diff --git a/src/main/java/cn/mesalab/config/ApplicationConfig.java b/src/main/java/cn/mesalab/config/ApplicationConfig.java
index 531f0ff..97b8322 100644
--- a/src/main/java/cn/mesalab/config/ApplicationConfig.java
+++ b/src/main/java/cn/mesalab/config/ApplicationConfig.java
@@ -44,7 +44,7 @@ public class ApplicationConfig {
     public static final String HBASE_ZOOKEEPER_CLIENT_PORT= ConfigUtils.getStringProperty("hbase.zookeeper.client.port");
 
 
-    public static final Double BASELINE_KALMAN_Q = ConfigUtils.getDoubleProperty("baseline.kalman.q");
+    public static final Double BASELINE_KALMAN_P = ConfigUtils.getDoubleProperty("baseline.kalman.p");
     public static final Double BASELINE_KALMAN_R = ConfigUtils.getDoubleProperty("baseline.kalman.r");
 
     public static final Integer LOG_WRITE_COUNT = ConfigUtils.getIntProperty("log.write.count");
diff --git a/src/main/java/cn/mesalab/service/BaselineGeneration.java b/src/main/java/cn/mesalab/service/BaselineGeneration.java
index 1f2acc7..5a700e8 100644
--- a/src/main/java/cn/mesalab/service/BaselineGeneration.java
+++ b/src/main/java/cn/mesalab/service/BaselineGeneration.java
@@ -121,7 +121,7 @@ public class BaselineGeneration {
         // 获取IP列表
         List<String> destinationIps = DruidData.getServerIpList(allFromDruid);
         ThreadFactory generationThreadFactory = new ThreadFactoryBuilder()
-                .setNameFormat("baseline-load-data-%d").build();
+                .setNameFormat("baseline-generate-%d").build();
         ThreadPoolExecutor generationExecutor = new ThreadPoolExecutor(
                 threadNum, threadNum, 0L,
                 TimeUnit.MILLISECONDS, new LinkedBlockingQueue<>(1024), generationThreadFactory,
diff --git a/src/main/java/cn/mesalab/service/algorithm/KalmanFilter.java b/src/main/java/cn/mesalab/service/algorithm/KalmanFilter.java
index 11a40c3..69ae1de 100644
--- a/src/main/java/cn/mesalab/service/algorithm/KalmanFilter.java
+++ b/src/main/java/cn/mesalab/service/algorithm/KalmanFilter.java
@@ -20,7 +20,6 @@ public class KalmanFilter {
     private double mdelt;
     private double Gauss;
     private double kalmanGain;
-    private final static double Q = ApplicationConfig.BASELINE_KALMAN_Q;
     private final static double R = ApplicationConfig.BASELINE_KALMAN_R;
 
     public KalmanFilter() {
@@ -28,9 +27,7 @@ public class KalmanFilter {
     }
 
     public void initial(){
-        // TODO 调整
-        pdelt = 1;
-        mdelt = 1;
+        pdelt = ApplicationConfig.BASELINE_KALMAN_P;
     }
 
     private ArrayList<Integer> smoothSeries;
@@ -40,20 +37,18 @@ public class KalmanFilter {
         //第一个估计值
         predict = oldValue;
         current = value;
-        //高斯噪声方差
-        Gauss = Math.sqrt(pdelt * pdelt + mdelt * mdelt) + Q;
         //估计方差
-        kalmanGain = Math.sqrt((Gauss * Gauss)/(Gauss * Gauss + pdelt * pdelt)) + R;
+        kalmanGain = pdelt/(pdelt + R);
         //估计值
         estimate = (int) (kalmanGain * (current - predict) + predict);
         //新的估计方差
-        mdelt = Math.sqrt((1-kalmanGain) * Gauss * Gauss);
+        mdelt = (1-kalmanGain) * pdelt ;
 
         return estimate;
     }
 
-
     public void forcast(List<Integer> historicalSeries, Integer length){
+        // 滤波
         int oldvalue = (historicalSeries.stream().mapToInt(Integer::intValue).sum())/historicalSeries.size();
         smoothSeries = new ArrayList<Integer>();
         for(int i = 0; i < historicalSeries.size(); i++){
@@ -62,6 +57,7 @@ public class KalmanFilter {
             smoothSeries.add(oldvalue);
         }
 
+        // 平均
         forecastSeries = new ArrayList<>();
         Integer partitonNum = historicalSeries.size()/length;
         for(int i = 0; i<length; i++){
diff --git a/src/main/resources/application.properties b/src/main/resources/application.properties
index 1ee2d0c..b1494c5 100644
--- a/src/main/resources/application.properties
+++ b/src/main/resources/application.properties
@@ -49,8 +49,8 @@ baseline.historical.ratio.threshold=0.1
 baseline.historical.sparse.fill.percentile=0.95
 baseline.rational.percentile=0.95
 #Kalman Filter
-baseline.kalman.q=0.000001
-baseline.kalman.r=0.002
+baseline.kalman.p=0.000001
+baseline.kalman.r=4
 
 
 # 每更新1000个记录打印log