[Bug] 写UDAF的时候，如果不加group by level就能正常接收参数，如果加了group by level就报301

### Search before asking

- [X] I searched in the [issues](https://github.com/apache/iotdb/issues) and found nothing similar.

### Version

1.3.2

### Describe the bug and provide the minimal reproduce step

写了一个UDAF，用来统计数量，加了一个参数filter用来过滤数据，如果不加group by level就能够正常执行，如果加了level就不能够正常执行，抛出异常301
不加的sql如下所示：
如果加了 group by level就会报错，如下所示：
我用count这种内置的聚合函数就没问题，即使参数filter这种没有定义的，而且，这个countFill也能够正常接收参数，filter能够传进去，
报错日志如下：
UDAF代码如下：
`package org.apache.iotdb.udf.api;

import org.apache.iotdb.udf.api.customizer.config.UDAFConfigurations;
import org.apache.iotdb.udf.api.customizer.parameter.UDFParameterValidator;
import org.apache.iotdb.udf.api.customizer.parameter.UDFParameters;
import org.apache.iotdb.udf.api.type.Type;
import org.apache.iotdb.udf.api.utils.ExecuteEval;
import org.apache.iotdb.udf.api.utils.ResultValue;
import org.apache.tsfile.block.column.Column;
import org.apache.tsfile.utils.BitMap;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.nio.ByteBuffer;
import java.util.Arrays;
import java.util.List;

/**
* 个数统计函数
* 获取所有符合条件的个数
* @author ylx
*/
public class CountFill implements UDAF {

private Logger logger = LoggerFactory.getLogger(CountFill.class);

private Type dataType;

private List filter;
static class CountState implements State {

Long count;

@Override
public void reset() {
count = 0L;
}

@Override
public byte[] serialize() {
ByteBuffer buffer = ByteBuffer.allocate(Double.BYTES + Long.BYTES);
buffer.putLong(count);

return buffer.array();
}

@Override
public void deserialize(byte[] bytes) {
ByteBuffer buffer = ByteBuffer.wrap(bytes);
count = buffer.getLong();
}
}

/**
* 在初始化方法beforeStart调用前执行，用于检测UDFParameters中用户输入的参数是否合法。该方法与 UDTF 的validate相同。
*/
@Override
public void validate(UDFParameterValidator validator) throws Exception {
validator
.validateInputSeriesNumber(1)
.validateInputSeriesDataType(0, Type.INT32, Type.INT64, Type.FLOAT, Type.DOUBLE,Type.TEXT);
}

/**
* 初始化方法，在 UDAF 处理输入数据前，调用用户自定义的初始化行为。与 UDTF 不同的是，这里的 configuration 是 UDAFConfiguration 类型
* @param parameters
* @param configurations
*/
@Override
public void beforeStart(UDFParameters parameters, UDAFConfigurations configurations) {
String inputFilter = parameters.getString("filter");
if (inputFilter !=null ){
filter = (Arrays.asList(inputFilter.split(",")));
}
dataType = parameters.getDataType(0);
configurations.setOutputDataType(Type.INT64);
}

/**
* 创建State对象，一般只需要调用默认构造函数，然后按需修改默认的初始值即可。
*/
@Override
public State createState() {
CountState countState = new CountState();
countState.count = 0L;
return countState;
}

/**
* 根据传入的数据Column[]批量地更新State对象，注意 column[0] 总是代表时间列。另外BitMap表示之前已经被过滤掉的数据，您在编写该方法时需要手动判断对应的数据是否被过滤掉
* values
*
* @param state state to be updated
* @param columns input columns from IoTDB TsBlock, time column is always the last column, the
* remaining columns are their parameter value columns
* @param bitMap define some filtered position in columns
*/
@Override
public void addInput(State state, Column[] columns, BitMap bitMap) {
CountState countState = (CountState) state;
switch (dataType) {
case INT32:
case INT64:
case FLOAT:
case DOUBLE:
case TEXT:
case BOOLEAN:
default:
addIntInput(countState, columns, bitMap);
break;
}
}

public void addIntInput(CountState state, Column[] columns, BitMap bitMap) {
int count = columns[0].getPositionCount();
for (int i = 0; i < count; i++) {
Boolean filterFlag = true;
if (bitMap != null && !bitMap.isMarked(i)) {
continue;
}
if (filter != null && filter.size()>0){
for (String fill : filter) {
String comparisonOperators = ExecuteEval.findComparisonOperators(fill);
String replace = fill.replace(comparisonOperators, "").trim();
if (ExecuteEval.isString(replace,dataType)){
// 如果是字符串，则将其拼接上单引号
replace = ("'"+replace+"'").trim();
switch (dataType){
case INT32:
// int类型
filterFlag =ExecuteEval.evalString("('"+columns[0].getInt(i)+"'"+comparisonOperators+replace+")?true:false");
break;
case INT64:
filterFlag =ExecuteEval.evalString("('"+columns[0].getLong(i)+"'"+comparisonOperators+replace+")?true:false");
break;
case FLOAT:
filterFlag =ExecuteEval.evalString("('"+columns[0].getFloat(i)+"'"+comparisonOperators+replace+")?true:false");
break;
case DOUBLE:
filterFlag =ExecuteEval.evalString("('"+columns[0].getDouble(i)+"'"+comparisonOperators+replace+")?true:false");
break;
case TEXT:
filterFlag =ExecuteEval.evalString("('"+columns[0].getObject(i)+"'"+comparisonOperators+replace+")?true:false");
break;
default:
break;
}

}else {
switch (dataType){
case INT32:
// int类型
filterFlag =ExecuteEval.evalString("("+columns[0].getInt(i)+comparisonOperators+replace+")?true:false");
break;
case INT64:
filterFlag =ExecuteEval.evalString("("+columns[0].getLong(i)+comparisonOperators+replace+")?true:false");
break;
case FLOAT:
filterFlag =ExecuteEval.evalString("("+columns[0].getFloat(i)+comparisonOperators+replace+")?true:false");
break;
case DOUBLE:
filterFlag =ExecuteEval.evalString("("+columns[0].getDouble(i)+comparisonOperators+replace+")?true:false");
break;
case TEXT:
filterFlag =ExecuteEval.evalString("("+columns[0].getObject(i)+comparisonOperators+replace+")?true:false");
break;
default:
break;
}
}
}
}
if (!columns[0].isNull(i) && filterFlag) {
state.count++;
}
}
}

/**
* 将rhs状态合并至state状态中。在分布式场景下，同一组的数据可能分布在不同节点上，IoTDB 会为每个节点上的部分数据生成一个State对象，然后调用该方法合并成完整的State。
*
* @param state current state
* @param rhs right-hand-side state to be merged
*/
@Override
public void combineState(State state, State rhs) {
CountState avgState = (CountState) state;
CountState avgRhs = (CountState) rhs;

avgState.count += avgRhs.count;
}

/**
* 根据State中的数据，计算出最终的聚合结果。注意根据聚合的语义，每一组只能输出一个值。
*
* @param state final state
* @param resultValue used to collect output data points
*/
@Override
public void outputFinal(State state, ResultValue resultValue) {
CountState avgState = (CountState) state;

resultValue.setLong(avgState.count);
}

}`

### What did you expect to see?

无

### What did you see instead?

无

### Anything else?

无

### Are you willing to submit a PR?

- [ ] I'm willing to submit a PR!

Start by reproducing the two described UDAF queries on IoTDB 1.3.2, then inspect CountFill's beforeStart, addInput, and state methods alongside the group-by-level aggregation path. Done means the parameterized UDAF executes with group by level without error 301 and retains the non-grouped behavior.

Written by the indexing model from the issue text.