![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/09\/20260913031745-6aa615d903b99.png\")
<\/p>\n<\/p>\n<\/p>\n<\/p>\n
\u968f\u7740\u8fb9\u7f18\u8ba1\u7b97\u7b97\u529b\u7684\u5feb\u901f\u63d0\u5347，\u7aef\u4fa7 AI \u90e8\u7f72\u5df2\u7ecf\u6210\u4e3a\u5d4c\u5165\u5f0f\u9886\u57df\u7684\u6838\u5fc3\u53d1\u5c55\u65b9\u5411\u3002\u6839\u636e IDC 2026 \u5e74\u9884\u6d4b，\u5230 2027 \u5e74\u8d85\u8fc7 80% \u7684 AI \u63a8\u7406\u4efb\u52a1\u5c06\u5728\u7aef\u4fa7\u8bbe\u5907\u5b8c\u6210，\u76f8\u6bd4\u4e91\u7aef\u90e8\u7f72\u5177\u5907\u4f4e\u5ef6\u8fdf\u3001\u9ad8\u9690\u79c1\u3001\u4f4e\u5e26\u5bbd\u6210\u672c\u4e09\u5927\u6838\u5fc3\u4f18\u52bf\u3002<\/p>\n<\/p>\n
\u5f53\u524d\u7aef\u4fa7 AI \u843d\u5730\u9762\u4e34\u4e09\u5927\u75db\u70b9：<\/p>\n<\/p>\n
\u672c\u6587\u62c6\u89e3\u7684\u4e24\u4e2a\u9879\u76ee\u5747\u6765\u81ea 2026 \u5e74 9 \u6708\u7b2c 1 \u5468 GitHub \u8d8b\u52bf\u699c\u524d 10：<\/p>\n<\/p>\n
![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/09\/20260913031746-6aa615dac9e01.png\")
<\/p>\n<\/p>\n
TinySDLC-Agent \u9488\u5bf9\u5d4c\u5165\u5f0f\u573a\u666f\u505a\u4e86\u4e09\u5c42\u67b6\u6784\u4f18\u5316：<\/p>\n<\/p>\n
\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
\n\u2502 \u5e94\u7528\u5c42：\u6a21\u578b\u76d1\u63a7\u3001OTA\u7ba1\u7406\u3001\u65e5\u5fd7\u4e0a\u62a5 \u2502
\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524
\n\u2502 \u4e2d\u95f4\u5c42：MQTT\/CoAP\u534f\u8bae\u6808\u3001\u5dee\u5206\u66f4\u65b0\u5f15\u64ce \u2502
\n\u251c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2524
\n\u2502 \u786c\u4ef6\u62bd\u8c61\u5c42：\u5185\u5b58\u7ba1\u7406\u3001Flash\u64cd\u4f5c\u63a5\u53e3 \u2502
\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518<\/p>\n
\u6839\u636e\u5b98\u65b9\u6587\u6863，\u8be5 Agent \u76f8\u6bd4\u4f20\u7edf\u4e91\u7aef SDLC Agent \u505a\u4e86\u4e09\u5927\u6838\u5fc3\u88c1\u526a：<\/p>\n<\/p>\n
EdgeVisionKit \u91c7\u7528\u4e86 "\u8bad\u7ec3\u540e\u91cf\u5316 + \u7b97\u5b50\u878d\u5408 + \u786c\u4ef6\u52a0\u901f" \u4e09\u7ea7\u4f18\u5316 pipeline：<\/p>\n<\/p>\n
![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/09\/20260913031748-6aa615dcb8f89.png\")
<\/p>\n<\/p>\n
\u672c\u6b21\u5b9e\u6218\u91c7\u7528\u4e3b\u6d41\u5d4c\u5165\u5f0f AI \u5f00\u53d1\u677f RK3588 \u4f5c\u4e3a\u786c\u4ef6\u5e73\u53f0：<\/p>\n<\/p>\n
\u786c\u4ef6\u53c2\u6570（\u6765\u81ea\u745e\u82af\u5fae\u5b98\u65b9 datasheet）：<\/p>\n
\u00a0<\/p>\n
\u8f6f\u4ef6\u73af\u5883：<\/p>\n<\/p>\n
# \u4ece\u9879\u76eeGitHub\u4e3b\u9875\u83b7\u53d6TinySDLC-Agent\u6e90\u7801：https:\/\/github.com\/tinysdlc\/agent
\nsudo apt install gcc-arm-linux-gnueabihf libssl-dev make cmake<\/p>\n
\u6253\u5f00config.h\u6587\u4ef6，\u6839\u636e\u7aef\u4fa7\u8d44\u6e90\u914d\u7f6e\u529f\u80fd\u5f00\u5173：<\/p>\n<\/p>\n
\/\/ config.h \u88c1\u526a\u914d\u7f6e\u793a\u4f8b
\n#define ENABLE_OTA_UPDATE 1 \/\/ \u542f\u7528OTA\u529f\u80fd
\n#define ENABLE_MODEL_MONITOR 1 \/\/ \u542f\u7528\u6a21\u578b\u6027\u80fd\u76d1\u63a7
\n#define ENABLE_LOG_UPLOAD 0 \/\/ \u5173\u95ed\u65e5\u5fd7\u4e0a\u4f20（\u8282\u7701\u5e26\u5bbd）
\n#define MAX_MEMORY_USAGE 1024*1024 \/\/ \u6700\u5927\u5185\u5b58\u5360\u75281MB<\/p>\n
mkdir build && cd build
\ncmake .. -DCMAKE_TOOLCHAIN_FILE=..\/cmake\/arm64-toolchain.cmake
\nmake -j4<\/p>\n
\u7f16\u8bd1\u5b8c\u6210\u540e\u751f\u6210\u7684\u4e8c\u8fdb\u5236\u6587\u4ef6tiny_sdlc_agent\u5927\u5c0f\u4ec5 1.2MB\u3002<\/p>\n
# \u590d\u5236\u5230\u5f00\u53d1\u677f
\nscp tiny_sdlc_agent rock@192.168.1.100:\/home\/rock\/
\n# \u8fd0\u884cAgent
\nssh rock@192.168.1.100 ".\/tiny_sdlc_agent –config agent_config.yaml"<\/p>\n
# \u4ece\u9879\u76eeGitHub\u4e3b\u9875\u83b7\u53d6EdgeVisionKit\u6e90\u7801：https:\/\/github.com\/edgevision\/kit
\npip3 install edgevisionkit==1.0.0<\/p>\n
\u4ee5 YOLOv8n \u76ee\u6807\u68c0\u6d4b\u6a21\u578b\u4e3a\u4f8b：<\/p>\n<\/p>\n
# model_optimize.py
\nfrom edgevisionkit import Optimizer, Platform<\/p>\n
# \u521d\u59cb\u5316\u4f18\u5316\u5668，\u6307\u5b9a\u76ee\u6807\u5e73\u53f0\u4e3aRK3588
\nopt = Optimizer(platform=Platform.RK3588, precision="int8")<\/p>\n
# \u52a0\u8f7d\u539f\u59cbPyTorch\u6a21\u578b
\nopt.load_model("yolov8n.pt")<\/p>\n
# \u7528\u6821\u51c6\u6570\u636e\u96c6\u505a\u91cf\u5316（100\u5f20\u4ee3\u8868\u6027\u56fe\u7247\u5373\u53ef）
\nopt.calibrate(dataset_path=".\/calibration_images\/")<\/p>\n
# \u6267\u884c\u4f18\u5316：\u91cf\u5316+\u7b97\u5b50\u878d\u5408+NPU\u9002\u914d
\nopt.optimize()<\/p>\n
# \u5bfc\u51faRK3588\u9002\u914d\u7684rknn\u6a21\u578b
\nopt.export("yolov8n_quantized.rknn")<\/p>\n
\u8fd0\u884c\u811a\u672c：<\/p>\n<\/p>\n
python3 model_optimize.py<\/p>\n
\u4f18\u5316\u540e\u6a21\u578b\u4f53\u79ef\u4ece 6.2MB \u964d\u4f4e\u5230 1.8MB，\u7cbe\u5ea6\u635f\u5931\u4ec5 1.7%（\u5b98\u65b9\u8bc4\u6d4b\u6570\u636e）\u3002<\/p>\n<\/p>\n
\/\/ inference.c \u5b8c\u6574\u63a8\u7406\u4ee3\u7801
\n#include <stdio.h>
\n#include <stdlib.h>
\n#include "rknn_api.h"
\n#include "image_utils.h"<\/p>\n
#define MODEL_PATH "yolov8n_quantized.rknn"
\n#define INPUT_WIDTH 640
\n#define INPUT_HEIGHT 640<\/p>\n
int main() {
\n int ret;
\n rknn_context ctx;<\/p>\n
\/\/ 1. \u52a0\u8f7d\u6a21\u578b
\n ret = rknn_init(&ctx, MODEL_PATH, 0, NULL);
\n if (ret < 0) {
\n printf("\u6a21\u578b\u52a0\u8f7d\u5931\u8d25，\u9519\u8bef\u7801：%d\\\\n", ret);
\n return -1;
\n }<\/p>\n
\/\/ 2. \u83b7\u53d6\u6a21\u578b\u8f93\u5165\u8f93\u51fa\u4fe1\u606f
\n rknn_input_output_num io_num;
\n rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num));<\/p>\n
\/\/ 3. \u9884\u5904\u7406\u56fe\u7247
\n image_t img;
\n image_load("test.jpg", &img);
\n image_resize(&img, INPUT_WIDTH, INPUT_HEIGHT);
\n image_normalize(&img, 0.0f, 1.0f\/255.0f);<\/p>\n
\/\/ 4. \u8bbe\u7f6e\u8f93\u5165
\n rknn_input inputs[1] = {0};
\n inputs[0].index = 0;
\n inputs[0].buf = img.data;
\n inputs[0].size = INPUT_WIDTH * INPUT_HEIGHT * 3;
\n inputs[0].pass_through = 0;
\n inputs[0].type = RKNN_TENSOR_UINT8;
\n inputs[0].fmt = RKNN_TENSOR_NHWC;
\n rknn_inputs_set(ctx, io_num.n_input, inputs);<\/p>\n
\/\/ 5. \u6267\u884c\u63a8\u7406
\n ret = rknn_run(ctx, NULL);
\n if (ret < 0) {
\n printf("\u63a8\u7406\u5931\u8d25，\u9519\u8bef\u7801：%d\\\\n", ret);
\n return -1;
\n }<\/p>\n
\/\/ 6. \u83b7\u53d6\u8f93\u51fa
\n rknn_output outputs[3] = {0};
\n for (int i = 0; i < 3; i++) {
\n outputs[i].index = i;
\n outputs[i].want_float = 1;
\n }
\n rknn_outputs_get(ctx, io_num.n_output, outputs, NULL);<\/p>\n
\/\/ 7. \u540e\u5904\u7406：\u89e3\u6790\u68c0\u6d4b\u7ed3\u679c
\n detection_result_t results[100];
\n int result_count = post_process_yolov8(outputs, results, 0.25f, 0.45f);<\/p>\n
\/\/ 8. \u6253\u5370\u7ed3\u679c
\n printf("\u68c0\u6d4b\u5230%d\u4e2a\u76ee\u6807：\\\\n", result_count);
\n for (int i = 0; i < result_count; i++) {
\n printf("\u7c7b\u522b：%s，\u7f6e\u4fe1\u5ea6：%.2f，\u5750\u6807：(%d,%d,%d,%d)\\\\n",
\n class_names[results[i].class_id], results[i].confidence,
\n results[i].x1, results[i].y1, results[i].x2, results[i].y2);
\n }<\/p>\n
\/\/ 9. \u91ca\u653e\u8d44\u6e90
\n rknn_outputs_release(ctx, io_num.n_output, outputs);
\n rknn_destroy(ctx);
\n image_free(&img);<\/p>\n
return 0;
\n}<\/p>\n
\u7f16\u8bd1\u8fd0\u884c：<\/p>\n<\/p>\n
gcc inference.c -o inference -lrknn_api -limage_utils
\n.\/inference<\/p>\n
| \u6a21\u578b\u4f53\u79ef<\/td>\n | 6.2MB<\/td>\n | 1.8MB<\/td>\n | 71%<\/td>\n<\/tr>\n |
| \u63a8\u7406\u901f\u5ea6<\/td>\n | 12fps<\/td>\n | 48fps<\/td>\n | 300%<\/td>\n<\/tr>\n |
| CPU \u5360\u7528<\/td>\n | 85%<\/td>\n | 15%<\/td>\n | 82% \u964d\u4f4e<\/td>\n<\/tr>\n |
| \u5185\u5b58\u5360\u7528<\/td>\n | 128MB<\/td>\n | 32MB<\/td>\n | 75% \u964d\u4f4e<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n4.2 \u4f18\u5316\u5efa\u8bae<\/h4>\n
|
![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/09\/20260913031750-6aa615de81359.png\")
<\/p>\n<\/p>\n