我用debezium得到了乱七八糟的代码：

"doulist_name": "2013 豆瓣电影��碑榜】"

mysql数据库中有中文单词，我使用debezium将数据发送给kafka。我发现在消费信息时，中文单词变成了乱码，如何解决这个问题？有什么配置我可以用吗？
当我使用flume和kafka producer生成中文单词时，效果很好
配置的一部分：

key.converter=org.apache.kafka.connect.json.JsonConverter
value.converter=org.apache.kafka.connect.json.JsonConverter
key.converter.schemas.enable=true
value.converter.schemas.enable=true
internal.key.converter=org.apache.kafka.connect.json.JsonConverter
internal.value.converter=org.apache.kafka.connect.json.JsonConverter
internal.key.converter.schemas.enable=false
internal.value.converter.schemas.enable=false

connector.class=io.debezium.connector.mysql.MySqlConnector
database.server.id=18405
database.server.name=mysqlfullfillment
database.whitelist=test
database.history.kafka.bootstrap.servers=192.168.0.100:9092
database.history.kafka.topic=dbhistory.fullfillment-local
include.schema.changes=true
transforms=unwrap
transforms.unwrap.type=io.debezium.transforms.UnwrapFromEnvelope

mysql字符集：utf8 mysql config picture
版本：debezium v0.7.5，kafka v1.1.1
添加：
当我用控制台测试它的时候 ./kafka-console-consumer.sh --zookeeper 192.168.0.100:2181 --topic mysqlfullfillment.test.doulist 我有乱码

"doulist_name": "2013 豆瓣电影��碑榜】"

在我的Spark代码里，我得到了同样的混乱代码：

def main(args: Array[String]) {
    val spark = SparkSession
      .builder()
      .master("local")
      .appName("KafkaWordCount")
      .config("spark.streaming.stopGracefullyOnShutdown", "true")
      .getOrCreate()
    simpleTestCode(spark)
  }

  def simpleTestCode(spark: SparkSession): Unit = {
    val kafkaParams = Map[String, Object](
      "bootstrap.servers" -> "localhost:9092",
      "key.deserializer" -> classOf[StringDeserializer],
      "value.deserializer" -> classOf[StringDeserializer],
      "group.id" -> "KafkaWordCountgroup",
      "auto.offset.reset" -> "latest",
      "enable.auto.commit" -> (true: java.lang.Boolean)
    )
    val topics = Array("mysqlfullfillment.test.doulist")
    val ssc = new StreamingContext(spark.sparkContext, Seconds(2))

    ssc.checkpoint("/home/feng/software/code/bigdata/spark-warehouse")
    val stream = KafkaUtils.createDirectStream[String, String](
      ssc,
      PreferBrokers,
      Subscribe[String, String](topics, kafkaParams)
    )

    stream.map(mapFunc = record => (record.key, record.value)).foreachRDD(
      r => r.collect().foreach(t => print("message:" + t)))

    ssc.start()
    ssc.awaitTermination()
  }