竞赛网站访问日志分析

def contest(utils: Utils): Unit = {val data = utils.sc.textFile("data/contest_log.txt")val users = data.map(line => line.split(',')(3)).distinct()println(users.count())val pages = data.map(line => line.split(',')(1)).distinct()println(pages.count())val session_with_time = data.map(line => (line.split(',')(5).substring(0, 7), 1))println(session_with_time.reduceByKey(_ + _).collect() mkString("\n", "\n", "\n"))}

影评

1. 求被评分次数最多的 10 部电影，并给出评分次数（电影名，评分次数）

2. 分别求男性，女性当中评分最高的 10 部电影（性别，电影名，影评分）

def solveQuest2(utils: Utils): Unit = {//(userID, sex)val userID_sex: RDD[(String, String)] = utils.usersRdd.map(x => (x._1, x._2))//(userID, (movieID, rating))val userID_movieID_rating: RDD[(String, (String, String))] = utils.ratingsRdd.map(x => (x._1, (x._2, x._3)))val userID_movieID_movie = utils.movieRdd.map(x => (x._1, x._2))//(userID, (sex, (movieID, rating)))  ---> (sex, movieID, rating)val movieID_rating: RDD[(String, String, String)] = userID_sex.join(userID_movieID_rating).map(x => (x._2._1, x._2._2._1, x._2._2._2))val movieID_rating_F = movieID_rating.filter(x => x._1 == "F").map(x => (x._2, x)).join(userID_movieID_movie).map(x => (x._2._1._1, x._2._2, x._2._1._3)).sortBy(_._3, false).take(10)val movieID_rating_M = movieID_rating.filter(x => x._1 == "M").map(x => (x._2, x)).join(userID_movieID_movie).map(x => (x._2._1._1, x._2._2, x._2._1._3)).sortBy(_._3, false).take(10)movieID_rating_F.union(movieID_rating_M).foreach { case (x, y, z) => println(x + ":" + y + ":" + z) }//((sex, movieID), Iterable[(sex, movieID, rating)])  ---> (movieID, (sex, avg))//   val movieID_sex_avg:RDD[(String, (String, Double))]=movieID_rating.groupBy(x=> (x._1, x._2))//      .map(x=> {//      var sum,avg=0d//      val list:List[(String, String, String)]=x._2.toList//      if(list.size >50){list.map(x=> ( sum +=x._3.toInt ))//        avg=sum*1.0/list.size}//      (x._1._2, (x._1._1, avg))//    })//    //(movieID, movieName)//    val movieID_movieName:RDD[(String, String)]=utils.movieRdd.map(x=> (x._1, x._2))//    sex_movieID_avg与movie进行关联 (movieID, ((sex, avg), movieName)) ---> (sex, movieName, avg)//    val sex_movieName_avg:RDD[(String, String, Double)]=movieID_sex_avg.join(movieID_movieName)//      .map(x=> (x._2._1._1, x._2._2, x._2._1._2)).sortBy(x=> (x._1, x._3),false)//    sex_movieName_avg.take(10).foreach(println(_))//    sex_movieName_avg.filter(_._1=="F").take(10).foreach(println(_))}

3. 分别求男性，女性看过评分次数最多的 10 部电影（性别，电影名）

  def solveQuest3(utils: Utils): Unit = {val userID_sex = utils.usersRdd.map(x => (x._1, x._2))val movieID_movie = utils.movieRdd.map(x => (x._1, x._2))val userID_movieID_times = utils.ratingsRdd.map(x => (x._1, (x._2, 1)))val userID_movieID_times_sexs = userID_movieID_times.join(userID_sex)val userID_movieID_times_sexs_movie = userID_movieID_times_sexs.map(x => (x._2._1._1, (x._1, x._2._1._2, x._2._2))).join(movieID_movie)val sex_movieID_times_movie = userID_movieID_times_sexs_movie.map(x => (x._2._1._3, (x._1, x._2._2, x._2._1._2)))val movie_times_M = sex_movieID_times_movie.filter(x => x._1 == "M").map(x => ((x._2._1, x._2._2), x._2._3)).reduceByKey(_ + _)println("male rating times top10")movie_times_M.top(10)(Ordering.by(_._2)).foreach(println(_))val movie_times_F = sex_movieID_times_movie.filter(x => x._1 == "F").map(x => ((x._2._1, x._2._2), x._2._3)).reduceByKey(_ + _)println("male rating times top10")movie_times_F.top(10)(Ordering.by(_._2)).foreach(println(_))}

4. 年龄段在“18-24”的男人，最喜欢看（评分次数最多的）10部电影

  def solveQuest4(utils: Utils): Unit = {val movieID_movie = utils.movieRdd.map(x => (x._1, x._2))val userID_movieID_times = utils.ratingsRdd.map(x => (x._1, (x._2, 1)))val userID_age = utils.usersRdd.filter(x => x._2 == "M" && x._3 == "18").map(x => (x._1, x._3))val userID_movieID_times_age = userID_movieID_times.join(userID_age)val userID_movieID_times_age_movie = userID_movieID_times_age.map(x => (x._2._1._1, (x._1, x._2._1._2, x._2._2))).join(movieID_movie)val movie_times = userID_movieID_times_age_movie.map(x => ((x._1, x._2._2), x._2._1._2)).reduceByKey(_ + _)println("age in 18-24 male rating times top10")movie_times.top(10)(Ordering.by(_._2)).foreach(println(_))}

5. 求 movieid = 2116 这部电影各年龄段（因为年龄就只有 7 个，就按这个 7 个分就好了）的平均影评（年龄段，影评分）

  def solveQuest5(utils: Utils): Unit = {val userID_rating = utils.ratingsRdd.filter(_._2 == "2116").map(x => (x._1, x._3.toDouble))val userID_age = utils.usersRdd.map(x => (x._1, x._3))val age_rating_times = userID_age.join(userID_rating).map(x => (x._2._1, (x._2._2, 1)))val age_avg = age_rating_times.reduceByKey((a, b) => (a._1 + b._1, a._2 + b._2)).map(x => (x._1, x._2._1 / x._2._2))println("movie 2116 in every age avg")age_avg.foreach(println(_))}

6. 求最喜欢看电影（影评次数最多）的那位女性评最高分的 10 部电影的平均影评分（观影者，电影名，影评分）

  def solveQuest6(utils: Utils): Unit = {val userID_sex = utils.usersRdd.map(x => (x._1, x._2))val movieID_movie = utils.movieRdd.map(x => (x._1, x._2))val userID_movieID_times = utils.ratingsRdd.map(x => (x._1, (x._2, 1)))val userID_movieID_times_F = userID_movieID_times.join(userID_sex).filter(_._2._2 == "F").map(x => (x._2._1._1, (x._1, x._2._1._2)))val uid_time = userID_movieID_times_F.join(movieID_movie).map(x => (x._2._1._1, x._2._1._2)).reduceByKey(_ + _)val uid = uid_time.top(1)(Ordering.by(_._2))(0)._1val mid_rating = utils.ratingsRdd.filter(_._1 == uid).map(x => (x._2, x._3))val mid_movive_rating = movieID_movie.join(mid_rating).map(x => (x._1, x._2._1, x._2._2.toDouble))val top10 = mid_movive_rating.top(10)(Ordering.by(_._3))println("movie fav F highest raing top 10")top10.foreach(println(_))}

7. 求好片（平均评分>=4.0）最多的那个年份的最好看（平均评分最高）的 10 部电影

  def solveQuest7(utils: Utils): Unit = {val mid_movie = utils.movieRdd.map(x => (x._1, x._2.substring(0, x._2.length - 7)))val mid_year = utils.movieRdd.map(x => (x._1, x._2.substring(x._2.length - 5, x._2.length - 1)))val mid_rat = utils.ratingsRdd.map(x => (x._2, x._3.toDouble))val mid_avg_ge4 = mid_rat.map(x => (x._1, (x._2, 1))).reduceByKey((a, b) => (a._1 + b._1, a._2 + b._2)).map(x => (x._1, x._2._1 / x._2._2)).filter(_._2 >= 4.0)val year_times = mid_year.join(mid_avg_ge4).map(x => (x._2._1, 1)).reduceByKey(_ + _)val year = year_times.top(1)(Ordering.by(_._2))(0)._1val year_mid_avg = mid_year.join(mid_avg_ge4).filter(_._2._1 == year).map(x => (x._1, x._2._2))val top10 = year_mid_avg.join(mid_movie).map(x => (x._2._2, x._2._1)).top(10)(Ordering.by(_._2))top10.foreach(println(_))}

8.求 1997 年上映的电影中，评分最高的 10 部 Comedy 类电影

  def solveQuest8(utils: Utils): Unit = {val mid_movie_year_type = utils.movieRdd.map(x => (x._1, (x._2.substring(0, x._2.length - 7), x._2.substring(x._2.length - 5, x._2.length - 1), x._3)))val usem = mid_movie_year_type.filter(x => x._2._2 == "1997" && x._2._3.contains("Comedy"))val mid_rat = utils.ratingsRdd.map(x => (x._2, x._3.toDouble))val mid_avg = mid_rat.map(x => (x._1, (x._2, 1))).reduceByKey((a, b) => (a._1 + b._1, a._2 + b._2)).map(x => (x._1, x._2._1 / x._2._2))val movie1997_avg = usem.join(mid_avg).map(x => (x._1, x._2._1._1, x._2._2))val top10 = movie1997_avg.top(10)(Ordering.by(_._3))top10.foreach(println(_))}

9. 该影评库中各种类型电影中评价最高的 5 部电影（类型，电影名，平均影评分）

  def solveQuest9(utils: Utils): Unit = {val types = utils.movieRdd.map(_._3.split('|')).flatMap(x => x).distinct().map(x => (x, 1))val mid_rat = utils.ratingsRdd.map(x => (x._2, x._3.toDouble))val mid_avg = mid_rat.map(x => (x._1, (x._2, 1))).reduceByKey((a, b) => (a._1 + b._1, a._2 + b._2)).map(x => (x._1, x._2._1 / x._2._2))val mrdd_avg = utils.movieRdd.map(x => (x._1, (x._2, x._3.split('|')))).join(mid_avg).map(x => (x._2._1._2, (x._2._1._1, x._2._2)))val type_avg = mrdd_avg.map(x => {for (i <- 0 until (x._1.length - 1)) yield (x._1(i), x._2)}).flatMap(x => x)val types_avg = types.join(type_avg).map(x => (x._1, ArrayBuffer(x._2._2))).reduceByKey((a, b) => a ++= b)val tmp = types_avg.collect()tmp.foreach(x => {println("top5 in : " + x._1)utils.sc.makeRDD(x._2).top(5)(Ordering.by(_._2)).foreach(println(_))})}

10. 各年评分最高的电影类型（年份，类型，影评分）

  def solveQuest0(utils: Utils): Unit = {val movieID_name_year = utils.movieRdd.map(x => (x._1, x._2.substring(0, x._2.length - 7), x._2.substring(x._2.length - 5, x._2.length - 1), x._3))val years = movieID_name_year.map(_._3).distinct().sortBy(_.toInt).collect()for (year <- years) {val movieID_type = movieID_name_year.filter(_._3.equals(year)).map(x => (x._1, x._4))val aveRatings = utils.ratingsRdd.map(x => (x._2, x._3.toDouble)).join(movieID_type).map(x => (x._2._2, (x._2._1, 1))).reduceByKey((x, y) => (x._1 + y._1, x._2 + y._2)).map(x => (x._1, x._2._1 / x._2._2))val topType = aveRatings.top(1)(Ordering.by(_._2))(0)println("In " + year + ", the highest rating movie type is " + topType._1 + " with average rating as " + topType._2)}}

完整代码

import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}import scala.collection.mutable.ArrayBufferclass Utils {val conf = new SparkConf().setAppName("FileReview").setMaster("local")//初始化sc对象val sc = new SparkContext(conf)val movie = sc.textFile("ml-1m/movies.dat")val ratings = sc.textFile("ml-1m/ratings.dat")val users = sc.textFile("ml-1m/users.dat")val movieRdd: RDD[(String, String, String)] = movie.map(_.split("::")).map(m => (m(0), m(1), m(2)))val ratingsRdd: RDD[(String, String, String, String)] = ratings.map(_.split("::")).map(r => (r(0), r(1), r(2), r(3)))val usersRdd: RDD[(String, String, String, String, String)] = users.map(_.split("::")).map(u => (u(0), u(1), u(2), u(3), u(4)))
}object four {def contest(utils: Utils): Unit = {val data = utils.sc.textFile("data/contest_log.txt")val users = data.map(line => line.split(',')(3)).distinct()println(users.count())val pages = data.map(line => line.split(',')(1)).distinct()println(pages.count())val session_with_time = data.map(line => (line.split(',')(5).substring(0, 7), 1))println(session_with_time.reduceByKey(_ + _).collect() mkString("\n", "\n", "\n"))}/** 1. 求被评分次数最多的 10 部电影，并给出评分次数（电影名，评分次数）* */def solveQuest1(utils: Utils): Unit = {val movieID_rating: RDD[(String, Int)] = utils.ratingsRdd.map(x => (x._2, 1))val movieID_times: RDD[(String, Int)] = movieID_rating.reduceByKey(_ + _).sortBy(_._2, false)//获得电影id和电影名val movieID_name: RDD[(String, String)] = utils.movieRdd.map(x => (x._1, x._2))//关联movieID_times和movieID_name，获得电影id，电影名，评分次数val result: RDD[(String, Int)] = movieID_times.join(movieID_name).sortBy(_._2._1, false).map(x => (x._2._2, x._2._1))result.take(10).foreach(println(_))}/** 2. 分别求男性，女性当中评分最高的 10 部电影（性别，电影名，影评分）* */def solveQuest2(utils: Utils): Unit = {//(userID, sex)val userID_sex: RDD[(String, String)] = utils.usersRdd.map(x => (x._1, x._2))//(userID, (movieID, rating))val userID_movieID_rating: RDD[(String, (String, String))] = utils.ratingsRdd.map(x => (x._1, (x._2, x._3)))val userID_movieID_movie = utils.movieRdd.map(x => (x._1, x._2))//(userID, (sex, (movieID, rating)))  ---> (sex, movieID, rating)val movieID_rating: RDD[(String, String, String)] = userID_sex.join(userID_movieID_rating).map(x => (x._2._1, x._2._2._1, x._2._2._2))val movieID_rating_F = movieID_rating.filter(x => x._1 == "F").map(x => (x._2, x)).join(userID_movieID_movie).map(x => (x._2._1._1, x._2._2, x._2._1._3)).sortBy(_._3, false).take(10)val movieID_rating_M = movieID_rating.filter(x => x._1 == "M").map(x => (x._2, x)).join(userID_movieID_movie).map(x => (x._2._1._1, x._2._2, x._2._1._3)).sortBy(_._3, false).take(10)movieID_rating_F.union(movieID_rating_M).foreach { case (x, y, z) => println(x + ":" + y + ":" + z) }//((sex, movieID), Iterable[(sex, movieID, rating)])  ---> (movieID, (sex, avg))//   val movieID_sex_avg:RDD[(String, (String, Double))]=movieID_rating.groupBy(x=> (x._1, x._2))//      .map(x=> {//      var sum,avg=0d//      val list:List[(String, String, String)]=x._2.toList//      if(list.size >50){list.map(x=> ( sum +=x._3.toInt ))//        avg=sum*1.0/list.size}//      (x._1._2, (x._1._1, avg))//    })//    //(movieID, movieName)//    val movieID_movieName:RDD[(String, String)]=utils.movieRdd.map(x=> (x._1, x._2))//    sex_movieID_avg与movie进行关联 (movieID, ((sex, avg), movieName)) ---> (sex, movieName, avg)//    val sex_movieName_avg:RDD[(String, String, Double)]=movieID_sex_avg.join(movieID_movieName)//      .map(x=> (x._2._1._1, x._2._2, x._2._1._2)).sortBy(x=> (x._1, x._3),false)//    sex_movieName_avg.take(10).foreach(println(_))//    sex_movieName_avg.filter(_._1=="F").take(10).foreach(println(_))}/** 3. 分别求男性，女性看过评分次数最多的 10 部电影（性别，电影名）* */def solveQuest3(utils: Utils): Unit = {val userID_sex = utils.usersRdd.map(x => (x._1, x._2))val movieID_movie = utils.movieRdd.map(x => (x._1, x._2))val userID_movieID_times = utils.ratingsRdd.map(x => (x._1, (x._2, 1)))val userID_movieID_times_sexs = userID_movieID_times.join(userID_sex)val userID_movieID_times_sexs_movie = userID_movieID_times_sexs.map(x => (x._2._1._1, (x._1, x._2._1._2, x._2._2))).join(movieID_movie)val sex_movieID_times_movie = userID_movieID_times_sexs_movie.map(x => (x._2._1._3, (x._1, x._2._2, x._2._1._2)))val movie_times_M = sex_movieID_times_movie.filter(x => x._1 == "M").map(x => ((x._2._1, x._2._2), x._2._3)).reduceByKey(_ + _)println("male rating times top10")movie_times_M.top(10)(Ordering.by(_._2)).foreach(println(_))val movie_times_F = sex_movieID_times_movie.filter(x => x._1 == "F").map(x => ((x._2._1, x._2._2), x._2._3)).reduceByKey(_ + _)println("male rating times top10")movie_times_F.top(10)(Ordering.by(_._2)).foreach(println(_))}/** 4. 年龄段在“18-24”的男人，最喜欢看（评分次数最多的）10部电影* */def solveQuest4(utils: Utils): Unit = {val movieID_movie = utils.movieRdd.map(x => (x._1, x._2))val userID_movieID_times = utils.ratingsRdd.map(x => (x._1, (x._2, 1)))val userID_age = utils.usersRdd.filter(x => x._2 == "M" && x._3 == "18").map(x => (x._1, x._3))val userID_movieID_times_age = userID_movieID_times.join(userID_age)val userID_movieID_times_age_movie = userID_movieID_times_age.map(x => (x._2._1._1, (x._1, x._2._1._2, x._2._2))).join(movieID_movie)val movie_times = userID_movieID_times_age_movie.map(x => ((x._1, x._2._2), x._2._1._2)).reduceByKey(_ + _)println("age in 18-24 male rating times top10")movie_times.top(10)(Ordering.by(_._2)).foreach(println(_))}/** 5. 求 movieid = 2116 这部电影各年龄段（因为年龄就只有 7 个，就按这个 7 个分就好了）的平均影评（年龄段，影评分）* */def solveQuest5(utils: Utils): Unit = {val userID_rating = utils.ratingsRdd.filter(_._2 == "2116").map(x => (x._1, x._3.toDouble))val userID_age = utils.usersRdd.map(x => (x._1, x._3))val age_rating_times = userID_age.join(userID_rating).map(x => (x._2._1, (x._2._2, 1)))val age_avg = age_rating_times.reduceByKey((a, b) => (a._1 + b._1, a._2 + b._2)).map(x => (x._1, x._2._1 / x._2._2))println("movie 2116 in every age avg")age_avg.foreach(println(_))}/** 6. 求最喜欢看电影（影评次数最多）的那位女性评最高分的 10 部电影的平均影评分（观影者，电影名，影评分）* */def solveQuest6(utils: Utils): Unit = {val userID_sex = utils.usersRdd.map(x => (x._1, x._2))val movieID_movie = utils.movieRdd.map(x => (x._1, x._2))val userID_movieID_times = utils.ratingsRdd.map(x => (x._1, (x._2, 1)))val userID_movieID_times_F = userID_movieID_times.join(userID_sex).filter(_._2._2 == "F").map(x => (x._2._1._1, (x._1, x._2._1._2)))val uid_time = userID_movieID_times_F.join(movieID_movie).map(x => (x._2._1._1, x._2._1._2)).reduceByKey(_ + _)val uid = uid_time.top(1)(Ordering.by(_._2))(0)._1val mid_rating = utils.ratingsRdd.filter(_._1 == uid).map(x => (x._2, x._3))val mid_movive_rating = movieID_movie.join(mid_rating).map(x => (x._1, x._2._1, x._2._2.toDouble))val top10 = mid_movive_rating.top(10)(Ordering.by(_._3))println("movie fav F highest raing top 10")top10.foreach(println(_))}/** 7. 求好片（平均评分>=4.0）最多的那个年份的最好看（平均评分最高）的 10 部电影* */def solveQuest7(utils: Utils): Unit = {val mid_movie = utils.movieRdd.map(x => (x._1, x._2.substring(0, x._2.length - 7)))val mid_year = utils.movieRdd.map(x => (x._1, x._2.substring(x._2.length - 5, x._2.length - 1)))val mid_rat = utils.ratingsRdd.map(x => (x._2, x._3.toDouble))val mid_avg_ge4 = mid_rat.map(x => (x._1, (x._2, 1))).reduceByKey((a, b) => (a._1 + b._1, a._2 + b._2)).map(x => (x._1, x._2._1 / x._2._2)).filter(_._2 >= 4.0)val year_times = mid_year.join(mid_avg_ge4).map(x => (x._2._1, 1)).reduceByKey(_ + _)val year = year_times.top(1)(Ordering.by(_._2))(0)._1val year_mid_avg = mid_year.join(mid_avg_ge4).filter(_._2._1 == year).map(x => (x._1, x._2._2))val top10 = year_mid_avg.join(mid_movie).map(x => (x._2._2, x._2._1)).top(10)(Ordering.by(_._2))top10.foreach(println(_))}/** 8.求 1997 年上映的电影中，评分最高的 10 部 Comedy 类电影* */def solveQuest8(utils: Utils): Unit = {val mid_movie_year_type = utils.movieRdd.map(x => (x._1, (x._2.substring(0, x._2.length - 7), x._2.substring(x._2.length - 5, x._2.length - 1), x._3)))val usem = mid_movie_year_type.filter(x => x._2._2 == "1997" && x._2._3.contains("Comedy"))val mid_rat = utils.ratingsRdd.map(x => (x._2, x._3.toDouble))val mid_avg = mid_rat.map(x => (x._1, (x._2, 1))).reduceByKey((a, b) => (a._1 + b._1, a._2 + b._2)).map(x => (x._1, x._2._1 / x._2._2))val movie1997_avg = usem.join(mid_avg).map(x => (x._1, x._2._1._1, x._2._2))val top10 = movie1997_avg.top(10)(Ordering.by(_._3))top10.foreach(println(_))}/** 9. 该影评库中各种类型电影中评价最高的 5 部电影（类型，电影名，平均影评分）* */def solveQuest9(utils: Utils): Unit = {val types = utils.movieRdd.map(_._3.split('|')).flatMap(x => x).distinct().map(x => (x, 1))val mid_rat = utils.ratingsRdd.map(x => (x._2, x._3.toDouble))val mid_avg = mid_rat.map(x => (x._1, (x._2, 1))).reduceByKey((a, b) => (a._1 + b._1, a._2 + b._2)).map(x => (x._1, x._2._1 / x._2._2))val mrdd_avg = utils.movieRdd.map(x => (x._1, (x._2, x._3.split('|')))).join(mid_avg).map(x => (x._2._1._2, (x._2._1._1, x._2._2)))val type_avg = mrdd_avg.map(x => {for (i <- 0 until (x._1.length - 1)) yield (x._1(i), x._2)}).flatMap(x => x)val types_avg = types.join(type_avg).map(x => (x._1, ArrayBuffer(x._2._2))).reduceByKey((a, b) => a ++= b)val tmp = types_avg.collect()tmp.foreach(x => {println("top5 in : " + x._1)utils.sc.makeRDD(x._2).top(5)(Ordering.by(_._2)).foreach(println(_))})}/** 10. 各年评分最高的电影类型（年份，类型，影评分）* */def solveQuest0(utils: Utils): Unit = {val movieID_name_year = utils.movieRdd.map(x => (x._1, x._2.substring(0, x._2.length - 7), x._2.substring(x._2.length - 5, x._2.length - 1), x._3))val years = movieID_name_year.map(_._3).distinct().sortBy(_.toInt).collect()for (year <- years) {val movieID_type = movieID_name_year.filter(_._3.equals(year)).map(x => (x._1, x._4))val aveRatings = utils.ratingsRdd.map(x => (x._2, x._3.toDouble)).join(movieID_type).map(x => (x._2._2, (x._2._1, 1))).reduceByKey((x, y) => (x._1 + y._1, x._2 + y._2)).map(x => (x._1, x._2._1 / x._2._2))val topType = aveRatings.top(1)(Ordering.by(_._2))(0)println("In " + year + ", the highest rating movie type is " + topType._1 + " with average rating as " + topType._2)}}def main(args: Array[String]): Unit = {val utils = new Utils()}
}

实验总结及问题

学会使用什么做什么事情

spark rdd复杂操作

遇到什么问题，如何解决

flatmap & reduceByKey 算子使用问题，查看官方文档解决

还有什么问题尚未解决，可能是什么原因导致的

暂无

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