以下是一个基于Python的宠物临时寄养双向匹配系统，采用模块化设计，包含核心匹配算法、押金担保和监控功能。系统遵循PEP8规范，添加详细注释，支持新手友好操作。
""" 宠物临时寄养双向匹配系统 核心功能：寄养人发布空间与收费、宠物主人发需求、智能匹配、实时监控、押金担保 技术栈：Python 3.8+（无第三方依赖，纯标准库实现） """
import json from datetime import datetime, timedelta from typing import List, Dict, Optional, Tuple
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数据模型定义（实体类）
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class User: """用户基类""" def init(self, user_id: str, name: str, phone: str, location: str): self.user_id = user_id # 唯一标识 self.name = name self.phone = phone self.location = location # 城市/区域 self.rating = 5.0 # 初始评分（5星制） self.reviews = [] # 评价记录
class Sitter(User): """寄养人：提供寄养服务""" def init(self, user_id: str, name: str, phone: str, location: str, space_desc: str, price: float, max_pets: int, accepted_types: List[str]): super().init(user_id, name, phone, location) self.space_desc = space_desc # 空间描述（如"独立阳台+空调房"） self.price = price # 每日收费（元） self.max_pets = max_pets # 最大寄养数量 self.accepted_types = accepted_types # 接受的宠物类型（如["猫", "小型犬"]） self.available_dates = [] # 可寄养日期范围（(start, end)元组列表）
class PetOwner(User): """宠物主人：发布寄养需求""" def init(self, user_id: str, name: str, phone: str, location: str, pet_type: str, pet_size: str, special_needs: str = ""): super().init(user_id, name, phone, location) self.pet_type = pet_type # 宠物类型（如"布偶猫"） self.pet_size = pet_size # 体型（小型/中型/大型） self.special_needs = special_needs # 特殊需求（如"需喂药"）
class Listing: """寄养信息发布""" def init(self, sitter: Sitter, start_date: str, end_date: str): self.listing_id = f"L{datetime.now().strftime('%Y%m%d%H%M%S')}" self.sitter = sitter self.start_date = start_date # 格式"YYYY-MM-DD" self.end_date = end_date self.posted_time = datetime.now()
class Request: """寄养需求发布""" def init(self, owner: PetOwner, pet_name: str, start_date: str, end_date: str, budget: float, description: str = ""): self.request_id = f"R{datetime.now().strftime('%Y%m%d%H%M%S')}" self.owner = owner self.pet_name = pet_name self.start_date = start_date self.end_date = end_date self.budget = budget # 最高预算（元/天） self.description = description # 额外说明 self.posted_time = datetime.now()
class Match: """匹配记录""" def init(self, listing: Listing, request: Request, deposit: float): self.match_id = f"M{datetime.now().strftime('%Y%m%d%H%M%S')}" self.listing = listing self.request = request self.deposit = deposit # 押金（元） self.status = "pending" # pending/confirmed/completed/cancelled self.monitor_logs = [] # 监控日志（时间, 内容）
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核心系统逻辑
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class PetSittingSystem: def init(self): self.sitters: List[Sitter] = [] # 寄养人列表 self.owners: List[PetOwner] = [] # 宠物主人列表 self.listings: List[Listing] = [] # 寄养信息发布 self.requests: List[Request] = [] # 寄养需求发布 self.matches: List[Match] = [] # 匹配记录 self.deposit_pool = {} # 押金池 {match_id: amount}
def register_sitter(self, name: str, phone: str, location: str,
space_desc: str, price: float, max_pets: int,
accepted_types: List[str]) -> Sitter:
"""注册寄养人"""
sitter_id = f"S{len(self.sitters)+1}"
sitter = Sitter(sitter_id, name, phone, location, space_desc,
price, max_pets, accepted_types)
self.sitters.append(sitter)
return sitter
def register_owner(self, name: str, phone: str, location: str,
pet_type: str, pet_size: str, special_needs: str = "") -> PetOwner:
"""注册宠物主人"""
owner_id = f"O{len(self.owners)+1}"
owner = PetOwner(owner_id, name, phone, location, pet_type,
pet_size, special_needs)
self.owners.append(owner)
return owner
def post_listing(self, sitter: Sitter, start_date: str, end_date: str) -> Listing:
"""寄养人发布寄养信息"""
# 校验日期格式
try:
datetime.strptime(start_date, "%Y-%m-%d")
datetime.strptime(end_date, "%Y-%m-%d")
except ValueError:
raise ValueError("日期格式错误，请使用YYYY-MM-DD")
listing = Listing(sitter, start_date, end_date)
self.listings.append(listing)
return listing
def post_request(self, owner: PetOwner, pet_name: str, start_date: str,
end_date: str, budget: float, description: str = "") -> Request:
"""宠物主人发布寄养需求"""
try:
datetime.strptime(start_date, "%Y-%m-%d")
datetime.strptime(end_date, "%Y-%m-%d")
except ValueError:
raise ValueError("日期格式错误，请使用YYYY-MM-DD")
request = Request(owner, pet_name, start_date, end_date, budget, description)
self.requests.append(request)
return request
def _date_overlap(self, s1: str, e1: str, s2: str, e2: str) -> bool:
"""检查两个日期区间是否重叠"""
s1_dt, e1_dt = datetime.strptime(s1, "%Y-%m-%d"), datetime.strptime(e1, "%Y-%m-%d")
s2_dt, e2_dt = datetime.strptime(s2, "%Y-%m-%d"), datetime.strptime(e2, "%Y-%m-%d")
return s1_dt <= e2_dt and s2_dt <= e1_dt
def match_sitter(self, request: Request) -> List[Tuple[Listing, float]]:
"""核心匹配算法：返回按匹配度排序的寄养信息列表"""
matched = []
for listing in self.listings:
sitter = listing.sitter
# 基本条件过滤
if not (self._date_overlap(listing.start_date, listing.end_date,
request.start_date, request.end_date) and
sitter.location == request.owner.location and
request.pet_type.split()[0] in sitter.accepted_types and # 匹配宠物大类
listing.sitter.price <= request.budget and
sitter.max_pets > 0): # 简化：假设寄养人有空位
continue
# 计算匹配度（综合评分、价格、距离等因素）
price_score = 1 - (listing.sitter.price / request.budget) # 价格越低分越高
rating_score = sitter.rating / 5.0 # 评分越高分越高
match_score = 0.6 * rating_score + 0.4 * price_score # 权重分配
matched.append((listing, round(match_score, 2)))
# 按匹配度降序排序
return sorted(matched, key=lambda x: x[1], reverse=True)
def create_match(self, listing: Listing, request: Request, deposit: float) -> Match:
"""创建匹配记录（含押金担保）"""
if deposit < listing.sitter.price * 2: # 押金至少为2天费用
raise ValueError("押金不足，需至少为2天寄养费用")
match = Match(listing, request, deposit)
self.matches.append(match)
self.deposit_pool[match.match_id] = deposit # 存入押金池
listing.sitter.max_pets -= 1 # 占用寄养名额
return match
def add_monitor_log(self, match: Match, content: str):
"""添加实时监控日志（如宠物状态更新）"""
log_entry = (datetime.now().strftime("%Y-%m-%d %H:%M"), content)
match.monitor_logs.append(log_entry)
def complete_match(self, match: Match, is_successful: bool):
"""完成寄养，处理押金"""
if is_successful:
# 成功：退还押金给寄养人（简化处理，实际应转账）
print(f"押金{self.deposit_pool[match.match_id]}元已退还给寄养人{match.listing.sitter.name}")
else:
# 失败：扣除押金（示例逻辑）
penalty = self.deposit_pool[match.match_id] * 0.3 # 扣30%作为违约金
print(f"因寄养问题，扣除押金{penalty}元，剩余{self.deposit_pool[match.match_id]-penalty}元退还")
del self.deposit_pool[match.match_id]
match.listing.sitter.max_pets += 1 # 释放名额
match.status = "completed"
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示例使用与测试
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def demo(): """系统功能演示""" system = PetSittingSystem()
# 1. 注册用户
sitter1 = system.register_sitter(
name="李阿姨", phone="13800138000", location="北京市朝阳区",
space_desc="独立阳光房，带猫爬架和玩具", price=80, max_pets=2,
accepted_types=["猫", "小型犬"]
)
owner1 = system.register_owner(
name="张先生", phone="13900139000", location="北京市朝阳区",
pet_type="布偶猫", pet_size="小型", special_needs="需每日梳毛"
)
# 2. 发布寄养信息&需求
listing1 = system.post_listing(
sitter=sitter1, start_date="2024-08-01", end_date="2024-08-07"
)
request1 = system.post_request(
owner=owner1, pet_name="雪球", start_date="2024-08-01", end_date="2024-08-07",
budget=100, description="猫咪性格温顺，已绝育"
)
# 3. 智能匹配
matches = system.match_sitter(request1)
if not matches:
print("未找到匹配的寄养人")
return
best_listing, score = matches[0]
print(f"\n找到最佳匹配：{best_listing.sitter.name}，匹配度{score*100}%")
print(f"收费：{best_listing.sitter.price}元/天，空间：{best_listing.sitter.space_desc}")
# 4. 创建匹配（押金200元）
try:
match1 = system.create_match(best_listing, request1, deposit=200)
print(f"\n匹配成功！匹配ID：{match1.match_id}，押金已托管")
except ValueError as e:
print(f"匹配失败：{e}")
return
# 5. 实时监控（寄养期间更新）
system.add_monitor_log(match1, "雪球已适应新环境，进食正常")
system.add_monitor_log(match1, "今日梳毛完成，状态良好")
print("\n监控日志：")
for log in match1.monitor_logs:
print(f"{log[0]}：{log[1]}")
# 6. 完成寄养（模拟成功）
system.complete_match(match1, is_successful=True)
print(f"\n寄养完成，最终状态：{match1.status}")
if name == "main": demo()
系统核心功能说明
- 双向匹配机制
- 寄养人侧：发布空间描述、收费标准、可寄养日期、接受宠物类型
- 需求侧：提交宠物信息（类型/体型）、寄养时间、预算、特殊需求
- 匹配算法：综合考量时间重叠、地理位置、价格预算、宠物兼容性、寄养人评分，按匹配度排序（示例权重：评分60%+价格40%）
- 押金担保体系
- 押金规则：至少预付2天费用（防违约）
- 资金托管：系统暂存押金至 "deposit_pool"
- 结算逻辑：寄养成功后全额退还，失败时按比例扣除违约金（示例扣30%）
- 实时监控功能
- 寄养人可添加图文日志（时间+内容）
- 宠物主人随时查看寄养状态（示例中通过 "add_monitor_log"实现）
- 扩展能力
- 用户评价：通过 "reviews"列表记录，影响后续匹配评分
- 多宠物支持： "max_pets"控制寄养容量， "special_needs"满足个性化需求
- 数据持久化：可添加JSON/SQLite存储（示例中预留 "json"导入）
使用指南
- 环境准备：Python 3.8+，无需额外安装库
- 操作流程：
- 注册寄养人/宠物主人
- 发布寄养信息（寄养人）或需求（宠物主人）
- 系统自动匹配并返回推荐列表
- 确认匹配后支付押金，开始寄养
- 寄养期间更新监控日志
- 结束后结算押金，完成评价
技术优势
- 轻量化设计：纯标准库实现，无外部依赖，适合新手部署
- 模块化架构：实体类与业务逻辑分离，便于扩展（如添加支付接口）
- 鲁棒性处理：日期校验、参数合法性检查、异常捕获（如押金不足提示）
- 透明化匹配：返回匹配度分数，帮助用户理解推荐理由
该系统可有效解决宠物主人短期出行时的寄养难题，通过标准化流程和信用保障机制提升双方信任度。如需扩展Web界面，可基于Flask/Django框架对接本核心逻辑。 关注我，有更多编程干货等着你！
