💖💖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
大学生请假管理系统介绍
基于Hadoop+Spark大数据技术架构的大学生请假管理系统是一个集传统管理功能与智能数据分析于一体的综合性平台。该系统采用Django后端框架配合Vue前端技术，构建了完整的前后端分离架构，通过Hadoop分布式文件系统存储海量请假数据，利用Spark强大的内存计算能力实现实时数据处理与分析。系统涵盖学生请假申请、辅导员审批管理、班级信息维护、请假数据统计分析以及基于机器学习算法的请假次数预测等核心功能模块，为高校学生事务管理提供了数据驱动的解决方案。平台通过ElementUI组件库打造直观友好的用户界面，结合Echarts数据可视化技术，将复杂的请假数据以图表形式直观展现，帮助管理者快速掌握学生请假规律和趋势。系统运用Pandas和NumPy进行数据预处理，通过Spark SQL进行复杂查询分析，实现了从数据采集、存储、处理到预测的完整大数据处理链条，为传统学生管理工作注入了智能化元素。
大学生请假管理系统演示视频
大学生请假管理系统演示图片
大学生请假管理系统代码展示
from pyspark.sql import SparkSession
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import LinearRegression
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
import pandas as pd
from datetime import datetime, timedelta
spark = SparkSession.builder.appName("StudentLeaveManagement").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
@csrf_exempt
def submit_leave_application(request):
if request.method == 'POST':
leave_data = json.loads(request.body)
student_id = leave_data.get('student_id')
leave_type = leave_data.get('leave_type')
start_date = leave_data.get('start_date')
end_date = leave_data.get('end_date')
reason = leave_data.get('reason')
emergency_contact = leave_data.get('emergency_contact')
current_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
leave_duration = (datetime.strptime(end_date, '%Y-%m-%d') - datetime.strptime(start_date, '%Y-%m-%d')).days + 1
application_data = {
'student_id': student_id,
'leave_type': leave_type,
'start_date': start_date,
'end_date': end_date,
'leave_duration': leave_duration,
'reason': reason,
'emergency_contact': emergency_contact,
'application_time': current_time,
'status': 'pending'
}
df = spark.createDataFrame([application_data])
df.write.mode('append').option('header', 'true').csv('hdfs://localhost:9000/student_leave_data')
historical_leaves = spark.read.option('header', 'true').csv('hdfs://localhost:9000/student_leave_data').filter(f"student_id = '{student_id}'")
recent_leaves_count = historical_leaves.filter("application_time >= date_sub(current_date(), 30)").count()
risk_level = 'high' if recent_leaves_count >= 3 else 'medium' if recent_leaves_count >= 1 else 'low'
return JsonResponse({'status': 'success', 'application_id': f"LA_{student_id}_{current_time.replace(' ', '_')}", 'risk_level': risk_level})
@csrf_exempt
def analyze_leave_data(request):
if request.method == 'GET':
leave_df = spark.read.option('header', 'true').csv('hdfs://localhost:9000/student_leave_data')
leave_df.createOrReplaceTempView('leave_applications')
monthly_stats = spark.sql("""
SELECT DATE_FORMAT(start_date, 'yyyy-MM') as month,
leave_type,
COUNT(*) as application_count,
AVG(leave_duration) as avg_duration,
COUNT(CASE WHEN status = 'approved' THEN 1 END) as approved_count
FROM leave_applications
WHERE start_date >= date_sub(current_date(), 365)
GROUP BY DATE_FORMAT(start_date, 'yyyy-MM'), leave_type
ORDER BY month DESC, leave_type
""").collect()
class_stats = spark.sql("""
SELECT class_id,
COUNT(*) as total_applications,
COUNT(CASE WHEN leave_type = 'sick' THEN 1 END) as sick_leaves,
COUNT(CASE WHEN leave_type = 'personal' THEN 1 END) as personal_leaves,
AVG(leave_duration) as avg_duration
FROM leave_applications la
JOIN student_info si ON la.student_id = si.student_id
WHERE la.start_date >= date_sub(current_date(), 180)
GROUP BY class_id
ORDER BY total_applications DESC
""").collect()
peak_analysis = spark.sql("""
SELECT DAYOFWEEK(start_date) as day_of_week,
COUNT(*) as leave_count,
AVG(leave_duration) as avg_duration
FROM leave_applications
WHERE start_date >= date_sub(current_date(), 365)
GROUP BY DAYOFWEEK(start_date)
ORDER BY leave_count DESC
""").collect()
result_data = {
'monthly_trends': [row.asDict() for row in monthly_stats],
'class_statistics': [row.asDict() for row in class_stats],
'peak_patterns': [row.asDict() for row in peak_analysis]
}
return JsonResponse({'status': 'success', 'analysis_data': result_data})
@csrf_exempt
def predict_leave_frequency(request):
if request.method == 'POST':
request_data = json.loads(request.body)
student_id = request_data.get('student_id')
prediction_months = request_data.get('months', 3)
historical_df = spark.read.option('header', 'true').csv('hdfs://localhost:9000/student_leave_data')
student_data = historical_df.filter(f"student_id = '{student_id}'")
student_data.createOrReplaceTempView('student_leaves')
monthly_leaves = spark.sql("""
SELECT DATE_FORMAT(start_date, 'yyyy-MM') as month,
COUNT(*) as leave_count,
SUM(leave_duration) as total_duration,
AVG(leave_duration) as avg_duration
FROM student_leaves
GROUP BY DATE_FORMAT(start_date, 'yyyy-MM')
ORDER BY month
""")
pandas_df = monthly_leaves.toPandas()
if len(pandas_df) < 3:
return JsonResponse({'status': 'insufficient_data', 'message': 'Need at least 3 months of historical data'})
pandas_df['month_num'] = pd.to_datetime(pandas_df['month']).dt.to_period('M').view(dtype='int64')
feature_assembler = VectorAssembler(inputCols=['month_num'], outputCol='features')
spark_df = spark.createDataFrame(pandas_df)
feature_df = feature_assembler.transform(spark_df)
lr_model = LinearRegression(featuresCol='features', labelCol='leave_count')
trained_model = lr_model.fit(feature_df)
latest_month = pandas_df['month_num'].max()
future_months = []
predictions = []
for i in range(1, prediction_months + 1):
future_month = latest_month + i
future_df = spark.createDataFrame([(future_month,)], ['month_num'])
future_features = feature_assembler.transform(future_df)
prediction = trained_model.transform(future_features).collect()[0]['prediction']
future_months.append(future_month)
predictions.append(max(0, round(prediction, 2)))
historical_avg = pandas_df['leave_count'].mean()
trend_direction = 'increasing' if predictions[-1] > historical_avg else 'decreasing' if predictions[-1] < historical_avg else 'stable'
return JsonResponse({
'status': 'success',
'student_id': student_id,
'historical_average': round(historical_avg, 2),
'predictions': predictions,
'trend': trend_direction,
'model_accuracy': round(trained_model.summary.r2, 3)
})
大学生请假管理系统文档展示
💖💖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
