNT(*) as pickup_frequency FROM ride_locations WHERE pickup_time >= date_sub(current_timestamp(), INTERVAL 7 DAY) GROUP BY pickup_latitude, pickup_longitude HAVING COUNT(*) > 10")
hotspot_pandas = hotspot_analysis.toPandas()
hotspot_pandas['lat_rounded'] = np.round(hotspot_pandas['pickup_latitude'], 3)
hotspot_pandas['lon_rounded'] = np.round(hotspot_pandas['pickup_longitude'], 3)
hotspot_clusters = hotspot_pandas.groupby(['lat_rounded', 'lon_rounded'])['pickup_frequency'].sum().reset_index()
top_hotspots = hotspot_clusters.nlargest(20, 'pickup_frequency')
route_analysis = spark.sql("SELECT pickup_district, dropoff_district, COUNT(*) as route_frequency, AVG(fare_amount) as avg_route_fare, AVG(distance) as avg_route_distance FROM popular_routes GROUP BY pickup_district, dropoff_district ORDER BY route_frequency DESC LIMIT 15")
route_data = route_analysis.toPandas()
route_data['profit_per_km'] = route_data['avg_route_fare'] / route_data['avg_route_distance']
profitable_routes = route_data.nlargest(10, 'profit_per_km')
supply_demand_gap = spark.sql("SELECT district_name, total_demand, available_drivers, (total_demand - available_drivers) as gap_ratio FROM district_supply_demand WHERE analysis_date = current_date()")
gap_analysis = supply_demand_gap.toPandas()
gap_analysis['shortage_level'] = np.where(gap_analysis['gap_ratio'] > 50, 'Critical', np.where(gap_analysis['gap_ratio'] > 20, 'High', 'Normal'))
return JsonResponse({'district_summary': district_summary.to_dict('records'), '
基于大数据的网约车平台运营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
