diff --git a/contrib/FCOS/README.md b/contrib/FCOS/README.md
index a8aadca921ba899921a7876abbbc94945c0b2828..9e074273592855e3abff95d9ee0dc1c1f3750232 100644
--- a/contrib/FCOS/README.md
+++ b/contrib/FCOS/README.md
@@ -11,7 +11,7 @@
 
 ### 1.1支持的产品
 
-本产品以昇腾310（推理）卡为硬件平台。
+本产品以昇腾310（推理）、310B（推理）卡为硬件平台。
 
 ### 1.2支持的版本
 
@@ -109,7 +109,7 @@ pip3 install mmdet
 
 ## 4 模型转换
 
-本项目使用的模型是FCOS目标检测模型这个模型是一个无anchor检测器。FCOS直接把预测特征图上的每个位置$(x,y)$当作训练样本，若这个位置在某个ground truth box的内部，则视为正样本，该位置的类别标签$c$对应这个box的类别，反之则视为负样本。这个网络的输出为目标框的左上角坐标、右下角坐标、类别和置信度。本项目的onnx模型可以直接[下载](https://www.hiascend.com/zh/software/modelzoo/models/detail/1/6fcc4747a48245d29351c26cd052dd13)。下载后，里面自带的om模型是可以直接使用的，或者自行使用ATC工具将onnx模型转换成为om模型，模型转换工具的使用说明参考[链接](https://gitee.com/ascend/docs-openmind/blob/master/guide/mindx/sdk/tutorials/%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99.md)。
+本项目使用的模型是FCOS目标检测模型这个模型是一个无anchor检测器。FCOS直接把预测特征图上的每个位置$(x,y)$当作训练样本，若这个位置在某个ground truth box的内部，则视为正样本，该位置的类别标签$c$对应这个box的类别，反之则视为负样本。这个网络的输出为目标框的左上角坐标、右下角坐标、类别和置信度。本项目的onnx模型可以直接[下载](https://mindx.sdk.obs.cn-north-4.myhuaweicloud.com/mindxsdk-referenceapps%20/contrib/Fcos/ATC%20Fcos.zip)。下载后，里面自带的om模型是可以直接使用的，或者自行使用ATC工具将onnx模型转换成为om模型，模型转换工具的使用说明参考[链接](https://gitee.com/ascend/docs-openmind/blob/master/guide/mindx/sdk/tutorials/%E5%8F%82%E8%80%83%E8%B5%84%E6%96%99.md)。
 
 模型转换步骤如下：
 
@@ -117,10 +117,13 @@ pip3 install mmdet
 
 2.进入models文件夹目录下，设置环境变量如下：
 
+```
+. /usr/local/Ascend/ascend-toolkit/set_env.sh   # Ascend-cann-toolkit开发套件包默认安装路径，根据实际安装路径修改
+```
 
 设置完环境变量之后，就进行模型的转换：
 
-模型转换语句如下：
+模型转换语句如下，注意若推理芯片为310B，需将atc-env脚本中模型转换atc命令中的soc_version参数设置为Ascend310B1。
 
 ```
 atc --model=fcos.onnx --framework=5 --soc_version=Ascend310 --input_format=NCHW --input_shape="input:1,3,800,1333" --output=fcos_bs1 --precision_mode=allow_fp32_to_fp16
diff --git a/contrib/FireDetection/README.md b/contrib/FireDetection/README.md
index eb042cd4098b0bd002b0482b3083eba9858ecd86..adffeb172e5350854762213c3a382464900ecd28 100644
--- a/contrib/FireDetection/README.md
+++ b/contrib/FireDetection/README.md
@@ -2,7 +2,7 @@
 
 ## 1 介绍
 
-高速公路车辆火灾识别基于 MindX SDK 开发，在 Ascend 310 芯片上进行目标检测，将检测结果保存成图片。项目主要流程为：通过 live555 服务器进行拉流输入视频，然后进行视频解码将 H.264 格式的视频解码为图片，图片缩放后经过模型推理进行火焰和烟雾检测，识别结果经过后处理后利用 cv 可视化识别框，如果检测到烟雾和火灾进行告警。
+高速公路车辆火灾识别基于 MindX SDK 开发，可在 Ascend 310 及 Ascend 310B 芯片上进行目标检测，将检测结果保存成图片。项目主要流程为：通过 live555 服务器进行拉流输入视频，然后进行视频解码将 H.264 格式的视频解码为图片，图片缩放后经过模型推理进行火焰和烟雾检测，识别结果经过后处理后利用 cv 可视化识别框，如果检测到烟雾和火灾进行告警。
 
 ### 1.1 支持的产品
 
@@ -110,7 +110,7 @@ export PYTHONPATH="${MX_SDK_HOME}/python:${PYTHONPATH}"
 
   运行结果：生成`best_s_t.onnx`文件。
 
-- **步骤5** 将`best_s_t.onnx`文件重命名为`firedetection.onnx`，然后运行当前目录`FireDetection\model`下的`model_conver.sh`
+- **步骤5** 将`best_s_t.onnx`文件重命名为`firedetection.onnx`，然后运行当前目录`FireDetection\model`下的`model_conver.sh`。注意若推理芯片为310B，需将atc-env脚本中模型转换atc命令中的soc_version参数设置为Ascend310B1。
 
   ```bash
   bash model_conver.sh
diff --git a/contrib/FireDetection/model/model_conver.sh b/contrib/FireDetection/model/model_conver.sh
index 9700b8ba73d8bd0d6362627a3a321e02d644c113..b15ec6c48175cdb6a5da5a7f22ef0a923cc6bf75 100644
--- a/contrib/FireDetection/model/model_conver.sh
+++ b/contrib/FireDetection/model/model_conver.sh
@@ -13,11 +13,7 @@
 # limitations under the License.
 
 # set ATC environment variate
-export install_path=/usr/local/Ascend/ascend-toolkit/latest
-export PATH=/usr/local/python3.9.2/bin:${install_path}/arm64-linux/atc/ccec_compiler/bin:${install_path}/arm64-linux/atc/bin:$PATH
-export PYTHONPATH=${install_path}/arm64-linux/atc/python/site-packages:${install_path}/arm64-linux/atc/python/site-packages/auto_tune.egg/auto_tune:${install_path}/arm64-linux/atc/python/site-packages/schedule_search.egg
-export LD_LIBRARY_PATH=${install_path}/arm64-linux/atc/lib64:$LD_LIBRARY_PATH
-export ASCEND_OPP_PATH=${install_path}/opp
+. /usr/local/Ascend/ascend-toolkit/set_env.sh   # The path where Ascend-cann-toolkit is located
 
 # atc transform model
 atc \
diff --git a/contrib/HelmetIdentification/Models/atc-env.sh b/contrib/HelmetIdentification/Models/atc-env.sh
index 2afbfa4dc6a21a4515ef803776c435316ccf5477..fa4c4c2608cb6c728d9ebf10462238f7286a2df5 100644
--- a/contrib/HelmetIdentification/Models/atc-env.sh
+++ b/contrib/HelmetIdentification/Models/atc-env.sh
@@ -1,11 +1,7 @@
 #!/bin/bash
 
 # This is used to convert onnx model file to .om model file.
-export install_path=/usr/local/Ascend/ascend-toolkit/latest
-export PATH=/usr/local/python3.9.2/bin:${install_path}/arm64-linux/atc/ccec_compiler/bin:${install_path}/arm64-linux/atc/bin:$PATH
-export PYTHONPATH=${install_path}/arm64-linux/atc/python/site-packages:${install_path}/arm64-linux/atc/python/site-packages/auto_tune.egg/auto_tune:${install_path}/arm64-linux/atc/python/site-packages/schedule_search.egg
-export LD_LIBRARY_PATH=${install_path}/arm64-linux/atc/lib64:$LD_LIBRARY_PATH
-export ASCEND_OPP_PATH=${install_path}/opp
+. /usr/local/Ascend/ascend-toolkit/set_env.sh   # The path where Ascend-cann-toolkit is located
 export Home="./path/"
 # Home is set to the path where the model is located
 
diff --git a/contrib/HelmetIdentification/Models/main-env.sh b/contrib/HelmetIdentification/Models/main-env.sh
deleted file mode 100644
index 7cbd319a45445f723be7484a15c5ab38537a2257..0000000000000000000000000000000000000000
--- a/contrib/HelmetIdentification/Models/main-env.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/bin/bash
-
-export MX_SDK_HOME=${MX_SDK_HOME}
-export install_path=/usr/local/Ascend/ascend-toolkit/latest
-export PATH=/usr/local/python3.9.2/bin:/usr/local/ffmpeg/bin/:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin
-export PYTHONPATH=/usr/local/python3.9.2/bin:${MX_SDK_HOME}/python
-export LD_LIBRARY_PATH=${MX_SDK_HOME}/lib:${MX_SDK_HOME}/opensource/lib:${MX_SDK_HOME}/opensource/lib64:${install_path}/acllib/lib64:/usr/local/Ascend/driver/lib64:${MX_SDK_HOME}/include:${MX_SDK_HOME}/python
-
-export GST_PLUGIN_SCANNER=${MX_SDK_HOME}/opensource/libexec/gstreamer-1.0/gst-plugin-scanner
-export GST_PLUGIN_PATH=${MX_SDK_HOME}/opensource/lib/gstreamer-1.0:${MX_SDK_HOME}/lib/plugins
-export ASCEND_OPP_PATH=${install_path}/opp
-export GST_DEBUG=3
-
-# ${MX_SDK_HOME}为远程SDK安装路径
\ No newline at end of file
diff --git a/contrib/HelmetIdentification/README.md b/contrib/HelmetIdentification/README.md
index 61f6b0eb482c5927a1283c966b5cb0c56c58b5c4..40bbe0e2535bf5df0529d74f1e9501e8ef49c683 100644
--- a/contrib/HelmetIdentification/README.md
+++ b/contrib/HelmetIdentification/README.md
@@ -6,7 +6,7 @@
 
 ### 1.1 支持的产品
 
-昇腾310(推理)
+昇腾310(推理)、昇腾310B(推理)
 
 ### 1.2 支持的版本
 
@@ -29,7 +29,6 @@ MindX SDK安装前准备可参考《用户指南》，[安装教程](https://git
   ├──Helmet_yolov5.cfg  #后处理配置文件
   ├──HelmetDetection.pipline # 安全帽识别推理流程pipline
   ├──imgclass.names  # 模型所有可识别类
-  ├──main-env.sh   # 环境变量设置脚本
   ├──main.py         # 推理运行程序
   ├──modify_yolov5s_slice.py  #slice算子修改脚本
   ├──dy_resize.py  # resize算子修改
@@ -38,7 +37,7 @@ MindX SDK安装前准备可参考《用户指南》，[安装教程](https://git
   ├──MxpiSelectedFrame # 跳帧插件
 ├── Test  
   ├──performance_test_main.py # 性能测试脚本  
-  ├──select.py # 测试集筛选脚本  
+  ├──test_select.py # 测试集筛选脚本  
   ├──parse_voc.py # 测试数据集解析脚本  
   ├──testmain.py # 测试主程序  
   ├──map_calculate.py # 精度计算程序
@@ -66,29 +65,12 @@ MindX SDK安装前准备可参考《用户指南》，[安装教程](https://git
 
 
 
-在运行脚本main.py前（2.2章节），需要通过环境配置脚本main-env.sh设置环境变量,运行命令：
+在运行脚本main.py前（2.2章节），需要执行如下两个环境配置脚本设置环境变量,运行命令：
 
 ```shell
-source main-env.sh
+. /usr/local/Ascend/ascend-toolkit/set_env.sh   # Ascend-cann-toolkit开发套件包默认安装路径，根据实际安装路径修改
+. ${MX_SDK_HOME}/mxVision/set_env.sh   # ${MX_SDK_HOME}替换为用户的SDK安装路径
 ```
-- 环境变量介绍
-
-```bash
-export MX_SDK_HOME=${MX_SDK_HOME}
-export install_path=/usr/local/Ascend/ascend-toolkit/latest
-export PATH=/usr/local/python3.9.2/bin:${install_path}/arm64-linux/atc/ccec_compiler/bin:${install_path}/arm64-linux/atc/bin:${install_path}/atc/bin
-export PYTHONPATH=/usr/local/python3.9.2/bin:${MX_SDK_HOME}/python
-export ${MX_SDK_HOME}/lib:${MX_SDK_HOME}/opensource/lib:${MX_SDK_HOME}/opensource/lib64:${install_path}/acllib/lib64:/usr/local/Ascend/driver/lib64:${MX_SDK_HOME}/include:${MX_SDK_HOME}/python
-
-export GST_PLUGIN_SCANNER=${MX_SDK_HOME}/opensource/libexec/gstreamer-1.0/gst-plugin-scanner
-export GST_PLUGIN_PATH=${MX_SDK_HOME}/opensource/lib/gstreamer-1.0:${MX_SDK_HOME}/lib/plugins
-export ASCEND_OPP_PATH=${install_path}/opp
-export GST_DEBUG=3
-```
-
-注：其中SDK安装路径${MX_SDK_HOME}替换为用户的SDK安装路径;install_path替换为开发套件包所在路径。LD_LIBRARY_PATH用以加载开发套件包中llib库。GST_DEBUG用以mxpi_rtspsrc取流地址配置不正确时出现warning日志提示。
-
-
 
 ## 3.推理
 
@@ -141,13 +123,13 @@ python3 modify_yolov5s_slice.py YOLOv5_s.onnx
 
 可以得到修改好后的YOLOv5_s.onnx模型
 
-3. 最后运行atc-env脚本将onnx转为om模型，运行命令如下：
+3. 最后运行atc-env脚本将onnx转为om模型，运行命令如下。注意若推理芯片为310B，需将atc-env脚本中模型转换atc命令中的soc_version参数设置为Ascend310B1。
 
 ```shell
 sh atc-env.sh
 ```
 
-提示 **ATC run success** 说明转换成功
+提示 **ATC run success** 说明转换成功。
 
 脚本中包含atc命令:
 
@@ -316,13 +298,13 @@ python3 performance_test_main.py
     ├── JPEGImages                 # 数据集原图片
   ```
 
-注：将数据集中的三个文件放置于项目的根目录Test文件下，与**select.py**同目录。
+注：将数据集中的三个文件放置于项目的根目录Test文件下，与**test_select.py**同目录。
 
 ###### 3.2.2测试数据集筛选
 
-依据数据集中ImageSets文件夹中test.txt文件，从原始数据集中筛选出测试数据集，该程序**select.py**放在源码根目录Test中，在同目录下创建文件夹TestImages用来存储筛选的数据。在该目录下运行命令：
+依据数据集中ImageSets文件夹中test.txt文件，从原始数据集中筛选出测试数据集，该程序**test_select.py**放在源码根目录Test中，在同目录下创建文件夹TestImages用来存储筛选的数据。在该目录下运行命令：
 ```shell
-python3.9.2 select.py
+python3.9.2 test_select.py
 ```
 
 程序运行后在根目录Test中会存放筛选出的测试集图片共1517张。
diff --git a/contrib/HelmetIdentification/Test/select.py b/contrib/HelmetIdentification/Test/select.py
deleted file mode 100644
index d74aa03269b163f12437cec6ba8fa7480da8d064..0000000000000000000000000000000000000000
--- a/contrib/HelmetIdentification/Test/select.py
+++ /dev/null
@@ -1,33 +0,0 @@
-# Copyright 2021 Huawei Technologies Co., Ltd
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-
-# http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import shutil
-import cv2
-
-with open("ImageSets/Main/test.txt", "r") as f:
-    data = f.readlines()
-    text_data = []
-    for line in data:
-        line_new = line.strip('\n')  # Remove the newline character of each element in the list
-        text_data.append(line_new)
-    print(text_data)
-
-path = 'JPEGImages'
-save_path = 'TestImages'
-
-for item in os.listdir(path):
-    file_name = item.split('.')[0]
-    if file_name in text_data:
-        img = cv2.imread(path + '/' + item)
-        cv2.imwrite(save_path + '/' + file_name + ".jpg", img)
\ No newline at end of file
diff --git a/contrib/OpenCVPlugin/README.md b/contrib/OpenCVPlugin/README.md
index ec45cc3894d8da456618587f32bab03bb61526a0..1959196526f09962110cdcd14a50ea411a42fdab 100644
--- a/contrib/OpenCVPlugin/README.md
+++ b/contrib/OpenCVPlugin/README.md
@@ -12,7 +12,7 @@
 
 ### 1.1 支持的产品
 
-本项目以昇腾Atlas310卡为主要的硬件平台。
+本项目以昇腾Atlas310、Atlas310B卡为主要的硬件平台。
 
 
 ### 1.2 支持的版本
diff --git a/contrib/facemaskdetection/README.md b/contrib/facemaskdetection/README.md
index bb9f1ade71205720104f247f9a34ffd0d6928338..535924b7ac3021f9036e0cc9ae5d714151c56e6f 100644
--- a/contrib/facemaskdetection/README.md
+++ b/contrib/facemaskdetection/README.md
@@ -5,7 +5,7 @@
 
 ### 1.1 支持的产品
 
-本项目以昇腾Atlas310卡为主要的硬件平台。
+本项目以昇腾Atlas310、Atlas310B卡为主要的硬件平台。
 
 ### 1.2 支持的版本
 
@@ -54,25 +54,13 @@ CANN版本为5.0.4。
 | opencv-python       | 4.5.2.54     | 用于识别结果画框              | python3 -m pip install opencv-python                       |
 
 
-在编译运行项目前，需要设置环境变量：
+在编译运行项目前，需要执行如下两个环境配置脚本设置环境变量：
 
-- 环境变量介绍
-
-```
-export MX_SDK_HOME=${MX_SDK_HOME}
-export install_path=/usr/local/Ascend/ascend-toolkit/latest
-export PATH=/usr/local/python3.9.2/bin:${install_path}/arm64-linux/atc/ccec_compiler/bin:${install_path}/arm64-linux/atc/bin:${install_path}/atc/bin:$PATH
-export PYTHONPATH=/usr/local/python3.9.2/bin:${MX_SDK_HOME}/python
-export ${MX_SDK_HOME}/lib:${MX_SDK_HOME}/opensource/lib:${MX_SDK_HOME}/opensource/lib64:${install_path}/acllib/lib64:/usr/local/Ascend/driver/lib64:${MX_SDK_HOME}/include:${MX_SDK_HOME}/python
-
-export GST_PLUGIN_SCANNER=${MX_SDK_HOME}/opensource/libexec/gstreamer-1.0/gst-plugin-scanner
-export GST_PLUGIN_PATH=${MX_SDK_HOME}/opensource/lib/gstreamer-1.0:${MX_SDK_HOME}/lib/plugins
-export ASCEND_OPP_PATH=${install_path}/opp
-export GST_DEBUG=3
+```shell
+. /usr/local/Ascend/ascend-toolkit/set_env.sh   # Ascend-cann-toolkit开发套件包默认安装路径，根据实际安装路径修改
+. ${MX_SDK_HOME}/mxVision/set_env.sh   # ${MX_SDK_HOME}替换为用户的SDK安装路径
 ```
 
-
-
 ## 依赖下载
 
 所用模型与软件依赖如下表所示。
@@ -92,21 +80,13 @@ export GST_DEBUG=3
 
 pb文件转换为om文件
 
-1. 设置环境变量：
+1. 执行如下脚本设置环境变量：
 
 ```
-export install_path=/usr/local/Ascend/ascend-toolkit/latest
-
-export PATH=/usr/local/python3.9.2/bin:${install_path}/atc/ccec_compiler/bin:${install_path}/atc/bin:$PATH
-
-export PYTHONPATH=${install_path}/atc/python/site-packages:${install_path}/atc/python/site-packages/auto_tune.egg/auto_tune:${install_path}/atc/python/site-packages/schedule_search.egg
-
-export LD_LIBRARY_PATH=${install_path}/atc/lib64:$LD_LIBRARY_PATH
-
-export ASCEND_OPP_PATH=${install_path}/opp
+. /usr/local/Ascend/ascend-toolkit/set_env.sh   # Ascend-cann-toolkit开发套件包默认安装路径，根据实际安装路径修改
 ```
 
-2. 运行atc工具将pb模型文件转为om模型，运行命令如下：
+2. 运行atc工具将pb模型文件转为om模型，运行命令如下。注意若推理芯片为310B，需将atc-env脚本中模型转换atc命令中的soc_version参数设置为Ascend310B1。
 
 ```
 atc --model=./face_mask_detection.pb --framework=3 --output=./aipp --output_type=FP32 --soc_version=Ascend310 --input_shape="data_1:1,260,260,3" --input_format=NHWC --insert_op_conf=./face_mask.aippconfig
diff --git a/contrib/facemaskdetection/model_conversion.sh b/contrib/facemaskdetection/model_conversion.sh
index 0ef69f34553ab0ae15e80d262b5a213f4f674d32..b2f24f98810753632361b3f0c2df524a4374d2e6 100644
--- a/contrib/facemaskdetection/model_conversion.sh
+++ b/contrib/facemaskdetection/model_conversion.sh
@@ -22,16 +22,7 @@
 # 设置环境变量（请确认install_path路径是否正确）
 # Set environment PATH (Please confirm that the install_path is correct).
 
-export install_path=/usr/local/Ascend/ascend-toolkit/latest
-
-export PATH=/usr/local/python3.9.2/bin:${install_path}/atc/ccec_compiler/bin:${install_path}/atc/bin:$PATH
-
-export PYTHONPATH=${install_path}/atc/python/site-packages:${install_path}/atc/python/site-packages/auto_tune.egg/auto_tune:${install_path}/atc/python/site-packages/schedule_search.egg
-
-export LD_LIBRARY_PATH=${install_path}/atc/lib64:$LD_LIBRARY_PATH
-
-export ASCEND_OPP_PATH=${install_path}/opp
-
+. /usr/local/Ascend/ascend-toolkit/set_env.sh   # The path where Ascend-cann-toolkit is located
 
 
 atc --model=./face_mask_detection.pb --framework=3 --output=./aipp --output_type=FP32 --soc_version=Ascend310 --input_shape="data_1:1,260,260,3" --input_format=NHWC --insert_op_conf=./face_mask.aippconfig
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