💖💖作者：计算机编程小咖 💙💙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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健康指导平台小程序介绍
健康指导平台小程序是一个基于现代移动互联网技术开发的综合性健康管理系统，采用Java+SpringBoot或Python+Django双技术栈后端架构，结合uni-app跨平台前端框架和MySQL数据库，构建了完整的C/S+B/S混合架构体系。该系统以健康管理为核心，整合了个人健身指导、营养管理、社交互动等多个功能模块，为用户提供全方位的健康生活服务。系统包含用户管理、教练信息管理、健身目标设定、个性化健身计划制定、每日健身任务分配、任务执行记录、计划反馈机制、健身进度跟踪、运动记录统计、多样化运动类型支持、体重变化记录、专业营养建议、饮食记录管理、个性化饮食建议、健身课程展示、课程预约功能、私人教练匹配、私教预约系统、灵活时间段管理、举报处理机制、论坛分类管理、互动社区交流、轮播图动态展示等30余个核心功能模块。通过科学的数据分析和智能化的个人中心管理，用户可以实现健身计划的精准制定、执行过程的实时监控、营养摄入的合理搭配以及健身社区的深度互动，形成了一个集健身指导、营养管理、社交分享于一体的完整生态系统，有效解决了传统健身管理分散化、缺乏专业指导、缺少社交互动等痛点问题。
健康指导平台小程序演示视频
健康指导平台小程序演示图片
健康指导平台小程序代码展示
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;
import com.baomidou.mybatisplus.core.conditions.query.QueryWrapper;
import java.util.*;
@Service
public class HealthPlatformCoreService {
@Autowired
private FitnessGoalMapper fitnessGoalMapper;
@Autowired
private FitnessPlanMapper fitnessPlanMapper;
@Autowired
private ExerciseRecordMapper exerciseRecordMapper;
@Autowired
private NutritionRecordMapper nutritionRecordMapper;
private SparkSession spark = SparkSession.builder().appName("HealthDataAnalysis").config("spark.master", "local[*]").getOrCreate();
public Map<String, Object> generatePersonalizedFitnessPlan(Long userId, FitnessGoal fitnessGoal) {
Map<String, Object> result = new HashMap<>();
QueryWrapper<ExerciseRecord> recordWrapper = new QueryWrapper<>();
recordWrapper.eq("user_id", userId).orderByDesc("create_time").last("LIMIT 30");
List<ExerciseRecord> recentRecords = exerciseRecordMapper.selectList(recordWrapper);
Dataset<Row> exerciseDataset = spark.read().option("header", "true").csv("exercise_data.csv");
exerciseDataset.createOrReplaceTempView("exercise_analysis");
String analysisQuery = "SELECT exercise_type, AVG(duration) as avg_duration, AVG(calories_burned) as avg_calories FROM exercise_analysis WHERE user_fitness_level = '" + fitnessGoal.getFitnessLevel() + "' GROUP BY exercise_type ORDER BY avg_calories DESC";
Dataset<Row> analysisResult = spark.sql(analysisQuery);
List<Row> recommendedExercises = analysisResult.collectAsList();
FitnessPlan fitnessPlan = new FitnessPlan();
fitnessPlan.setUserId(userId);
fitnessPlan.setGoalId(fitnessGoal.getId());
fitnessPlan.setPlanType(fitnessGoal.getGoalType());
fitnessPlan.setTargetWeight(fitnessGoal.getTargetWeight());
fitnessPlan.setDuration(calculatePlanDuration(fitnessGoal));
List<Map<String, Object>> weeklyPlan = new ArrayList<>();
for(int week = 1; week <= fitnessPlan.getDuration(); week++) {
Map<String, Object> weekPlan = new HashMap<>();
weekPlan.put("week", week);
List<Map<String, Object>> dailyExercises = new ArrayList<>();
for(int day = 1; day <= 7; day++) {
Map<String, Object> dayExercise = new HashMap<>();
dayExercise.put("day", day);
if(recommendedExercises.size() > 0) {
Row exercise = recommendedExercises.get((week + day) % recommendedExercises.size());
dayExercise.put("exercise_type", exercise.getString(0));
dayExercise.put("duration", exercise.getDouble(1));
dayExercise.put("target_calories", exercise.getDouble(2));
}
dailyExercises.add(dayExercise);
}
weekPlan.put("exercises", dailyExercises);
weeklyPlan.add(weekPlan);
}
fitnessPlan.setPlanContent(weeklyPlan.toString());
fitnessPlan.setCreateTime(new Date());
fitnessPlanMapper.insert(fitnessPlan);
result.put("success", true);
result.put("planId", fitnessPlan.getId());
result.put("weeklyPlan", weeklyPlan);
result.put("estimatedCaloriesBurn", calculateTotalCalories(weeklyPlan));
return result;
}
public Map<String, Object> analyzeNutritionAndGenerateAdvice(Long userId) {
Map<String, Object> result = new HashMap<>();
QueryWrapper<NutritionRecord> nutritionWrapper = new QueryWrapper<>();
nutritionWrapper.eq("user_id", userId).orderByDesc("record_date").last("LIMIT 14");
List<NutritionRecord> recentNutrition = nutritionRecordMapper.selectList(nutritionWrapper);
Dataset<Row> nutritionDataset = spark.read().option("header", "true").csv("nutrition_data.csv");
nutritionDataset.createOrReplaceTempView("nutrition_analysis");
String nutritionQuery = "SELECT food_category, AVG(calories) as avg_calories, AVG(protein) as avg_protein, AVG(carbs) as avg_carbs, AVG(fat) as avg_fat FROM nutrition_analysis GROUP BY food_category";
Dataset<Row> nutritionAnalysis = spark.sql(nutritionQuery);
List<Row> nutritionStandards = nutritionAnalysis.collectAsList();
double totalCalories = 0, totalProtein = 0, totalCarbs = 0, totalFat = 0;
for(NutritionRecord record : recentNutrition) {
totalCalories += record.getCalories();
totalProtein += record.getProtein();
totalCarbs += record.getCarbs();
totalFat += record.getFat();
}
double avgDailyCalories = totalCalories / 14;
double avgDailyProtein = totalProtein / 14;
double avgDailyCarbs = totalCarbs / 14;
double avgDailyFat = totalFat / 14;
List<String> nutritionAdvice = new ArrayList<>();
if(avgDailyCalories < 1500) {
nutritionAdvice.add("您的日均热量摄入偏低，建议增加健康碳水化合物和优质蛋白质的摄入");
} else if(avgDailyCalories > 2500) {
nutritionAdvice.add("您的日均热量摄入偏高，建议控制高热量食物，增加蔬菜水果比例");
}
if(avgDailyProtein < 50) {
nutritionAdvice.add("蛋白质摄入不足，建议增加瘦肉、鸡蛋、豆类等优质蛋白质来源");
}
if(avgDailyCarbs > 300) {
nutritionAdvice.add("碳水化合物摄入过多，建议选择复合碳水化合物，减少精制糖摄入");
}
Map<String, Object> nutritionSummary = new HashMap<>();
nutritionSummary.put("avgDailyCalories", avgDailyCalories);
nutritionSummary.put("avgDailyProtein", avgDailyProtein);
nutritionSummary.put("avgDailyCarbs", avgDailyCarbs);
nutritionSummary.put("avgDailyFat", avgDailyFat);
nutritionSummary.put("recommendedCalories", calculateRecommendedCalories(userId));
result.put("success", true);
result.put("nutritionSummary", nutritionSummary);
result.put("advice", nutritionAdvice);
result.put("analysisDate", new Date());
return result;
}
public Map<String, Object> generateProgressReport(Long userId) {
Map<String, Object> result = new HashMap<>();
QueryWrapper<ExerciseRecord> exerciseWrapper = new QueryWrapper<>();
exerciseWrapper.eq("user_id", userId).orderByDesc("exercise_date").last("LIMIT 30");
List<ExerciseRecord> exerciseRecords = exerciseRecordMapper.selectList(exerciseWrapper);
Dataset<Row> progressDataset = spark.read().option("header", "true").csv("progress_data.csv");
progressDataset.createOrReplaceTempView("progress_analysis");
String progressQuery = "SELECT DATE_FORMAT(exercise_date, '%Y-%m') as month, SUM(duration) as total_duration, SUM(calories_burned) as total_calories, COUNT(*) as exercise_count FROM progress_analysis WHERE user_id = " + userId + " GROUP BY DATE_FORMAT(exercise_date, '%Y-%m') ORDER BY month DESC LIMIT 6";
Dataset<Row> monthlyProgress = spark.sql(progressQuery);
List<Row> progressData = monthlyProgress.collectAsList();
double totalDuration = 0, totalCalories = 0;
int totalExerciseDays = exerciseRecords.size();
Map<String, Integer> exerciseTypeCount = new HashMap<>();
for(ExerciseRecord record : exerciseRecords) {
totalDuration += record.getDuration();
totalCalories += record.getCaloriesBurned();
exerciseTypeCount.merge(record.getExerciseType(), 1, Integer::sum);
}
String mostFrequentExercise = exerciseTypeCount.entrySet().stream()
.max(Map.Entry.comparingByValue())
.map(Map.Entry::getKey)
.orElse("暂无数据");
double avgDailyDuration = totalDuration / 30;
double avgDailyCalories = totalCalories / 30;
List<Map<String, Object>> monthlyStats = new ArrayList<>();
for(Row row : progressData) {
Map<String, Object> monthStat = new HashMap<>();
monthStat.put("month", row.getString(0));
monthStat.put("totalDuration", row.getDouble(1));
monthStat.put("totalCalories", row.getDouble(2));
monthStat.put("exerciseCount", row.getLong(3));
monthlyStats.add(monthStat);
}
Map<String, Object> progressSummary = new HashMap<>();
progressSummary.put("totalExerciseDays", totalExerciseDays);
progressSummary.put("totalDuration", totalDuration);
progressSummary.put("totalCaloriesBurned", totalCalories);
progressSummary.put("avgDailyDuration", avgDailyDuration);
progressSummary.put("avgDailyCalories", avgDailyCalories);
progressSummary.put("mostFrequentExercise", mostFrequentExercise);
progressSummary.put("monthlyProgress", monthlyStats);
List<String> progressInsights = generateProgressInsights(avgDailyDuration, totalExerciseDays, mostFrequentExercise);
result.put("success", true);
result.put("progressSummary", progressSummary);
result.put("insights", progressInsights);
result.put("reportDate", new Date());
return result;
}
private int calculatePlanDuration(FitnessGoal goal) {
return Math.max(4, Math.min(12, goal.getTargetWeight() != null ?
(int)Math.abs(goal.getTargetWeight() - goal.getCurrentWeight()) : 8));
}
private double calculateTotalCalories(List<Map<String, Object>> weeklyPlan) {
return weeklyPlan.stream().mapToDouble(week ->
((List<Map<String, Object>>)week.get("exercises")).stream()
.mapToDouble(day -> (Double)day.getOrDefault("target_calories", 0.0))
.sum()).sum();
}
private double calculateRecommendedCalories(Long userId) {
return 2000.0;
}
private List<String> generateProgressInsights(double avgDuration, int totalDays, String mostFrequent) {
List<String> insights = new ArrayList<>();
if(avgDuration < 30) {
insights.add("建议增加每日运动时长至30分钟以上，以获得更好的健身效果");
}
if(totalDays < 15) {
insights.add("运动频率偏低，建议保持每周至少3-4次的运动习惯");
}
insights.add("您最常进行的运动是" + mostFrequent + "，可以考虑增加运动类型的多样性");
return insights;
}
}
健康指导平台小程序文档展示
💖💖作者：计算机编程小咖 💙💙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
