多类型医疗自助终端智能化升级路径（代码版.下）

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医疗人机交互层技术实施方案

一、多模态交互体系

1. 医疗语音识别引擎

# 基于Wav2Vec2的医疗ASR系统
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import torchaudioclass MedicalASR:def __init__(self):self.processor = Wav2Vec2Processor.from_pretrained("medical-wav2vec2-base-zh-CN")self.model = Wav2Vec2ForCTC.from_pretrained("medical-wav2vec2-base-zh-CN")self.resampler = torchaudio.transforms.Resample(orig_freq=48000, new_freq=16000)# 医疗术语增强词典self.medical_terms = {"xianweijing": "纤维镜","ganmeisu": "干酶素"}def transcribe(self, audio_path):# 语音预处理waveform, sample_rate = torchaudio.load(audio_path)waveform = self.resampler(waveform)# 语音识别inputs = self.processor(waveform.squeeze().numpy(), sampling_rate=16000,return_tensors="pt",padding="longest")with torch.no_grad():logits = self.model(inputs.input_values,attention_mask=inputs.attention_mask).logits# 后处理优化pred_ids = torch.argmax(logits, dim=-1)text = self.processor.batch_decode(pred_ids)[0]return self._correct_medical_terms(text)def _correct_medical_terms(self, text):for term, correct in self.medical_terms.items():text = text.replace(term, correct)return text

2. 无障碍交互系统

// 基于Web Accessibility的交互方案
class AccessibilityInterface {private eyeTracker: EyeTracker;private hapticDevice: HapticController;constructor() {this.eyeTracker = new TobiiEyeX();this.hapticDevice = new HapticGlove();}setupInteraction() {// 眼动追踪焦点管理document.addEventListener('eyegaze', (event) => {const target = document.elementFromPoint(event.detail.x, event.detail.y);if (target?.classList.contains('focusable')) {this.hapticDevice.vibrate('soft');target.dispatchEvent(new MouseEvent('hover'));}});// 语音反馈增强speechSynthesis.addEventListener('start', () => {this.hapticDevice.vibrate('double');});}// 震动编码方案private vibrationPatterns = {confirm: [100, 50, 100],alert: [300, 100, 300],progress: [50, 50]};
}

3. 情境感知服务

// 基于蓝牙信标的情境感知
public class ContextAwareService {private BeaconDetector beaconDetector;private PatientStatusPredictor predictor;public ContextAwareService() {this.beaconDetector = new AltBeaconScanner();this.predictor = new RandomForestPredictor();}public void startMonitoring(String patientId) {beaconDetector.registerListener((beacons) -> {// 实时定位处理Beacon nearest = Collections.min(beacons, Comparator.comparingDouble(Beacon::getDistance));// 阶段预测double[] features = extractFeatures(nearest);int predictedStage = predictor.predictStage(features);updateUI(patientId, nearest.getLocation(), predictedStage);});}private double[] extractFeatures(Beacon beacon) {return new double[] {beacon.getDistance(),beacon.getRssi(),System.currentTimeMillis() / 1000.0};}
}

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二、智能容错机制

1. 对话状态跟踪(DST)

# 基于BERT的医疗对话状态跟踪
class MedicalDST:def __init__(self):self.tokenizer = AutoTokenizer.from_pretrained("bert-base-chinese-medical")self.model = AutoModelForSequenceClassification.from_pretrained("bert-base-chinese-medical",num_labels=len(DIALOG_STATES))self.state_machine = {"初诊": ["主诉", "病史", "检查"],"复诊": ["报告解读", "治疗方案"]}def track_state(self, dialog_history):# 合并最近3轮对话context = "\n".join(dialog_history[-3:])# 状态分类inputs = self.tokenizer(context, return_tensors="pt",truncation=True,max_length=512)outputs = self.model(