💖💖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
基于大数据的华为游戏排行数据可视化分析系统介绍
华为游戏排行数据可视化分析系统是一个基于大数据技术栈构建的综合性分析平台，采用Hadoop+Spark分布式计算框架处理海量游戏数据，通过Django后端框架和Vue前端技术实现数据的采集、存储、分析与可视化展示。系统集成了HDFS分布式文件系统进行数据存储，利用Spark SQL和Pandas进行高效的数据处理与分析，结合MySQL数据库管理结构化数据，最终通过Echarts和ElementUI组件构建直观的可视化界面。系统涵盖华为游戏排行管理、游戏物理属性分析、市场品类分析、榜单生态分析、热度趋势分析等核心功能模块，为用户提供全方位的游戏数据洞察。通过可视化大屏展示，系统能够实时呈现游戏市场动态，帮助用户深入理解华为游戏生态的发展规律和市场表现，为游戏行业决策提供数据支撑。
基于大数据的华为游戏排行数据可视化分析系统演示视频
基于大数据的华为游戏排行数据可视化分析系统演示图片
基于大数据的华为游戏排行数据可视化分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
import pandas as pd
from django.http import JsonResponse
from django.views import View
import json
import mysql.connector
spark = SparkSession.builder.appName("HuaweiGameRankingAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
class GamePhysicalAttributeAnalysis(View):
def post(self, request):
try:
game_df = spark.read.option("header", "true").csv("hdfs://localhost:9000/huawei_games/game_data.csv")
game_df = game_df.withColumn("size_mb", col("size").cast("double"))
game_df = game_df.withColumn("rating_score", col("rating").cast("double"))
game_df = game_df.filter(col("size_mb").isNotNull() & col("rating_score").isNotNull())
size_categories = when(col("size_mb") < 100, "小型游戏").when(col("size_mb") < 500, "中型游戏").otherwise("大型游戏")
game_df = game_df.withColumn("size_category", size_categories)
size_distribution = game_df.groupBy("size_category").agg(count("*").alias("game_count"), avg("rating_score").alias("avg_rating"), avg("download_count").cast("double").alias("avg_downloads"))
size_rating_correlation = game_df.select(corr("size_mb", "rating_score").alias("correlation")).collect()[0]["correlation"]
performance_analysis = game_df.groupBy("size_category").agg(avg("loading_time").cast("double").alias("avg_loading_time"), avg("crash_rate").cast("double").alias("avg_crash_rate"), count(when(col("rating_score") >= 4.0, 1)).alias("high_rating_count"))
compatibility_stats = game_df.groupBy("android_version").agg(count("*").alias("compatible_games"), avg("rating_score").alias("version_avg_rating"))
memory_usage_analysis = game_df.select(avg("ram_usage").cast("double").alias("avg_ram"), stddev("ram_usage").cast("double").alias("ram_stddev"), max("ram_usage").cast("double").alias("max_ram"))
graphics_performance = game_df.filter(col("graphics_level").isNotNull()).groupBy("graphics_level").agg(avg("fps").cast("double").alias("avg_fps"), count("*").alias("game_count"))
battery_consumption = game_df.select(avg("battery_drain").cast("double").alias("avg_battery_drain"), percentile_approx("battery_drain", 0.5).alias("median_battery_drain"))
network_requirement = game_df.groupBy("network_type").agg(count("*").alias("game_count"), avg("data_usage").cast("double").alias("avg_data_usage"))
result_data = {"size_distribution": size_distribution.toPandas().to_dict('records'), "size_rating_correlation": float(size_rating_correlation) if size_rating_correlation else 0, "performance_metrics": performance_analysis.toPandas().to_dict('records'), "compatibility_data": compatibility_stats.toPandas().to_dict('records'), "memory_statistics": memory_usage_analysis.toPandas().to_dict('records')[0], "graphics_data": graphics_performance.toPandas().to_dict('records'), "battery_metrics": battery_consumption.toPandas().to_dict('records')[0], "network_data": network_requirement.toPandas().to_dict('records')}
return JsonResponse({"code": 200, "message": "游戏物理属性分析完成", "data": result_data})
except Exception as e:
return JsonResponse({"code": 500, "message": f"分析过程出现错误: {str(e)}"})
class GameMarketCategoryAnalysis(View):
def post(self, request):
try:
market_df = spark.read.option("header", "true").csv("hdfs://localhost:9000/huawei_games/market_data.csv")
market_df = market_df.withColumn("download_count", col("downloads").cast("long"))
market_df = market_df.withColumn("revenue", col("revenue").cast("double"))
market_df = market_df.withColumn("release_date", to_date(col("release_date"), "yyyy-MM-dd"))
category_overview = market_df.groupBy("category").agg(count("*").alias("total_games"), sum("download_count").alias("total_downloads"), avg("rating").cast("double").alias("avg_rating"), sum("revenue").alias("total_revenue"))
category_trends = market_df.filter(col("release_date") >= date_sub(current_date(), 365)).groupBy("category", month("release_date").alias("month")).agg(count("*").alias("monthly_releases"), avg("download_count").alias("avg_monthly_downloads"))
competitive_analysis = market_df.withColumn("competition_score", (col("download_count") / sum("download_count").over(Window.partitionBy("category"))) * 100)
top_categories = category_overview.orderBy(desc("total_downloads")).limit(10)
revenue_analysis = market_df.groupBy("category").agg(avg("revenue").alias("avg_revenue"), sum("revenue").alias("category_revenue"), (sum("revenue") / sum("download_count")).alias("revenue_per_download"))
user_preference = market_df.groupBy("category", "age_group").agg(count("*").alias("game_count"), avg("user_rating").cast("double").alias("preference_score"))
seasonal_pattern = market_df.withColumn("season", when(month("release_date").isin([12, 1, 2]), "冬季").when(month("release_date").isin([3, 4, 5]), "春季").when(month("release_date").isin([6, 7, 8]), "夏季").otherwise("秋季"))
seasonal_stats = seasonal_pattern.groupBy("category", "season").agg(count("*").alias("seasonal_releases"), avg("download_count").alias("seasonal_avg_downloads"))
growth_rate = market_df.withColumn("days_since_release", datediff(current_date(), "release_date")).groupBy("category").agg(avg("days_since_release").alias("avg_age"), (sum("download_count") / avg("days_since_release")).alias("daily_growth_rate"))
market_share = category_overview.withColumn("market_share", (col("total_downloads") / sum("total_downloads").over(Window.partitionBy())) * 100)
result_data = {"category_overview": category_overview.toPandas().to_dict('records'), "trend_analysis": category_trends.toPandas().to_dict('records'), "top_categories": top_categories.toPandas().to_dict('records'), "revenue_metrics": revenue_analysis.toPandas().to_dict('records'), "user_preferences": user_preference.toPandas().to_dict('records'), "seasonal_patterns": seasonal_stats.toPandas().to_dict('records'), "growth_metrics": growth_rate.toPandas().to_dict('records'), "market_share_data": market_share.toPandas().to_dict('records')}
return JsonResponse({"code": 200, "message": "游戏市场品类分析完成", "data": result_data})
except Exception as e:
return JsonResponse({"code": 500, "message": f"市场分析出现错误: {str(e)}"})
class GameHeatTrendAnalysis(View):
def post(self, request):
try:
heat_df = spark.read.option("header", "true").csv("hdfs://localhost:9000/huawei_games/heat_trend_data.csv")
heat_df = heat_df.withColumn("search_volume", col("search_count").cast("long"))
heat_df = heat_df.withColumn("discussion_count", col("discussions").cast("long"))
heat_df = heat_df.withColumn("trend_date", to_date(col("date"), "yyyy-MM-dd"))
heat_df = heat_df.withColumn("heat_score", (col("search_volume") * 0.4 + col("discussion_count") * 0.3 + col("download_velocity").cast("double") * 0.3))
daily_trends = heat_df.groupBy("game_name", "trend_date").agg(avg("heat_score").alias("daily_heat"), sum("search_volume").alias("daily_searches"), sum("discussion_count").alias("daily_discussions"))
weekly_aggregation = daily_trends.withColumn("week_start", date_trunc("week", "trend_date")).groupBy("game_name", "week_start").agg(avg("daily_heat").alias("weekly_avg_heat"), sum("daily_searches").alias("weekly_searches"))
trend_direction = heat_df.withColumn("prev_heat", lag("heat_score").over(Window.partitionBy("game_name").orderBy("trend_date"))).withColumn("heat_change", col("heat_score") - col("prev_heat"))
trend_classification = trend_direction.withColumn("trend_type", when(col("heat_change") > 10, "急速上升").when(col("heat_change") > 0, "稳步上升").when(col("heat_change") < -10, "急速下降").when(col("heat_change") < 0, "缓慢下降").otherwise("平稳"))
peak_detection = heat_df.withColumn("is_peak", when(col("heat_score") > avg("heat_score").over(Window.partitionBy("game_name")) + 2 * stddev("heat_score").over(Window.partitionBy("game_name")), 1).otherwise(0))
correlation_analysis = heat_df.select(corr("search_volume", "download_velocity").alias("search_download_corr"), corr("discussion_count", "rating_change").alias("discussion_rating_corr"))
seasonal_heat = heat_df.withColumn("month", month("trend_date")).groupBy("month").agg(avg("heat_score").alias("monthly_avg_heat"), count("*").alias("data_points"))
top_trending_games = heat_df.groupBy("game_name").agg(avg("heat_score").alias("avg_heat_score"), max("heat_score").alias("peak_heat"), stddev("heat_score").alias("heat_volatility")).orderBy(desc("avg_heat_score")).limit(20)
category_heat_trends = heat_df.groupBy("category", month("trend_date").alias("month")).agg(avg("heat_score").alias("category_heat"), count("*").alias("games_tracked"))
result_data = {"daily_trends": daily_trends.toPandas().to_dict('records'), "weekly_data": weekly_aggregation.toPandas().to_dict('records'), "trend_directions": trend_classification.toPandas().to_dict('records'), "peak_moments": peak_detection.filter(col("is_peak") == 1).toPandas().to_dict('records'), "correlation_metrics": correlation_analysis.toPandas().to_dict('records')[0], "seasonal_analysis": seasonal_heat.toPandas().to_dict('records'), "trending_rankings": top_trending_games.toPandas().to_dict('records'), "category_trends": category_heat_trends.toPandas().to_dict('records')}
return JsonResponse({"code": 200, "message": "游戏热度趋势分析完成", "data": result_data})
except Exception as e:
return JsonResponse({"code": 500, "message": f"趋势分析出现错误: {str(e)}"})
基于大数据的华为游戏排行数据可视化分析系统文档展示
💖💖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
