精彩專欄推薦訂閱：在 下方專欄👇🏻👇🏻👇🏻👇🏻

💖🔥作者主頁：計算機畢設木哥🔥 💖

一、項目介紹

基于Django的飲食計劃推薦與交流分享平臺是一個專門為用戶提供個性化飲食建議和營養管理的綜合性Web應用系統。該平臺采用Python作為主要開發語言，結合Django框架構建穩定的后端服務架構，前端使用Vue.js配合ElementUI組件庫打造現代化的用戶交互界面，數據存儲依托MySQL數據庫確保信息的可靠性和一致性。系統設計了管理員和普通用戶兩種角色，管理員負責維護食物信息庫、管理飲食計劃模板、監控用戶健康數據、發布營養建議內容、設置健康目標參數、維護健康食譜資源以及管理系統排行榜功能，還具備食物類別的智能預測能力。普通用戶可以完成注冊登錄、瀏覽豐富的食物營養信息、獲取個性化飲食計劃推薦、查閱專業健康食譜、追蹤個人健康數據變化、接受系統營養建議以及設定個人健康目標。平臺通過科學的算法分析用戶的健康狀況和飲食偏好，為每位用戶量身定制適合的飲食方案，同時提供社交分享功能促進用戶間的經驗交流，構建了一個集個性化推薦、營養管理、社區互動于一體的現代化飲食健康管理平臺。

選題背景
隨著現代生活節奏的加快和工作壓力的增大，人們對健康飲食的關注度日益提升，但普遍缺乏科學的營養知識和個性化的飲食指導。傳統的飲食管理方式往往依賴于標準化的營養表和通用的飲食建議，無法針對個體差異提供精準的營養方案。市面上雖然存在一些飲食管理應用，但大多功能單一，要么只提供基礎的熱量計算，要么僅限于食譜展示，缺乏系統性的個性化推薦機制和用戶交流互動功能。同時，不同年齡段、體質狀況、運動習慣的人群對營養需求存在顯著差異，而現有的飲食管理工具往往采用"一刀切"的方式，難以滿足用戶的個性化需求。另外，缺乏專業營養師指導的普通用戶在面對海量的飲食信息時容易迷失方向，需要一個智能化的平臺來整合營養知識、分析個人狀況并提供科學的飲食建議，這為開發一個綜合性的飲食計劃推薦與交流平臺提供了現實需求基礎。

選題意義
開發基于Django的飲食計劃推薦與交流分享平臺具有多方面的實際意義。從技術實現角度來看，該項目綜合運用了Python編程、Django Web框架、Vue.js前端技術、MySQL數據庫管理等多項技術，能夠幫助開發者深入理解現代Web應用的完整開發流程，掌握前后端分離的系統架構設計方法，提升數據庫設計和優化能力，為今后從事相關技術工作奠定扎實基礎。從用戶價值層面分析，平臺能夠為普通用戶提供科學的營養指導和個性化的飲食建議，幫助用戶建立健康的飲食習慣，在一定程度上改善生活質量。系統的交流分享功能能夠促進用戶間的經驗交流，形成良性的健康飲食社區氛圍。從社會意義角度考慮，該平臺可以普及營養健康知識，提高公眾的健康意識，為推動全民健康生活方式的形成貢獻微薄力量。雖然作為一個畢業設計項目，其影響范圍相對有限，但通過實際的系統開發和功能實現，能夠驗證技術方案的可行性，為后續的功能擴展和商業化應用提供參考依據。

二、開發環境

開發語言：Python
數據庫：MySQL
系統架構：B/S
后端框架：Django
前端：Vue+ElementUI
開發工具：PyCharm

三、視頻展示

四、項目展示

登錄模塊：

首頁模塊：

管理模塊：

五、代碼展示

from pyspark.sql import SparkSession
from django.shortcuts import render, get_object_or_404
from django.http import JsonResponse
from django.contrib.auth.decorators import login_required
from django.views.decorators.csrf import csrf_exempt
from django.db.models import Q, Avg
from .models import FoodInfo, DietPlan, UserHealthData, NutritionSuggestion
import json
import numpy as np
from sklearn.cluster import KMeans
from datetime import datetime, timedeltaspark = SparkSession.builder.appName("DietRecommendation").getOrCreate()@login_required
@csrf_exempt
def generate_personalized_diet_plan(request):if request.method == 'POST':user = request.userdata = json.loads(request.body)user_weight = data.get('weight', 70)user_height = data.get('height', 170)activity_level = data.get('activity_level', 'moderate')health_goal = data.get('health_goal', 'maintain')dietary_restrictions = data.get('dietary_restrictions', [])bmi = user_weight / ((user_height / 100) ** 2)if bmi < 18.5:calorie_multiplier = 1.2elif bmi > 25:calorie_multiplier = 0.8else:calorie_multiplier = 1.0base_calorie = 1800 if user.profile.gender == 'female' else 2200if activity_level == 'low':activity_multiplier = 0.9elif activity_level == 'high':activity_multiplier = 1.3else:activity_multiplier = 1.0target_calories = int(base_calorie * calorie_multiplier * activity_multiplier)available_foods = FoodInfo.objects.exclude(name__in=dietary_restrictions)breakfast_foods = available_foods.filter(meal_type='breakfast')lunch_foods = available_foods.filter(meal_type='lunch')dinner_foods = available_foods.filter(meal_type='dinner')snack_foods = available_foods.filter(meal_type='snack')breakfast_selection = breakfast_foods.order_by('?')[:3]lunch_selection = lunch_foods.order_by('?')[:4]dinner_selection = dinner_foods.order_by('?')[:4]snack_selection = snack_foods.order_by('?')[:2]total_protein = sum([food.protein_per_100g * 1.5 for food in breakfast_selection])total_protein += sum([food.protein_per_100g * 2.0 for food in lunch_selection])total_protein += sum([food.protein_per_100g * 2.0 for food in dinner_selection])total_carbs = sum([food.carb_per_100g * 1.5 for food in breakfast_selection])total_carbs += sum([food.carb_per_100g * 2.0 for food in lunch_selection])total_carbs += sum([food.carb_per_100g * 2.0 for food in dinner_selection])if health_goal == 'lose_weight' and total_calories > target_calories:portion_adjustment = target_calories / total_calories * 0.9elif health_goal == 'gain_weight' and total_calories < target_calories:portion_adjustment = target_calories / total_calories * 1.1else:portion_adjustment = 1.0diet_plan = DietPlan.objects.create(user=user,target_calories=target_calories,breakfast_foods=','.join([str(f.id) for f in breakfast_selection]),lunch_foods=','.join([str(f.id) for f in lunch_selection]),dinner_foods=','.join([str(f.id) for f in dinner_selection]),snack_foods=','.join([str(f.id) for f in snack_selection]),portion_adjustment=portion_adjustment,created_date=datetime.now())return JsonResponse({'status': 'success','diet_plan_id': diet_plan.id,'target_calories': target_calories,'breakfast_items': len(breakfast_selection),'lunch_items': len(lunch_selection),'dinner_items': len(dinner_selection),'estimated_protein': round(total_protein * portion_adjustment, 1),'estimated_carbs': round(total_carbs * portion_adjustment, 1)})@login_required
@csrf_exempt
def analyze_user_health_data(request):if request.method == 'POST':user = request.userdata = json.loads(request.body)weight = data.get('weight')blood_pressure_systolic = data.get('bp_systolic')blood_pressure_diastolic = data.get('bp_diastolic')heart_rate = data.get('heart_rate')sleep_hours = data.get('sleep_hours')exercise_minutes = data.get('exercise_minutes')water_intake = data.get('water_intake', 2000)recent_data = UserHealthData.objects.filter(user=user,record_date__gte=datetime.now() - timedelta(days=30)).order_by('-record_date')health_score = 100if blood_pressure_systolic > 140 or blood_pressure_diastolic > 90:health_score -= 15elif blood_pressure_systolic > 130 or blood_pressure_diastolic > 85:health_score -= 8if heart_rate > 100 or heart_rate < 60:health_score -= 10if sleep_hours < 7:health_score -= (7 - sleep_hours) * 5elif sleep_hours > 9:health_score -= (sleep_hours - 9) * 3if exercise_minutes < 150:health_score -= (150 - exercise_minutes) / 10if water_intake < 1500:health_score -= (1500 - water_intake) / 100elif water_intake > 3000:health_score -= (water_intake - 3000) / 200if recent_data.count() >= 5:weight_trend = []for record in recent_data[:5]:weight_trend.append(record.weight)weight_variance = np.var(weight_trend)if weight_variance > 4:health_score -= 5health_data = UserHealthData.objects.create(user=user,weight=weight,blood_pressure_systolic=blood_pressure_systolic,blood_pressure_diastolic=blood_pressure_diastolic,heart_rate=heart_rate,sleep_hours=sleep_hours,exercise_minutes=exercise_minutes,water_intake=water_intake,health_score=health_score,record_date=datetime.now())risk_factors = []if health_score < 70:risk_factors.append('整體健康狀況需要關注')if blood_pressure_systolic > 130:risk_factors.append('血壓偏高，建議減少鈉鹽攝入')if sleep_hours < 7:risk_factors.append('睡眠不足，影響新陳代謝')if exercise_minutes < 100:risk_factors.append('運動量不足，建議增加有氧運動')return JsonResponse({'status': 'success','health_score': health_score,'risk_factors': risk_factors,'trend_analysis': '數據已記錄，建議持續監測','recommendations': generate_health_recommendations(health_score, recent_data)})@login_required
def intelligent_food_category_prediction(request):user = request.useruser_preferences = DietPlan.objects.filter(user=user).values_list('breakfast_foods', 'lunch_foods', 'dinner_foods')all_food_preferences = []for pref in user_preferences:breakfast_ids = pref[0].split(',') if pref[0] else []lunch_ids = pref[1].split(',') if pref[1] else []dinner_ids = pref[2].split(',') if pref[2] else []all_food_preferences.extend(breakfast_ids + lunch_ids + dinner_ids)preferred_food_objects = FoodInfo.objects.filter(id__in=all_food_preferences)category_features = []for food in preferred_food_objects:features = [food.calories_per_100g / 100,food.protein_per_100g / 50,food.carb_per_100g / 100,food.fat_per_100g / 50,1 if food.is_vegetarian else 0,1 if food.is_low_sodium else 0,1 if food.is_high_fiber else 0]category_features.append(features)if len(category_features) >= 3:kmeans = KMeans(n_clusters=min(3, len(category_features)), random_state=42)clusters = kmeans.fit_predict(category_features)cluster_centers = kmeans.cluster_centers_all_foods = FoodInfo.objects.all()recommendations = []for food in all_foods:if food.id not in [int(x) for x in all_food_preferences if x.isdigit()]:food_features = np.array([food.calories_per_100g / 100,food.protein_per_100g / 50,food.carb_per_100g / 100,food.fat_per_100g / 50,1 if food.is_vegetarian else 0,1 if food.is_low_sodium else 0,1 if food.is_high_fiber else 0])distances = [np.linalg.norm(food_features - center) for center in cluster_centers]min_distance = min(distances)if min_distance < 0.5:similarity_score = (1 - min_distance) * 100recommendations.append({'food_id': food.id,'food_name': food.name,'category': food.category,'similarity_score': round(similarity_score, 2),'predicted_preference': 'high' if similarity_score > 80 else 'medium'})recommendations.sort(key=lambda x: x['similarity_score'], reverse=True)top_recommendations = recommendations[:10]else:popular_foods = FoodInfo.objects.annotate(avg_rating=Avg('dietplan__user__healthdata__health_score')).order_by('-avg_rating')[:10]top_recommendations = []for food in popular_foods:top_recommendations.append({'food_id': food.id,'food_name': food.name,'category': food.category,'similarity_score': 75.0,'predicted_preference': 'medium'})return JsonResponse({'status': 'success','total_analyzed_foods': len(category_features),'recommendations': top_recommendations,'prediction_accuracy': 'high' if len(category_features) >= 10 else 'medium','algorithm_used': 'k-means_clustering'})

六、項目文檔展示

七、總結

基于Django的飲食計劃推薦與交流分享平臺項目成功地將現代Web開發技術與健康管理需求相結合，構建了一個功能完善的飲食健康管理系統。項目采用Python語言作為核心開發工具，通過Django框架搭建了穩定高效的后端服務架構，配合Vue.js和ElementUI打造了用戶友好的前端交互界面，MySQL數據庫確保了數據存儲的可靠性和查詢效率。

該平臺的核心價值在于為用戶提供個性化的飲食推薦服務，通過智能算法分析用戶的身體狀況、健康目標和飲食偏好，生成科學合理的飲食計劃。系統集成了用戶管理、食物信息維護、健康數據追蹤、營養建議推送等多項功能模塊，形成了完整的健康管理生態。特別是引入機器學習技術實現的食物類別預測功能，能夠根據用戶歷史選擇智能推薦相似食物，提升了用戶體驗的個性化程度。

從技術實現層面來看，項目充分體現了現代軟件工程的開發理念，采用前后端分離的架構模式，確保了系統的可維護性和擴展性。通過實際的開發實踐，不僅掌握了Web應用開發的核心技能，也深入理解了數據處理、算法應用和用戶交互設計的重要性，為今后的技術發展奠定了堅實基礎。

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💖🔥作者主頁：計算機畢設木哥🔥 💖