企业官网建设流程全解析

我将为您创建一个智能旅行规划系统，基于性价比算法推荐高性价比的酒店和景点组合。

项目结构

travel_planner_ai/

├── main.py # 主程序入口

├── budget_analyzer.py # 预算分析模块

├── hotel_recommender.py # 酒店推荐模块

├── attraction_recommender.py # 景点推荐模块

├── route_optimizer.py # 路线优化模块

├── utils.py # 工具函数

├── data_loader.py # 数据加载模块

├── requirements.txt # 依赖包列表

├── README.md # 项目说明

└── knowledge_cards.md # 核心知识点卡片

1. requirements.txt

pandas>=1.3.0

numpy>=1.21.0

scikit-learn>=1.0.0

geopy>=2.2.0

matplotlib>=3.5.0

seaborn>=0.11.0

requests>=2.26.0

json>=2.0.9

datetime>=4.3

typing-extensions>=4.0.0

2. utils.py - 工具函数模块

import json

import math

from datetime import datetime, timedelta

from typing import List, Dict, Any, Tuple

import pandas as pd

import numpy as np

class TravelUtils:

"""旅行规划工具类"""

@staticmethod

def calculate_distance(lat1: float, lon1: float, lat2: float, lon2: float) -> float:

"""

计算两个地理坐标之间的距离（公里）

使用Haversine公式

Args:

lat1, lon1: 第一个点的经纬度

lat2, lon2: 第二个点的经纬度

Returns:

两点间的距离（公里）

"""

# 将十进制度数转化为弧度

lat1_rad = math.radians(lat1)

lon1_rad = math.radians(lon1)

lat2_rad = math.radians(lat2)

lon2_rad = math.radians(lon2)

# Haversine公式

dlon = lon2_rad - lon1_rad

dlat = lat2_rad - lat1_rad

a = math.sin(dlat/2)**2 + math.cos(lat1_rad) * math.cos(lat2_rad) * math.sin(dlon/2)**2

c = 2 * math.asin(math.sqrt(a))

# 地球半径（公里）

r = 6371.0

return c * r

@staticmethod

def format_currency(amount: float, currency: str = "CNY") -> str:

"""

格式化货币显示

Args:

amount: 金额

currency: 货币符号

Returns:

格式化后的货币字符串

"""

if currency == "CNY":

return f"¥{amount:.2f}"

elif currency == "USD":

return f"${amount:.2f}"

else:

return f"{currency}{amount:.2f}"

@staticmethod

def validate_budget(budget: float, min_budget: float = 500) -> Tuple[bool, str]:

"""

验证预算合理性

Args:

budget: 用户输入的预算

min_budget: 最低预算要求

Returns:

(是否有效, 错误信息)

"""

if budget <= 0:

return False, "预算必须大于0"

if budget < min_budget:

return False, f"预算过低，建议至少{min_budget}元以确保基本旅行体验"

return True, ""

@staticmethod

def validate_duration(days: int) -> Tuple[bool, str]:

"""

验证旅行天数合理性

Args:

days: 旅行天数

Returns:

(是否有效, 错误信息)

"""

if days <= 0:

return False, "旅行天数必须大于0"

if days > 30:

return False, "单次旅行不建议超过30天"

return True, ""

@staticmethod

def calculate_daily_budget(total_budget: float, days: int,

accommodation_ratio: float = 0.4,

attraction_ratio: float = 0.3,

food_ratio: float = 0.2,

transport_ratio: float = 0.1) -> Dict[str, float]:

"""

计算各项支出的日均预算

Args:

total_budget: 总预算

days: 旅行天数

accommodation_ratio: 住宿预算占比

attraction_ratio: 景点预算占比

food_ratio: 餐饮预算占比

transport_ratio: 交通预算占比

Returns:

各项日均预算字典

"""

daily_total = total_budget / days

return {

"accommodation": daily_total * accommodation_ratio,

"attraction": daily_total * attraction_ratio,

"food": daily_total * food_ratio,

"transport": daily_total * transport_ratio,

"total": daily_total

}

@staticmethod

def get_season_info(date: datetime = None) -> Dict[str, Any]:

"""

获取指定日期的季节信息

Args:

date: 指定日期，默认为今天

Returns:

季节信息字典

"""

if date is None:

date = datetime.now()

month = date.month

if month in [3, 4, 5]:

season = "春季"

weather = "温和"

crowd_level = "中等"

elif month in [6, 7, 8]:

season = "夏季"

weather = "炎热"

crowd_level = "较高"

elif month in [9, 10, 11]:

season = "秋季"

weather = "凉爽"

crowd_level = "中等"

else:

season = "冬季"

weather = "寒冷"

crowd_level = "较低"

return {

"season": season,

"weather": weather,

"crowd_level": crowd_level,

"month": month

}

@staticmethod

def save_travel_plan(plan: Dict[str, Any], filename: str = None) -> str:

"""

保存旅行计划到JSON文件

Args:

plan: 旅行计划字典

filename: 文件名，默认为自动生成

Returns:

保存的文件路径

"""

if filename is None:

timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")

filename = f"travel_plan_{timestamp}.json"

# 确保目录存在

import os

os.makedirs("plans", exist_ok=True)

filepath = os.path.join("plans", filename)

# 添加保存时间

plan["saved_at"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

with open(filepath, 'w', encoding='utf-8') as f:

json.dump(plan, f, ensure_ascii=False, indent=2)

return filepath

@staticmethod

def load_sample_data() -> Dict[str, Any]:

"""

加载示例数据

Returns:

包含示例酒店和景点数据的字典

"""

return {

"hotels": [

{

"id": 1,

"name": "如家快捷酒店",

"city": "北京",

"price": 180,

"rating": 4.2,

"location": [39.9042, 116.4074],

"amenities": ["免费WiFi", "24小时前台", "空调"],

"distance_to_center": 5.2

},

{

"id": 2,

"name": "汉庭酒店",

"city": "北京",

"price": 220,

"rating": 4.4,

"location": [39.9163, 116.3972],

"amenities": ["免费WiFi", "早餐", "健身房"],

"distance_to_center": 3.8

},

{

"id": 3,

"name": "7天连锁酒店",

"city": "上海",

"price": 160,

"rating": 4.0,

"location": [31.2304, 121.4737],

"amenities": ["免费WiFi", "24小时前台"],

"distance_to_center": 4.5

},

{

"id": 4,

"name": "锦江之星",

"city": "上海",

"price": 190,

"rating": 4.3,

"location": [31.2222, 121.4581],

"amenities": ["免费WiFi", "早餐", "商务中心"],

"distance_to_center": 2.1

}

],

"attractions": [

{

"id": 1,

"name": "故宫博物院",

"city": "北京",

"ticket_price": 60,

"rating": 4.8,

"location": [39.9163, 116.3972],

"category": "历史文化",

"visit_duration": 4

},

{

"id": 2,

"name": "天安门广场",

"city": "北京",

"ticket_price": 0,

"rating": 4.6,

"location": [39.9031, 116.3974],

"category": "历史地标",

"visit_duration": 2

},

{

"id": 3,

"name": "外滩",

"city": "上海",

"ticket_price": 0,

"rating": 4.7,

"location": [31.2397, 121.4916],

"category": "城市风光",

"visit_duration": 3

},

{

"id": 4,

"name": "东方明珠",

"city": "上海",

"ticket_price": 180,

"rating": 4.4,

"location": [31.2397, 121.4999],

"category": "现代建筑",

"visit_duration": 2

}

]

}

class DataValidator:

"""数据验证器类"""

@staticmethod

def validate_hotel_data(hotel: Dict[str, Any]) -> Tuple[bool, List[str]]:

"""

验证酒店数据完整性

Args:

hotel: 酒店数据字典

Returns:

(是否有效, 错误信息列表)

"""

errors = []

required_fields = ["name", "city", "price", "rating", "location"]

for field in required_fields:

if field not in hotel:

errors.append(f"缺少必需字段: {field}")

if "price" in hotel and hotel["price"] < 0:

errors.append("价格不能为负数")

if "rating" in hotel and (hotel["rating"] < 0 or hotel["rating"] > 5):

errors.append("评分必须在0-5之间")

if "location" in hotel and len(hotel["location"]) != 2:

errors.append("位置必须是[纬度, 经度]格式")

return len(errors) == 0, errors

@staticmethod

def validate_attraction_data(attraction: Dict[str, Any]) -> Tuple[bool, List[str]]:

"""

验证景点数据完整性

Args:

attraction: 景点数据字典

Returns:

(是否有效, 错误信息列表)

"""

errors = []

required_fields = ["name", "city", "ticket_price", "rating", "location"]

for field in required_fields:

if field not in attraction:

errors.append(f"缺少必需字段: {field}")

if "ticket_price" in attraction and attraction["ticket_price"] < 0:

errors.append("门票价格不能为负数")

if "rating" in attraction and (attraction["rating"] < 0 or attraction["rating"] > 5):

errors.append("评分必须在0-5之间")

if "visit_duration" in attraction and attraction["visit_duration"] <= 0:

errors.append("游览时长必须大于0")

return len(errors) == 0, errors

3. data_loader.py - 数据加载模块

import pandas as pd

import json

import os

from typing import Dict, List, Any, Optional

from utils import TravelUtils, DataValidator

class DataLoader:

"""数据加载器类"""

def __init__(self, data_dir: str = "data"):

"""

初始化数据加载器

Args:

data_dir: 数据目录路径

"""

self.data_dir = data_dir

self.hotels_data = []

self.attractions_data = []

self.cities_data = {}

# 确保数据目录存在

os.makedirs(data_dir, exist_ok=True)

def load_hotels_from_csv(self, filename: str) -> List[Dict[str, Any]]:

"""

从CSV文件加载酒店数据

Args:

filename: CSV文件名

Returns:

酒店数据列表

"""

filepath = os.path.join(self.data_dir, filename)

try:

df = pd.read_csv(filepath, encoding='utf-8')

hotels = []

for _, row in df.iterrows():

hotel = {

"id": int(row.get("id", len(hotels) + 1)),

"name": row["name"],

"city": row["city"],

"price": float(row["price"]),

"rating": float(row["rating"]),

"location": [float(row["latitude"]), float(row["longitude"])],

"amenities": row.get("amenities", "").split(","),

"distance_to_center": float(row.get("distance_to_center", 0))

}

# 验证数据

is_valid, errors = DataValidator.validate_hotel_data(hotel)

if is_valid:

hotels.append(hotel)

else:

print(f"跳过无效酒店数据: {hotel['name']}, 错误: {errors}")

self.hotels_data = hotels

print(f"成功加载 {len(hotels)} 条酒店数据")

return hotels

except FileNotFoundError:

print(f"文件未找到: {filepath}")

return []

except Exception as e:

print(f"加载酒店数据时出错: {e}")

return []

def load_attractions_from_csv(self, filename: str) -> List[Dict[str, Any]]:

"""

从CSV文件加载景点数据

Args:

filename: CSV文件名

Returns:

景点数据列表

"""

filepath = os.path.join(self.data_dir, filename)

try:

df = pd.read_csv(filepath, encoding='utf-8')

attractions = []

for _, row in df.iterrows():

attraction = {

"id": int(row.get("id", len(attractions) + 1)),

"name": row["name"],

"city": row["city"],

"ticket_price": float(row["ticket_price"]),

"rating": float(row["rating"]),

"location": [float(row["latitude"]), float(row["longitude"])],

"category": row.get("category", "其他"),

"visit_duration": int(row.get("visit_duration", 2))

}

# 验证数据

is_valid, errors = DataValidator.validate_attraction_data(attraction)

if is_valid:

attractions.append(attraction)

else:

print(f"跳过无效景点数据: {attraction['name']}, 错误: {errors}")

self.attractions_data = attractions

print(f"成功加载 {len(attractions)} 条景点数据")

return attractions

except FileNotFoundError:

print(f"文件未找到: {filepath}")

return []

except Exception as e:

print(f"加载景点数据时出错: {e}")

return []

def load_data_from_json(self, filename: str) -> Dict[str, Any]:

"""

从JSON文件加载数据

Args:

filename: JSON文件名

Returns:

数据字典

"""

filepath = os.path.join(self.data_dir, filename)

try:

with open(filepath, 'r', encoding='utf-8') as f:

data = json.load(f)

if "hotels" in data:

self.hotels_data = data["hotels"]

if "attractions" in data:

self.attractions_data = data["attractions"]

print(f"成功从JSON文件加载数据")

return data

except FileNotFoundError:

print(f"文件未找到: {filepath}")

return {}

except Exception as e:

print(f"加载JSON数据时出错: {e}")

return {}

def save_data_to_json(self, data: Dict[str, Any], filename: str) -> bool:

"""

保存数据到JSON文件

Args:

data: 要保存的数据

filename: 文件名

Returns:

是否保存成功

"""

filepath = os.path.join(self.data_dir, filename)

try:

with open(filepath, 'w', encoding='utf-8') as f:

json.dump(data, f, ensure_ascii=False, indent=2)

print(f"数据已保存至: {filepath}")

return True

except Exception as e:

print(f"保存数据时出错: {e}")

return False

def get_hotels_by_city(self, city: str) -> List[Dict[str, Any]]:

"""

根据城市筛选酒店

Args:

city: 城市名称

Returns:

该城市的酒店列表

"""

return [hotel for hotel in self.hotels_data if hotel["city"] == city]

def get_attractions_by_city(self, city: str) -> List[Dict[str, Any]]:

"""

根据城市筛选景点

Args:

city: 城市名称

Returns:

该城市的景点列表

"""

return [attraction for attraction in self.attractions_data if attraction["city"] == city]

def create_sample_csv_files(self):

"""创建示例CSV文件"""

# 示例酒店数据

hotels_df = pd.DataFrame([

{

"id": 1, "name": "如家快捷酒店", "city": "北京", "price": 180,

"rating": 4.2, "latitude": 39.9042, "longitude": 116.4074,

"amenities": "免费WiFi,24小时前台,空调", "distance_to_center": 5.2

},

{

"id": 2, "name": "汉庭酒店", "city": "北京", "price": 220,

"rating": 4.4, "latitude": 39.9163, "longitude": 116.3972,

"amenities": "免费WiFi,早餐,健身房", "distance_to_center": 3.8

},

{

"id": 3, "name": "7天连锁酒店", "city": "上海", "price": 160,

"rating": 4.0, "latitude": 31.2304, "longitude": 121.4737,

"amenities": "免费WiFi,24小时前台", "distance_to_center": 4.5

},

{

"id": 4, "name": "锦江之星", "city": "上海", "price": 190,

"rating": 4.3, "latitude": 31.2222, "longitude": 121.4581,

"amenities": "免费WiFi,早餐,商务中心", "distance_to_center": 2.1

},

{

"id": 5, "name": "维也纳酒店", "city": "广州", "price": 280,

"rating": 4.6, "latitude": 23.1291, "longitude": 113.2644,

"amenities": "免费WiFi,早餐,健身房,游泳池", "distance_to_center": 6.8

}

])

# 示例景点数据

attractions_df = pd.DataFrame([

{

"id": 1, "name": "故宫博物院", "city": "北京", "ticket_price": 60,

"rating": 4.8, "latitude": 39.9163, "longitude": 116.3972,

"category": "历史文化", "visit_duration": 4

},

{

"id": 2, "name": "天安门广场", "city": "北京", "ticket_price": 0,

"rating": 4.6, "latitude": 39.9031, "longitude": 116.3974,

"category": "历史地标", "visit_duration": 2

},

{

"id": 3, "name": "颐和园", "city": "北京", "ticket_price": 30,

"rating": 4.7, "latitude": 39.9999, "longitude": 116.2750,

"category": "皇家园林", "visit_duration": 5

},

{

"id": 4, "name": "外滩", "city": "上海", "ticket_price": 0,

"rating": 4.7, "latitude": 31.2397, "longitude": 121.4916,

"category": "城市风光", "visit_duration": 3

},

{

"id": 5, "name": "东方明珠", "city": "上海", "ticket_price": 180,

"rating": 4.4, "latitude": 31.2397, "longitude": 121.4999,

"category": "现代建筑", "visit_duration": 2

},

{

"id": 6, "name": "豫园", "city": "上海", "ticket_price": 40,

"rating": 4.3, "latitude": 31.2278, "longitude": 121.4926,

"category": "古典园林", "visit_duration": 2

}

])

# 保存CSV文件

hotels_df.to_csv(os.path.join(self.data_dir, "hotels.csv"), index=False, encoding='utf-8')

attractions_df.to_csv(os.path.join(self.data_dir, "attractions.csv"), index=False, encoding='utf-8')

print("示例CSV文件已创建在 data/ 目录下")

# 示例使用

if __name__ == "__main__":

loader = DataLoader()

loader.create_sample_csv_files()

# 加载数据

hotels = loader.load_hotels_from_csv("hotels.csv")

attractions = loader.load_attractions_from_csv("attractions.csv")

print(f"加载了 {len(hotels)} 家酒店和 {len(attractions)} 个景点")

4. budget_analyzer.py - 预算分析模块

import numpy as np

from typing import Dict, List, Tuple

from utils import Trav

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