💖💖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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基于大数据的专业智能导学系统介绍
基于大数据的专业智能导学系统是一套采用现代化技术栈构建的综合性教育管理平台，该系统充分运用大数据技术对学习行为和教学资源进行智能化分析与管理。系统采用B/S架构设计，支持Java+SpringBoot和Python+Django双技术栈实现，前端采用Vue+ElementUI框架构建现代化用户界面，后台数据存储基于MySQL数据库，确保系统的稳定性和扩展性。系统核心功能涵盖用户管理、资源信息管理、在线学习、交流论坛等多个模块，其中资源类型分类管理和资源信息模块为学习者提供丰富的学习材料，在线学习模块支持个性化学习路径定制，交流论坛及论坛分类功能促进师生互动交流，举报记录和系统日志确保平台运行安全。特别值得关注的是，系统集成了数据分析和数据备份功能，通过大数据技术对学习行为、资源使用情况、论坛互动数据进行深度挖掘分析，生成可视化数据看板，为教育管理决策提供科学依据。同时，系统还配置了轮播图管理、公告信息管理、关于我们等内容管理模块，以及个人中心、修改密码等个性化服务功能，形成了一个功能完善、技术先进的智能化教育导学平台，能够有效提升教学质量和学习效率，为现代化教育管理提供强有力的技术支撑。
基于大数据的专业智能导学系统演示视频
基于大数据的专业智能导学系统演示图片
基于大数据的专业智能导学系统代码展示
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.springframework.web.bind.annotation.*;
import org.springframework.beans.factory.annotation.Autowired;
import java.util.*;
@RestController
@RequestMapping("/api")
public class IntelligentTutorialController {
@Autowired
private SparkSession sparkSession = SparkSession.builder()
.appName("IntelligentTutorialSystem")
.master("local[*]")
.config("spark.sql.adaptive.enabled", "true")
.getOrCreate();
@PostMapping("/dataAnalysis/studentBehavior")
public Map<String, Object> analyzeStudentLearningBehavior(@RequestBody Map<String, Object> params) {
String userId = (String) params.get("userId");
String timeRange = (String) params.get("timeRange");
Dataset<Row> learningRecords = sparkSession.sql(
"SELECT user_id, resource_id, learning_duration, access_time, completion_status " +
"FROM learning_records WHERE user_id = '" + userId + "' AND access_time >= '" + timeRange + "'"
);
Dataset<Row> aggregatedData = learningRecords.groupBy("resource_id")
.agg(
org.apache.spark.sql.functions.sum("learning_duration").alias("total_duration"),
org.apache.spark.sql.functions.count("*").alias("access_count"),
org.apache.spark.sql.functions.avg("completion_status").alias("avg_completion")
);
List<Row> behaviorPatterns = aggregatedData.collectAsList();
Map<String, Object> analysisResult = new HashMap<>();
List<Map<String, Object>> patterns = new ArrayList<>();
for (Row row : behaviorPatterns) {
Map<String, Object> pattern = new HashMap<>();
pattern.put("resourceId", row.getString(0));
pattern.put("totalDuration", row.getLong(1));
pattern.put("accessCount", row.getLong(2));
pattern.put("avgCompletion", row.getDouble(3));
patterns.add(pattern);
}
Dataset<Row> difficultyAnalysis = sparkSession.sql(
"SELECT resource_type, AVG(learning_duration) as avg_time, " +
"COUNT(*) as total_attempts FROM learning_records lr " +
"JOIN resource_info ri ON lr.resource_id = ri.id " +
"WHERE lr.user_id = '" + userId + "' GROUP BY resource_type"
);
List<Row> difficultyData = difficultyAnalysis.collectAsList();
Map<String, Double> learningDifficulty = new HashMap<>();
for (Row row : difficultyData) {
learningDifficulty.put(row.getString(0), row.getDouble(1));
}
analysisResult.put("behaviorPatterns", patterns);
analysisResult.put("learningDifficulty", learningDifficulty);
analysisResult.put("analysisTime", new Date());
return analysisResult;
}
@PostMapping("/resourceManagement/intelligentRecommend")
public Map<String, Object> intelligentResourceRecommendation(@RequestBody Map<String, Object> params) {
String userId = (String) params.get("userId");
String currentSubject = (String) params.get("currentSubject");
Dataset<Row> userPreferences = sparkSession.sql(
"SELECT resource_type, resource_category, AVG(completion_status) as preference_score " +
"FROM learning_records lr JOIN resource_info ri ON lr.resource_id = ri.id " +
"WHERE lr.user_id = '" + userId + "' GROUP BY resource_type, resource_category " +
"HAVING preference_score > 0.6 ORDER BY preference_score DESC"
);
Dataset<Row> similarUsers = sparkSession.sql(
"SELECT lr2.user_id, COUNT(*) as similarity_count " +
"FROM learning_records lr1 JOIN learning_records lr2 " +
"ON lr1.resource_id = lr2.resource_id " +
"WHERE lr1.user_id = '" + userId + "' AND lr2.user_id != '" + userId + "' " +
"GROUP BY lr2.user_id ORDER BY similarity_count DESC LIMIT 5"
);
List<String> similarUserIds = new ArrayList<>();
for (Row row : similarUsers.collectAsList()) {
similarUserIds.add(row.getString(0));
}
String userIdList = String.join("','", similarUserIds);
Dataset<Row> collaborativeRecommendations = sparkSession.sql(
"SELECT ri.id, ri.title, ri.resource_type, ri.difficulty_level, " +
"COUNT(*) as recommendation_score " +
"FROM learning_records lr JOIN resource_info ri ON lr.resource_id = ri.id " +
"WHERE lr.user_id IN ('" + userIdList + "') " +
"AND ri.resource_category = '" + currentSubject + "' " +
"AND ri.id NOT IN (SELECT resource_id FROM learning_records WHERE user_id = '" + userId + "') " +
"GROUP BY ri.id, ri.title, ri.resource_type, ri.difficulty_level " +
"ORDER BY recommendation_score DESC LIMIT 10"
);
List<Row> recommendations = collaborativeRecommendations.collectAsList();
List<Map<String, Object>> recommendedResources = new ArrayList<>();
for (Row row : recommendations) {
Map<String, Object> resource = new HashMap<>();
resource.put("resourceId", row.getString(0));
resource.put("title", row.getString(1));
resource.put("resourceType", row.getString(2));
resource.put("difficultyLevel", row.getString(3));
resource.put("recommendationScore", row.getLong(4));
recommendedResources.add(resource);
}
Map<String, Object> result = new HashMap<>();
result.put("recommendedResources", recommendedResources);
result.put("totalCount", recommendedResources.size());
result.put("recommendationType", "collaborative_filtering");
result.put("generateTime", new Date());
return result;
}
@PostMapping("/forum/intelligentModeration")
public Map<String, Object> intelligentForumContentModeration(@RequestBody Map<String, Object> params) {
String forumCategoryId = (String) params.get("categoryId");
Integer timeWindow = (Integer) params.get("timeWindowHours");
Dataset<Row> forumActivities = sparkSession.sql(
"SELECT user_id, content, post_time, reply_count, like_count, report_count " +
"FROM forum_posts WHERE category_id = '" + forumCategoryId + "' " +
"AND post_time >= DATE_SUB(NOW(), INTERVAL " + timeWindow + " HOUR)"
);
Dataset<Row> suspiciousContent = forumActivities.filter(
"LENGTH(content) < 10 OR report_count > 2 OR " +
"UPPER(content) LIKE '%SPAM%' OR UPPER(content) LIKE '%广告%'"
);
Dataset<Row> userActivityStats = sparkSession.sql(
"SELECT user_id, COUNT(*) as post_count, " +
"AVG(reply_count) as avg_replies, SUM(report_count) as total_reports " +
"FROM forum_posts WHERE category_id = '" + forumCategoryId + "' " +
"AND post_time >= DATE_SUB(NOW(), INTERVAL " + timeWindow + " HOUR) " +
"GROUP BY user_id HAVING post_count > 10 OR total_reports > 5"
);
List<Row> suspiciousUsers = userActivityStats.collectAsList();
List<Map<String, Object>> moderationAlerts = new ArrayList<>();
for (Row row : suspiciousUsers) {
Map<String, Object> alert = new HashMap<>();
alert.put("userId", row.getString(0));
alert.put("postCount", row.getLong(1));
alert.put("avgReplies", row.getDouble(2));
alert.put("totalReports", row.getLong(3));
alert.put("riskLevel", row.getLong(3) > 10 ? "HIGH" : "MEDIUM");
moderationAlerts.add(alert);
}
Dataset<Row> contentQualityAnalysis = sparkSession.sql(
"SELECT category_id, AVG(LENGTH(content)) as avg_content_length, " +
"AVG(reply_count) as avg_engagement, COUNT(*) as total_posts " +
"FROM forum_posts WHERE post_time >= DATE_SUB(NOW(), INTERVAL " + timeWindow + " HOUR) " +
"GROUP BY category_id"
);
List<Row> qualityMetrics = contentQualityAnalysis.collectAsList();
Map<String, Object> qualityAnalysis = new HashMap<>();
for (Row row : qualityMetrics) {
qualityAnalysis.put("avgContentLength", row.getDouble(1));
qualityAnalysis.put("avgEngagement", row.getDouble(2));
qualityAnalysis.put("totalPosts", row.getLong(3));
}
Map<String, Object> moderationResult = new HashMap<>();
moderationResult.put("suspiciousContentCount", suspiciousContent.count());
moderationResult.put("moderationAlerts", moderationAlerts);
moderationResult.put("qualityAnalysis", qualityAnalysis);
moderationResult.put("analysisTimeWindow", timeWindow + " hours");
moderationResult.put("processTime", new Date());
return moderationResult;
}
}
基于大数据的专业智能导学系统文档展示
💖💖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
