客户收益 = (缺陷漏检率↓×月产量) + (人工成本↓×员工数) + (客户评分↑×客单价)

<?php
// 安装：composer require php-opencv/php-opencv
use CV\Mat, CV\CascadeClassifier;class PhoneCaseInspector {// 加载预训练的缺陷检测模型public function __construct() {$this->model = new CascadeClassifier();$this->model->load('/models/scratch_detection.xml');}/*** 检测产品表面划痕* @param string $imagePath 产品图片路径* @return array 缺陷位置列表*/public function detectScratches(string $imagePath): array {// 1. 加载图像$image = Mat::load($imagePath);// 2. 缺陷检测（核心代码）$defects = new Mat();$this->model->detectMultiScale($image, $defects);// 3. 转换为易懂的结果$results = [];for ($i = 0; $i < $defects->rows; $i++) {$x = $defects->atRow($i)->get(0)->val[0];$y = $defects->atRow($i)->get(0)->val[1];$width = $defects->atRow($i)->get(0)->val[2];$height = $defects->atRow($i)->get(0)->val[3];$results[] = ['position' => "左上角({$x},{$y})",'size' => "{$width}x{$height}像素",'type' => '划痕'];}return $results;}
}/* 使用示例 - 电子厂质检场景 */
$inspector = new PhoneCaseInspector();
$defects = $inspector->detectScratches('/data/phone_case_001.jpg');if (!empty($defects)) {echo "发现缺陷：".count($defects)."处\n";print_r($defects);
} else {echo "产品合格！";
}/* 输出示例：
发现缺陷：2处
Array ([0] => Array ([position] => 左上角(105,320)[size] => 25x25像素[type] => 划痕)[1] => Array ([position] => 左上角(450,80)[size] => 15x15像素[type] => 划痕)
)
*/

<?php
class FactoryInspector {/*** 融合视觉和声音进行质检* @param string $imagePath 产品图像路径* @param string $audioPath 设备录音路径* @return string 质检结果*/public function multimodalInspection(string $imagePath, string $audioPath): string {// 1. 视觉检测$visionResult = $this->checkVisualDefects($imagePath);// 2. 声音分析$audioResult = $this->analyzeMachineSound($audioPath);// 3. 融合决策（核心逻辑）if ($visionResult['defect_level'] > 0 || $audioResult['abnormal']) {$score = $visionResult['defect_score'] * 0.7 + $audioResult['risk_score'] * 0.3;if ($score > 0.8) return "严重缺陷：需停产检修";if ($score > 0.5) return "中度缺陷：需人工复检";return "轻微缺陷：可继续生产";}return "产品合格";}// 视觉检测简化实现private function checkVisualDefects(string $imagePath): array {// 实际项目调用6.1的检测类return ['defect_level' => 1, 'defect_score' => 0.6];}// 声音分析简化实现private function analyzeMachineSound(string $audioPath): array {// 分析音频特征（实际项目用FFT算法）return ['abnormal' => true, 'risk_score' => 0.7];}
}// 使用示例 - 汽车零件厂
$inspector = new FactoryInspector();
$result = $inspector->multimodalInspection('/data/engine_part.jpg', '/data/machine_audio.wav'
);
echo "质检结果：$result"; // 输出：质检结果：中度缺陷：需人工复检

<?php
// 在工厂边缘设备运行的质检服务
$server = new Swoole\HTTP\Server("0.0.0.0", 9502);// 处理质检请求
$server->on('request', function ($request, $response) {$data = json_decode($request->getContent(), true);// 启动异步检测（不阻塞主线程）go(function () use ($data, $response) {// 1. 下载图片$image = file_get_contents($data['image_url']);file_put_contents('/tmp/product.jpg', $image);// 2. 执行检测$inspector = new PhoneCaseInspector();$result = $inspector->detectScratches('/tmp/product.jpg');// 3. 返回结果$response->header('Content-Type', 'application/json');$response->end(json_encode($result));});
});echo "质检服务已启动，监听9502端口\n";
$server->start();

// 自动生成损失报告
function generateLossReport(int $defectCount): string {$productPrice = 200; // 产品单价$monthlyOutput = 10000; // 月产量$lossRate = $defectCount / $monthlyOutput;return sprintf("发现缺陷：%d处，月损失估算：￥%d\n年损失预计：￥%d（按10%%漏检率计算）",$defectCount,$lossRate * $monthlyOutput * $productPrice,$lossRate * $monthlyOutput * $productPrice * 12);
}

// 模型量化压缩（减小75%体积）
$model = new ONNXModel('defect_detection.onnx');
$model->quantize()->save('defect_detection_quant.onnx');

# Nginx配置GPU加速
location /inference {proxy_pass http://gpu-server:8000;proxy_set_header X-GPU-Priority "high";
}

class EdgeScheduler {public function process(array $data) {$cpuLoad = sys_getloadavg()[0];if ($cpuLoad < 70) {// 本地处理return $this->localProcess($data);} else {// 转发到空闲设备return $this->sendToNeighbor($data);}}
}

class ImageEnhancer {public function adjustLighting(Mat $image): Mat {// 1. 自动亮度调整CV::equalizeHist($image, $image);// 2. 阴影修复（核心代码）$lab = new Mat();CV::cvtColor($image, $lab, CV::COLOR_BGR2Lab);$channels = [];CV::split($lab, $channels);CV::medianBlur($channels[0], $channels[0], 5);CV::merge($channels, $lab);// 3. 转回原格式CV::cvtColor($lab, $image, CV::COLOR_Lab2BGR);return $image;}
}

class AudioFilter {public function removeMachineNoise(string $audioPath): string {// 1. 加载设备噪音样本$noiseProfile = $this->loadNoiseProfile('machine123');// 2. 降噪处理（核心算法）$cleanAudio = $this->applyNoiseReduction($audioPath, $noiseProfile);// 3. 保存处理结果file_put_contents('/tmp/clean_audio.wav', $cleanAudio);return '/tmp/clean_audio.wav';}
}

class EdgeRecovery {public function syncData(array $data) {try {// 尝试发送数据$this->sendToCloud($data);} catch (Exception $e) {// 失败时本地缓存$this->saveToLocal($data);// 定时重试（每5分钟）Swoole\Timer::tick(300000, function() {if ($this->isCloudOnline()) {$this->retryFailedData();}});}}
}