diff --git a/README.md b/README.md
index 750bd8f..4d58068 100644
--- a/README.md
+++ b/README.md
@@ -18,10 +18,11 @@
 
 ## libtorch 安装
 1. 下载： https://download.pytorch.org/libtorch/cpu/libtorch-macos-2.1.0.zip
-2. 配置环境变量：*注意三个地址都配置到libtorch的根目录，不要加lib和incloud*
+2. macos 配置环境变量：*注意三个地址都配置到libtorch的根目录，不要加lib和incloud*
 export LIBTORCH_INCLUDE=/Users/tom/Downloads/libtorch
 export LIBTORCH_LIB=/Users/tom/Downloads/libtorch
 export LIBTORCH=/Users/tom/Downloads/libtorch
 export DYLD_LIBRARY_PATH=/Users/tom/Downloads/libtorch/lib:$DYLD_LIBRARY_PATH
 3. ubuntu22.04.3版本上使用 Tesla M40（NVIDIA-Linux-x86_64-535.129.03.run） cuda118版本，测试tch cuda版本通过（缺少包可以拷贝到debug下）
+- `export LD_LIBRARY_PATH=${LIBTORCH}/lib:$LD_LIBRARY_PATH`
 - `cargo test --test tch_test`
\ No newline at end of file
diff --git a/src/normalize_data.rs b/src/normalize_data.rs
index fc97fb3..e57c2ea 100644
--- a/src/normalize_data.rs
+++ b/src/normalize_data.rs
@@ -85,6 +85,61 @@ async fn normalize(df: Option<DataFrame>) -> anyhow::Result<DataFrame>{
     Ok(DataFrame::empty())
 }
 
+pub fn tch_normalization(tch_matrix: &[Vec<f64>]) -> Vec<Vec<f64>> {
+    // 计算均值向量
+    let mean_vector = calculate_mean_vector(tch_matrix);
+    // 计算标准差向量
+    let std_vector = calculate_standard_deviation_vector(tch_matrix);
+    // 归一化处理
+    let normalized_tch_matrix = normalize_matrix(tch_matrix, &mean_vector, &std_vector);
+    normalized_tch_matrix
+}
+
+fn calculate_mean_vector(tch_matrix: &[Vec<f64>]) -> Vec<f64> {
+    let mut mean_vector = vec![];
+    for row in tch_matrix {
+        let mut sum = 0.0;
+        for &value in row {
+            sum += value;
+        }
+        mean_vector.push(sum / tch_matrix.len() as f64);
+    }
+    mean_vector
+}
+fn calculate_standard_deviation_vector(tch_matrix: &[Vec<f64>]) -> Vec<f64> {
+    let mut std_vector = vec![];
+    for row in tch_matrix {
+        let mut variance = 0.0;
+        let mean = calculate_mean(row);
+        for &value in row {
+            variance += (value - mean) * (value - mean);
+        }
+        std_vector.push(variance.sqrt() as f64);
+    }
+    std_vector
+}
+
+fn calculate_mean(numbers: &Vec<f64>) -> f64 {
+    let sum = numbers.iter().sum::<f64>();
+    let length = numbers.len() as f64;
+    sum as f64 / length
+}
+
+fn normalize_matrix(
+    tch_matrix: &[Vec<f64>],
+    mean_vector: &Vec<f64>,
+    std_vector: &Vec<f64>,
+) -> Vec<Vec<f64>> {
+    let mut normalized_tch_matrix = vec![Vec::new(); tch_matrix.len()];
+    for (i, row) in tch_matrix.iter().enumerate() {
+        let mut normalized_row = vec![0.0; row.len()];
+        for (j, &value) in row.iter().enumerate() {
+            normalized_row[j] = (value - mean_vector[i]) / std_vector[i];
+        }
+        normalized_tch_matrix.push(normalized_row);
+    }
+    normalized_tch_matrix
+}
 
 
 
@@ -93,11 +148,28 @@ async fn normalize(df: Option<DataFrame>) -> anyhow::Result<DataFrame>{
 mod test {
     use polars::prelude::{CsvReader, SerReader};
 
-    use crate::normalize_data::traverse_source_directory;
+    use crate::normalize_data::{tch_normalization, traverse_source_directory};
     use crate::util::Envs;
 
     use super::normalize;
 
+    #[test]
+    pub fn normalization_works(){
+        std::env::set_var("RUST_LOG", "qlib_data=debug");
+        // 初始化日志
+        tracing_subscriber::fmt::init();
+
+        let tch_matrix: Vec<Vec<f64>> = vec![
+            vec![1.0, 2.0, 3.0],
+            vec![4.0, 5.0, 6.0],
+            vec![7.0, 8.0, 9.0],
+        ];
+        let normalized_tch_matrix = tch_normalization(&tch_matrix);
+        for row in normalized_tch_matrix {
+            tracing::debug!("row: {:?}", row);
+        }
+    }
+
     #[tokio::test]
     pub async fn normalize_format_works()->anyhow::Result<()>{
         std::env::set_var("RUST_LOG", "qlib_data=debug");
diff --git a/tests/tch_test.rs b/tests/tch_test.rs
index f13b01d..804b51f 100644
--- a/tests/tch_test.rs
+++ b/tests/tch_test.rs
@@ -1,11 +1,7 @@
 #[cfg(test)]
 mod alstm {
     use tch::nn::OptimizerConfig;
-    use tch::{
-        kind,
-        nn::{self, Module},
-        Device, Tensor,
-    };
+    use tch::{kind, nn::{self, Module}, Device, Tensor, Kind};
 
     fn my_module(p: nn::Path, dim: i64) -> impl nn::Module {
         let x1 = p.zeros("x1", &[dim]);
@@ -13,11 +9,37 @@ mod alstm {
         nn::func(move |xs| xs * &x1 + xs.exp() * &x2)
     }
 
+    fn normalize_data(input: &Tensor) -> Tensor {
+        let mean = input.mean(Kind::Float);
+        let std = input.std(false);
+
+        let normalized_input = input - mean;
+        normalized_input / std
+    }
+
+    #[test]
+    fn normalize_data_works(){
+        // 加载数据，这里以一个简单的示例为例，实际应用中，您需要根据实际情况加载数据
+        let data = vec![
+            Tensor::from_slice(&vec![1.0, 2.0, 3.0, 4.0, 5.0]),
+            Tensor::from_slice(&vec![6.0, 7.0, 8.0, 9.0, 10.0]),
+        ];
+
+        // 数据标准化
+        let normalized_data = data.iter().map(|tensor| normalize_data(tensor)).collect::<Vec<_>>();
+
+        // 打印标准化后的数据
+        for (i, tensor) in normalized_data.iter().enumerate() {
+            println!("Data normalized {}: {}", i, tensor);
+        }
+    }
+
     #[test]
     fn alstm_build_model_cuda_works() {
         let vs = nn::VarStore::new(Device::Cuda(0));
         let my_module = my_module(vs.root(), 7);
         let mut opt = nn::Sgd::default().build(&vs, 1e-2).unwrap();
+
         for _idx in 1..5000 {
             // Dummy mini-batches made of zeros.
             let xs = Tensor::zeros(&[7], kind::FLOAT_CUDA);