一、个人简介
💖💖作者：计算机编程果茶熊 💙💙个人简介：曾长期从事计算机专业培训教学，担任过编程老师，同时本人也热爱上课教学，擅长Java、微信小程序、Python、Golang、安卓Android等多个IT方向。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 计算机毕业设计选题 💕💕文末获取源码联系计算机编程果茶熊
二、系统介绍
开发语言：Java+Python 数据库：MySQL 系统架构：B/S 后端框架：SpringBoot(Spring+SpringMVC+Mybatis)+Django 前端：Vue+HTML+CSS+JavaScript+jQuery
蛋糕烘焙分享平台是一个基于SpringBoot+Vue技术栈开发的垂直化分享社区系统。该平台专注于烘焙爱好者的交流互动，提供了完整的用户管理、内容发布、互动交流和商城购物功能模块。用户可以在平台上注册登录，发布自己的烘焙作品和制作心得，通过图文并茂的方式分享烘焙技巧和创意配方。系统支持作品的分类管理，用户可以按照蛋糕类型、难度等级、制作时间等维度进行内容筛选和浏览。平台集成了完善的互动功能，包括点赞、评论、收藏和关注机制，形成了良好的社区氛围。同时系统还提供了烘焙用品商城模块，用户可以直接购买制作工具和原材料，实现了从学习到实践的闭环服务。管理员可以通过后台系统对用户行为、内容质量和商品信息进行统一管理，确保平台内容的健康发展。整个系统采用前后端分离的架构设计，后端使用SpringBoot框架提供RESTful API接口，前端采用Vue.js构建响应式用户界面，数据库使用MySQL存储用户和内容信息，通过Redis实现缓存优化，提升了系统的性能和用户体验。
三、视频解说
四、部分功能展示
五、部分代码展示
import org.apache.spark.sql.SparkSession;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;
import org.springframework.web.multipart.MultipartFile;
import org.springframework.data.domain.Page;
import org.springframework.data.domain.PageRequest;
import org.springframework.transaction.annotation.Transactional;
import java.util.*;
import java.time.LocalDateTime;
@Service
public class BakingShareService {
@Autowired
private BakingWorkRepository bakingWorkRepository;
@Autowired
private UserRepository userRepository;
@Autowired
private RedisTemplate<String, Object> redisTemplate;
@Autowired
private FileUploadService fileUploadService;
private SparkSession spark = SparkSession.builder().appName("BakingDataAnalysis").master("local[*]").getOrCreate();
@Transactional
public Result publishBakingWork(BakingWorkDto workDto, List<MultipartFile> images, Long userId) {
User user = userRepository.findById(userId).orElseThrow(() -> new BusinessException("用户不存在"));
if (user.getStatus() != 1) {
return Result.error("账户状态异常，无法发布作品");
}
BakingWork work = new BakingWork();
work.setTitle(workDto.getTitle());
work.setDescription(workDto.getDescription());
work.setIngredients(workDto.getIngredients());
work.setSteps(workDto.getSteps());
work.setCategoryId(workDto.getCategoryId());
work.setDifficultyLevel(workDto.getDifficultyLevel());
work.setMakeTime(workDto.getMakeTime());
work.setUserId(userId);
work.setCreateTime(LocalDateTime.now());
work.setStatus(0);
work.setLikeCount(0);
work.setCommentCount(0);
work.setViewCount(0);
List<String> imageUrls = new ArrayList<>();
for (MultipartFile image : images) {
String imageUrl = fileUploadService.uploadImage(image, "baking-works");
imageUrls.add(imageUrl);
}
work.setImages(String.join(",", imageUrls));
BakingWork savedWork = bakingWorkRepository.save(work);
String userKey = "user:publish:count:" + userId;
redisTemplate.opsForValue().increment(userKey, 1);
redisTemplate.expire(userKey, Duration.ofDays(1));
String categoryKey = "category:works:" + workDto.getCategoryId();
redisTemplate.opsForZSet().add(categoryKey, savedWork.getId(), System.currentTimeMillis());
return Result.success(savedWork);
}
public Result getUserInteractionData(Long userId, String timeRange) {
List<BakingWork> userWorks = bakingWorkRepository.findByUserId(userId);
Map<String, Object> interactionData = new HashMap<>();
int totalLikes = userWorks.stream().mapToInt(BakingWork::getLikeCount).sum();
int totalComments = userWorks.stream().mapToInt(BakingWork::getCommentCount).sum();
int totalViews = userWorks.stream().mapToInt(BakingWork::getViewCount).sum();
interactionData.put("totalLikes", totalLikes);
interactionData.put("totalComments", totalComments);
interactionData.put("totalViews", totalViews);
interactionData.put("worksCount", userWorks.size());
LocalDateTime startTime = getStartTimeByRange(timeRange);
List<BakingWork> recentWorks = bakingWorkRepository.findByUserIdAndCreateTimeAfter(userId, startTime);
Map<String, Integer> dailyData = new HashMap<>();
for (BakingWork work : recentWorks) {
String dateKey = work.getCreateTime().toLocalDate().toString();
dailyData.put(dateKey, dailyData.getOrDefault(dateKey, 0) + 1);
}
interactionData.put("dailyPublishData", dailyData);
Dataset<Row> workDataset = spark.createDataFrame(userWorks, BakingWork.class);
Dataset<Row> categoryStats = workDataset.groupBy("categoryId").agg(
functions.count("id").alias("count"),
functions.avg("likeCount").alias("avgLikes"),
functions.max("viewCount").alias("maxViews")
);
List<Row> categoryResults = categoryStats.collectAsList();
Map<Long, Map<String, Object>> categoryAnalysis = new HashMap<>();
for (Row row : categoryResults) {
Long categoryId = row.getLong(0);
Map<String, Object> stats = new HashMap<>();
stats.put("count", row.getLong(1));
stats.put("avgLikes", row.getDouble(2));
stats.put("maxViews", row.getLong(3));
categoryAnalysis.put(categoryId, stats);
}
interactionData.put("categoryAnalysis", categoryAnalysis);
return Result.success(interactionData);
}
@Transactional
public Result processProductOrder(Long userId, Long productId, Integer quantity, String deliveryAddress) {
User user = userRepository.findById(userId).orElseThrow(() -> new BusinessException("用户不存在"));
Product product = productRepository.findById(productId).orElseThrow(() -> new BusinessException("商品不存在"));
if (product.getStock() < quantity) {
return Result.error("商品库存不足");
}
String lockKey = "product:stock:lock:" + productId;
Boolean lockAcquired = redisTemplate.opsForValue().setIfAbsent(lockKey, "1", Duration.ofSeconds(10));
if (!lockAcquired) {
return Result.error("系统繁忙，请稍后重试");
}
try {
product = productRepository.findById(productId).get();
if (product.getStock() < quantity) {
return Result.error("商品库存不足");
}
BigDecimal totalAmount = product.getPrice().multiply(new BigDecimal(quantity));
if (user.getBalance().compareTo(totalAmount) < 0) {
return Result.error("账户余额不足");
}
Order order = new Order();
order.setUserId(userId);
order.setProductId(productId);
order.setQuantity(quantity);
order.setUnitPrice(product.getPrice());
order.setTotalAmount(totalAmount);
order.setDeliveryAddress(deliveryAddress);
order.setStatus("PENDING");
order.setCreateTime(LocalDateTime.now());
order.setOrderNumber(generateOrderNumber());
Order savedOrder = orderRepository.save(order);
product.setStock(product.getStock() - quantity);
product.setSalesCount(product.getSalesCount() + quantity);
productRepository.save(product);
user.setBalance(user.getBalance().subtract(totalAmount));
userRepository.save(user);
String userOrderKey = "user:orders:" + userId;
redisTemplate.opsForList().leftPush(userOrderKey, savedOrder.getId());
redisTemplate.expire(userOrderKey, Duration.ofDays(30));
String productSalesKey = "product:sales:count:" + productId;
redisTemplate.opsForValue().increment(productSalesKey, quantity);
Dataset<Row> orderDataset = spark.read().format("jdbc")
.option("url", "jdbc:mysql://localhost:3306/baking_db")
.option("dbtable", "orders")
.option("user", "root")
.option("password", "password")
.load();
Dataset<Row> salesAnalysis = orderDataset
.filter(col("createTime").gt(LocalDateTime.now().minusDays(30)))
.groupBy("productId")
.agg(sum("quantity").alias("totalSales"), avg("totalAmount").alias("avgAmount"));
List<Row> salesResults = salesAnalysis.collectAsList();
for (Row row : salesResults) {
Long pid = row.getLong(0);
Long totalSales = row.getLong(1);
Double avgAmount = row.getDouble(2);
String salesStatsKey = "product:sales:stats:" + pid;
Map<String, Object> stats = new HashMap<>();
stats.put("totalSales", totalSales);
stats.put("avgAmount", avgAmount);
redisTemplate.opsForHash().putAll(salesStatsKey, stats);
redisTemplate.expire(salesStatsKey, Duration.ofHours(6));
}
return Result.success(savedOrder);
} finally {
redisTemplate.delete(lockKey);
}
}
}
六、部分文档展示
七、END
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