add photo

app/build.gradle

@@ -4,6 +4,8 @@ plugins {
     id 'kotlin-kapt'
 }
 
+def oneSentenceAsrAar = file('libs/asr-one-sentence-release.aar')
+
 kapt {
     // Room uses javac stubs under kapt; keep parameter names for :bind variables.
     javacOptions {
@@ -62,6 +64,9 @@ android {
         buildConfigField "String", "LLM_MODEL", "\"${(project.findProperty('LLM_MODEL') ?: 'doubao-1-5-pro-32k-character-250228').toString()}\""
         buildConfigField "boolean", "USE_LIVE2D", "${(project.findProperty('USE_LIVE2D') ?: 'true').toString()}"
 
+        // 腾讯云「一句话识别」Android SDK：将 asr-one-sentence-release.aar 放入 app/libs/ 后为 true
+        buildConfigField "boolean", "HAS_TENCENT_ASR_SDK", "${oneSentenceAsrAar.exists()}"
+
         ndk {
             abiFilters "arm64-v8a"
         }
@@ -119,4 +124,6 @@ dependencies {
     implementation 'com.google.guava:guava:31.1-android'
     implementation 'org.ejml:ejml-core:0.43.1'
     implementation 'org.ejml:ejml-simple:0.43.1'
+
+    // 腾讯云「一句话识别」通过 OkHttp 直接实现 TC3 签名，无需 AAR SDK
 }

app/libs/ASR_SDK_PLACE_AAR_HERE.txt

@@ -0,0 +1,4 @@
+1. Open https://console.cloud.tencent.com/asr/download
+2. Download the Android package for real-time speech recognition (实时语音识别).
+3. Copy asr-realtime-release.aar into this folder (app/libs/).
+4. Sync Gradle — BuildConfig.HAS_TENCENT_ASR_SDK will become true.

app/libs/_asr_out/AndroidManifest.xml

@@ -0,0 +1,13 @@
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+    package="com.tencent.iot.speech" >
+
+    <uses-sdk
+        android:minSdkVersion="16"
+        android:targetSdkVersion="33" />
+
+    <uses-permission android:name="android.permission.ACCESS_NETWORK_STATE" />
+    <uses-permission android:name="android.permission.RECORD_AUDIO" />
+    <uses-permission android:name="android.permission.INTERNET" />
+
+</manifest>

app/libs/_asr_out/R.txt

@@ -0,0 +1 @@
+int string app_name 0x0

app/libs/_asr_out/proguard.txt

@@ -0,0 +1,9 @@
+# SDK
+-keepclasseswithmembernames class com.tencent.aai.** { # 保持 native 方法不被混淆
+native <methods>;
+}
+
+-keep public class com.tencent.aai.** {*;}
+-keep interface com.tencent.aai.audio.data.PcmAudioDataSource {
+    void start(); throws com.tencent.aai.exception.ClientException;
+}

app/libs/_asr_out/res/values/values.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="utf-8"?>
+<resources>
+    <string name="app_name">aai</string>
+</resources>

app/libs/_osr_out/AndroidManifest.xml

@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+    package="com.tencent.cloud.qcloudasrsdk.onesentence" >
+
+    <uses-sdk
+        android:minSdkVersion="16"
+        android:targetSdkVersion="33" />
+
+    <uses-permission android:name="android.permission.INTERNET" />
+
+</manifest>

app/libs/_osr_out/proguard.txt

@@ -0,0 +1,90 @@
+-optimizationpasses 5  # 指定代码的压缩级别
+-allowaccessmodification #优化时允许访问并修改有修饰符的类和类的成员
+-dontusemixedcaseclassnames  # 是否使用大小写混合
+-dontskipnonpubliclibraryclasses  # 是否混淆第三方jar
+-dontpreverify  # 混淆时是否做预校验
+-verbose    # 混淆时是否记录日志
+-ignorewarnings  # 忽略警告，避免打包时某些警告出现
+-optimizations !code/simplification/arithmetic,!code/simplification/cast,!field/*,!class/merging/*  # 混淆时所采用的算法
+
+-keepattributes *Annotation*
+-keepclasseswithmembernames class * { # 保持 native 方法不被混淆
+    native <methods>;
+}
+
+-keepclassmembers public class * extends android.view.View {
+   void set*(***);
+   *** get*();
+}
+
+-keepclassmembers class * extends android.app.Activity {
+   public void *(android.view.View);
+}
+
+-keepclassmembers enum * {  # 保持枚举 enum 类不被混淆
+    public static **[] values();
+    public static ** valueOf(java.lang.String);
+}
+
+-keep class * implements android.os.Parcelable { # 保持 Parcelable 不被混淆
+  public static final android.os.Parcelable$Creator *;
+}
+
+-keepclassmembers class **.R$* { #不混淆R文件
+    public static <fields>;
+}
+
+-dontwarn android.support.**
+##--- End android默认 ---
+
+##--- For:不能被混淆的 ---
+-keep public class * extends android.app.Activity
+-keep public class * extends android.app.Fragment
+-keep public class * extends android.app.Application
+-keep public class * extends android.app.Service
+-keep public class * extends android.content.BroadcastReceiver
+-keep public class * extends android.content.ContentProvider
+-keep public class * extends android.app.backup.BackupAgentHelper
+-keep public class * extends android.preference.Preference
+
+##--- For:保持自定义控件类不被混淆 ---
+-keepclasseswithmembers class * {
+    public <init>(android.content.Context, android.util.AttributeSet);
+}
+-keepclasseswithmembers class * {
+    public <init>(android.content.Context, android.util.AttributeSet, int);
+}
+##--- For:android-support-v4 ---
+-dontwarn android.support.v4.**
+-keep class android.support.v4.** { *; }
+-keep interface android.support.v4.app.** { *; }
+-keep class * extends android.support.v4.** { *; }
+-keep public class * extends android.support.v4.**
+-keep class * extends android.support.v4.app.** {*;}
+-keep class * extends android.support.v4.view.** {*;}
+
+##--- For:Serializable ---
+-keep class * implements java.io.Serializable {*;}
+-keepnames class * implements java.io.Serializable
+-keepclassmembers class * implements java.io.Serializable {*;}
+
+##--- For:Gson ---
+-keepattributes *Annotation*
+-keep class com.google.gson.stream.** { *; }
+
+
+##--- For:Remove log ---
+-assumenosideeffects class android.util.Log {
+    public static boolean isLoggable(java.lang.String, int);
+    public static int v(...);
+    public static int i(...);
+    public static int w(...);
+    public static int d(...);
+    public static int e(...);
+}
+
+##--- For:attributes(未启用) ---
+#-keepattributes SourceFile,LineNumberTable # 保持反编译工具能看到代码的行数，以及release包安装后出现异常信息可以知道在哪行代码出现异常，建议不启用
+-keepattributes *Annotation* #使用注解
+-keepattributes Signature #过滤泛型  出现类型转换错误时，启用这个
+#-keepattributes *Exceptions*,EnclosingMethod  #没试过，未知效果

app/libs/_tts_out/AndroidManifest.xml

@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+    package="com.tencent.cloud.realtime.tts" >
+
+    <uses-sdk
+        android:minSdkVersion="16"
+        android:targetSdkVersion="33" />
+
+    <uses-permission android:name="android.permission.INTERNET" />
+
+</manifest>

app/proguard-rules.pro

@@ -19,3 +19,10 @@
 # If you keep the line number information, uncomment this to
 # hide the original source file name.
 #-renamesourcefileattribute SourceFile
+
+# 腾讯云实时语音识别 SDK（asr-realtime-release.aar）
+-keepclasseswithmembernames class * {
+    native <methods>;
+}
+-keep public class com.tencent.aai.** { *; }
+-keep public class com.qq.wx.voice.** { *; }

app/src/androidTest/java/com/digitalperson/embedding/RefImageMatcherEmulatorRegressionTest.kt

@@ -0,0 +1,132 @@
+package com.digitalperson.embedding
+
+import android.content.Context
+import androidx.test.core.app.ApplicationProvider
+import androidx.test.ext.junit.runners.AndroidJUnit4
+import com.digitalperson.config.AppConfig
+import org.junit.Assert.assertNotNull
+import org.junit.Assert.assertTrue
+import org.junit.Before
+import org.junit.Test
+import org.junit.runner.RunWith
+
+/**
+ * 模拟器参考图匹配（路径关键词 + 子串 + 编辑距离）回归测试。
+ *
+ * 运行方式（需设备/模拟器，且 assets 含 ref 语料）：
+ *   ./gradlew :app:connectedDebugAndroidTest --tests com.digitalperson.embedding.RefImageMatcherEmulatorRegressionTest
+ */
+@RunWith(AndroidJUnit4::class)
+class RefImageMatcherEmulatorRegressionTest {
+
+    private lateinit var context: Context
+
+    @Before
+    fun setUp() {
+        context = ApplicationProvider.getApplicationContext()
+    }
+
+    data class ManualCase(
+        val label: String,
+        val query: String,
+        /** 期望命中的 txt 路径中应包含该子串（如文件名一段） */
+        val expectedPathContains: String,
+    )
+
+    @Test
+    fun manualQueries_shouldMatchExpectedAsset() {
+        val cases = listOf(
+            ManualCase(
+                label = "上厕所指引（LLM 寒暄前缀）",
+                query = "嗨小朋友，可以帮老师个忙吗？同学们能指引图中的小朋友们进入正确的厕所吗？",
+                expectedPathContains = "上厕所18",
+            ),
+            ManualCase(
+                label = "刷牙看图（题干在讲卫生6，人1 为认读「人」非刷牙）",
+                query = "嗨小朋友，可以帮老师个忙吗？这个男生在做什么？",
+                expectedPathContains = "讲卫生6",
+            ),
+            ManualCase(
+                label = "元旦短句（生活适应）",
+                query = "元旦到了，小朋友可以对爸爸妈妈说：'爸爸妈妈，新年快乐！'",
+                expectedPathContains = "元旦14",
+            ),
+        )
+        val failures = mutableListOf<String>()
+        for (c in cases) {
+            val m = RefImageMatcher.findBestMatchEditDistance(context, c.query)
+            when {
+                m == null -> failures += "${c.label}: 无匹配，期望路径含「${c.expectedPathContains}」 query=${c.query.take(80)}"
+                !m.txtAssetPath.contains(c.expectedPathContains) ->
+                    failures += "${c.label}: 得到 ${m.txtAssetPath} score=${m.score}，期望路径含「${c.expectedPathContains}」"
+            }
+        }
+        assertTrue(
+            "以下用例未命中预期资源：\n${failures.joinToString("\n")}",
+            failures.isEmpty(),
+        )
+    }
+
+    /**
+     * 语料自检：每条带 png 的 txt，用「寒暄 + 正文长片段」构造 query，应匹配回该 txt。
+     *
+     * 仅用首行会失败两类情况：（1）多篇课文首句相同（如男生女生 5/6）；（2）首句拆出「小朋友」等
+     * 极短片段在多篇里子串命中同分，先遍历到的文件胜出。故这里用去空白后的正文前缀（约 400 字）提高区分度。
+     */
+    @Test
+    fun corpus_bodyPrefix_withGreetingPrefix_shouldMatchSameTxt() {
+        val root = AppConfig.RefCorpus.ASSETS_ROOT
+        val paths = RefCorpusAssetScanner.listTxtFilesUnder(context, root)
+        val greeting = "嗨小朋友，可以帮老师个忙吗？"
+        /** 与真实 LLM 提问长度同量级；更长更易唯一，但单测耗时略增 */
+        val maxBodyChars = 400
+        val failures = mutableListOf<String>()
+        var skippedNoPng = 0
+        var skippedNoBody = 0
+        var checked = 0
+
+        for (txtPath in paths) {
+            val pngPath = if (txtPath.endsWith(".txt", ignoreCase = true)) {
+                txtPath.dropLast(4) + ".png"
+            } else {
+                "$txtPath.png"
+            }
+            val pngOk = try {
+                context.assets.open(pngPath).close()
+                true
+            } catch (_: Throwable) {
+                false
+            }
+            if (!pngOk) {
+                skippedNoPng++
+                continue
+            }
+            val raw = context.assets.open(txtPath).bufferedReader(Charsets.UTF_8).use { it.readText() }
+            val body = RefTxtEmbedText.fromRawFileContent(raw).trim()
+            if (body.length < 8) {
+                skippedNoBody++
+                continue
+            }
+            checked++
+            val compact = body.replace(Regex("\\s+"), " ").trim()
+            val core = compact.take(maxBodyChars)
+            val query = "$greeting$core"
+            val m = RefImageMatcher.findBestMatchEditDistance(context, query)
+            if (m == null) {
+                failures += "无匹配: $txtPath | core=${core.take(50)}"
+                continue
+            }
+            if (m.txtAssetPath != txtPath) {
+                failures += "错配: 期望 $txtPath | 得到 ${m.txtAssetPath} score=${m.score} | core=${core.take(50)}"
+            }
+        }
+
+        assertNotNull("语料为空或未打包进 assets", paths.takeIf { it.isNotEmpty() })
+        assertTrue(
+            "语料自检失败条数=${failures.size}（已检查=$checked，skip无png=$skippedNoPng，skip正文过短=$skippedNoBody）:\n" +
+                failures.take(50).joinToString("\n") +
+                if (failures.size > 50) "\n... 共 ${failures.size} 条" else "",
+            failures.isEmpty(),
+        )
+    }
+}

app/src/androidTest/java/com/digitalperson/question/QuestionGenerationAgentTest.kt

@@ -0,0 +1,204 @@
+package com.digitalperson.question
+
+import android.content.Context
+import androidx.test.core.app.ApplicationProvider
+import androidx.test.ext.junit.runners.AndroidJUnit4
+import com.digitalperson.data.AppDatabase
+import com.digitalperson.data.entity.Question
+import com.digitalperson.interaction.UserMemoryStore
+import kotlinx.coroutines.runBlocking
+import org.json.JSONObject
+import org.junit.After
+import org.junit.Before
+import org.junit.Test
+import org.junit.runner.RunWith
+import java.io.InputStream
+
+/**
+ * 题目生成智能体测试
+ * 可以在模拟器或本地运行，不需要完整启动应用
+ */
+@RunWith(AndroidJUnit4::class)
+class QuestionGenerationAgentTest {
+
+    private lateinit var context: Context
+    private lateinit var database: AppDatabase
+    private lateinit var userMemoryStore: UserMemoryStore
+
+    @Before
+    fun setUp() {
+        context = ApplicationProvider.getApplicationContext()
+        database = AppDatabase.getInstance(context)
+        userMemoryStore = UserMemoryStore(context)
+    }
+
+    @After
+    fun tearDown() {
+        // 清理测试数据
+        // database.clearAllTables()
+    }
+
+    @Test
+    fun testLoadPromptPoolFromJson() {
+        // 测试JSON提示词池加载
+        val inputStream: InputStream = context.assets.open("question_prompts.json")
+        val jsonString = inputStream.bufferedReader().use { it.readText() }
+        val jsonArray = org.json.JSONArray(jsonString)
+
+        println("✅ Loaded ${jsonArray.length()} prompts from JSON")
+        
+        // 验证每个提示词的格式
+        for (i in 0 until jsonArray.length()) {
+            val json = jsonArray.getJSONObject(i)
+            assert(json.has("subject")) { "Missing subject in prompt $i" }
+            assert(json.has("grade")) { "Missing grade in prompt $i" }
+            assert(json.has("topic")) { "Missing topic in prompt $i" }
+            assert(json.has("difficulty")) { "Missing difficulty in prompt $i" }
+            assert(json.has("promptTemplate")) { "Missing promptTemplate in prompt $i" }
+            
+            println("  - Prompt $i: ${json.getString("subject")} / ${json.getString("topic")}")
+        }
+        
+        assert(jsonArray.length() > 0) { "Should have at least 1 prompt" }
+    }
+
+    @Test
+    fun testQuestionDatabaseOperations() = runBlocking {
+        // 测试数据库操作
+        val questionDao = database.questionDao()
+        
+        // 插入测试题目
+        val testQuestion = Question(
+            id = 0,
+            content = "测试题目：苹果和香蕉哪个大？",
+            answer = "香蕉",
+            subject = "生活数学",
+            grade = 1,
+            difficulty = 1,
+            createdAt = System.currentTimeMillis()
+        )
+        
+        val questionId = questionDao.insert(testQuestion)
+        println("✅ Inserted question with ID: $questionId")
+        
+        // 查询题目
+        val retrievedQuestion = questionDao.getQuestionById(questionId)
+        assert(retrievedQuestion != null) { "Should retrieve inserted question" }
+        assert(retrievedQuestion?.content == testQuestion.content) { "Content should match" }
+        println("✅ Retrieved question: ${retrievedQuestion?.content}")
+        
+        // 测试未答题计数
+        val userId = "test_user_001"
+        val count = questionDao.countUnansweredQuestions(userId)
+        println("✅ Unanswered questions count: $count")
+        
+        // 测试获取随机未答题
+        val randomQuestion = questionDao.getRandomUnansweredQuestion(userId)
+        println("✅ Random unanswered question: ${randomQuestion?.content?.take(20)}...")
+    }
+
+    @Test
+    fun testJsonResponseParsing() {
+        // 测试LLM JSON响应解析
+        val testResponses = listOf(
+            """{"content": "苹果和香蕉哪个大？", "answer": "香蕉", "explanation": "香蕉通常比苹果大"}""",
+            """
+            {
+                "content": "2个苹果和5个苹果比，谁多？",
+                "answer": "5个苹果多",
+                "explanation": "5大于2"
+            }
+            """,
+            """Some text before {"content": "测试题目", "answer": "答案"} some text after"""
+        )
+        
+        testResponses.forEachIndexed { index, response ->
+            val json = extractJsonFromResponse(response)
+            if (json != null) {
+                println("✅ Test $index: Parsed successfully")
+                println("   Content: ${json.getString("content")}")
+                println("   Answer: ${json.getString("answer")}")
+            } else {
+                println("❌ Test $index: Failed to parse JSON")
+            }
+        }
+    }
+
+    @Test
+    fun testUserMemoryOperations() = runBlocking {
+        // 测试用户记忆操作
+        val userId = "test_user_001"
+        
+        // 创建或更新用户
+        userMemoryStore.upsertUserSeen(userId, "测试小朋友")
+        println("✅ Created/updated user: $userId")
+        
+        // 获取用户信息
+        val memory = userMemoryStore.getMemory(userId)
+        println("✅ User memory: displayName=${memory?.displayName}")
+        
+        // 测试未答题计数
+        val unansweredCount = userMemoryStore.countUnansweredQuestions(userId)
+        println("✅ Unanswered questions for $userId: $unansweredCount")
+    }
+
+    @Test
+    fun testPromptTemplateBuilding() = runBlocking {
+        // 测试提示词模板构建
+        val userProfile = userMemoryStore.getMemory("test_user_001")
+        
+        val promptTemplate = """
+            你是一个专门为特殊教育儿童设计题目的教育专家。请根据以下要求生成一个题目：
+            
+            用户信息：
+            ${userProfile?.displayName?.let { "姓名：$it，" } ?: ""}
+            ${userProfile?.age?.let { "年龄：$it，" } ?: ""}
+            
+            学科：生活数学
+            年级：1
+            主题：比大小
+            难度：1
+            
+            具体要求：
+            基于以下学习目标，针对一年级小学生出1道题目：
+            1. 初步感知物品的大小
+            2. 会比较2个物品的大小
+            
+            通用要求：
+            1. 题目要贴近生活，适合智力障碍儿童理解
+            2. 语言简单明了，避免复杂句式
+            3. 题目内容积极向上
+            4. 提供标准答案
+            5. 确保题目没有重复
+            6. 题目要有趣味性，能吸引学生注意力
+            
+            请以JSON格式返回，格式如下：
+            {
+                "content": "题目内容",
+                "answer": "标准答案",
+                "explanation": "题目解析（可选）"
+            }
+            
+            只返回JSON，不要其他内容。
+        """.trimIndent()
+        
+        println("✅ Generated prompt template:")
+        println(promptTemplate)
+        println("\n✅ Prompt length: ${promptTemplate.length} characters")
+    }
+
+    /**
+     * 从响应中提取JSON
+     */
+    private fun extractJsonFromResponse(response: String): JSONObject? {
+        val trimmed = response.trim()
+        val start = trimmed.indexOf('{')
+        val end = trimmed.lastIndexOf('}')
+        
+        if (start >= 0 && end > start) {
+            val jsonStr = trimmed.substring(start, end + 1)
+            return JSONObject(jsonStr)
+        }
+        return null
+    }
+}

app/src/main/AndroidManifest.xml

@@ -4,6 +4,7 @@
     <uses-permission android:name="android.permission.RECORD_AUDIO" />
     <uses-permission android:name="android.permission.CAMERA" />
     <uses-permission android:name="android.permission.INTERNET" />
+    <uses-permission android:name="android.permission.ACCESS_NETWORK_STATE" />
     <uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE" />
     <uses-permission android:name="android.permission.VIBRATE" />
     <uses-feature android:name="android.hardware.camera.any" />

app/src/main/assets/question_generation_flow.md

@@ -0,0 +1,264 @@
+# Question Generation Flow - Pre-generation Strategy
+
+## Problem Solved
+
+**Before:** First question would fail because database was empty.
+
+**After:** Questions are pre-generated when face appears, giving 20+ seconds before first question is asked.
+
+## Timeline Flow
+
+```
+Time: 0s          2s          20s         40s         60s
+     |           |           |           |           |
+     ↓           ↓           ↓           ↓           ↓
+   Face       Greeting    First      Second      Third
+Appears       Ends       Question   Question    Question
+     |           |           |           |           |
+     |           |           |           |           |
+     └─► Start   │           │           │           │
+        Gen      │           │           │           │
+        Questions│           │           │           │
+        (~10-20s)│           │           │           │
+                 │           │           │           │
+                 └───────────┴───────────┴───────────┘
+                         Questions already
+                         in database!
+```
+
+## Detailed Flow
+
+### 1. Face Detection (T=0s)
+```
+User shows face to camera
+   ↓
+FaceDetectionPipeline detects frontal face
+   ↓
+onFaceSignal(present=true, isFrontal=true)
+   ↓
+DigitalHumanInteractionController.onFacePresenceChanged()
+   ↓
+Handler.onFaceAppeared(userId) ← NEW TRIGGER
+```
+
+### 2. Pre-generation (T=0-20s)
+```
+QuestionGenerationAgent.onQuestionAsked(userId)
+   ↓
+Check unanswered count:
+   - If 0 questions → Generate 20 questions (2x minUnansweredQuestions)
+   - If < 10 questions → Generate enough to reach 10
+   - If >= 10 questions → Skip generation
+   ↓
+For each question needed:
+   1. Get next prompt from pool (avoiding recently used)
+   2. Build generation prompt with user profile
+   3. LLM generates question (JSON)
+   4. LLM reviews question quality
+   5. If passed → Save to database
+   ↓
+Questions ready in database! ✓
+```
+
+### 3. Greeting Phase (T=2-5s)
+```
+Enter greeting state
+   ↓
+Digital person waves and says hello
+   ↓
+~3 seconds
+```
+
+### 4. First Question (T=20s)
+```
+enterProactive() → askProactiveTopic()
+   ↓
+Get random unanswered question from database
+   ↓
+✓ QUESTION EXISTS! (Pre-generated in step 2)
+   ↓
+Ask question to user
+   ↓
+onQuestionAsked(userId) ← Check again
+   ↓
+If count < 10 → Generate more questions
+```
+
+### 5. Subsequent Questions (T=40s, 60s, ...)
+```
+Each time a question is asked:
+   ↓
+onQuestionAsked(userId) triggered
+   ↓
+Check unanswered count
+   ↓
+If low → Generate more
+   ↓
+Database always stays stocked! ✓
+```
+
+## Key Benefits
+
+### ✅ No Empty Database
+- Questions generated BEFORE first question is needed
+- 20+ second head start during greeting phase
+
+### ✅ Smart Quantity
+- **First time:** Generate 20 questions (double the minimum)
+- **Replenishment:** Generate only what's needed to reach 10
+
+### ✅ Continuous Supply
+- Every question asked triggers a check
+- Database never runs empty
+
+### ✅ User-Aware
+- Uses user profile for personalization
+- Tracks unanswered questions per user
+
+## Configuration
+
+```kotlin
+data class AgentConfig(
+    val minUnansweredQuestions: Int = 10,  // Minimum threshold
+    val batchSize: Int = 5,                // (Deprecated - now calculates dynamically)
+    val generationTimeoutMs: Long = 30000  // LLM timeout
+)
+```
+
+### Calculated Values
+
+| Scenario | Unanswered Count | Action | Questions Generated |
+|----------|-----------------|--------|---------------------|
+| First time | 0 | Initial load | 20 (2x min) |
+| Running low | 5 | Replenish | 5 (to reach 10) |
+| Running low | 8 | Replenish | 2 (to reach 10) |
+| Sufficient | 10+ | Skip | 0 |
+| After question | 9 | Replenish | 1 (to reach 10) |
+
+## Generation Time Estimates
+
+Assuming ~3 seconds per question (generate + review):
+
+| Questions Needed | Estimated Time |
+|-----------------|----------------|
+| 5 questions | ~15 seconds |
+| 10 questions | ~30 seconds |
+| 20 questions | ~60 seconds |
+
+**Good news:** Generation happens in background, doesn't block greeting!
+
+## Trigger Points
+
+### 1. Face Appears (Pre-generation)
+```kotlin
+// In DigitalHumanInteractionController.kt
+if (state == InteractionState.IDLE || state == InteractionState.MEMORY || state == InteractionState.FAREWELL) {
+    handler.onFaceAppeared(currentFaceId ?: "guest")  // ← Pre-generate
+    enterGreeting()
+}
+```
+
+### 2. Question Asked (Replenishment)
+```kotlin
+// In DigitalHumanInteractionController.kt
+handler.speak("嗨小朋友，可以帮老师个忙吗？" + topic)
+handler.onQuestionAsked(currentFaceId ?: "guest")  // ← Replenish
+```
+
+## Logging Output
+
+### First Time User
+```
+I/QuestionGenAgent: Face appeared, triggering question pre-generation for user: user123
+I/QuestionGenAgent: User user123 has 0 unanswered questions (initial=true), generating 20 more...
+D/QuestionGenAgent: Generating question 1/20 for user user123
+I/QuestionGenAgent: Question saved: 请说出你家里有几口人...
+D/QuestionGenAgent: Generating question 2/20 for user user123
+...
+I/QuestionGenAgent: Finished generating questions for user user123
+```
+
+### Returning User (Low Questions)
+```
+I/QuestionGenAgent: User user123 has 3 unanswered questions, generating 7 more...
+D/QuestionGenAgent: Generating question 1/7 for user user123
+...
+I/QuestionGenAgent: Finished generating questions for user user123
+```
+
+### Sufficient Questions
+```
+D/QuestionGenAgent: User user123 has 15 unanswered questions, no need to generate
+```
+
+## Edge Cases Handled
+
+### ❌ No Face Detection
+- No pre-generation triggered
+- User must show face first
+
+### ❌ LLM Generation Fails
+- Gracefully skips failed question
+- Continues with next question
+- Logs error for debugging
+
+### ❌ Review Fails
+- Question discarded
+- Not saved to database
+- Moves to next question
+
+### ❌ Database Empty at Question Time
+- Very unlikely (20s head start)
+- Falls back to default question: "你喜欢什么颜色呀？"
+
+## Performance Optimization
+
+### Asynchronous Generation
+```kotlin
+agentScope.launch {
+    checkAndGenerateForUser(userId)  // Non-blocking
+}
+```
+- Doesn't block UI thread
+- Doesn't block greeting
+- Runs completely in background
+
+### Smart Prompt Selection
+- Tracks last 10 used prompts
+- Avoids repetition
+- Ensures diversity
+
+### Delay Between Questions
+```kotlin
+delay(1000)  // 1 second between each question
+```
+- Prevents overwhelming LLM
+- Allows proper processing time
+
+## Testing Scenarios
+
+### Test 1: First Time User
+1. Clear database
+2. Show face to camera
+3. Check logs: Should see "generating 20 more"
+4. Wait 20 seconds
+5. First question should work ✓
+
+### Test 2: Returning User
+1. User already has 5 unanswered questions
+2. Show face to camera
+3. Check logs: Should see "generating 5 more"
+4. Questions should replenish ✓
+
+### Test 3: Continuous Usage
+1. User answers questions continuously
+2. Each question triggers check
+3. Database should stay above 10 questions ✓
+
+## Future Enhancements
+
+- [ ] Priority-based generation (generate harder questions as user progresses)
+- [ ] Track question difficulty and adjust based on user performance
+- [ ] Pre-generate during idle time (not just on face appearance)
+- [ ] Cache generated questions for offline use
+- [ ] Support multiple users with different question pools

app/src/main/assets/question_prompts_README.md

@@ -0,0 +1,221 @@
+# Question Generation Agent - Prompt Pool Guide
+
+## Overview
+
+The Question Generation Agent uses a JSON-based prompt pool to generate diverse educational questions for special needs children. The system automatically loads prompts from `question_prompts.json` and intelligently selects prompts to ensure diversity.
+
+## File Location
+
+```
+app/src/main/assets/question_prompts.json
+```
+
+## JSON Structure
+
+Each prompt in the pool follows this structure:
+
+```json
+{
+  "subject": "生活适应",
+  "grade": 1,
+  "topic": "认识家庭成员",
+  "difficulty": 1,
+  "promptTemplate": "生成一个关于认识家庭成员的题目，适合自闭症儿童，要求题目贴近日常生活，语言简单易懂"
+}
+```
+
+### Fields Description
+
+| Field | Type | Description | Example |
+|-------|------|-------------|---------|
+| `subject` | String | Subject category | "生活适应", "生活语文" |
+| `grade` | Integer | Grade level (1-6) | 1 |
+| `topic` | String | Specific topic name | "认识家庭成员" |
+| `difficulty` | Integer | Difficulty level (1-5) | 1 |
+| `promptTemplate` | String | Detailed instruction for LLM | "生成一个关于...的题目" |
+
+## How to Add New Prompts
+
+### Step 1: Open the JSON file
+
+Open `app/src/main/assets/question_prompts.json` in any text editor.
+
+### Step 2: Add a new prompt object
+
+Add a new object to the JSON array:
+
+```json
+{
+  "subject": "生活适应",
+  "grade": 1,
+  "topic": "你的新主题",
+  "difficulty": 2,
+  "promptTemplate": "生成一个关于[主题描述]的题目，要求[具体要求]"
+}
+```
+
+### Step 3: Save and restart
+
+The prompts are loaded when the app starts. Restart the app to load new prompts.
+
+## Prompt Template Guidelines
+
+### Good Prompt Templates
+
+✅ **Specific and detailed:**
+```json
+"promptTemplate": "生成一个关于认识家庭成员的题目，包括爸爸、妈妈、爷爷、奶奶等，要求描述家庭成员之间的关系和称呼"
+```
+
+✅ **Include context:**
+```json
+"promptTemplate": "生成一个关于交通安全的题目，例如红绿灯、斑马线、过马路等，要求强调安全规则和实际应用场景"
+```
+
+✅ **Specify requirements:**
+```json
+"promptTemplate": "生成一个关于认识颜色的题目，包括红、黄、蓝、绿等基本颜色，要求联系实际生活中的物品，如红色的苹果、黄色的香蕉等"
+```
+
+### Bad Prompt Templates
+
+❌ **Too vague:**
+```json
+"promptTemplate": "生成一个题目"
+```
+
+❌ **Too broad:**
+```json
+"promptTemplate": "生成一个关于数学的题目"
+```
+
+❌ **Not specific to special education:**
+```json
+"promptTemplate": "生成一个难一点的题目"
+```
+
+## Current Prompt Categories
+
+### 生活适应 (Life Adaptation) - 25 prompts
+- 认识家庭成员 (Family Members)
+- 认识日常用品 (Daily Items)
+- 认识天气 (Weather)
+- 认识季节 (Seasons)
+- 交通安全 (Traffic Safety)
+- 认识食物 (Food)
+- 个人卫生 (Personal Hygiene)
+- 认识动物 (Animals)
+- 情绪识别 (Emotion Recognition)
+- 社交礼仪 (Social Etiquette)
+- 时间概念 (Time Concepts)
+- 认识颜色 (Colors)
+- 认识形状 (Shapes)
+- 数数练习 (Counting)
+- 身体部位 (Body Parts)
+- 穿衣自理 (Dressing)
+- 整理物品 (Organizing)
+- 认识数字 (Numbers)
+- 简单加减法 (Basic Math)
+- 认识钱币 (Money)
+- 认识地图 (Maps)
+- 紧急情况 (Emergencies)
+- 公共场合行为 (Public Behavior)
+- 认识职业 (Professions)
+- 节日文化 (Festivals)
+
+### 生活语文 (Life Chinese) - 6 prompts
+- 认识汉字 (Chinese Characters)
+- 简单句子理解 (Sentence Understanding)
+- 词语配对 (Word Pairing)
+- 看图说话 (Picture Description)
+- 反义词 (Antonyms)
+- 量词使用 (Measure Words)
+- 标点符号 (Punctuation)
+
+## Diversity Mechanism
+
+The system ensures question diversity through:
+
+1. **Prompt Pool Rotation**: Intelligently selects prompts that haven't been used recently
+2. **Recent Usage Tracking**: Remembers the last 10 used prompts and avoids them
+3. **Topic Tracking**: Records generated topics to prevent duplicate questions
+4. **Automatic Replenishment**: Generates new questions when the unanswered count drops below threshold
+
+## Configuration
+
+You can configure the agent in `QuestionGenerationAgent.kt`:
+
+```kotlin
+data class AgentConfig(
+    val minUnansweredQuestions: Int = 10,  // Minimum questions before triggering generation
+    val batchSize: Int = 5,                // Number of questions to generate per batch
+    val generationTimeoutMs: Long = 30000  // LLM generation timeout
+)
+```
+
+## Trigger Mechanism
+
+Questions are generated **event-driven**, not time-driven:
+
+1. User asks a question to the child
+2. `onQuestionAsked(userId)` is triggered
+3. System checks if unanswered questions < `minUnansweredQuestions`
+4. If yes, generates `batchSize` new questions
+5. Each question goes through: Generate → Review → Save to Database
+
+## Testing
+
+To manually trigger question generation:
+
+```kotlin
+questionGenerationAgent.triggerGeneration(userId)
+```
+
+## Troubleshooting
+
+### Prompts not loading
+- Check if `question_prompts.json` exists in `app/src/main/assets/`
+- Verify JSON syntax is valid (no trailing commas, proper quotes)
+- Check Logcat for error messages
+
+### Questions not generating
+- Ensure LLM is properly initialized
+- Check if `llmManager` is not null
+- Verify the user has been seen by the face detection system
+
+### Low diversity
+- Add more prompts to the JSON file
+- Increase `maxRecentPrompts` in the agent
+- Check if prompts have varied `topic` values
+
+## Best Practices
+
+1. **Be Specific**: Write detailed prompt templates that specify the exact requirements
+2. **Include Examples**: Provide examples in the prompt template to guide the LLM
+3. **Vary Topics**: Create many different topics within each subject
+4. **Consider Difficulty**: Use appropriate difficulty levels (1-5) for special education
+5. **Test Prompts**: Test each new prompt to ensure it generates quality questions
+6. **Keep Updated**: Regularly review and update prompts based on generated question quality
+
+## Example: Adding a New Subject
+
+```json
+{
+  "subject": "生活数学",
+  "grade": 1,
+  "topic": "比较大小",
+  "difficulty": 1,
+  "promptTemplate": "生成一个关于比较大小的题目，例如哪个更大、哪个更小，要求使用具体的物品如苹果、球等作为比较对象"
+},
+{
+  "subject": "生活数学",
+  "grade": 1,
+  "topic": "简单分类",
+  "difficulty": 2,
+  "promptTemplate": "生成一个关于分类的题目，例如把水果和蔬菜分开，把动物和植物分开，要求分类标准明确"
+}
+```
+
+## Support
+
+For questions or issues, check the Logcat output for error messages and review this guide.

app/src/main/assets/question_prompts_example.json

@@ -0,0 +1,305 @@
+{
+  "_comment": "This is an EXAMPLE file showing how to add more prompts. Copy the prompts you want to the main question_prompts.json file.",
+  
+  "_examples": [
+    {
+      "_description": "Example 1: Life Math - Comparing Sizes",
+      "subject": "生活数学",
+      "grade": 1,
+      "topic": "比较大小",
+      "difficulty": 1,
+      "promptTemplate": "生成一个关于比较大小的题目，例如哪个更大、哪个更小，要求使用具体的物品如苹果、球等作为比较对象"
+    },
+    {
+      "_description": "Example 2: Life Math - Simple Classification",
+      "subject": "生活数学",
+      "grade": 1,
+      "topic": "简单分类",
+      "difficulty": 2,
+      "promptTemplate": "生成一个关于分类的题目，例如把水果和蔬菜分开，把动物和植物分开，要求分类标准明确"
+    },
+    {
+      "_description": "Example 3: Life Adaptation - School Rules",
+      "subject": "生活适应",
+      "grade": 1,
+      "topic": "学校规则",
+      "difficulty": 2,
+      "promptTemplate": "生成一个关于学校规则的题目，例如上课要举手发言、不能在走廊奔跑等，要求强调遵守规则的重要性"
+    },
+    {
+      "_description": "Example 4: Life Chinese - Simple Reading",
+      "subject": "生活语文",
+      "grade": 1,
+      "topic": "简单阅读",
+      "difficulty": 3,
+      "promptTemplate": "生成一个简单阅读理解题目，提供2-3句话的短文，然后提出一个关于短文内容的问题"
+    },
+    {
+      "_description": "Example 5: Life Adaptation - Healthy Habits",
+      "subject": "生活适应",
+      "grade": 1,
+      "topic": "健康习惯",
+      "difficulty": 2,
+      "promptTemplate": "生成一个关于健康习惯的题目，例如早睡早起、多喝水、多运动等，要求解释为什么这些习惯好"
+    },
+    {
+      "_description": "Example 6: Life Adaptation - Using Phone",
+      "subject": "生活适应",
+      "grade": 1,
+      "topic": "使用电话",
+      "difficulty": 3,
+      "promptTemplate": "生成一个关于如何使用电话的题目，包括拨号、接听、挂断等基本操作，要求在紧急情况下如何求助"
+    },
+    {
+      "_description": "Example 7: Life Chinese - Sentence Completion",
+      "subject": "生活语文",
+      "grade": 1,
+      "topic": "句子填空",
+      "difficulty": 2,
+      "promptTemplate": "生成一个句子填空题，例如'我喜欢吃___'，要求提供合适的选项让学生选择"
+    },
+    {
+      "_description": "Example 8: Life Adaptation - Weather Clothing",
+      "subject": "生活适应",
+      "grade": 1,
+      "topic": "天气与穿衣",
+      "difficulty": 2,
+      "promptTemplate": "生成一个关于根据天气选择衣服的题目，例如下雨天穿雨衣、冬天穿棉袄等，要求联系实际生活场景"
+    }
+  ],
+  
+  "_instructions": "To add these prompts to your system:",
+  "_step1": "1. Open app/src/main/assets/question_prompts.json",
+  "_step2": "2. Copy the objects from the _examples array (without the _description field)",
+  "_step3": "3. Paste them into the main JSON array",
+  "_step4": "4. Save the file and restart the app"
+}
+
+
+
+[
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "认识家庭成员",
+    "difficulty": 1,
+    "promptTemplate": "生成一个关于认识家庭成员的题目，适合智力障碍儿童，要求题目贴近日常生活，语言简单易懂"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "认识日常用品",
+    "difficulty": 1,
+    "promptTemplate": "生成一个关于认识日常用品的题目，例如牙刷、毛巾、杯子等，要求描述物品的用途和使用场景"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "认识天气",
+    "difficulty": 1,
+    "promptTemplate": "生成一个关于认识天气的题目，包括晴天、雨天、阴天等，要求联系实际生活场景"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "认识季节",
+    "difficulty": 2,
+    "promptTemplate": "生成一个关于认识四季的题目，要求描述不同季节的特点和对应的活动"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "交通安全",
+    "difficulty": 2,
+    "promptTemplate": "生成一个关于交通安全的题目，例如红绿灯、斑马线、过马路等，要求强调安全规则"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "认识食物",
+    "difficulty": 1,
+    "promptTemplate": "生成一个关于认识常见食物的题目，包括水果、蔬菜、主食等，要求联系实际饮食场景"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "个人卫生",
+    "difficulty": 1,
+    "promptTemplate": "生成一个关于个人卫生的题目，例如洗手、刷牙、洗澡等，要求强调卫生习惯的重要性"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "认识动物",
+    "difficulty": 1,
+    "promptTemplate": "生成一个关于认识常见动物的题目，包括猫、狗、鸟、鱼等，要求描述动物的特征和习性"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "情绪识别",
+    "difficulty": 2,
+    "promptTemplate": "生成一个关于识别情绪的题目，例如高兴、伤心、生气、害怕等，要求联系实际情境"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "社交礼仪",
+    "difficulty": 2,
+    "promptTemplate": "生成一个关于社交礼仪的题目，例如打招呼、说谢谢、对不起等，要求强调礼貌用语的使用场景"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "时间概念",
+    "difficulty": 2,
+    "promptTemplate": "生成一个关于时间概念的题目，例如早上、中午、晚上、昨天、今天、明天等，要求联系日常生活作息"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "认识颜色",
+    "difficulty": 1,
+    "promptTemplate": "生成一个关于认识颜色的题目，包括红、黄、蓝、绿等基本颜色，要求联系实际生活中的物品"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "认识形状",
+    "difficulty": 1,
+    "promptTemplate": "生成一个关于认识形状的题目，例如圆形、方形、三角形等，要求联系实际生活中的物品"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "数数练习",
+    "difficulty": 1,
+    "promptTemplate": "生成一个关于数数的题目，要求10以内的数量，联系实际场景如水果、玩具等"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "身体部位",
+    "difficulty": 1,
+    "promptTemplate": "生成一个关于认识身体部位的题目，例如手、脚、头、眼睛、耳朵等，要求描述部位的功能"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "穿衣自理",
+    "difficulty": 2,
+    "promptTemplate": "生成一个关于穿衣自理的题目，要求描述穿衣的步骤和注意事项"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "整理物品",
+    "difficulty": 2,
+    "promptTemplate": "生成一个关于整理物品的题目，例如整理书包、玩具、房间等，要求强调整理的重要性"
+  },
+  {
+    "subject": "生活语文",
+    "grade": 1,
+    "topic": "认识汉字",
+    "difficulty": 1,
+    "promptTemplate": "生成一个关于认识简单汉字的题目，要求选择日常生活中常用的高频汉字"
+  },
+  {
+    "subject": "生活语文",
+    "grade": 1,
+    "topic": "简单句子理解",
+    "difficulty": 2,
+    "promptTemplate": "生成一个关于简单句子理解的题目，要求句子简短，贴近生活场景"
+  },
+  {
+    "subject": "生活语文",
+    "grade": 1,
+    "topic": "词语配对",
+    "difficulty": 1,
+    "promptTemplate": "生成一个关于词语配对的题目，例如苹果-水果、猫-动物等，要求逻辑关系简单明了"
+  },
+  {
+    "subject": "生活语文",
+    "grade": 1,
+    "topic": "看图说话",
+    "difficulty": 2,
+    "promptTemplate": "生成一个看图说话的题目，用文字描述一个简单场景，让学生用一两句话描述看到的内容"
+  },
+  {
+    "subject": "生活语文",
+    "grade": 1,
+    "topic": "反义词",
+    "difficulty": 2,
+    "promptTemplate": "生成一个关于反义词的题目，例如大-小、高-矮、快-慢等，要求选择常用的反义词对"
+  },
+  {
+    "subject": "生活语文",
+    "grade": 1,
+    "topic": "量词使用",
+    "difficulty": 2,
+    "promptTemplate": "生成一个关于量词使用的题目，例如一个苹果、一本书、一只猫等，要求选择常见的量词搭配"
+  },
+  {
+    "subject": "生活语文",
+    "grade": 1,
+    "topic": "标点符号",
+    "difficulty": 2,
+    "promptTemplate": "生成一个关于标点符号的题目，主要涉及句号、问号、感叹号的基本使用"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "认识数字",
+    "difficulty": 1,
+    "promptTemplate": "生成一个关于认识数字的题目，要求1-10的数字识别和书写"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "简单加减法",
+    "difficulty": 2,
+    "promptTemplate": "生成一个关于简单加减法的题目，要求10以内的加减法，联系实际场景"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "认识钱币",
+    "difficulty": 3,
+    "promptTemplate": "生成一个关于认识钱币的题目，包括元、角、分的认识，要求联系实际购物场景"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "认识地图",
+    "difficulty": 3,
+    "promptTemplate": "生成一个关于认识简单地图的题目，例如家庭、学校的平面图，要求理解基本方位"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "紧急情况",
+    "difficulty": 3,
+    "promptTemplate": "生成一个关于紧急情况处理的题目，例如着火、地震、迷路等，要求强调安全自救知识"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "公共场合行为",
+    "difficulty": 2,
+    "promptTemplate": "生成一个关于公共场合行为规范的题目，例如图书馆、医院、公交车等场景的礼仪"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "认识职业",
+    "difficulty": 2,
+    "promptTemplate": "生成一个关于认识常见职业的题目，例如医生、老师、警察、消防员等，要求描述职业的工作内容"
+  },
+  {
+    "subject": "生活适应",
+    "grade": 1,
+    "topic": "节日文化",
+    "difficulty": 2,
+    "promptTemplate": "生成一个关于传统节日的题目，例如春节、中秋节、端午节等，要求介绍节日的习俗和意义"
+  }
+]

app/src/main/java/com/digitalperson/UnityDigitalPersonActivity.kt

@@ -6,6 +6,7 @@ import android.content.Context
 import android.content.pm.PackageManager
 import android.content.res.ColorStateList
 import android.graphics.Color
+import android.graphics.BitmapFactory
 import android.os.Build
 import android.os.Bundle
 import android.os.Handler
@@ -16,6 +17,7 @@ import android.util.Log
 import android.view.MotionEvent
 import android.view.ViewGroup
 import android.widget.Button
+import android.widget.ImageView
 import android.widget.TextView
 import android.widget.Toast
 import androidx.core.app.ActivityCompat
@@ -25,7 +27,6 @@ import android.view.View
 import androidx.lifecycle.Lifecycle
 import androidx.lifecycle.LifecycleOwner
 import androidx.lifecycle.LifecycleRegistry
-import android.widget.ImageView
 import com.unity3d.player.UnityPlayer
 import com.unity3d.player.UnityPlayerActivity
 import com.digitalperson.audio.AudioProcessor
@@ -33,6 +34,7 @@ import com.digitalperson.asr.AsrManager
 import com.digitalperson.cloud.CloudApiManager
 import com.digitalperson.cloud.CloudReflectionHelper
 import com.digitalperson.config.AppConfig
+import com.digitalperson.embedding.RefImageMatcher
 import com.digitalperson.question.QuestionGenerationAgent
 import com.digitalperson.data.AppDatabase
 import com.digitalperson.face.FaceDetectionPipeline
@@ -48,8 +50,6 @@ import com.digitalperson.tts.TtsController
 import com.digitalperson.util.FileHelper
 import com.digitalperson.vad.VadManager
 import kotlinx.coroutines.*
-import com.digitalperson.embedding.RefImageMatcher
-import android.graphics.BitmapFactory
 
 class UnityDigitalPersonActivity : UnityPlayerActivity(), LifecycleOwner {
     
@@ -260,6 +260,11 @@ class UnityDigitalPersonActivity : UnityPlayerActivity(), LifecycleOwner {
             recordButtonGlow = chatLayout.findViewById(R.id.record_button_glow)
             refMatchImageView = chatLayout.findViewById(R.id.ref_match_image)
 
+            if (!AppConfig.SHOW_DEBUG_TEXT) {
+                chatHistoryText.visibility = View.GONE
+                chatLayout.findViewById<View>(R.id.scroll_view).visibility = View.GONE
+            }
+
             // 根据配置设置按钮可见性
             if (AppConfig.USE_HOLD_TO_SPEAK) {
                 holdToSpeakButton.visibility = View.VISIBLE
@@ -366,6 +371,8 @@ class UnityDigitalPersonActivity : UnityPlayerActivity(), LifecycleOwner {
                 override fun onLlmCalled(text: String) {
                     Log.d("UnityDigitalPerson", "LLM called with: $text")
                     interactionCoordinator.onUserAsrText(text)
+                    // 用用户问题提前匹配：比等 LLM 回复更早显示图片（模拟器/真机通用）
+                    maybeShowMatchedRefImage(text)
                 }
             })
             setAudioProcessor(audioProcessor)
@@ -664,6 +671,7 @@ class UnityDigitalPersonActivity : UnityPlayerActivity(), LifecycleOwner {
     }
     
     private fun appendChat(text: String) {
+        if (!AppConfig.SHOW_DEBUG_TEXT) return
         runOnUiThread {
             chatHistoryText.append(text + "\n")
         }
@@ -696,6 +704,8 @@ class UnityDigitalPersonActivity : UnityPlayerActivity(), LifecycleOwner {
             override fun onSpeak(text: String) {
                 ttsController.enqueueSegment(text)
                 ttsController.enqueueEnd()
+                // 主动发言（问候/主动提问）也尝试匹配参考图片
+                maybeShowMatchedRefImage(text)
             }
 
             override fun onRequestCloudReply(prompt: String) {
@@ -759,13 +769,22 @@ class UnityDigitalPersonActivity : UnityPlayerActivity(), LifecycleOwner {
 
     private fun maybeShowMatchedRefImage(text: String) {
         val imageView = refMatchImageView ?: return
-        // Unity Activity already has coroutines
+        // 每次匹配前先清掉上一张图
-        CoroutineScope(SupervisorJob() + Dispatchers.IO).launch {
+        runOnUiThread {
+            imageView.setImageBitmap(null)
+            imageView.visibility = View.GONE
+        }
+        ioScope.launch {
             val match = RefImageMatcher.findBestMatch(applicationContext, text)
-            if (match == null) return@launch
+            if (match == null) {
+                Log.d("RefImageMatch", "未找到匹配图片 query=\"${text.take(80)}\"")
+                return@launch
+            }
+            Log.d("RefImageMatch", "匹配成功 score=${match.score} path=${match.pngAssetPath} query=\"${text.take(80)}\"")
             val bitmap = try {
                 assets.open(match.pngAssetPath).use { BitmapFactory.decodeStream(it) }
-            } catch (_: Throwable) {
+            } catch (e: Throwable) {
+                Log.w("RefImageMatch", "图片加载失败 path=${match.pngAssetPath}", e)
                 null
             }
             if (bitmap == null) return@launch

app/src/main/java/com/digitalperson/asr/AsrManager.kt

@@ -6,6 +6,7 @@ import android.util.Log
 import com.digitalperson.BuildConfig
 import com.digitalperson.audio.AudioProcessor
 import com.digitalperson.config.AppConfig
+import com.digitalperson.env.RuntimeEnv
 import com.digitalperson.engine.SenseVoiceEngineRKNN
 import com.digitalperson.util.FileHelper
 import kotlinx.coroutines.Dispatchers
@@ -23,7 +24,6 @@ class AsrManager(private val context: Context) {
     
     private var senseVoice: SenseVoiceEngineRKNN? = null
     private val nativeLock = Any()
-    
     private val asrQueue = Channel<Pair<FloatArray, FloatArray>>(capacity = Channel.UNLIMITED)
     
     private var audioProcessor: AudioProcessor? = null
@@ -48,6 +48,10 @@ class AsrManager(private val context: Context) {
     }
     
     fun initSenseVoiceModel(): Boolean {
+        if (RuntimeEnv.isEmulator()) {
+            Log.w(TAG, "ASR: emulator detected; skip local RKNN init and use cloud ASR")
+            return false
+        }
         return try {
             Log.i(TAG, "ASR: init SenseVoice RKNN (scheme A)")
             
@@ -134,22 +138,46 @@ class AsrManager(private val context: Context) {
                 
                 saveAsrAudio(originalSeg, processedSeg)
 
-                val raw = synchronized(nativeLock) {
+                val localText = synchronized(nativeLock) {
                     val e = senseVoice
                     if (e == null || !e.isInitialized) {
-                        Log.e(TAG, "ASR failed: SenseVoice engine not initialized")
                         ""
                     } else {
                         try {
-                            e.transcribeBuffer(processedSeg)
+                            removeTokens(e.transcribeBuffer(processedSeg))
-                        } catch (e: Throwable) {
+                        } catch (t: Throwable) {
-                            Log.e(TAG, "ASR transcribe failed: ${e.message}")
+                            Log.e(TAG, "ASR transcribe failed: ${t.message}")
                             ""
                         }
                     }
+                }.trim()
+
+                val text = if (localText.isNotBlank()) {
+                    localText
+                } else {
+                    // 模拟器或本地 RKNN 未就绪：使用腾讯云「一句话识别」SDK（app/libs/asr-one-sentence-release.aar）
+                    val shouldTryTencent =
+                        BuildConfig.HAS_TENCENT_ASR_SDK && (RuntimeEnv.isEmulator() || !isInitialized())
+                    if (!shouldTryTencent) {
+                        Log.e(
+                            TAG,
+                            "ASR failed: local RKNN not ready and Tencent SDK unavailable " +
+                                "(add libs/asr-one-sentence-release.aar or fix SenseVoice init)"
+                        )
+                        ""
+                    } else {
+                        withContext(Dispatchers.IO) {
+                            try {
+                                // 云端 ASR 使用原始录音（未经 AEC/NS）：
+                                // 模拟器上 AEC/NS 不可用，processedSeg 可能被处理成近似静音
+                                TencentOneSentenceAsr.transcribePcm16Mono(originalSeg)
+                            } catch (t: Throwable) {
+                                Log.e(TAG, "Tencent ASR failed: ${t.message}")
+                                ""
+                            }
+                        }.trim()
+                    }
                 }
-                Log.d(TAG, "ASR raw result: $raw")
-                val text = removeTokens(raw)
 
                 val filterResult = filterText(text)
                 if (filterResult != null) {
@@ -220,4 +248,5 @@ class AsrManager(private val context: Context) {
         }
         return null
     }
+
 }

app/src/main/java/com/digitalperson/asr/TencentOneSentenceAsr.kt

@@ -0,0 +1,216 @@
+package com.digitalperson.asr
+
+import android.util.Base64
+import android.util.Log
+import com.digitalperson.config.AppConfig
+import okhttp3.MediaType.Companion.toMediaType
+import okhttp3.OkHttpClient
+import okhttp3.Request
+import okhttp3.RequestBody.Companion.toRequestBody
+import org.json.JSONObject
+import java.nio.ByteBuffer
+import java.nio.ByteOrder
+import java.security.MessageDigest
+import java.text.SimpleDateFormat
+import java.util.Date
+import java.util.Locale
+import java.util.TimeZone
+import java.util.concurrent.TimeUnit
+import javax.crypto.Mac
+import javax.crypto.spec.SecretKeySpec
+
+/**
+ * 腾讯云「一句话识别」REST API 直接实现（TC3-HMAC-SHA256 签名）。
+ *
+ * 不依赖 SDK AAR，而是用 OkHttp 自行签名并发起 HTTP 请求。
+ * 签名时间戳从服务器 Date 响应头获取，彻底规避模拟器时钟偏差导致的
+ * AuthFailure.SignatureExpire 错误。
+ *
+ * 文档：https://cloud.tencent.com/document/product/1093/35646
+ */
+object TencentOneSentenceAsr {
+
+    private const val TAG = "TencentOneSentenceAsr"
+    private const val HOST = "asr.tencentcloudapi.com"
+    private const val ACTION = "SentenceRecognition"
+    private const val VERSION = "2019-06-14"
+
+    private val client = OkHttpClient.Builder()
+        .connectTimeout(10, TimeUnit.SECONDS)
+        .readTimeout(30, TimeUnit.SECONDS)
+        .build()
+
+    /**
+     * 将 FloatArray (16kHz mono, -1..1) 通过腾讯云一句话识别转为文字。
+     * 阻塞直到 HTTP 响应返回或超时。请在 IO 线程中调用。
+     */
+    fun transcribePcm16Mono(pcmFloat: FloatArray): String {
+        val appId = AppConfig.QCloud.APP_ID.trim()
+        val sid   = AppConfig.QCloud.SECRET_ID.trim()
+        val skey  = AppConfig.QCloud.SECRET_KEY.trim()
+        if (appId.isEmpty() || sid.isEmpty() || skey.isEmpty()) {
+            Log.e(TAG, "APP_ID / SECRET_ID / SECRET_KEY 为空")
+            return ""
+        }
+        if (pcmFloat.isEmpty()) return ""
+
+        val pcmBytes  = floatToPcm16Bytes(pcmFloat)
+        val pcmBase64 = Base64.encodeToString(pcmBytes, Base64.NO_WRAP)
+
+        // 诊断：检查音频幅度，若 RMS 接近 0 说明麦克风没采集到声音
+        val rms = kotlin.math.sqrt(pcmFloat.fold(0.0) { acc, v -> acc + v * v } / pcmFloat.size)
+        val maxAmp = pcmFloat.maxOf { kotlin.math.abs(it) }
+        Log.d(TAG, "一句话识别：${pcmFloat.size} 采样点，${pcmFloat.size / 16000.0}s，${pcmBytes.size} bytes  RMS=${"%.4f".format(rms)} maxAmp=${"%.4f".format(maxAmp)}")
+        if (maxAmp < 0.01f) {
+            Log.w(TAG, "⚠ 音频幅度极低（maxAmp=${"%.5f".format(maxAmp)}），模拟器麦克风可能没有采集到声音！请检查：模拟器扩展控制 → 麦克风 → 使用宿主机麦克风")
+        }
+
+        // 从服务器取时间，修正模拟器时钟偏差
+        val timestamp = fetchServerTimestamp()
+        val date = utcDate(timestamp)
+
+        val payload = buildPayload(appId, pcmBase64, pcmBytes.size)
+        val auth    = buildAuthorization(sid, skey, date, timestamp, payload)
+
+        val request = Request.Builder()
+            .url("https://$HOST")
+            .addHeader("Authorization",    auth)
+            .addHeader("Content-Type",     "application/json; charset=utf-8")
+            .addHeader("Host",             HOST)
+            .addHeader("X-TC-Action",      ACTION)
+            .addHeader("X-TC-Version",     VERSION)
+            .addHeader("X-TC-Timestamp",   timestamp.toString())
+            .post(payload.toRequestBody("application/json; charset=utf-8".toMediaType()))
+            .build()
+
+        return try {
+            val response = client.newCall(request).execute()
+            val body = response.body?.string().orEmpty()
+            Log.d(TAG, "API 响应: ${body.take(400)}")
+            parseResult(body)
+        } catch (e: Exception) {
+            Log.e(TAG, "HTTP 请求失败: ${e.message}", e)
+            ""
+        }
+    }
+
+    // ─── 工具方法 ──────────────────────────────────────────────────────────
+
+    /**
+     * 向服务器发送 HEAD 请求，从 Date 响应头获取精确时间戳。
+     * 若请求失败则回退到设备时钟（可能有偏差）。
+     */
+    private fun fetchServerTimestamp(): Long {
+        return try {
+            val req  = Request.Builder().url("https://$HOST").head().build()
+            val resp = client.newCall(req).execute()
+            val dateHeader = resp.header("Date")
+            resp.close()
+            if (dateHeader != null) {
+                val sdf = SimpleDateFormat("EEE, dd MMM yyyy HH:mm:ss z", Locale.ENGLISH)
+                val serverTs = sdf.parse(dateHeader)?.time?.div(1000) ?: deviceTimestamp()
+                val deviceTs = deviceTimestamp()
+                val offset   = serverTs - deviceTs
+                if (kotlin.math.abs(offset) > 60) {
+                    Log.w(TAG, "设备时钟偏差 ${offset}s，使用服务器时间修正（设备=${deviceTs}, 服务器=${serverTs}）")
+                }
+                serverTs
+            } else {
+                deviceTimestamp()
+            }
+        } catch (e: Exception) {
+            Log.w(TAG, "获取服务器时间失败: ${e.message}，使用设备时间")
+            deviceTimestamp()
+        }
+    }
+
+    private fun deviceTimestamp() = System.currentTimeMillis() / 1000
+
+    private fun utcDate(timestamp: Long): String {
+        val sdf = SimpleDateFormat("yyyy-MM-dd", Locale.US)
+        sdf.timeZone = TimeZone.getTimeZone("UTC")
+        return sdf.format(Date(timestamp * 1000))
+    }
+
+    private fun buildPayload(appId: String, base64: String, dataLen: Int): String =
+        JSONObject().apply {
+            put("ProjectId",      0)
+            put("SubServiceType", 2)
+            put("EngSerViceType", "16k_zh")
+            put("SourceType",     1)   // 1 = 数据流
+            put("VoiceFormat",    "pcm")
+            put("UsrAudioKey",    "digital-person-asr")
+            put("FilterDirty",    0)
+            put("FilterModal",    0)
+            put("FilterPunc",     0)
+            put("ConvertNumMode", 1)
+            put("Data",           base64)
+            put("DataLen",        dataLen)
+        }.toString()
+
+    // ─── TC3-HMAC-SHA256 签名 ──────────────────────────────────────────────
+
+    private fun buildAuthorization(
+        secretId:  String,
+        secretKey: String,
+        date:      String,
+        timestamp: Long,
+        payload:   String,
+    ): String {
+        val payloadHash = sha256Hex(payload)
+        val canonicalRequest = listOf(
+            "POST", "/", "",
+            "content-type:application/json; charset=utf-8",
+            "host:$HOST",
+            "",
+            "content-type;host",
+            payloadHash,
+        ).joinToString("\n")
+
+        val credentialScope = "$date/asr/tc3_request"
+        val stringToSign    = "TC3-HMAC-SHA256\n$timestamp\n$credentialScope\n${sha256Hex(canonicalRequest)}"
+
+        val signingKey = hmacSha256(
+            hmacSha256(hmacSha256("TC3$secretKey".toByteArray(), date), "asr"),
+            "tc3_request",
+        )
+        val signature = hmacSha256(signingKey, stringToSign).joinToString("") { "%02x".format(it) }
+
+        return "TC3-HMAC-SHA256 Credential=$secretId/$credentialScope, SignedHeaders=content-type;host, Signature=$signature"
+    }
+
+    private fun parseResult(json: String): String {
+        if (json.isBlank()) return ""
+        return try {
+            val response = JSONObject(json).optJSONObject("Response") ?: return ""
+            val error = response.optJSONObject("Error")
+            if (error != null) {
+                Log.e(TAG, "API 错误: ${error.optString("Code")} - ${error.optString("Message")}")
+                return ""
+            }
+            response.optString("Result").also { text ->
+                if (text.isNotBlank()) Log.d(TAG, "识别结果: \"$text\"")
+            }
+        } catch (e: Exception) {
+            Log.w(TAG, "解析响应失败: ${json.take(300)}")
+            ""
+        }
+    }
+
+    private fun sha256Hex(data: String): String {
+        val md = MessageDigest.getInstance("SHA-256")
+        return md.digest(data.toByteArray(Charsets.UTF_8)).joinToString("") { "%02x".format(it) }
+    }
+
+    private fun hmacSha256(key: ByteArray, data: String): ByteArray {
+        val mac = Mac.getInstance("HmacSHA256")
+        mac.init(SecretKeySpec(key, "HmacSHA256"))
+        return mac.doFinal(data.toByteArray(Charsets.UTF_8))
+    }
+
+    private fun floatToPcm16Bytes(samples: FloatArray): ByteArray {
+        val buf = ByteBuffer.allocate(samples.size * 2).order(ByteOrder.LITTLE_ENDIAN)
+        samples.forEach { buf.putShort((it.coerceIn(-1f, 1f) * 32767f).toInt().toShort()) }
+        return buf.array()
+    }
+}

app/src/main/java/com/digitalperson/config/AppConfig.kt

@@ -116,6 +116,19 @@ object AppConfig {
         const val MODEL_FILE = "bge-small-zh-v1.5.rknn"
     }
 
+    /**
+     * 模拟器上 [RefImageMatcher] 使用编辑距离时的最低归一化分（与 BGE 余弦阈值不可混用）。
+     * 分数 = 1 - Levenshtein / max(len)，越接近 1 越像。
+     */
+    object RefMatchEmulator {
+        /**
+         * 模拟器混合评分（路径关键词命中率 + 编辑距离）阈值。
+         * 路径关键词：1 个词命中 ≈ 0.25，已足够确认话题相关性。
+         * 原 0.82 是纯编辑距离阈值，字面差异大时根本达不到，故降至 0.20。
+         */
+        const val MIN_NORMALIZED_EDIT_SCORE = 0.20f
+    }
+
     /**
      * app/note/ref 通过 Gradle 额外 assets 目录打入 apk 后，在 assets 中的根路径为 `ref/`。
      */

app/src/main/java/com/digitalperson/embedding/EditDistanceSimilarity.kt

@@ -0,0 +1,49 @@
+package com.digitalperson.embedding
+
+import kotlin.math.max
+import kotlin.math.min
+
+/**
+ * 基于 Levenshtein 的字符级相似度（模拟器兜底，无语义，仅用于联调/演示）。
+ *
+ * 分数：1 - dist / max(len1, len2)，与余弦相似度不可直接对比阈值。
+ */
+object EditDistanceSimilarity {
+
+    fun normalizedScore(a: String, b: String): Float {
+        val s1 = a.trim()
+        val s2 = b.trim()
+        if (s1.isEmpty() && s2.isEmpty()) return 1f
+        if (s1.isEmpty() || s2.isEmpty()) return 0f
+        val dist = levenshtein(s1, s2)
+        val denom = max(s1.length, s2.length).coerceAtLeast(1)
+        return 1f - dist.toFloat() / denom.toFloat()
+    }
+
+    /**
+     * 经典双行 DP，O(n·m)；仅适用于模拟器上中等规模语料。
+     */
+    fun levenshtein(s1: String, s2: String): Int {
+        val n = s1.length
+        val m = s2.length
+        if (n == 0) return m
+        if (m == 0) return n
+        var prev = IntArray(m + 1) { it }
+        var curr = IntArray(m + 1)
+        for (i in 1..n) {
+            curr[0] = i
+            val c1 = s1[i - 1]
+            for (j in 1..m) {
+                val cost = if (c1 == s2[j - 1]) 0 else 1
+                curr[j] = min(
+                    min(prev[j] + 1, curr[j - 1] + 1),
+                    prev[j - 1] + cost
+                )
+            }
+            val tmp = prev
+            prev = curr
+            curr = tmp
+        }
+        return prev[m]
+    }
+}

app/src/main/java/com/digitalperson/embedding/RefEmbeddingIndexer.kt

@@ -4,6 +4,7 @@ import android.content.Context
 import android.util.Log
 import com.digitalperson.config.AppConfig
 import com.digitalperson.data.AppDatabase
+import com.digitalperson.data.dao.QuestionDao
 import com.digitalperson.data.entity.Question
 import com.digitalperson.data.entity.RefTextEmbedding
 import com.digitalperson.data.util.floatArrayToEmbeddingBytes
@@ -27,15 +28,15 @@ object RefEmbeddingIndexer {
         val dao = db.refTextEmbeddingDao()
         val questionDao = db.questionDao()
 
-        if (!BgeEmbedding.initialize(app)) {
-            Log.e(TAG, "[RefEmbed] BGE 初始化失败，跳过 ref 语料索引")
-            return@withContext
-        }
-
         val root = AppConfig.RefCorpus.ASSETS_ROOT
         val paths = RefCorpusAssetScanner.listTxtFilesUnder(app, root)
         Log.i(TAG, "[RefEmbed] 发现 ${paths.size} 个 txt（root=$root）")
 
+        val bgeOk = BgeEmbedding.initialize(app)
+        if (!bgeOk) {
+            Log.w(TAG, "[RefEmbed] BGE 未就绪（常见于模拟器），仅扫描题库；ref 配图匹配可用编辑距离")
+        }
+
         var skipped = 0
         var embedded = 0
         var empty = 0
@@ -50,28 +51,9 @@ object RefEmbeddingIndexer {
                 continue
             }
 
-            // 题库：遇到包含 ?/？ 的行，写入 questions
+            ingestQuestionsFromRaw(raw, path, questionDao)
-            val subject = extractSubjectFromRaw(raw)
+
-            val grade = extractGradeFromPath(path)
+            if (!bgeOk) continue
-            val questionLines = extractQuestionLines(raw)
-            for (line in questionLines) {
-                val content = line.trim()
-                if (content.isEmpty()) continue
-                val exists = questionDao.findByContentSubjectGrade(content, subject, grade)
-                if (exists == null) {
-                    questionDao.insert(
-                        Question(
-                            id = 0,
-                            content = content,
-                            answer = null,
-                            subject = subject,
-                            grade = grade,
-                            difficulty = 1,
-                            createdAt = System.currentTimeMillis()
-                        )
-                    )
-                }
-            }
 
             val embedText = RefTxtEmbedText.fromRawFileContent(raw)
             if (embedText.isEmpty()) {
@@ -110,10 +92,34 @@ object RefEmbeddingIndexer {
 
         Log.i(
             TAG,
-            "[RefEmbed] 完成 embedded=$embedded skipped=$skipped empty=$empty failed=$failed cacheSize=${RefEmbeddingMemoryCache.size()}"
+            "[RefEmbed] 完成 embedded=$embedded skipped=$skipped empty=$empty failed=$failed cacheSize=${RefEmbeddingMemoryCache.size()} bgeOk=$bgeOk"
         )
     }
 
+    private fun ingestQuestionsFromRaw(raw: String, path: String, questionDao: QuestionDao) {
+        val subject = extractSubjectFromRaw(raw)
+        val grade = extractGradeFromPath(path)
+        val questionLines = extractQuestionLines(raw)
+        for (line in questionLines) {
+            val content = line.trim()
+            if (content.isEmpty()) continue
+            val exists = questionDao.findByContentSubjectGrade(content, subject, grade)
+            if (exists == null) {
+                questionDao.insert(
+                    Question(
+                        id = 0,
+                        content = content,
+                        answer = null,
+                        subject = subject,
+                        grade = grade,
+                        difficulty = 1,
+                        createdAt = System.currentTimeMillis()
+                    )
+                )
+            }
+        }
+    }
+
     private fun extractSubjectFromRaw(raw: String): String? {
         val line = raw.lineSequence()
             .map { it.trimEnd() }

app/src/main/java/com/digitalperson/embedding/RefImageMatcher.kt

@@ -3,6 +3,7 @@ package com.digitalperson.embedding
 import android.content.Context
 import android.util.Log
 import com.digitalperson.config.AppConfig
+import com.digitalperson.env.RuntimeEnv
 import kotlin.math.sqrt
 
 data class RefImageMatch(
@@ -16,7 +17,8 @@ object RefImageMatcher {
     private const val TAG = AppConfig.TAG
 
     /**
-     * @param threshold 余弦相似度阈值（向量已归一化时等价于 dot product）。
+     * @param threshold 真机 BGE：余弦相似度阈值（向量已归一化时等价于 dot product）。
+     * 模拟器：忽略该参数，使用 [AppConfig.RefMatchEmulator.MIN_NORMALIZED_EDIT_SCORE]（编辑距离归一化分）。
      */
     fun findBestMatch(
         context: Context,
@@ -26,6 +28,10 @@ object RefImageMatcher {
         val query = text.trim()
         if (query.isEmpty()) return null
 
+        if (RuntimeEnv.isEmulator()) {
+            return findBestMatchEditDistance(context, query)
+        }
+
         if (!BgeEmbedding.isReady()) {
             val ok = BgeEmbedding.initialize(context.applicationContext)
             if (!ok) {
@@ -78,6 +84,203 @@ object RefImageMatcher {
         )
     }
 
+    /**
+     * 模拟器：不加载 BGE，用**路径关键词命中率**（主）+ 编辑距离（辅）混合评分。
+     *
+     * 路径关键词：取最深目录名按 "-" 分割，如 "一年级上-生活适应-社会生活-元旦"
+     * → ["一年级上", "生活适应", "社会生活", "元旦"]。
+     * 命中率 = 命中数 / 关键词总数（1 个词命中 ≈ 0.25，足以通过 0.20 阈值）。
+     *
+     * 纯编辑距离原先用 0.82 阈值，但 LLM 回复文本与参考短句字面差异很大，
+     * 即使话题相同也难达标；改为关键词方案后准确率大幅提升。
+     */
+    /** 模拟器混合匹配逻辑；对 [androidTest] 暴露以便回归「本应命中却未命中」的用例。 */
+    internal fun findBestMatchEditDistance(context: Context, query: String): RefImageMatch? {
+        val app = context.applicationContext
+        val root = AppConfig.RefCorpus.ASSETS_ROOT
+        val paths = RefCorpusAssetScanner.listTxtFilesUnder(app, root)
+        val minScore = AppConfig.RefMatchEmulator.MIN_NORMALIZED_EDIT_SCORE
+        val qNorm = normalizeTextForEmuMatch(query.trim())
+        if (qNorm.isEmpty()) return null
+
+        var bestPath: String? = null
+        var bestScore = -1f
+        var bestSubstr = -1f
+        var bestEdit = -1f
+
+        for (path in paths) {
+            // 主：路径关键词命中率（无 IO，O(1)）
+            val kwScore = pathKeywordMatchScore(path, qNorm)
+
+            // 辅：内容匹配（有 IO，仅在关键词有命中或尚无候选时读取）
+            // 策略：① 子串包含（query 句子 ⊆ candidate 或 candidate 句子 ⊆ query 句子）
+            //        ② 逐句编辑距离
+            // candidate 是 txt 去掉 # 行后的全文（可能同时含问题和答案），
+            // query 中某句若直接出现在 candidate 里，说明话题完全命中。
+            // 必须对每条 txt 做内容打分：若仅在 bestScore<0 或 kwScore>0 时才读盘，
+            // 会先被其它文件的弱编辑分「占坑」，导致题干与某文件完全一致却从未被打开（如「上厕所」目录 kw 未命中但正文含原句）。
+            var substrScore = 0f
+            var editScore = 0f
+            try {
+                val raw = app.assets.open(path).bufferedReader(Charsets.UTF_8).use { it.readText() }
+                val candidate = normalizeTextForEmuMatch(RefTxtEmbedText.fromRawFileContent(raw))
+                if (candidate.isNotEmpty()) {
+                    val querySentences = splitSentences(qNorm)
+                    val candidateSentences = splitSentences(candidate)
+                    // ① 子串：query 句 ⊆ candidate，或 candidate 句 ⊆ query 句。
+                    // 极短片段（如「小朋友」）在多篇课文里都有，一律给高分会错配；按匹配长度分级。
+                    substrScore = querySentences.maxOfOrNull { qs ->
+                        var s = 0f
+                        if (qs.length >= 4 && candidate.contains(qs)) {
+                            s = maxOf(s, emulatorSubstringScoreForLength(qs.length))
+                        }
+                        for (cs in candidateSentences) {
+                            if (cs.length >= 6 && qs.contains(cs)) {
+                                s = maxOf(s, emulatorSubstringScoreForLength(cs.length) * 0.92f)
+                            }
+                        }
+                        s
+                    } ?: 0f
+                    // ② 编辑距离（逐句 vs 逐句，取最高分）
+                    editScore = querySentences.maxOfOrNull { qs ->
+                        candidateSentences.maxOfOrNull { cs ->
+                            EditDistanceSimilarity.normalizedScore(qs, cs)
+                        } ?: 0f
+                    } ?: 0f
+                }
+            } catch (e: Exception) {
+                Log.w(TAG, "[RefMatchEmu] read fail $path: ${e.message}")
+            }
+
+            val score = maxOf(kwScore, substrScore, editScore)
+            if (score > 0f) {
+                Log.v(TAG, "[RefMatchEmu] candidate score=$score (kw=$kwScore substr=$substrScore edit=$editScore) path=$path")
+            }
+            if (isBetterEmulatorCandidate(score, substrScore, editScore, bestScore, bestSubstr, bestEdit)) {
+                bestScore = score
+                bestSubstr = substrScore
+                bestEdit = editScore
+                bestPath = path
+            }
+        }
+
+        val txtPath = bestPath ?: run {
+            Log.d(TAG, "[RefMatchEmu] 无候选文件 query=${qNorm.take(60)}")
+            return null
+        }
+        if (bestScore < minScore) {
+            Log.d(TAG, "[RefMatchEmu] 分数不足 bestScore=$bestScore minScore=$minScore bestPath=$txtPath query=${qNorm.take(60)}")
+            return null
+        }
+
+        val pngPath = if (txtPath.endsWith(".txt", ignoreCase = true)) {
+            txtPath.dropLast(4) + ".png"
+        } else {
+            "$txtPath.png"
+        }
+        val exists = try {
+            context.assets.open(pngPath).close()
+            true
+        } catch (_: Throwable) {
+            false
+        }
+        if (!exists) return null
+
+        Log.d(TAG, "[RefMatchEmu] best=$txtPath score=$bestScore query=${qNorm.take(30)}")
+        return RefImageMatch(
+            txtAssetPath = txtPath,
+            pngAssetPath = pngPath,
+            score = bestScore
+        )
+    }
+
+    /**
+     * 从文件路径提取话题关键词，计算与查询文本的关键词命中率。
+     * 如路径含目录 "一年级上-生活适应-社会生活-元旦" → 关键词 ["一年级上","生活适应","社会生活","元旦"]。
+     */
+    private fun pathKeywordMatchScore(path: String, query: String): Float {
+        val keywords = extractPathTopicKeywords(path)
+        if (keywords.isEmpty()) return 0f
+        val matches = keywords.count { kw -> queryMatchesPathKeyword(query, kw) }
+        return matches.toFloat() / keywords.size
+    }
+
+    /** 统一全角/半角标点后再匹配，避免代码或 ASR 里半角 `:` 与语料全角 `：` 导致长句子串匹配失败。 */
+    private fun normalizeTextForEmuMatch(s: String): String = buildString(s.length) {
+        for (ch in s) {
+            append(
+                when (ch) {
+                    '\uFF1A', '\uFE55', ':' -> ':'
+                    '\uFF0C' -> ','
+                    '\uFF01' -> '!'
+                    '\uFF1F' -> '?'
+                    '\uFF1B' -> ';'
+                    else -> ch
+                },
+            )
+        }
+    }
+
+    /** 总分相同时优先子串分、再比编辑分，避免「元旦到了，小朋友」等前缀在多篇课文同分却先命中排序靠前者。 */
+    private fun isBetterEmulatorCandidate(
+        score: Float,
+        substr: Float,
+        edit: Float,
+        bestScore: Float,
+        bestSubstr: Float,
+        bestEdit: Float,
+    ): Boolean {
+        if (bestScore < 0f) return true
+        when {
+            score > bestScore + 1e-5f -> return true
+            score + 1e-5f < bestScore -> return false
+            substr > bestSubstr + 1e-5f -> return true
+            substr + 1e-5f < bestSubstr -> return false
+            else -> return edit > bestEdit + 1e-5f
+        }
+    }
+
+    /** 路径片段与 query 的包含关系；题干常省略词头（如目录「上厕所」、句子里只有「厕所」）。 */
+    private fun queryMatchesPathKeyword(query: String, kw: String): Boolean {
+        if (query.contains(kw)) return true
+        // 去掉首字再匹配，避免「个人生活」用 takeLast(2) 误匹配到泛泛的「生活」
+        if (kw.length >= 3) {
+            val rest = kw.substring(1)
+            if (rest.length >= 2 && query.contains(rest)) return true
+        }
+        return false
+    }
+
+    /** 子串命中得分：越长说明越具区分度；过短（如「小朋友」）分数低，减少跨课文误配。 */
+    private fun emulatorSubstringScoreForLength(len: Int): Float = when {
+        len >= 18 -> 0.95f
+        len >= 12 -> 0.90f
+        len >= 8 -> 0.82f
+        len >= 6 -> 0.68f
+        len >= 4 -> 0.48f
+        else -> 0f
+    }
+
+    /**
+     * 按中文/英文句子分隔符拆分，返回非空句子列表。
+     * 用于模拟器编辑距离辅助评分：逐句比对，避免 LLM 前导寒暄句拉低得分。
+     */
+    private fun splitSentences(text: String): List<String> {
+        val parts = text.split(Regex("[，。！？；,!?;\n]+"))
+            .map { it.trim() }
+            .filter { it.length >= 2 }
+        return parts.ifEmpty { listOf(text) }
+    }
+
+    /** 取最深目录名，按 "-" 分割并过滤掉纯数字和单字符片段。 */
+    private fun extractPathTopicKeywords(path: String): List<String> {
+        val deepestDir = path.split("/").dropLast(1).lastOrNull() ?: return emptyList()
+        return deepestDir.split("-")
+            .map { it.replace(Regex("\\d+"), "").trim() }
+            .filter { it.length >= 2 }
+            .distinct()
+    }
+
     private fun dot(a: FloatArray, b: FloatArray): Float {
         var s = 0f
         for (i in a.indices) s += a[i] * b[i]

app/src/main/java/com/digitalperson/env/RuntimeEnv.kt

@@ -0,0 +1,34 @@
+package com.digitalperson.env
+
+import android.os.Build
+
+object RuntimeEnv {
+    fun isEmulator(): Boolean {
+        val fingerprint = Build.FINGERPRINT.orEmpty()
+        val model = Build.MODEL.orEmpty()
+        val brand = Build.BRAND.orEmpty()
+        val device = Build.DEVICE.orEmpty()
+        val product = Build.PRODUCT.orEmpty()
+        val hardware = Build.HARDWARE.orEmpty()
+        val manufacturer = Build.MANUFACTURER.orEmpty()
+
+        var hits = 0
+        fun hit(b: Boolean) { if (b) hits++ }
+
+        hit(fingerprint.startsWith("generic", ignoreCase = true))
+        hit(fingerprint.contains("unknown", ignoreCase = true))
+        hit(model.contains("google_sdk", ignoreCase = true))
+        hit(model.contains("emulator", ignoreCase = true))
+        hit(model.contains("android sdk built for", ignoreCase = true))
+        hit(manufacturer.contains("genymotion", ignoreCase = true))
+        hit(brand.startsWith("generic", ignoreCase = true) && device.startsWith("generic", ignoreCase = true))
+        hit(product.contains("sdk", ignoreCase = true))
+        hit(product.contains("emulator", ignoreCase = true))
+        hit(hardware.contains("goldfish", ignoreCase = true))
+        hit(hardware.contains("ranchu", ignoreCase = true))
+
+        // Require multiple signals to avoid false positives on weird ROMs.
+        return hits >= 2
+    }
+}
+

app/src/main/java/com/digitalperson/face/FaceDetectionPipeline.kt

@@ -5,6 +5,7 @@ import android.graphics.Bitmap
 import android.util.Log
 import com.digitalperson.config.AppConfig
 import com.digitalperson.engine.RetinaFaceEngineRKNN
+import com.digitalperson.env.RuntimeEnv
 import java.util.ArrayDeque
 import java.util.concurrent.atomic.AtomicBoolean
 import kotlinx.coroutines.CoroutineScope
@@ -35,6 +36,12 @@ class FaceDetectionPipeline(
     private val onResult: (FaceDetectionResult) -> Unit,
     private val onPresenceChanged: (present: Boolean, isFrontal: Boolean, faceIdentityId: String?, recognizedName: String?) -> Unit,
 ) {
+    companion object {
+        /** 模拟器固定人脸 ID，对应 UserMemory 中的 userId */
+        const val EMULATOR_FACE_ID   = "face_emulator"
+        /** 模拟器固定显示名，直接作为 recognizedName 传给 coordinator */
+        const val EMULATOR_FACE_NAME = "小黑"
+    }
     private val appContext = context.applicationContext
     private val engine = RetinaFaceEngineRKNN()
     private val recognizer = FaceRecognizer(appContext)
@@ -50,6 +57,11 @@ class FaceDetectionPipeline(
     private val fusionQualities = ArrayDeque<Float>()
 
     fun initialize(): Boolean {
+        if (RuntimeEnv.isEmulator()) {
+            Log.i(AppConfig.TAG, "[Face] 模拟器模式：跳过 RKNN 初始化，固定返回身份「$EMULATOR_FACE_NAME」")
+            initialized.set(true)
+            return true
+        }
         val detectorOk = engine.initialize(appContext)
         val recognizerOk = recognizer.initialize()
         val ok = detectorOk && recognizerOk
@@ -68,6 +80,31 @@ class FaceDetectionPipeline(
             return
         }
 
+        // 模拟器：跳过 RKNN 检测，固定上报一张居中正脸
+        if (RuntimeEnv.isEmulator()) {
+            scope.launch {
+                try {
+                    val w = bitmap.width
+                    val h = bitmap.height
+                    val fakeBox = FaceBox(
+                        left   = w * 0.25f,
+                        top    = h * 0.15f,
+                        right  = w * 0.75f,
+                        bottom = h * 0.85f,
+                        score  = 0.99f,
+                    )
+                    withContext(Dispatchers.Main) {
+                        onPresenceChanged(true, true, EMULATOR_FACE_ID, EMULATOR_FACE_NAME)
+                        onResult(FaceDetectionResult(w, h, listOf(fakeBox)))
+                    }
+                } finally {
+                    bitmap.recycle()
+                    frameInFlight.set(false)
+                }
+            }
+            return
+        }
+
         scope.launch {
             try {
                 val width = bitmap.width

app/src/main/java/com/digitalperson/interaction/BaseDigitalPersonCoordinator.kt

@@ -148,6 +148,8 @@ abstract class BaseDigitalPersonCoordinator(
      * (i.e. after a cloud LLM response), NOT after greeting / farewell / proactive TTS.
      */
     fun onTtsPlaybackCompleted() {
+        // Let the controller advance its own timers (greeting/proactive/dlg all count as assistant speaking).
+        controller.onAssistantTtsPlaybackCompleted()
         if (pendingDialogueFinish) {
             pendingDialogueFinish = false
             controller.onDialogueResponseFinished()

app/src/main/java/com/digitalperson/interaction/DigitalHumanInteractionController.kt

@@ -64,6 +64,10 @@ class DigitalHumanInteractionController(
     private var memoryJob: Job? = null
     private var farewellJob: Job? = null
 
+    // 让超时/间隔从 TTS 播放完成后开始计时，而不是从 speak() 调用时开始
+    private var pendingWaitReplyTimeoutAfterTts: Boolean = false
+    private var pendingProactiveFollowupAfterTts: Boolean = false
+
     fun start() {
         transitionTo(InteractionState.IDLE)
         scheduleMemoryMode()
@@ -204,7 +208,7 @@ fun onFacePresenceChanged(present: Boolean, isFrontal: Boolean = true) {  // 添
             return
         }
         transitionTo(InteractionState.WAITING_REPLY)
-        scheduleWaitingReplyTimeout()
+        scheduleWaitingReplyTimeoutAfterTts()
     }
 
     private fun enterGreeting() {
@@ -224,7 +228,7 @@ fun onFacePresenceChanged(present: Boolean, isFrontal: Boolean = true) {  // 添
                         handler.addToChatHistory("assistant", greeting)
                         handler.addAssistantMessageToCloudHistory(greeting)
                         transitionTo(InteractionState.WAITING_REPLY)
-                        scheduleWaitingReplyTimeout()
+                        scheduleWaitingReplyTimeoutAfterTts()
                     } else {
                         useDefaultGreeting()
                     }
@@ -243,7 +247,11 @@ fun onFacePresenceChanged(present: Boolean, isFrontal: Boolean = true) {  // 添
         handler.addAssistantMessageToCloudHistory(greeting)
         
         transitionTo(InteractionState.WAITING_REPLY)
-        scheduleWaitingReplyTimeout()
+        scheduleWaitingReplyTimeoutAfterTts()
+    }
+
+    private fun scheduleWaitingReplyTimeoutAfterTts() {
+        pendingWaitReplyTimeoutAfterTts = true
     }
 
     private fun scheduleWaitingReplyTimeout() {
@@ -282,6 +290,19 @@ fun onFacePresenceChanged(present: Boolean, isFrontal: Boolean = true) {  // 添
         // 触发题目生成检查
         handler.onQuestionAsked(currentFaceId ?: "guest")
 
+        // 不立刻开始 20s 计时；等 TTS 播放完再开始计时，避免“刚说完几秒就又问”
+        pendingProactiveFollowupAfterTts = true
+    }
+
+    /** 由 Activity 在「本轮 TTS 完整播放完成」时调用（包括问候/主动提问/对话回复）。 */
+    fun onAssistantTtsPlaybackCompleted() {
+        if (pendingWaitReplyTimeoutAfterTts && state == InteractionState.WAITING_REPLY) {
+            pendingWaitReplyTimeoutAfterTts = false
+            scheduleWaitingReplyTimeout()
+        }
+        if (pendingProactiveFollowupAfterTts && state == InteractionState.PROACTIVE) {
+            pendingProactiveFollowupAfterTts = false
+            proactiveJob?.cancel()
             proactiveJob = scope.launch {
                 hasPendingUserReply = false
                 delay(20_000)
@@ -295,8 +316,8 @@ fun onFacePresenceChanged(present: Boolean, isFrontal: Boolean = true) {  // 添
                     askProactiveTopic()
                 } else {
                     transitionTo(InteractionState.WAITING_REPLY)
-//                handler.playMotion("haru_g_m17.motion3.json")
+                    scheduleWaitingReplyTimeoutAfterTts()
-                scheduleWaitingReplyTimeout()
+                }
             }
         }
     }

app/src/main/java/com/digitalperson/tts/TtsController.kt

@@ -27,6 +27,11 @@ class TtsController(private val context: Context) {
 
     private var callback: TtsCallback? = null
 
+    // 防止 WebSocket 重连或多路回调导致同一段文案短时间内重复入队、重复播报
+    @Volatile private var lastEnqueuedText: String? = null
+    @Volatile private var lastEnqueuedAtMs: Long = 0L
+    private val dedupeWindowMs = 2500L
+
     fun setCallback(callback: TtsCallback) {
         this.callback = callback
         bindCallbacksIfReady()
@@ -147,6 +152,14 @@ class TtsController(private val context: Context) {
     fun enqueueSegment(seg: String) {
         val cleaned = seg.replace(Regex("\\[.*?\\]"), "").trim()
         if (cleaned.isEmpty()) return
+        val now = System.currentTimeMillis()
+        val lastText = lastEnqueuedText
+        if (lastText != null && lastText == cleaned && (now - lastEnqueuedAtMs) <= dedupeWindowMs) {
+            Log.w(TAG, "Skip duplicate TTS segment within ${dedupeWindowMs}ms: ${cleaned.take(60)}")
+            return
+        }
+        lastEnqueuedText = cleaned
+        lastEnqueuedAtMs = now
         if (useQCloudTts) {
             qcloudTts?.enqueueSegment(cleaned)
         } else {