### 实现一个与园林技术相关的网站项目实训 #### 1. 网站架构概述 为了构建一个与园林技术相关的网站，可以采用 Flask 或 Django 框架作为后端基础。以下是主要模块的功能划分： - **用户管理**：包括注册、登录等功能。 - **园林设计方案展示**：允许用户上传和查看园林设计图纸及相关说明。 - **3D模型生成器**：根据用户的输入自动生成简单的3D模型参数。 - **知识库查询**：提供关于园林建筑施工技术和材料选择的知识检索功能。 --- #### 2. 用户管理系统代码示例 ##### 注册功能 ```python from flask import Flask, request, jsonify import hashlib app = Flask(__name__) users_db = {} # 使用字典模拟数据库 @app.route('/register', methods=['POST']) def register(): data = request.json username = data.get('username') password = data.get('password') if not username or not password: return jsonify({"error": "Missing fields"}), 400 hashed_password = hashlib.sha256(password.encode()).hexdigest() users_db[username] = {"password": hashed_password} return jsonify({"message": "User registered successfully!"}), 201 ``` ##### 登录功能 ```python @app.route('/login', methods=['POST']) def login(): data = request.json username = data.get('username') password = data.get('password') user = users_db.get(username) if not user or user['password'] != hashlib.sha256(password.encode()).hexdigest(): return jsonify({"error": "Invalid credentials"}), 401 return jsonify({"message": "Login successful!"}), 200 ``` --- #### 3. 园林设计方案展示 ##### 文件上传与保存 ```python import os UPLOAD_FOLDER = './uploads' os.makedirs(UPLOAD_FOLDER, exist_ok=True) @app.route('/upload_design', methods=['POST']) def upload_design(): file = request.files.get('file') description = request.form.get('description') if not file: return jsonify({"error": "No file provided"}), 400 filename = os.path.join(UPLOAD_FOLDER, file.filename) file.save(filename) designs_db.append({ "filename": filename, "description": description }) return jsonify({"message": "Design uploaded successfully!"}), 201 ``` --- #### 4. 3D模型生成器 利用自然语言处理技术将文字描述转化为3D模型参数[^2]。 ##### 文本转3D参数函数 ```python import requests import json def text_to_3d_params(description): api_key = "YOUR_API_KEY" url = "https://api.example.com/generate_scene" payload = { "prompt": f"Convert the following scene into 3D parameters: {description}", "model": "scene-generator-v2", "key": api_key } headers = {'Content-Type': 'application/json'} response = requests.post(url, data=json.dumps(payload), headers=headers) try: result = response.json() params = result["params"] return params except Exception as e: return {"error": str(e)} ``` 调用示例： ```python @app.route('/generate_3d_model', methods=['POST']) def generate_3d_model(): description = request.json.get('description') if not description: return jsonify({"error": "Description is required"}), 400 params = text_to_3d_params(description) return jsonify(params), 200 ``` --- #### 5. 知识库查询系统 基于向量检索的问答系统[^4]，可以通过以下方式实现： ##### 初始化向量数据库 ```python from sentence_transformers import SentenceTransformer from sklearn.metrics.pairwise import cosine_similarity model = SentenceTransformer('all-MiniLM-L6-v2') # 加载预训练模型 knowledge_base = [ ("What are space composition principles?", "Space composition involves arranging elements to create harmony."), ("How do you choose materials for garden construction?", "Material selection depends on durability and aesthetics.") ] embeddings = model.encode([q for q, _ in knowledge_base]) ``` ##### 查询逻辑 ```python @app.route('/query_knowledge', methods=['POST']) def query_knowledge(): question = request.json.get('question') if not question: return jsonify({"error": "Question is required"}), 400 query_embedding = model.encode(question).reshape(1, -1) similarities = cosine_similarity(query_embedding, embeddings)[0] most_similar_index = similarities.argmax() _, answer = knowledge_base[most_similar_index] return jsonify({"answer": answer}), 200 ``` --- ###