JETSON ORIN NANO 进阶教程 本章主要Jetson特色的AI功能使用 新页面 安装jtop jtop 是英伟达专为 ​ ​Jetson 系列边缘计算设备​ ​开发的​ ​交互式系统监控工具。 1 安装jtop 安装jtop需要的依赖库 sudo apt update sudo apt install python3 sudo apt install python3-pip 安装jtop sudo pip3 install -U pip -i https://pypi.tuna.tsinghua.edu.cn/simple sudo pip3 install jetson-stats -i https://pypi.tuna.tsinghua.edu.cn/simple sudo systemctl restart jtop.service 2 jtop的使用 安装完成后在控制台中输入 jtop 打开主界面 2.1 监视各个模块的工作信息 2.2 控制风扇 2.3 查看内置软件信息 安装CUDA Jetson CUDA 是NVIDIA为边缘AI设计的并行计算平台，基于ARM的Jetson系列硬件，支持 GPU加速 深度学习与 实时推理 ，低 功耗高性能 。 安装CUDA JETSON SDK sudo apt update sudo apt install nvidia-jetpack 对开发板进行压力测试 以下测试仅在 JetPack 6.1 (rev1) 和 JetPack 5.1.5 版本中通过验证 调整功耗上限： sudo nvpmodel -m 2 #nano 模式2为super sudo jetson_clocks --fan cpu压力测试： sudo apt install stress stress --cpu 8 --io 4 --vm 2 --vm-bytes 128M --hdd 1 --hdd-bytes 1024M gpu压力测试： git clone https://github.com/anseeto/jetson-gpu-burn/ cd jetson-gpu-burn make ./gpu_burn 100000 状态查询： sudo jtop Orin Nano 整机最大功率为 30W. Orin NX  整机最大功率为 40W . 编译安装opencv with cuda JetPack预装的opencv没有启用cuda，需要自行编译安装。 1. 脚本一键安装 #!/bin/bash # opencv_install.sh # Modified from https://github.com/AastaNV/JEP/blob/master/script/install_opencv4.10.0_Jetpack6.1.sh version="4.10.0" folder="workspace" remove_old="" set -e # Parse command-line arguments for arg in "$@"; do case $arg in --version=*) version="${arg#*=}" ;; --folder=*) folder="${arg#*=}" ;; --remove-old=*) remove_old="${arg#*=}" ;; --help|-h) echo "Usage: $0 [--version=4.x.x] [--folder=dir] [--remove-old=yes/no]" exit 0 ;; *) echo "Unknown parameter: $arg" echo "Usage: $0 [--version=4.x.x] [--folder=dir] [--remove-old=yes/no]" exit 1 ;; esac done # Create installation directory if it doesn't exist if [ ! -d "$folder" ]; then echo "Creating directory: $folder" mkdir -p "$folder" fi cd "$folder" || exit # Old OpenCV removal logic if [ -z "$remove_old" ]; then read -rp "Do you want to remove system-installed OpenCV? (yes/no): " remove_old fi case "$remove_old" in [yY] | [yY][eE][sS]) echo "** Removing system OpenCV packages" sudo apt -y purge *libopencv* sudo apt -y autoremove ;; *) echo "** Skipping system OpenCV removal" ;; esac echo "------------------------------------" echo "** Installing dependencies (1/4)" echo "------------------------------------" sudo apt-get update sudo apt-get install -y build-essential cmake git libgtk2.0-dev pkg-config \ libavcodec-dev libavformat-dev libswscale-dev libgstreamer1.0-dev \ libgstreamer-plugins-base1.0-dev python3-dev python3-numpy libtbb2 \ libtbb-dev libjpeg-dev libpng-dev libtiff-dev libv4l-dev v4l-utils qv4l2 curl # Verify essential dependencies installed for dep in g++ cmake git pkg-config; do if ! command -v "$dep" > /dev/null; then echo "Error: $dep installation failed" exit 1 fi done echo "------------------------------------" echo "** Downloading OpenCV ${version} (2/4)" echo "------------------------------------" # Check if source files already exist download_opencv=false download_contrib=false if [ ! -f "opencv-${version}.zip" ]; then echo "Downloading opencv-${version}.zip" wget -O opencv-${version}.zip https://github.com/opencv/opencv/archive/${version}.zip || { echo "Download failed! Check your internet connection or verify the version exists" exit 1 } download_opencv=true else echo "opencv-${version}.zip exists, skipping download" fi if [ ! -f "opencv_contrib-${version}.zip" ]; then echo "Downloading opencv_contrib-${version}.zip" wget -O opencv_contrib-${version}.zip https://github.com/opencv/opencv_contrib/archive/${version}.zip || { echo "Download failed! Check your internet connection or verify the version exists" exit 1 } download_contrib=true else echo "opencv_contrib-${version}.zip exists, skipping download" fi # Unpack source files if [ ! -d "opencv-${version}" ] || $download_opencv; then if [ -d "opencv-${version}" ]; then echo "Removing existing opencv-${version} directory" rm -rf "opencv-${version}" fi echo "Unpacking opencv-${version}.zip" unzip -q opencv-${version}.zip || { echo "Extraction failed! File may be corrupt" exit 1 } fi if [ ! -d "opencv_contrib-${version}" ] || $download_contrib; then if [ -d "opencv_contrib-${version}" ]; then echo "Removing existing opencv_contrib-${version} directory" rm -rf "opencv_contrib-${version}" fi echo "Unpacking opencv_contrib-${version}.zip" unzip -q opencv_contrib-${version}.zip || { echo "Extraction failed! File may be corrupt" exit 1 } fi # Clean up zip files after successful extraction if [ $? -eq 0 ]; then rm -f opencv-${version}.zip opencv_contrib-${version}.zip fi cd opencv-${version} || exit echo "------------------------------------" echo "** Building OpenCV ${version} (3/4)" echo "------------------------------------" mkdir -p release cd release # Auto-detect CUDA architecture cuda_arch="" if command -v nvidia-smi &> /dev/null; then gpu_name=$(nvidia-smi --query-gpu=name --format=csv,noheader | head -n1) if [[ $gpu_name == *"Orin"* ]] || [[ $gpu_name == *"Jetson"* ]]; then cuda_arch="8.7" elif [[ $gpu_name == *"A100"* ]]; then cuda_arch="8.0" fi fi cmake_cmd="cmake -D WITH_CUDA=ON -D WITH_CUDNN=ON -D OPENCV_GENERATE_PKGCONFIG=ON " cmake_cmd+="-D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib-${version}/modules " cmake_cmd+="-D WITH_GSTREAMER=ON -D WITH_LIBV4L=ON -D BUILD_opencv_python3=ON " cmake_cmd+="-D BUILD_TESTS=OFF -D BUILD_PERF_TESTS=OFF -D BUILD_EXAMPLES=OFF " cmake_cmd+="-D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local " # Add CUDA architecture if detected if [ -n "$cuda_arch" ]; then echo "Detected NVIDIA GPU: ${gpu_name}, using CUDA_ARCH_BIN=${cuda_arch}" cmake_cmd+="-D CUDA_ARCH_BIN=${cuda_arch} -D CUDA_ARCH_PTX=\"\" " else echo "No supported GPU detected, skipping CUDA architecture flags" fi # Execute CMake configuration echo "CMake command: $cmake_cmd .." $cmake_cmd .. || { echo "CMake configuration failed" exit 1 } # Parallel build (leave one core for system stability) cpu_cores=$(($(nproc) - 1)) [ $cpu_cores -lt 1 ] && cpu_cores=1 echo "Building with ${cpu_cores} CPU cores" make -j${cpu_cores} || { echo "Compilation failed" exit 1 } echo "------------------------------------" echo "** Installing OpenCV ${version} (4/4)" echo "------------------------------------" sudo make install || { echo "Installation failed" exit 1 } # Add environment variables to .bashrc (only if not already present) bashrc=~/.bashrc env_lines=( "export LD_LIBRARY_PATH=/usr/local/lib:\$LD_LIBRARY_PATH" "export PYTHONPATH=/usr/local/lib/python3.10/site-packages/:\$PYTHONPATH" ) for line in "${env_lines[@]}"; do if ! grep -Fxq "$line" "$bashrc"; then echo "Adding to .bashrc: $line" echo "$line" >> "$bashrc" else echo "Environment variable already exists: $line" fi done source ~/.bashrc echo "** OpenCV ${version} installation completed" echo "Verification commands:" echo " pkg-config --modversion opencv4" echo " python3 -c 'import cv2; print(cv2.__version__)'" echo "** Installation successful!" 2. 手动安装 2.1 卸载自带opencv sudo apt-get purge libopencv* sudo apt autoremove sudo apt-get update 2.2 安装前置软件包 sudo apt-get update sudo apt-get install -y build-essential cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev sudo apt-get install -y libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev python3.10-dev python3-numpy sudo apt-get install -y libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libv4l-dev v4l-utils qv4l2 sudo apt-get install -y curl 2.3 获取opencv源码(以4.10.0版本为例) version=4.10.0 wget -O "opencv-${version}.zip" "https://github.com/opencv/opencv/archive/${version}.zip" wget -O "opencv_contrib-${version}.zip" "https://github.com/opencv/opencv_contrib/archive/${version}.zip" unzip "opencv-${version}.zip" unzip "opencv_contrib-${version}.zip" rm "opencv-${version}.zip" "opencv_contrib-${version}.zip" cd "opencv-${version}/" 2.4 编译源码 此步骤至少需要半小时以上。 中途可能会下载第三方软件包，建议提前确认网络环境。 mkdir build cd build/ cmake -D WITH_CUDA=ON -D WITH_CUDNN=ON -D CUDA_ARCH_BIN="8.7" -D CUDA_ARCH_PTX="" -D OPENCV_GENERATE_PKGCONFIG=ON -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib-${version}/modules -D WITH_GSTREAMER=ON -D WITH_LIBV4L=ON -D BUILD_opencv_python3=ON -D BUILD_TESTS=OFF -D BUILD_PERF_TESTS=OFF -D BUILD_EXAMPLES=OFF -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local .. make -j$(nproc) 2.5 安装 sudo make install echo 'export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH' >> ~/.bashrc echo 'export PYTHONPATH=/usr/local/lib/python3.10/site-packages/:$PYTHONPATH' >> ~/.bashrc source ~/.bashrc 三、验证测试 #--test_cuda.cpp #include #include #include #include #include // CPU 矩阵乘法 void cpu_matrix_mult(cv::Mat& a, cv::Mat& b, cv::Mat& result) {   for (int i = 0; i < 50; i++) {       result = a * b;   } } // GPU 矩阵乘法 void gpu_matrix_mult(cv::cuda::GpuMat& d_a, cv::cuda::GpuMat& d_b, cv::cuda::GpuMat& d_result) {   cv::cuda::Stream stream;   for (int i = 0; i < 50; i++) {       cv::cuda::gemm(d_a, d_b, 1.0, cv::cuda::GpuMat(), 0, d_result, 0, stream);       stream.waitForCompletion();   } } int main() {   try {       std::cout << "--- OpenCV CUDA Matrix Multiplication Test ---\n";           // 创建两个 1000x1000 的随机矩阵       cv::Mat mat_a(1000, 1000, CV_32FC1);       cv::Mat mat_b(1000, 1000, CV_32FC1);       cv::randu(mat_a, 0.0f, 1.0f);       cv::randu(mat_b, 0.0f, 1.0f);           cv::Mat cpu_result;           // CPU 测试       auto start_cpu = std::chrono::high_resolution_clock::now();       cpu_matrix_mult(mat_a, mat_b, cpu_result);       auto end_cpu = std::chrono::high_resolution_clock::now();       double cpu_time = std::chrono::duration_cast(end_cpu - start_cpu).count();           // GPU 测试       cv::cuda::GpuMat d_mat_a, d_mat_b, d_result;       d_mat_a.upload(mat_a);       d_mat_b.upload(mat_b);           auto start_gpu = std::chrono::high_resolution_clock::now();       gpu_matrix_mult(d_mat_a, d_mat_b, d_result);       auto end_gpu = std::chrono::high_resolution_clock::now();       double gpu_time = std::chrono::duration_cast(end_gpu - start_gpu).count();           // 下载结果进行验证       cv::Mat gpu_result;       d_result.download(gpu_result);           // 计算误差(一般为空)       double diff = cv::norm(cpu_result, gpu_result, cv::NORM_L2);       std::cout << "Result difference: " << diff << "\n";           std::cout << "Performance Results:\n"                 << " - CPU time: " << cpu_time << " ms\n"                 << " - GPU time: " << gpu_time << " ms\n"                 << " - Speedup: " << cpu_time / gpu_time << "x\n";           std::cout << "\n✅ CUDA matrix multiplication test completed\n";       return 0;       } catch (const cv::Exception& e) {       std::cerr << "OpenCV Error (" << e.err << "): " << e.what() << "\n";       return -1;   } catch (const std::exception& e) {       std::cerr << "Standard Error: " << e.what() << "\n";       return -2;   } } 编译运行 jetson@jetson-desktop:~/work$ g++ test_cuda.cpp -o test_cuda `pkg-config --cflags --libs opencv4` jetson@jetson-desktop:~/work$ ./test_cuda --- OpenCV CUDA Performance Test --- Performance Results: - CPU time: 2451 ms - GPU time: 918 ms - Speedup: 2.66993x ✅ CUDA performance test completed 安装使用pytorch和torchvision ​​ PyTorch 是 Python 中最流行、最易用的深度学习框架之一。它让开发者能够像操作普通 Python 代码一样，直观、灵活地设计和训练复杂的神经网络模型。其简洁的 API 设计和强大的 GPU 加速支持，使得从研究想法到实际部署的开发过程都极其高效便捷，广受开发者青睐。​ NVIDIA 为 Jetson 系列设备专门适配了对应的软件包，其版本依赖关系如下： PyTorch Version NVIDIA Framework  Container NVIDIA Framework  Wheel JetPack Version 2.8.0a0+5228986c39 25.06 - 6.2 2.8.0a0+5228986c39 25.05 - 6.2 2.7.0a0+79aa17489c 25.04 - 6.2 2.7.0a0+7c8ec84dab 25.03 - 6.2 2.7.0a0+6c54963f75 25.02 - 6.2 2.6.0a0+ecf3bae40a 25.01 - 6.1 2.6.0a0+df5bbc09d1 24.12 - 6.1 2.6.0a0+df5bbc0 24.11 - 6.1 2.5.0a0+e000cf0ad9 24.10 - 6.1 2.5.0a0+b465a5843b 24.09 24.09 6.1 2.5.0a0+872d972e41 24.08 - 6.0 2.4.0a0+3bcc3cddb5 24.07 24.07 6.0 2.4.0a0+f70bd71a48 24.06 24.06 6.0 2.4.0a0+07cecf4168 24.05 24.05 6.0 2.3.0a0+6ddf5cf85e 24.04 24.04 6.0 Developer Preview 2.3.0a0+40ec155e58 24.03 24.03 2.3.0a0+ebedce2 24.02 24.02 2.2.0a0+81ea7a4 23.12, 24.01 23.12, 24.01 2.2.0a0+6a974bec 23.11 23.11 2.1.0a   23.06 5.1.x 2.0.0   23.05 2.0.0a0+fe05266f   23.04 2.0.0a0+8aa34602   23.03 1.14.0a0+44dac51c   23.02, 23.01 1.13.0a0+936e930   22.11 5.0.2 1.13.0a0+d0d6b1f   22.09, 22,10 1.13.0a0+08820cb 22.07 22.07 1.13.0a0+340c412 22.06 22.06 5.0.1 1.12.0a0+8a1a93a9 22.05 22.05 5.0 1.12.0a0+bd13bc66   22.04 1.12.0a0+2c916ef   22.03 1.11.0a0+bfe5ad28   22.01 4.6.1 下面教程以 JetPack6.2.1 cuda12.6 版本为例 1.安装torch工具包 1.1下载并安装torch , torchvison wget https://pypi.jetson-ai-lab.io/jp6/cu126/+f/62a/1beee9f2f1470/torch-2.8.0-cp310-cp310-linux_aarch64.whl wget https://pypi.jetson-ai-lab.io/jp6/cu126/+f/907/c4c1933789645/torchvision-0.23.0-cp310-cp310-linux_aarch64.whl pip install torch-2.8.0-cp310-cp310-linux_aarch64.whl torchvision-0.23.0-cp310-cp310-linux_aarch64.whl -i https://pypi.tuna.tsinghua.edu.cn/simple 1.2 检测是否正确安装 使用python执行下面三个语句 jetson@jetson-desktop:~$ python Python 3.10.16 (main, Dec 11 2024, 16:18:56) [GCC 11.2.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import torch >>> print(torch.__version__) 2.8.0 >>> print(torch.cuda.is_available()) True 2. 运行YOLO11 YOLO 是一种实时目标检测算法，它将目标检测视为单阶段回归问题，通过将图像划分为网格并直接预测边界框与类别概率，实现高速且高精度的检测。YOLO系列因开源易用、部署灵活，广泛应用于自动驾驶、安防监控、工业质检等领域。 2.1 安装miniconda curl -L https://repo.anaconda.com/miniconda/Miniconda3-py310_25.3.1-1-Linux-aarch64.sh | bash source ~/.bashrc conda --version   2.2 conda换源 conda config --add channels https://mirrors.ustc.edu.cn/anaconda/pkgs/main/ conda config --add channels https://mirrors.ustc.edu.cn/anaconda/pkgs/free/ conda config --add channels https://mirrors.ustc.edu.cn/anaconda/cloud/conda-forge/ conda config --add channels https://mirrors.ustc.edu.cn/anaconda/cloud/msys2/ conda config --set show_channel_urls yes 2.3创建conda环境 conda create -n jetson-ai python=3.10 2.4 进入conda环境 conda activate jetson-ai 2.5 安装torch和torchvison wget https://pypi.jetson-ai-lab.io/jp6/cu126/+f/62a/1beee9f2f1470/torch-2.8.0-cp310-cp310-linux_aarch64.whl wget https://pypi.jetson-ai-lab.io/jp6/cu126/+f/907/c4c1933789645/torchvision-0.23.0-cp310-cp310-linux_aarch64.whl pip install torch-2.8.0-cp310-cp310-linux_aarch64.whl torchvision-0.23.0-cp310-cp310-linux_aarch64.whl -i https://pypi.tuna.tsinghua.edu.cn/simple 2.6 安装ultralytics pip install ultralytics -i https://pypi.tuna.tsinghua.edu.cn/simple 2.7 运行摄像头视频推理例程 接入摄像头，并在上面创建的环境中运行如下程序。 import cv2 import time from ultralytics import YOLO from ultralytics import YOLOWorld # Load the YOLO model model = YOLO("yolo11s.pt") # Open the video file video_path = 0 cap = cv2.VideoCapture(video_path) # Loop through the video frames while cap.isOpened(): # Read a frame from the video success, frame = cap.read() start = time.time() if success: # Run YOLO inference on the frame results = model(frame) inf_time = time.time() - start # Visualize the results on the frame annotated_frame = results[0].plot() fps = 1.0 / inf_time if inf_time > 0 else 0 # show FPS cv2.putText(annotated_frame, f"FPS: {fps:.2f}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2) cv2.imshow("YOLO Inference", annotated_frame) # Break the loop if 'q' is pressed if cv2.waitKey(1) & 0xFF == ord("q"): break else: # Break the loop if the end of the video is reached break # Release the video capture object and close the display window cap.release() cv2.destroyAllWindows() 更多信息可参考 Ultralytics YOLO11 -Ultralytics YOLO 文档 安装使用ollama 1. 运行以下指令安装ollama curl -fsSL https://ollama.com/install.sh | sh 2. 检查是否正确安装 jetson@jetson-desktop:~$ ollama -v ollama version is 0.9.6 3. 下载并运行deepseek-r1 1.5b模型 ollama run deepseek-r1:1.5b 安装使用Jetson-container 开始本节内容前请确定您的网络环境能正常拉取docker镜像 Jetson-container是NVIDIA为Jetson设备打造的轻量Docker环境，预装CUDA、cuDNN、TensorRT，快速部署AI边缘应用。 本节以 Jetson Orin NX 16GB ， JetPack6.2.1 运行 Comfy-UI 为例进行展示。您也可以参考 官方示例 Comfy-UI 是一款专业的节点式Stable Diffusion图形界面，拖拽连接即可构建文生图工作流，支持LoRA、ControlNet、视频扩散，低代码、易扩展等应用及特性。 1. 下载编译 jetson-container 环境 git clone https://github.com/dusty-nv/jetson-containers.git cd jetson-containersbash jetson-containers/install.sh 2. 拉取docker镜像并运行 jetson-containers run dustynv/comfyui:r36.4.3 3. 成功运行后命令行窗口输出如下 4. 打开对应服务的GUI网页 若在Jetson设备上打开则访问 http://0.0.0.0:8188 若在局域网内的其他设备则输入 http://:8188 5. 设定映射路径，将开发板里下载好的模型映射到容器内部目录 jetson-containers run ~/models/:/opt/ComfyUI/models/checkpoints dustynv/comfyui:r36.4.3 6. 按照需求搭建工作流并调整参数生成图像 关于ComfyUI的使用教程请参考其官网 ComfyUI | 用AI生成视频、图像、音频 使用TensorRT加速 c++ python绑定 网卡驱动 4G模块使用说明 移远通信LTE Standard EM05系列是一款专为IoT/M2M应用而设计的LTE Cat 4模块。采用节省空间的M.2（NGFF）封装类型，超薄、紧凑的设计使其更易于嵌入到小尺寸产品中。 EM05系列支持最大下行速率150 Mbps，最大上行速率50 Mbps,包含三个型号：EM05-CN、EM05-E和EM05-G。支持LTE-FDD、LTE-TDD、DC-HSDPA、HSPA+、HSDPA、HSUPA、WCDMA和CDMA等多种网络制式。 要在Jetson系列上使用EM-05模块需要重新编译系统内核并刷入新编译的系统。 方法一：替换新内核 1.1 拉取编译好的资料 1.2 修改设备树配置文件 1.3 移动内核和驱动到指定位置 1.4 应用更改并重启验证 方法二：编译刷入完整系统 2.1 获取源码和工具链 安装编译依赖工具 sudo apt install build-essential bc sudo apt-get install git bison flex libssl-dev zip libncurses-dev make git sudo apt-get install build-essential bc 创建工具链放置目录 mkdir $HOME/l4t-gcc-toolchain cd $HOME/l4t-gcc-toolchain 下载并解压交叉编译工具链 wget https://developer.nvidia.com/downloads/embedded/l4t/r36_release_v3.0/toolchain/aarch64--glibc--stable-2022.08-1.tar.bz2 tar xf aarch64--glibc--stable-2022.08-1.tar.bz2 进入到缓存好的刷机固件目录 cd $HOME/nvidia/nvidia_sdk/JetPack_6.2.1_Linux_JETSON_ORIN_NANO_TARGETS/Linux_for_Tegra/source  #根据实际目录进行修改 根据 release tag 拉取源码。 Jetson Linux Archive | NVIDIA Developer  ./source_sync.sh -k -t #替换为你自己的发行版本如 jetson_36.4.3 2.2 修改内核源码 本部分内容可参考 移远Linux&Andriod驱动技术手册 Jetson Orin Nano配置文件路径：arch/arm64/configs/defconfig 配置USB转串口驱动添加如下内容 CONFIG_USB_SERIAL=y CONFIG_USB_SERIAL_WWAN=y CONFIG_USB_SERIAL_OPTION=y CONFIG_USB_NET_DRIVERS=y CONFIG_USB_USBNET=y CONFIG_USB_NET_QMI_WWAN=y CONFIG_USB_WDM=y 修改 drivers/usb/serial/option.c，向USB转串口驱动添加VID和PID static const struct usb_device_id option_ids[] = { #if 1 // 2025-04-24 Added by Quectel   { USB_DEVICE(0x2C7C, 0x0125) }, #endif   ... ... } 使用USBNet驱动，文件路径：drivers/usb/serial/option.c static int option_probe(struct usb_serial *serial,           const struct usb_device_id *id) {   ... ... #if 1  // 2025-04-24 Added by Quectel if (serial->dev->descriptor.idVendor == cpu_to_le16(0x2C7C)) {       __u16 idProduct = le16_to_cpu(serial->dev->descriptor.idProduct);       struct usb_interface_descriptor *intf = &serial->interface->cur_altsetting->desc;       if (intf->bInterfaceClass != 0xFF || intf->bInterfaceSubClass == 0x42) {               //ECM, RNDIS, NCM, MBIM, ACM, UAC, ADB               return -ENODEV;       }       if ((idProduct&0xF000) == 0x0000) {               //MDM interface 4 is QMI               if (intf->bInterfaceNumber == 4 && intf->bNumEndpoints == 3                       && intf->bInterfaceSubClass == 0xFF && intf->bInterfaceProtocol == 0xFF)                       return -ENODEV;       } } #endif   /* Store the device flags so we can use them during attach. */   usb_set_serial_data(serial, (void *)device_flags);   return 0; } 添加零包机制，文件路径：drivers/usb/serial/usb_wwan.c static struct urb *usb_wwan_setup_urb(struct usb_serial_port *port,                     int endpoint,                     int dir, void *ctx, char *buf, int len,                     void (*callback) (struct urb *)) { ... ...   usb_fill_bulk_urb(urb, serial->dev,             usb_sndbulkpipe(serial->dev, endpoint) | dir,             buf, len, callback, ctx);   #if 1   //2025-04-24 Added by Quectel for zero packet   if (dir == USB_DIR_OUT) {       struct usb_device_descriptor *desc = &serial->dev->descriptor;       if (desc->idVendor == cpu_to_le16(0x2C7C))       urb->transfer_flags |= URB_ZERO_PACKET;   } #endif   return urb; } 添加Reset-resume机制（休眠唤醒）文件路径：drivers/usb/serial/option.c static struct usb_serial_driver option_1port_device = {   ... ... #ifdef CONFIG_PM   .suspend           = usb_wwan_suspend,   .resume            = usb_wwan_resume, #if 1  //2025-04-24 Added by Quectel   .reset_resume   = usb_wwan_resume, #endif #endif }; 添加QMI_WWAN驱动         移远提供了QMI_WWAN驱动源文件qmi_wwan_q.c，将其复制到 drivers/net/usb/ 目录下。同时修改drivers/net/usb/Makefile，使其能编译 qmi_wwan_q.c #Makefile # must insert qmi_wwan_q.o before qmi_wwan.o obj-${CONFIG_USB_NET_QMI_WWAN} += qmi_wwan_q.o obj-${CONFIG_USB_NET_QMI_WWAN} += qmi_wwan.o  2.3 编译内核 此步骤可能需要进行半个小时以上 创建输出目录 cd ../../                  mkdir kernel_out  编译安装内核 export CROSS_COMPILE=$HOME/l4t-gcc-toolchain/aarch64--glibc--stable-2022.08-1/bin/aarch64-buildroot-linux-gnu-        #设置交叉编译工具 make -C kernel                     #构建 Jetson Linux 内核镜像 sudo -E make install -C kernel     #安装内核模块和树内模块 将内核镜像复制到刷机目录下 cp kernel/kernel-jammy-src/arch/arm64/boot/Image ../Linux_for_Tegra/kernel/Image 构建NVIDIA树外模块（驱动程序）  export CROSS_COMPILE=$HOME/l4t-gcc-toolchain/aarch64--glibc--stable-2022.08-1/bin/aarch64-buildroot-linux-gnu- export KERNEL_HEADERS=$PWD/kernel/kernel-jammy-src make modules 安装到刷机目录下 export INSTALL_MOD_PATH=$HOME/nvidia/nvidia_sdk/JetPack_6.2.1_Linux_JETSON_ORIN_NANO_TARGETS/Linux_for_Tegra/rootfs/ #根据实际目录进行修改 sudo -E make modules_install 开始构建DTB export CROSS_COMPILE=$HOME/l4t-gcc-toolchain/aarch64--glibc--stable-2022.08-1/bin/aarch64-buildroot-linux-gnu- export KERNEL_HEADERS=$PWD/kernel/kernel-jammy-src make dtbs 将生成的dtb文件拷贝到刷机目录 cp kernel-devicetree/generic-dts/dtbs/* /Linux_for_Tegra/kernel/dtb/ 2.4 刷入系统 进入刷机固件缓存目录 cd $HOME/nvidia/nvidia_sdk/JetPack_6.2.1_Linux_JETSON_ORIN_NANO_TARGETS/Linux_for_Tegra/source  #根据实际目录进行修改 使用命令行刷机： super模式 sudo ./tools/kernel_flash/l4t_initrd_flash.sh --external-device nvme0n1p1 -c tools/kernel_flash/flash_l4t_t234_nvme.xml -p "-c bootloader/generic/cfg/flash_t234_qspi.xml" --showlogs --network usb0 jetson-orin-nano-devkit-super internal 普通模式 sudo ./flash.sh jetson-orin-nano-devkit-nvme internal 2.5 开机验证 将Quectel_Qconnectmanager上传到开发板 解压并编译 unizp Quectel_QConnectManager_Linux_V1.6.5.zip cd Quectel_QConnectManager_Linux_V1.6.5 make 成功运行便可4G拨号上网 jetson@jetson-desktop:~/Downloads/Quectel_QConnectManager_Linux_V1.6.5$ sudo ./quectel-CM [08-08_11:39:04:415] QConnectManager_Linux_V1.6.5 [08-08_11:39:04:416] Find /sys/bus/usb/devices/1-2.2 idVendor=0x2c7c idProduct=0x125, bus=0x001, dev=0x007 [08-08_11:39:04:416] Auto find qmichannel = /dev/cdc-wdm0 [08-08_11:39:04:417] Auto find usbnet_adapter = wwan0 [08-08_11:39:04:417] netcard driver = qmi_wwan_q, driver version = V1.2.6 [08-08_11:39:04:417] Modem works in QMI mode [08-08_11:39:04:447] cdc_wdm_fd = 7 [08-08_11:39:04:523] Get clientWDS = 5 [08-08_11:39:04:557] Get clientDMS = 1 [08-08_11:39:04:589] Get clientNAS = 2 [08-08_11:39:04:620] Get clientUIM = 1 [08-08_11:39:04:653] Get clientWDA = 1 [08-08_11:39:04:684] requestBaseBandVersion EM05CNFDR08A03M1G_ND [08-08_11:39:04:812] requestGetSIMStatus SIMStatus: SIM_READY [08-08_11:39:04:876] requestGetProfile[pdp:1 index:1] ctnet///0/IPV4V6 [08-08_11:39:04:908] requestRegistrationState2 MCC: 460, MNC: 11, PS: Attached, DataCap: LTE [08-08_11:39:04:940] requestQueryDataCall IPv4ConnectionStatus: DISCONNECTED [08-08_11:39:04:941] ip addr flush dev wwan0 [08-08_11:39:04:947] ip link set dev wwan0 down [08-08_11:39:05:003] requestSetupDataCall WdsConnectionIPv4Handle: 0x8723e530 [08-08_11:39:05:132] ip link set dev wwan0 up [08-08_11:39:05:141] No default.script found, it should be in '/usr/share/udhcpc/' or '/etc//udhcpc' depend on your udhcpc version! [08-08_11:39:05:142] busybox udhcpc -f -n -q -t 5 -i wwan0 udhcpc: started, v1.30.1 udhcpc: sending discover udhcpc: sending select for 10.21.181.66 udhcpc: lease of 10.21.181.66 obtained, lease time 7200 [08-08_11:39:05:282] ip -4 address flush dev wwan0 [08-08_11:39:05:286] ip -4 address add 10.21.181.66/30 dev wwan0 [08-08_11:39:05:292] ip -4 route add default via 10.21.181.65 dev wwan0 查看网卡信息 jetson@jetson-desktop:~$ ifconfig wwan0 wwan0: flags=193 mtu 1500 inet 10.21.181.66 netmask 255.255.255.252 inet6 fe80::5804:41ff:feda:ce83 prefixlen 64 scopeid 0x20 ether 5a:04:41:da:ce:83 txqueuelen 1000 (Ethernet) RX packets 9 bytes 2304 (2.3 KB) RX errors 0 dropped 0 overruns 0 frame 0 TX packets 22 bytes 1854 (1.8 KB) TX errors 0 dropped 0 overruns 0 carrier 0 collisions 0 连通性测试