一、个人简介
💖💖作者：计算机编程果茶熊 💙💙个人简介：曾长期从事计算机专业培训教学，担任过编程老师，同时本人也热爱上课教学，擅长Java、微信小程序、Python、Golang、安卓Android等多个IT方向。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 计算机毕业设计选题 💕💕文末获取源码联系计算机编程果茶熊
二、系统介绍
大数据框架：Hadoop+Spark（Hive需要定制修改） 开发语言：Java+Python（两个版本都支持） 数据库：MySQL 后端框架：SpringBoot(Spring+SpringMVC+Mybatis)+Django（两个版本都支持） 前端：Vue+Echarts+HTML+CSS+JavaScript+jQuery 基于大数据的国际顶尖运动员比赛生涯数据分析与可视化系统是一个专门面向体育数据分析领域的综合性平台。该系统采用Hadoop+Spark大数据处理架构，结合Python数据分析技术栈，构建了完整的运动员职业生涯数据管理和分析体系。系统核心功能涵盖运动员基础信息管理、比赛数据采集处理、群体特征分析、环境因素影响评估、巅峰表现识别以及生涯轨迹追踪等多个维度。通过集成Spark SQL进行大规模数据查询，利用Pandas和NumPy完成统计分析计算，最终通过Vue+ElementUI+Echarts技术栈实现动态可视化展示。系统支持对海量比赛记录进行深度挖掘，能够识别运动员不同阶段的竞技状态变化规律，分析训练环境、比赛场地、对手实力等外部因素对成绩的影响程度，为体育科研人员和教练团队提供数据支撑。整个系统基于MySQL数据库存储结构化数据，通过HDFS处理非结构化比赛视频和图像资料，形成了完整的体育大数据生态环境。
三、基于大数据的国际顶尖运动员比赛生涯数据分析与可视化系统-视频解说
计算机毕设推荐：基于大数据的运动员比赛生涯分析与可视化系统开发
四、基于大数据的国际顶尖运动员比赛生涯数据分析与可视化系统-功能展示
五、基于大数据的国际顶尖运动员比赛生涯数据分析与可视化系统-代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, avg, max, min, count, when, desc, asc
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
spark = SparkSession.builder.appName("AthleteDataAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
def athlete_group_analysis(sport_type, age_range, performance_threshold):
athlete_df = spark.sql(f"""
SELECT athlete_id, name, age, sport_type, country,
AVG(score) as avg_score, COUNT(*) as match_count,
MAX(score) as best_score, MIN(score) as worst_score
FROM athlete_matches
WHERE sport_type = '{sport_type}'
AND age BETWEEN {age_range[0]} AND {age_range[1]}
GROUP BY athlete_id, name, age, sport_type, country
HAVING AVG(score) >= {performance_threshold}
""")
performance_stats = athlete_df.select(
avg("avg_score").alias("group_avg_performance"),
max("best_score").alias("group_peak_score"),
min("worst_score").alias("group_lowest_score"),
count("athlete_id").alias("total_athletes")
).collect()[0]
country_distribution = athlete_df.groupBy("country").agg(
count("athlete_id").alias("athlete_count"),
avg("avg_score").alias("country_avg_score")
).orderBy(desc("athlete_count"))
age_performance_correlation = athlete_df.select("age", "avg_score").rdd.map(
lambda row: (row.age, row.avg_score)
).collect()
correlation_coefficient = np.corrcoef([x[0] for x in age_performance_correlation],
[x[1] for x in age_performance_correlation])[0,1]
result_data = {
'group_statistics': performance_stats.asDict(),
'country_analysis': country_distribution.collect(),
'age_correlation': correlation_coefficient,
'athlete_details': athlete_df.collect()
}
return result_data
def competition_environment_analysis(athlete_id, start_date, end_date):
environment_df = spark.sql(f"""
SELECT m.match_id, m.athlete_id, m.score, m.match_date,
e.venue, e.weather, e.temperature, e.humidity, e.audience_count,
e.altitude, e.surface_type
FROM athlete_matches m
JOIN environment_factors e ON m.match_id = e.match_id
WHERE m.athlete_id = {athlete_id}
AND m.match_date BETWEEN '{start_date}' AND '{end_date}'
""")
weather_impact = environment_df.groupBy("weather").agg(
avg("score").alias("avg_score_by_weather"),
count("match_id").alias("matches_in_weather"),
max("score").alias("best_score_in_weather")
).orderBy(desc("avg_score_by_weather"))
venue_performance = environment_df.groupBy("venue").agg(
avg("score").alias("avg_venue_score"),
count("match_id").alias("venue_matches"),
(max("score") - min("score")).alias("score_variance")
).orderBy(desc("avg_venue_score"))
temperature_groups = environment_df.withColumn(
"temp_range",
when(col("temperature") < 15, "cold")
.when(col("temperature") < 25, "moderate")
.otherwise("hot")
).groupBy("temp_range").agg(
avg("score").alias("temp_avg_score"),
count("match_id").alias("temp_matches")
)
audience_impact = environment_df.select("audience_count", "score").rdd.map(
lambda row: (row.audience_count, row.score)
).filter(lambda x: x[0] is not None and x[1] is not None).collect()
if len(audience_impact) > 1:
audience_correlation = np.corrcoef([x[0] for x in audience_impact],
[x[1] for x in audience_impact])[0,1]
else:
audience_correlation = 0.0
environment_analysis = {
'weather_impact': weather_impact.collect(),
'venue_performance': venue_performance.collect(),
'temperature_analysis': temperature_groups.collect(),
'audience_correlation': audience_correlation,
'total_matches_analyzed': environment_df.count()
}
return environment_analysis
def athlete_peak_analysis(athlete_id, analysis_period_months):
end_date = datetime.now()
start_date = end_date - timedelta(days=analysis_period_months * 30)
peak_df = spark.sql(f"""
SELECT athlete_id, score, match_date, opponent, competition_level,
LAG(score, 1) OVER (PARTITION BY athlete_id ORDER BY match_date) as prev_score,
LEAD(score, 1) OVER (PARTITION BY athlete_id ORDER BY match_date) as next_score,
ROW_NUMBER() OVER (PARTITION BY athlete_id ORDER BY score DESC) as score_rank
FROM athlete_matches
WHERE athlete_id = {athlete_id}
AND match_date BETWEEN '{start_date.strftime('%Y-%m-%d')}' AND '{end_date.strftime('%Y-%m-%d')}'
""")
peak_performances = peak_df.filter(col("score_rank") <= 5).orderBy(desc("score"))
performance_trends = peak_df.select("score", "match_date").orderBy("match_date").collect()
scores = [row.score for row in performance_trends]
dates = [row.match_date for row in performance_trends]
if len(scores) >= 3:
moving_avg_3 = []
for i in range(2, len(scores)):
moving_avg_3.append(np.mean(scores[i-2:i+1]))
trend_coefficient = np.polyfit(range(len(scores)), scores, 1)[0]
else:
moving_avg_3 = []
trend_coefficient = 0.0
consistency_metrics = peak_df.select(
avg("score").alias("mean_score"),
(max("score") - min("score")).alias("score_range")
).collect()[0]
score_std = np.std(scores) if len(scores) > 1 else 0.0
consistency_index = consistency_metrics.mean_score / (score_std + 0.1)
peak_periods = []
if len(scores) >= 5:
for i in range(len(scores) - 4):
period_scores = scores[i:i+5]
if np.mean(period_scores) > consistency_metrics.mean_score * 1.1:
peak_periods.append({
'start_date': dates[i],
'end_date': dates[i+4],
'avg_score': np.mean(period_scores),
'peak_score': max(period_scores)
})
peak_analysis_result = {
'top_performances': peak_performances.collect(),
'trend_coefficient': trend_coefficient,
'consistency_index': consistency_index,
'moving_averages': moving_avg_3,
'peak_periods': peak_periods,
'overall_stats': consistency_metrics.asDict()
}
return peak_analysis_result
六、基于大数据的国际顶尖运动员比赛生涯数据分析与可视化系统-文档展示
七、END
💛💛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 计算机毕业设计选题 💕💕文末获取源码联系计算机编程果茶熊
