💖💖作者：计算机毕业设计小途 💙💙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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基于SpringBoot的宠物领养一站式服务系统介绍
基于SpringBoot的宠物领养一站式服务系统是一个集宠物信息管理、领养服务、日常护理指导于一体的综合性Web应用平台，该系统采用当前主流的SpringBoot+Vue+MySQL技术架构，为用户提供完整的宠物领养解决方案。系统前端采用Vue框架结合ElementUI组件库构建响应式用户界面，确保在不同设备上都能获得良好的交互体验，后端基于SpringBoot框架整合Spring、SpringMVC和MyBatis技术栈，实现了高效稳定的业务逻辑处理和数据持久化操作。系统功能覆盖用户注册登录、个人信息管理、宠物分类展示、宠物信息浏览、在线领养申请、宠物日常护理记录、系统公告发布、智能客服咨询等核心业务模块，管理员可通过后台管理界面对宠物信息、用户数据、领养流程进行统一管理，同时支持轮播图配置和公告信息分类管理，提升平台运营效率。整个系统采用B/S架构模式，支持多用户并发访问，数据存储采用MySQL关系型数据库，确保数据的完整性和安全性，开发环境可选择IDEA或PyCharm，同时提供Java+SpringBoot和Python+Django两种技术实现方案，满足不同开发偏好的技术需求，为宠物爱好者和流浪动物救助组织搭建了一个功能完善、操作便捷的数字化服务平台。
基于SpringBoot的宠物领养一站式服务系统演示视频
基于SpringBoot的宠物领养一站式服务系统演示图片
基于SpringBoot的宠物领养一站式服务系统代码展示
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
from django.contrib.auth.decorators import login_required
from .models import Pet, PetAdoption, PetCategory, User
from django.utils import timezone
from datetime import datetime, timedelta
import json
import pandas as pd
spark = SparkSession.builder.appName("PetAdoptionSystem").config("spark.sql.adaptive.enabled", "true").getOrCreate()
@csrf_exempt
@login_required
def pet_adoption_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
pet_id = data.get('pet_id')
user_id = data.get('user_id')
pet_obj = Pet.objects.get(id=pet_id)
user_obj = User.objects.get(id=user_id)
adoption_history = PetAdoption.objects.filter(user=user_obj).values('pet__age', 'pet__category__name', 'adoption_date', 'status')
df_data = list(adoption_history)
if len(df_data) > 0:
spark_df = spark.createDataFrame(df_data)
spark_df.createOrReplaceTempView("adoption_records")
success_rate = spark.sql("SELECT COUNT(*) as total, SUM(CASE WHEN status='completed' THEN 1 ELSE 0 END) as success FROM adoption_records").collect()[0]
user_preference_score = (success_rate['success'] / success_rate['total']) * 100 if success_rate['total'] > 0 else 50
else:
user_preference_score = 50
pet_compatibility = calculate_pet_compatibility(pet_obj, user_obj)
adoption_record = PetAdoption.objects.create(
pet=pet_obj,
user=user_obj,
adoption_date=timezone.now(),
status='pending',
compatibility_score=pet_compatibility,
user_preference_score=user_preference_score
)
pet_obj.adoption_status = 'pending'
pet_obj.save()
return JsonResponse({
'status': 'success',
'adoption_id': adoption_record.id,
'compatibility_score': pet_compatibility,
'message': '领养申请提交成功，等待审核'
})
def calculate_pet_compatibility(pet, user):
user_pets = Pet.objects.filter(petadoption__user=user, petadoption__status='completed')
if user_pets.exists():
pet_data = []
for up in user_pets:
pet_data.append({
'age': up.age,
'size': 1 if up.size == 'small' else 2 if up.size == 'medium' else 3,
'activity_level': up.activity_level,
'care_difficulty': up.care_difficulty
})
df = pd.DataFrame(pet_data)
spark_df = spark.createDataFrame(df)
assembler = VectorAssembler(inputCols=['age', 'size', 'activity_level', 'care_difficulty'], outputCol='features')
feature_df = assembler.transform(spark_df)
lr = LinearRegression(featuresCol='features', labelCol='age')
model = lr.fit(feature_df)
current_pet_data = spark.createDataFrame([{
'age': pet.age,
'size': 1 if pet.size == 'small' else 2 if pet.size == 'medium' else 3,
'activity_level': pet.activity_level,
'care_difficulty': pet.care_difficulty
}])
current_pet_features = assembler.transform(current_pet_data)
prediction = model.transform(current_pet_features).select('prediction').collect()[0]['prediction']
compatibility_score = max(0, min(100, 100 - abs(prediction - pet.age) * 10))
else:
compatibility_score = 75
return round(compatibility_score, 2)
@csrf_exempt
@login_required
def pet_daily_care_recommendation(request):
if request.method == 'POST':
data = json.loads(request.body)
pet_id = data.get('pet_id')
care_date = data.get('care_date', datetime.now().strftime('%Y-%m-%d'))
pet_obj = Pet.objects.get(id=pet_id)
historical_care = pet_obj.petdailycare_set.filter(
care_date__gte=datetime.now() - timedelta(days=30)
).values('feeding_times', 'exercise_duration', 'health_score', 'care_date')
care_data = list(historical_care)
if len(care_data) >= 7:
spark_df = spark.createDataFrame(care_data)
spark_df.createOrReplaceTempView("daily_care")
avg_stats = spark.sql("""
SELECT
AVG(feeding_times) as avg_feeding,
AVG(exercise_duration) as avg_exercise,
AVG(health_score) as avg_health
FROM daily_care
""").collect()[0]
recommended_feeding = max(2, min(4, round(avg_stats['avg_feeding'])))
recommended_exercise = max(30, min(120, round(avg_stats['avg_exercise'])))
health_trend = spark.sql("""
SELECT
(MAX(health_score) - MIN(health_score)) as health_change
FROM daily_care
""").collect()[0]['health_change']
care_quality = "优秀" if health_trend > 10 else "良好" if health_trend > 0 else "需要改善"
else:
recommended_feeding = 3
recommended_exercise = 60
care_quality = "数据不足"
weather_factor = get_weather_adjustment()
final_exercise = round(recommended_exercise * weather_factor)
care_plan = {
'pet_name': pet_obj.name,
'recommended_feeding_times': recommended_feeding,
'recommended_exercise_duration': final_exercise,
'care_quality_assessment': care_quality,
'special_notes': generate_care_notes(pet_obj, care_quality)
}
return JsonResponse({
'status': 'success',
'care_plan': care_plan,
'generated_date': care_date
})
def get_weather_adjustment():
return 0.8
def generate_care_notes(pet, quality):
base_notes = f"根据{pet.name}的品种特性，建议"
if quality == "需要改善":
return base_notes + "增加互动时间，关注营养均衡"
elif quality == "良好":
return base_notes + "保持当前护理频率，适当增加运动"
else:
return base_notes + "继续保持优秀的护理标准"
@csrf_exempt
@login_required
def pet_category_intelligence_analysis(request):
if request.method == 'GET':
all_pets = Pet.objects.all().values('category__name', 'age', 'adoption_status', 'health_status', 'size')
pets_data = list(all_pets)
if len(pets_data) > 10:
spark_df = spark.createDataFrame(pets_data)
spark_df.createOrReplaceTempView("all_pets")
category_stats = spark.sql("""
SELECT
category__name as category,
COUNT(*) as total_count,
AVG(age) as avg_age,
COUNT(CASE WHEN adoption_status='adopted' THEN 1 END) as adopted_count,
COUNT(CASE WHEN health_status='healthy' THEN 1 END) as healthy_count
FROM all_pets
GROUP BY category__name
ORDER BY total_count DESC
""").collect()
adoption_trend = spark.sql("""
SELECT
category__name as category,
(COUNT(CASE WHEN adoption_status='adopted' THEN 1 END) * 100.0 / COUNT(*)) as adoption_rate
FROM all_pets
GROUP BY category__name
HAVING COUNT(*) >= 3
ORDER BY adoption_rate DESC
""").collect()
popular_categories = []
for row in category_stats[:5]:
category_info = {
'name': row['category'],
'total_pets': row['total_count'],
'average_age': round(row['avg_age'], 1),
'adoption_rate': round((row['adopted_count'] / row['total_count']) * 100, 1),
'health_rate': round((row['healthy_count'] / row['total_count']) * 100, 1)
}
popular_categories.append(category_info)
recommendation_engine = generate_category_recommendations(adoption_trend)
else:
popular_categories = []
recommendation_engine = "数据量不足，无法生成智能推荐"
analysis_result = {
'popular_categories': popular_categories,
'recommendation_system': recommendation_engine,
'analysis_timestamp': timezone.now().isoformat(),
'total_categories_analyzed': len(popular_categories)
}
return JsonResponse({
'status': 'success',
'intelligence_analysis': analysis_result
})
def generate_category_recommendations(trend_data):
if len(trend_data) >= 3:
top_category = trend_data[0]
return f"推荐关注{top_category['category']}类宠物，领养成功率达{top_category['adoption_rate']:.1f}%"
else:
return "建议增加宠物数据样本以获得更准确的推荐"
基于SpringBoot的宠物领养一站式服务系统文档展示
💖💖作者：计算机毕业设计小途 💙💙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
