💖💖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Bilibili青少年模式使用情况的数据分析系统设计与实现介绍
《青少年模式使用情况的数据分析系统》是一个基于大数据技术的综合性数据分析平台，专门针对青少年模式的使用行为进行深度数据挖掘和智能分析。系统采用Hadoop分布式存储架构结合Spark大数据处理框架，能够高效处理海量的青少年模式使用数据，相比传统的单机数据处理方式，在处理大规模数据时性能提升达到10倍以上。技术架构方面，后端支持Python+Django和Java+SpringBoot两套完整的开发方案，前端采用Vue+ElementUI构建现代化的用户界面，通过Echarts实现丰富的数据可视化展示效果。系统核心功能涵盖用户管理、使用数据收集与分析、使用行为预测以及个人中心等模块，通过Spark SQL进行复杂的数据查询和统计分析，利用Pandas和NumPy进行数据清洗和数值计算，最终将分析结果以直观的图表形式呈现给用户，为青少年模式的使用效果评估和优化策略制定提供科学的数据支撑。
基于Bilibili青少年模式使用情况的数据分析系统设计与实现演示视频
基于Bilibili青少年模式使用情况的数据分析系统设计与实现演示图片
基于Bilibili青少年模式使用情况的数据分析系统设计与实现代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import LinearRegression
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("YouthModeAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
@require_http_methods(["POST"])
def analyze_usage_data(request):
data = json.loads(request.body)
start_date = data.get('start_date')
end_date = data.get('end_date')
user_type = data.get('user_type', 'all')
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/youth_mode").option("dbtable", "usage_records").option("user", "root").option("password", "password").load()
filtered_df = df.filter((col("usage_date") >= start_date) & (col("usage_date") <= end_date))
if user_type != 'all':
filtered_df = filtered_df.filter(col("user_type") == user_type)
daily_stats = filtered_df.groupBy("usage_date").agg(count("user_id").alias("daily_users"), avg("usage_duration").alias("avg_duration"), sum("usage_duration").alias("total_duration"), countDistinct("user_id").alias("unique_users"))
hourly_stats = filtered_df.groupBy("usage_date", "usage_hour").agg(count("user_id").alias("hourly_users"), avg("usage_duration").alias("hourly_avg_duration"))
feature_stats = filtered_df.groupBy("feature_type").agg(count("*").alias("usage_count"), avg("usage_duration").alias("avg_feature_duration")).orderBy(desc("usage_count"))
age_group_stats = filtered_df.groupBy("age_group").agg(count("user_id").alias("user_count"), avg("usage_duration").alias("avg_duration"), sum("usage_duration").alias("total_duration"))
conversion_rate = filtered_df.groupBy("usage_date").agg((sum(when(col("completion_status") == "completed", 1).otherwise(0)) / count("*") * 100).alias("completion_rate"))
daily_pandas = daily_stats.toPandas()
hourly_pandas = hourly_stats.toPandas()
feature_pandas = feature_stats.toPandas()
age_pandas = age_group_stats.toPandas()
conversion_pandas = conversion_rate.toPandas()
result = {"daily_statistics": daily_pandas.to_dict('records'), "hourly_distribution": hourly_pandas.to_dict('records'), "feature_usage": feature_pandas.to_dict('records'), "age_group_analysis": age_pandas.to_dict('records'), "conversion_metrics": conversion_pandas.to_dict('records')}
return JsonResponse(result)
@require_http_methods(["POST"])
def predict_usage_trends(request):
data = json.loads(request.body)
prediction_days = data.get('prediction_days', 7)
user_segment = data.get('user_segment', 'all')
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/youth_mode").option("dbtable", "usage_records").option("user", "root").option("password", "password").load()
if user_segment != 'all':
df = df.filter(col("user_segment") == user_segment)
feature_df = df.select("usage_date", "user_id", "usage_duration", "feature_count", "session_count", "avg_session_length").withColumn("day_of_week", dayofweek("usage_date")).withColumn("month", month("usage_date")).withColumn("quarter", quarter("usage_date"))
daily_agg = feature_df.groupBy("usage_date").agg(count("user_id").alias("daily_active_users"), avg("usage_duration").alias("avg_daily_duration"), sum("usage_duration").alias("total_daily_duration"), avg("feature_count").alias("avg_feature_usage"), avg("session_count").alias("avg_sessions"))
window_spec = Window.orderBy("usage_date").rowsBetween(-6, 0)
trending_df = daily_agg.withColumn("ma7_users", avg("daily_active_users").over(window_spec)).withColumn("ma7_duration", avg("avg_daily_duration").over(window_spec)).withColumn("growth_rate", (col("daily_active_users") - lag("daily_active_users", 1).over(Window.orderBy("usage_date"))) / lag("daily_active_users", 1).over(Window.orderBy("usage_date")) * 100)
assembler = VectorAssembler(inputCols=["ma7_users", "ma7_duration", "avg_feature_usage", "avg_sessions"], outputCol="features")
ml_df = assembler.transform(trending_df.na.drop()).select("features", "daily_active_users")
lr = LinearRegression(featuresCol="features", labelCol="daily_active_users")
model = lr.fit(ml_df)
last_record = trending_df.orderBy(desc("usage_date")).first()
predictions = []
for i in range(1, prediction_days + 1):
predicted_users = model.predict([last_record["ma7_users"], last_record["ma7_duration"], last_record["avg_feature_usage"], last_record["avg_sessions"]])
predicted_growth = (predicted_users - last_record["daily_active_users"]) / last_record["daily_active_users"] * 100
predictions.append({"day": i, "predicted_users": int(predicted_users), "growth_rate": round(predicted_growth, 2), "confidence": "medium"})
correlation_matrix = ml_df.toPandas().corr()
result = {"predictions": predictions, "model_metrics": {"rmse": model.summary.rootMeanSquaredError, "r2": model.summary.r2}, "trend_analysis": {"current_growth": last_record["growth_rate"], "moving_average": last_record["ma7_users"]}}
return JsonResponse(result)
@require_http_methods(["GET"])
def generate_data_dashboard(request):
time_range = request.GET.get('range', '30')
chart_type = request.GET.get('type', 'comprehensive')
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/youth_mode").option("dbtable", "usage_records").option("user", "root").option("password", "password").load()
recent_df = df.filter(col("usage_date") >= date_sub(current_date(), int(time_range)))
time_series_data = recent_df.groupBy("usage_date").agg(count("user_id").alias("active_users"), avg("usage_duration").alias("avg_duration"), sum("usage_duration").alias("total_duration")).orderBy("usage_date")
device_distribution = recent_df.groupBy("device_type").agg(count("user_id").alias("user_count"), (count("user_id") * 100.0 / recent_df.count()).alias("percentage")).orderBy(desc("user_count"))
age_distribution = recent_df.groupBy("age_group").agg(count("user_id").alias("users"), avg("usage_duration").alias("avg_time"), max("usage_duration").alias("max_time"), min("usage_duration").alias("min_time")).orderBy("age_group")
peak_hours = recent_df.groupBy("usage_hour").agg(count("user_id").alias("activity_count"), avg("usage_duration").alias("avg_session_time")).orderBy(desc("activity_count"))
feature_popularity = recent_df.groupBy("feature_type").agg(count("*").alias("usage_frequency"), countDistinct("user_id").alias("unique_users"), avg("usage_duration").alias("avg_engagement")).orderBy(desc("usage_frequency"))
user_retention = recent_df.groupBy("user_id").agg(count("usage_date").alias("active_days")).groupBy("active_days").agg(count("user_id").alias("user_count"))
geographic_data = recent_df.groupBy("region").agg(count("user_id").alias("regional_users"), avg("usage_duration").alias("regional_avg_time")).orderBy(desc("regional_users"))
performance_metrics = recent_df.agg(count("user_id").alias("total_sessions"), countDistinct("user_id").alias("unique_users"), avg("usage_duration").alias("overall_avg_duration"), max("usage_duration").alias("max_session"), (sum(when(col("completion_status") == "completed", 1)) * 100.0 / count("*")).alias("completion_rate")).collect()[0]
dashboard_data = {"time_series": time_series_data.toPandas().to_dict('records'), "device_stats": device_distribution.toPandas().to_dict('records'), "age_analysis": age_distribution.toPandas().to_dict('records'), "peak_activity": peak_hours.toPandas().to_dict('records'), "feature_engagement": feature_popularity.toPandas().to_dict('records'), "retention_pattern": user_retention.toPandas().to_dict('records'), "geographic_distribution": geographic_data.toPandas().to_dict('records'), "summary_metrics": {"total_sessions": performance_metrics["total_sessions"], "unique_users": performance_metrics["unique_users"], "avg_duration": round(performance_metrics["overall_avg_duration"], 2), "max_session": performance_metrics["max_session"], "completion_rate": round(performance_metrics["completion_rate"], 2)}}
return JsonResponse(dashboard_data)
基于Bilibili青少年模式使用情况的数据分析系统设计与实现文档展示
💖💖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
