💖💖作者：计算机毕业设计江挽 💙💙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
基于微信小程序的考研资源共享平台介绍
基于微信小程序的考研资源共享平台是一个集资源共享、学习交流、备考指导于一体的综合性教育服务系统。该平台采用SpringBoot框架构建后端服务，结合uni-app开发的微信小程序前端，为考研学子提供便捷的移动端学习体验。系统实现了用户分类管理，支持学生和教师两种角色，学生可以浏览和下载考研资料、参与学习讨论、制定个人学习计划、提交作业并查看成绩，教师则可以发布课程信息、管理选课情况、布置和批改作业。平台设置了丰富的功能模块，包括资料分类展示、考研资源库、备考专区、学习交流论坛等，通过MySQL数据库存储用户数据和资源信息。系统还提供了报名活动功能，让用户参与各类考研相关活动，并建立了举报机制维护平台秩序。整个平台基于C/S和B/S混合架构，确保了系统的稳定性和扩展性，为广大考研群体搭建了一个高效、便民的学习资源共享环境。
基于微信小程序的考研资源共享平台演示视频
基于微信小程序的考研资源共享平台演示图片
基于微信小程序的考研资源共享平台代码展示
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;
@Service
public class ExamResourceService {
private SparkSession spark = SparkSession.builder()
.appName("ExamResourceAnalysis")
.master("local[*]")
.getOrCreate();
@Autowired
private ResourceMapper resourceMapper;
public List<ExamResource> getResourcesByCategory(String category) {
List<ExamResource> resources = resourceMapper.findByCategory(category);
Dataset<Row> resourceDF = spark.createDataFrame(resources, ExamResource.class);
resourceDF.createOrReplaceTempView("resources");
Dataset<Row> filteredData = spark.sql("SELECT * FROM resources WHERE downloadCount > 10 ORDER BY uploadTime DESC");
List<Row> results = filteredData.collectAsList();
List<ExamResource> filteredResources = new ArrayList<>();
for (Row row : results) {
ExamResource resource = new ExamResource();
resource.setId(row.getAs("id"));
resource.setTitle(row.getAs("title"));
resource.setCategory(row.getAs("category"));
resource.setDownloadCount(row.getAs("downloadCount"));
resource.setUploadTime(row.getAs("uploadTime"));
filteredResources.add(resource);
}
return filteredResources;
}
public Map<String, Object> getResourceStatistics() {
List<ExamResource> allResources = resourceMapper.findAll();
Dataset<Row> resourceDF = spark.createDataFrame(allResources, ExamResource.class);
resourceDF.createOrReplaceTempView("all_resources");
Dataset<Row> statsData = spark.sql("SELECT category, COUNT(*) as count, AVG(downloadCount) as avgDownloads FROM all_resources GROUP BY category");
List<Row> statsList = statsData.collectAsList();
Map<String, Object> statistics = new HashMap<>();
for (Row row : statsList) {
Map<String, Object> categoryStats = new HashMap<>();
categoryStats.put("count", row.getAs("count"));
categoryStats.put("avgDownloads", row.getAs("avgDownloads"));
statistics.put(row.getAs("category").toString(), categoryStats);
}
return statistics;
}
public boolean downloadResource(Long resourceId, Long userId) {
ExamResource resource = resourceMapper.findById(resourceId);
if (resource == null) {
return false;
}
DownloadRecord record = new DownloadRecord();
record.setResourceId(resourceId);
record.setUserId(userId);
record.setDownloadTime(new Date());
downloadRecordMapper.insert(record);
resource.setDownloadCount(resource.getDownloadCount() + 1);
resourceMapper.update(resource);
Dataset<Row> downloadDF = spark.createDataFrame(Arrays.asList(record), DownloadRecord.class);
downloadDF.createOrReplaceTempView("downloads");
Dataset<Row> userDownloads = spark.sql("SELECT COUNT(*) as totalDownloads FROM downloads WHERE userId = " + userId);
Long totalCount = userDownloads.first().getAs("totalDownloads");
if (totalCount > 50) {
UserLevel level = userLevelMapper.findByUserId(userId);
level.setLevel("VIP");
userLevelMapper.update(level);
}
return true;
}
public List<ForumPost> getForumPostsByTopic(String topic) {
List<ForumPost> posts = forumMapper.findByTopic(topic);
Dataset<Row> postDF = spark.createDataFrame(posts, ForumPost.class);
postDF.createOrReplaceTempView("forum_posts");
Dataset<Row> hotPosts = spark.sql("SELECT * FROM forum_posts WHERE replyCount > 5 AND likeCount > 10 ORDER BY createTime DESC LIMIT 20");
List<Row> hotPostRows = hotPosts.collectAsList();
List<ForumPost> result = new ArrayList<>();
for (Row row : hotPostRows) {
ForumPost post = new ForumPost();
post.setId(row.getAs("id"));
post.setTitle(row.getAs("title"));
post.setContent(row.getAs("content"));
post.setUserId(row.getAs("userId"));
post.setTopic(row.getAs("topic"));
post.setReplyCount(row.getAs("replyCount"));
post.setLikeCount(row.getAs("likeCount"));
post.setCreateTime(row.getAs("createTime"));
result.add(post);
}
return result;
}
public boolean createForumPost(ForumPost post) {
post.setCreateTime(new Date());
post.setReplyCount(0);
post.setLikeCount(0);
forumMapper.insert(post);
List<ForumPost> userPosts = forumMapper.findByUserId(post.getUserId());
Dataset<Row> userPostDF = spark.createDataFrame(userPosts, ForumPost.class);
userPostDF.createOrReplaceTempView("user_posts");
Dataset<Row> userStats = spark.sql("SELECT COUNT(*) as postCount, AVG(likeCount) as avgLikes FROM user_posts WHERE userId = " + post.getUserId());
Row statsRow = userStats.first();
Long postCount = statsRow.getAs("postCount");
Double avgLikes = statsRow.getAs("avgLikes");
UserContribution contribution = userContributionMapper.findByUserId(post.getUserId());
contribution.setPostCount(postCount.intValue());
contribution.setAvgLikes(avgLikes);
userContributionMapper.update(contribution);
return true;
}
public List<StudyPlan> getPersonalStudyPlans(Long userId) {
List<StudyPlan> plans = studyPlanMapper.findByUserId(userId);
Dataset<Row> planDF = spark.createDataFrame(plans, StudyPlan.class);
planDF.createOrReplaceTempView("study_plans");
Dataset<Row> planAnalysis = spark.sql("SELECT *, DATEDIFF(endDate, startDate) as planDays, completedTasks * 100.0 / totalTasks as completionRate FROM study_plans WHERE userId = " + userId);
List<Row> analysisRows = planAnalysis.collectAsList();
List<StudyPlan> enhancedPlans = new ArrayList<>();
for (Row row : analysisRows) {
StudyPlan plan = new StudyPlan();
plan.setId(row.getAs("id"));
plan.setUserId(row.getAs("userId"));
plan.setTitle(row.getAs("title"));
plan.setStartDate(row.getAs("startDate"));
plan.setEndDate(row.getAs("endDate"));
plan.setCompletedTasks(row.getAs("completedTasks"));
plan.setTotalTasks(row.getAs("totalTasks"));
plan.setPlanDays(row.getAs("planDays"));
plan.setCompletionRate(row.getAs("completionRate"));
enhancedPlans.add(plan);
}
return enhancedPlans;
}
public boolean updateStudyPlan(StudyPlan plan) {
studyPlanMapper.update(plan);
List<StudyPlan> allUserPlans = studyPlanMapper.findByUserId(plan.getUserId());
Dataset<Row> allPlansDF = spark.createDataFrame(allUserPlans, StudyPlan.class);
allPlansDF.createOrReplaceTempView("all_user_plans");
Dataset<Row> planStats = spark.sql("SELECT AVG(completedTasks * 100.0 / totalTasks) as avgCompletionRate, COUNT(*) as totalPlans FROM all_user_plans WHERE userId = " + plan.getUserId());
Row statsRow = planStats.first();
Double avgCompletion = statsRow.getAs("avgCompletionRate");
Long totalPlans = statsRow.getAs("totalPlans");
UserProgress progress = userProgressMapper.findByUserId(plan.getUserId());
progress.setAvgCompletionRate(avgCompletion);
progress.setTotalPlans(totalPlans.intValue());
userProgressMapper.update(progress);
if (avgCompletion > 80.0) {
Achievement achievement = new Achievement();
achievement.setUserId(plan.getUserId());
achievement.setType("STUDY_MASTER");
achievement.setEarnedDate(new Date());
achievementMapper.insert(achievement);
}
return true;
}
}
基于微信小程序的考研资源共享平台文档展示
💖💖作者：计算机毕业设计江挽 💙💙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
