💖💖作者：计算机编程小咖 💙💙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 深度学习实战项目
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校园学生健康监测管理系统介绍
校园学生健康监测管理系统是一套专门为高校学生健康管理需求而设计的综合性管理平台，采用先进的B/S架构模式，支持Java+SpringBoot和Python+Django两套完整的技术实现方案，前端采用Vue+ElementUI构建现代化的用户界面，后端通过MySQL数据库确保数据的安全存储和高效访问。该系统涵盖了校园健康管理的全流程业务，从基础的用户信息管理、医生信息维护、科室信息配置出发，建立了完整的医疗资源管理体系；通过健康知识库、健康饮食指导、健身项目推荐等功能模块，为学生提供全方位的健康教育服务；核心业务功能包括医生排班管理、在线预约挂号、取消挂号、预约提醒等就医流程管理，以及诊断记录存储、评价反馈收集等医疗服务跟踪；特别设计了健康数据采集、健康评估分析、个性化健身计划制定等智能化健康监测功能，帮助学生建立科学的健康管理档案；系统还配备了用户反馈机制、健康小贴士推送、系统日志记录、轮播图展示等辅助功能，以及完善的个人中心和系统管理模块，形成了一个功能完备、操作便捷、技术先进的校园健康监测管理解决方案，能够有效提升高校学生的健康管理水平和就医体验。
校园学生健康监测管理系统演示视频
校园学生健康监测管理系统演示图片
校园学生健康监测管理系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, when, desc
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
from django.utils.decorators import method_decorator
from django.views import View
from django.db import transaction
from datetime import datetime, timedelta
import json
import pandas as pd
spark = SparkSession.builder.appName("CampusHealthSystem").config("spark.sql.adaptive.enabled", "true").getOrCreate()
@method_decorator(csrf_exempt, name='dispatch')
class AppointmentBookingView(View):
def post(self, request):
try:
data = json.loads(request.body)
student_id = data.get('student_id')
doctor_id = data.get('doctor_id')
department_id = data.get('department_id')
appointment_date = data.get('appointment_date')
appointment_time = data.get('appointment_time')
booking_df = spark.sql(f"""
SELECT COUNT(*) as booking_count
FROM appointments
WHERE doctor_id = {doctor_id}
AND appointment_date = '{appointment_date}'
AND appointment_time = '{appointment_time}'
AND status != 'cancelled'
""")
current_bookings = booking_df.collect()[0]['booking_count']
if current_bookings >= 10:
return JsonResponse({'status': 'error', 'message': '该时段预约已满'})
doctor_schedule_df = spark.sql(f"""
SELECT * FROM doctor_schedules
WHERE doctor_id = {doctor_id}
AND schedule_date = '{appointment_date}'
AND start_time <= '{appointment_time}'
AND end_time >= '{appointment_time}'
""")
if doctor_schedule_df.count() == 0:
return JsonResponse({'status': 'error', 'message': '医生该时段不在班'})
with transaction.atomic():
appointment_id = f"APT{datetime.now().strftime('%Y%m%d%H%M%S')}{student_id}"
spark.sql(f"""
INSERT INTO appointments
(appointment_id, student_id, doctor_id, department_id, appointment_date, appointment_time, status, created_time)
VALUES ('{appointment_id}', {student_id}, {doctor_id}, {department_id},
'{appointment_date}', '{appointment_time}', 'booked', '{datetime.now()}')
""")
spark.sql(f"""
INSERT INTO appointment_reminders
(appointment_id, reminder_time, reminder_type, is_sent)
VALUES ('{appointment_id}', '{appointment_date} {appointment_time}', 'booking_confirm', false)
""")
queue_number_df = spark.sql(f"""
SELECT COUNT(*) + 1 as queue_number
FROM appointments
WHERE doctor_id = {doctor_id}
AND appointment_date = '{appointment_date}'
AND status = 'booked'
""")
queue_number = queue_number_df.collect()[0]['queue_number']
return JsonResponse({
'status': 'success',
'message': '预约成功',
'appointment_id': appointment_id,
'queue_number': queue_number,
'estimated_wait_time': queue_number * 15
})
except Exception as e:
return JsonResponse({'status': 'error', 'message': f'预约失败: {str(e)}'})
@method_decorator(csrf_exempt, name='dispatch')
class HealthAssessmentView(View):
def post(self, request):
try:
data = json.loads(request.body)
student_id = data.get('student_id')
height = float(data.get('height'))
weight = float(data.get('weight'))
blood_pressure_high = int(data.get('blood_pressure_high'))
blood_pressure_low = int(data.get('blood_pressure_low'))
heart_rate = int(data.get('heart_rate'))
sleep_hours = float(data.get('sleep_hours'))
exercise_frequency = int(data.get('exercise_frequency'))
bmi = weight / ((height / 100) ** 2)
bmi_score = 100 if 18.5 <= bmi <= 23.9 else (85 if 24 <= bmi <= 27.9 else 60)
bp_score = 100 if blood_pressure_high <= 120 and blood_pressure_low <= 80 else (
75 if blood_pressure_high <= 139 and blood_pressure_low <= 89 else 50)
hr_score = 100 if 60 <= heart_rate <= 100 else (80 if heart_rate <= 110 else 60)
sleep_score = 100 if 7 <= sleep_hours <= 9 else (80 if 6 <= sleep_hours <= 10 else 60)
exercise_score = min(100, exercise_frequency * 20)
total_score = (bmi_score * 0.25 + bp_score * 0.25 + hr_score * 0.2 + sleep_score * 0.15 + exercise_score * 0.15)
risk_level = 'low' if total_score >= 85 else ('medium' if total_score >= 70 else 'high')
historical_data_df = spark.sql(f"""
SELECT health_score, assessment_date
FROM health_assessments
WHERE student_id = {student_id}
ORDER BY assessment_date DESC
LIMIT 5
""")
trend_analysis = "稳定"
if historical_data_df.count() >= 2:
scores = [row['health_score'] for row in historical_data_df.collect()]
recent_avg = sum(scores[:2]) / 2
older_avg = sum(scores[2:]) / len(scores[2:]) if len(scores) > 2 else recent_avg
if recent_avg > older_avg + 5:
trend_analysis = "改善"
elif recent_avg < older_avg - 5:
trend_analysis = "下降"
recommendations = []
if bmi_score < 85:
recommendations.append("建议控制体重，保持均衡饮食")
if bp_score < 85:
recommendations.append("注意监测血压，减少盐分摄入")
if exercise_score < 80:
recommendations.append("增加运动频次，每周至少运动3次")
if sleep_score < 85:
recommendations.append("改善睡眠质量，保证7-9小时睡眠")
assessment_id = f"HSA{datetime.now().strftime('%Y%m%d%H%M%S')}{student_id}"
spark.sql(f"""
INSERT INTO health_assessments
(assessment_id, student_id, health_score, risk_level, bmi_value, trend_analysis,
recommendations, assessment_date)
VALUES ('{assessment_id}', {student_id}, {total_score:.2f}, '{risk_level}',
{bmi:.2f}, '{trend_analysis}', '{";".join(recommendations)}', '{datetime.now()}')
""")
return JsonResponse({
'status': 'success',
'assessment_id': assessment_id,
'health_score': round(total_score, 2),
'bmi': round(bmi, 2),
'risk_level': risk_level,
'trend_analysis': trend_analysis,
'recommendations': recommendations,
'detailed_scores': {
'bmi_score': bmi_score,
'blood_pressure_score': bp_score,
'heart_rate_score': hr_score,
'sleep_score': sleep_score,
'exercise_score': exercise_score
}
})
except Exception as e:
return JsonResponse({'status': 'error', 'message': f'健康评估失败: {str(e)}'})
@method_decorator(csrf_exempt, name='dispatch')
class HealthDataAnalysisView(View):
def get(self, request):
try:
time_range = request.GET.get('time_range', '30')
start_date = (datetime.now() - timedelta(days=int(time_range))).strftime('%Y-%m-%d')
health_trends_df = spark.sql(f"""
SELECT
DATE(assessment_date) as date,
AVG(health_score) as avg_score,
COUNT(*) as assessment_count,
AVG(bmi_value) as avg_bmi,
SUM(CASE WHEN risk_level = 'high' THEN 1 ELSE 0 END) as high_risk_count,
SUM(CASE WHEN risk_level = 'medium' THEN 1 ELSE 0 END) as medium_risk_count,
SUM(CASE WHEN risk_level = 'low' THEN 1 ELSE 0 END) as low_risk_count
FROM health_assessments
WHERE assessment_date >= '{start_date}'
GROUP BY DATE(assessment_date)
ORDER BY date
""")
trends_data = []
for row in health_trends_df.collect():
trends_data.append({
'date': str(row['date']),
'avg_score': round(row['avg_score'], 2),
'assessment_count': row['assessment_count'],
'avg_bmi': round(row['avg_bmi'], 2),
'risk_distribution': {
'high': row['high_risk_count'],
'medium': row['medium_risk_count'],
'low': row['low_risk_count']
}
})
department_stats_df = spark.sql(f"""
SELECT
d.department_name,
COUNT(a.appointment_id) as total_appointments,
AVG(CASE WHEN a.status = 'completed' THEN 5 ELSE 0 END) as avg_rating,
COUNT(CASE WHEN a.status = 'cancelled' THEN 1 END) as cancelled_count
FROM appointments a
JOIN departments d ON a.department_id = d.department_id
WHERE a.appointment_date >= '{start_date}'
GROUP BY d.department_name
ORDER BY total_appointments DESC
""")
department_data = []
for row in department_stats_df.collect():
department_data.append({
'department': row['department_name'],
'appointments': row['total_appointments'],
'avg_rating': round(row['avg_rating'], 2),
'cancellation_rate': round(row['cancelled_count'] / row['total_appointments'] * 100, 2) if row['total_appointments'] > 0 else 0
})
student_health_ranking_df = spark.sql(f"""
SELECT
s.student_name,
ha.health_score,
ha.risk_level,
COUNT(a.appointment_id) as appointment_count
FROM health_assessments ha
JOIN students s ON ha.student_id = s.student_id
LEFT JOIN appointments a ON s.student_id = a.student_id
WHERE ha.assessment_date >= '{start_date}'
GROUP BY s.student_name, ha.health_score, ha.risk_level
ORDER BY ha.health_score DESC
LIMIT 20
""")
ranking_data = []
for i, row in enumerate(student_health_ranking_df.collect(), 1):
ranking_data.append({
'rank': i,
'student_name': row['student_name'][:2] + '*' * (len(row['student_name']) - 2),
'health_score': row['health_score'],
'risk_level': row['risk_level'],
'appointment_count': row['appointment_count']
})
system_summary_df = spark.sql(f"""
SELECT
COUNT(DISTINCT s.student_id) as active_users,
COUNT(ha.assessment_id) as total_assessments,
COUNT(a.appointment_id) as total_appointments,
AVG(ha.health_score) as system_avg_score
FROM students s
LEFT JOIN health_assessments ha ON s.student_id = ha.student_id
LEFT JOIN appointments a ON s.student_id = a.student_id
WHERE (ha.assessment_date >= '{start_date}' OR a.appointment_date >= '{start_date}')
""")
summary_row = system_summary_df.collect()[0]
return JsonResponse({
'status': 'success',
'analysis_period': f'{time_range}天',
'health_trends': trends_data,
'department_statistics': department_data,
'health_ranking': ranking_data,
'system_summary': {
'active_users': summary_row['active_users'],
'total_assessments': summary_row['total_assessments'],
'total_appointments': summary_row['total_appointments'],
'avg_health_score': round(summary_row['system_avg_score'], 2) if summary_row['system_avg_score'] else 0
}
})
except Exception as e:
return JsonResponse({'status': 'error', 'message': f'数据分析失败: {str(e)}'})
校园学生健康监测管理系统文档展示
💖💖作者：计算机编程小咖 💙💙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 深度学习实战项目
