feat: 参数字段管理功能 - 类别和子类别可配置参数列表

app.py

@@ -523,6 +523,168 @@ def api_parse_images():
 
 # ============ 智能添加API ============
 
+# ============ 智能补充参数API ============
+
+@app.route('/api/models/<model_id>/smart-update', methods=['POST'])
+def api_smart_update_model(model_id):
+    """智能补充模型参数（只填充缺失字段）"""
+    data = request.get_json()
+    text = data.get('text', '')
+    images = data.get('images', [])
+    
+    if not text and not images:
+        return jsonify({'error': '文本或图片不能都为空'}), 400
+    
+    # 获取现有数据
+    models = load_data(MODELS_FILE)
+    model = next((m for m in models if m['id'] == model_id), None)
+    if not model:
+        return jsonify({'error': 'Model not found'}), 404
+    
+    # 解析新参数
+    parsed_list = parse_with_llm(text, 'model', images)
+    if not parsed_list:
+        return jsonify({'error': '解析失败'}), 500
+    
+    parsed = parsed_list[0]  # 补充只取第一个
+    
+    # 只填充缺失或为空的字段
+    updated_fields = []
+    for key, value in parsed.items():
+        if value is not None and value != '' and value != 0:
+            existing = model.get(key)
+            if existing is None or existing == '' or existing == 0:
+                model[key] = value
+                updated_fields.append(key)
+    
+    model['updated_at'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    model['raw_text'] = model.get('raw_text', '') + '\n' + text if text else model.get('raw_text', '')
+    if images:
+        existing_images = model.get('images', [])
+        model['images'] = existing_images + images
+    
+    save_data(MODELS_FILE, models)
+    
+    return jsonify({'success': True, 'updated_fields': updated_fields, 'model': model})
+
+@app.route('/api/gpus/<gpu_id>/smart-update', methods=['POST'])
+def api_smart_update_gpu(gpu_id):
+    """智能补充GPU参数（只填充缺失字段）"""
+    data = request.get_json()
+    text = data.get('text', '')
+    images = data.get('images', [])
+    
+    if not text and not images:
+        return jsonify({'error': '文本或图片不能都为空'}), 400
+    
+    gpus = load_data(GPUS_FILE)
+    gpu = next((g for g in gpus if g['id'] == gpu_id), None)
+    if not gpu:
+        return jsonify({'error': 'GPU not found'}), 404
+    
+    parsed_list = parse_with_llm(text, 'gpu', images)
+    if not parsed_list:
+        return jsonify({'error': '解析失败'}), 500
+    
+    parsed = parsed_list[0]
+    
+    updated_fields = []
+    for key, value in parsed.items():
+        if value is not None and value != '' and value != 0:
+            existing = gpu.get(key)
+            if existing is None or existing == '' or existing == 0:
+                gpu[key] = value
+                updated_fields.append(key)
+    
+    gpu['updated_at'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    gpu['raw_text'] = gpu.get('raw_text', '') + '\n' + text if text else gpu.get('raw_text', '')
+    if images:
+        existing_images = gpu.get('images', [])
+        gpu['images'] = existing_images + images
+    
+    save_data(GPUS_FILE, gpus)
+    
+    return jsonify({'success': True, 'updated_fields': updated_fields, 'gpu': gpu})
+
+@app.route('/api/cpus/<cpu_id>/smart-update', methods=['POST'])
+def api_smart_update_cpu(cpu_id):
+    """智能补充CPU参数（只填充缺失字段）"""
+    data = request.get_json()
+    text = data.get('text', '')
+    images = data.get('images', [])
+    
+    if not text and not images:
+        return jsonify({'error': '文本或图片不能都为空'}), 400
+    
+    cpus = load_data(CPUS_FILE)
+    cpu = next((c for c in cpus if c['id'] == cpu_id), None)
+    if not cpu:
+        return jsonify({'error': 'CPU not found'}), 404
+    
+    parsed_list = parse_with_llm(text, 'cpu', images)
+    if not parsed_list:
+        return jsonify({'error': '解析失败'}), 500
+    
+    parsed = parsed_list[0]
+    
+    updated_fields = []
+    for key, value in parsed.items():
+        if value is not None and value != '' and value != 0:
+            existing = cpu.get(key)
+            if existing is None or existing == '' or existing == 0:
+                cpu[key] = value
+                updated_fields.append(key)
+    
+    cpu['updated_at'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    cpu['raw_text'] = cpu.get('raw_text', '') + '\n' + text if text else cpu.get('raw_text', '')
+    if images:
+        existing_images = cpu.get('images', [])
+        cpu['images'] = existing_images + images
+    
+    save_data(CPUS_FILE, cpus)
+    
+    return jsonify({'success': True, 'updated_fields': updated_fields, 'cpu': cpu})
+
+@app.route('/api/items/<category_id>/<item_id>/smart-update', methods=['POST'])
+def api_smart_update_item(category_id, item_id):
+    """智能补充动态分类数据参数（只填充缺失字段）"""
+    data = request.get_json()
+    text = data.get('text', '')
+    images = data.get('images', [])
+    
+    if not text and not images:
+        return jsonify({'error': '文本或图片不能都为空'}), 400
+    
+    items_file = DATA_DIR / f'items_{category_id}.json'
+    items = load_data(items_file)
+    item = next((i for i in items if i['id'] == item_id), None)
+    if not item:
+        return jsonify({'error': 'Item not found'}), 404
+    
+    parsed_list = parse_with_llm(text, 'dynamic', images)
+    if not parsed_list:
+        return jsonify({'error': '解析失败'}), 500
+    
+    parsed = parsed_list[0]
+    
+    updated_fields = []
+    for key, value in parsed.items():
+        if value is not None and value != '' and value != 0:
+            existing = item.get(key)
+            if existing is None or existing == '' or existing == 0:
+                item[key] = value
+                updated_fields.append(key)
+    
+    item['updated_at'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    item['raw_text'] = item.get('raw_text', '') + '\n' + text if text else item.get('raw_text', '')
+    if images:
+        existing_images = item.get('images', [])
+        item['images'] = existing_images + images
+    
+    save_data(items_file, items)
+    
+    return jsonify({'success': True, 'updated_fields': updated_fields, 'item': item})
+
 @app.route('/api/models/smart-add', methods=['POST'])
 def api_smart_add_model():
     """智能添加模型（支持文本和多图解析，可能添加多个产品）"""

data/categories.json

@@ -11,7 +11,12 @@
         "id": "chat",
         "name": "对话模型",
         "icon": "ri-chat-3-line",
-        "key_features": ["context_length", "mmlu", "input_price", "output_price"],
+        "key_features": [
+          "context_length",
+          "mmlu",
+          "input_price",
+          "output_price"
+        ],
         "feature_labels": {
           "context_length": "上下文",
           "mmlu": "MMLU",
@@ -23,7 +28,11 @@
         "id": "code",
         "name": "代码模型",
         "icon": "ri-code-line",
-        "key_features": ["humaneval", "context_length", "input_price"],
+        "key_features": [
+          "humaneval",
+          "context_length",
+          "input_price"
+        ],
         "feature_labels": {
           "humaneval": "HumanEval",
           "context_length": "上下文",
@@ -34,7 +43,11 @@
         "id": "reasoning",
         "name": "推理模型",
         "icon": "ri-lightbulb-line",
-        "key_features": ["reasoning_capability", "mmlu", "context_length"],
+        "key_features": [
+          "reasoning_capability",
+          "mmlu",
+          "context_length"
+        ],
         "feature_labels": {
           "reasoning_capability": "推理能力",
           "mmlu": "MMLU",
@@ -45,7 +58,11 @@
         "id": "vision",
         "name": "视觉模型",
         "icon": "ri-image-line",
-        "key_features": ["vision_capability", "multimodal", "context_length"],
+        "key_features": [
+          "vision_capability",
+          "multimodal",
+          "context_length"
+        ],
         "feature_labels": {
           "vision_capability": "视觉能力",
           "multimodal": "多模态",
@@ -66,7 +83,12 @@
         "id": "gaming",
         "name": "游戏显卡",
         "icon": "ri-gamepad-line",
-        "key_features": ["memory_gb", "cuda_cores", "price_usd", "fp16_tflops"],
+        "key_features": [
+          "memory_gb",
+          "cuda_cores",
+          "price_usd",
+          "fp16_tflops"
+        ],
         "feature_labels": {
           "memory_gb": "显存",
           "cuda_cores": "CUDA核心",
@@ -78,7 +100,12 @@
         "id": "professional",
         "name": "专业显卡",
         "icon": "ri-building-line",
-        "key_features": ["memory_gb", "tensor_cores", "memory_bandwidth_gbs", "price_usd"],
+        "key_features": [
+          "memory_gb",
+          "tensor_cores",
+          "memory_bandwidth_gbs",
+          "price_usd"
+        ],
         "feature_labels": {
           "memory_gb": "显存",
           "tensor_cores": "Tensor核心",
@@ -90,7 +117,12 @@
         "id": "datacenter",
         "name": "数据中心",
         "icon": "ri-server-line",
-        "key_features": ["memory_gb", "tensor_cores", "memory_bandwidth_gbs", "fp16_tflops"],
+        "key_features": [
+          "memory_gb",
+          "tensor_cores",
+          "memory_bandwidth_gbs",
+          "fp16_tflops"
+        ],
         "feature_labels": {
           "memory_gb": "显存",
           "tensor_cores": "Tensor核心",
@@ -112,7 +144,12 @@
         "id": "desktop",
         "name": "桌面CPU",
         "icon": "ri-computer-line",
-        "key_features": ["cores", "threads", "boost_clock_ghz", "price_usd"],
+        "key_features": [
+          "cores",
+          "threads",
+          "boost_clock_ghz",
+          "price_usd"
+        ],
         "feature_labels": {
           "cores": "核心",
           "threads": "线程",
@@ -124,7 +161,12 @@
         "id": "server",
         "name": "服务器CPU",
         "icon": "ri-server-line",
-        "key_features": ["cores", "threads", "l3_cache_mb", "tdp_watts"],
+        "key_features": [
+          "cores",
+          "threads",
+          "l3_cache_mb",
+          "tdp_watts"
+        ],
         "feature_labels": {
           "cores": "核心",
           "threads": "线程",
@@ -136,7 +178,12 @@
         "id": "mobile",
         "name": "移动CPU",
         "icon": "ri-smartphone-line",
-        "key_features": ["cores", "threads", "base_clock_ghz", "tdp_watts"],
+        "key_features": [
+          "cores",
+          "threads",
+          "base_clock_ghz",
+          "tdp_watts"
+        ],
         "feature_labels": {
           "cores": "核心",
           "threads": "线程",
@@ -159,7 +206,12 @@
         "id": "flagship",
         "name": "旗舰手机",
         "icon": "ri-star-line",
-        "key_features": ["processor", "ram_gb", "storage_gb", "price"],
+        "key_features": [
+          "processor",
+          "ram_gb",
+          "storage_gb",
+          "price"
+        ],
         "feature_labels": {
           "processor": "处理器",
           "ram_gb": "内存",
@@ -171,7 +223,12 @@
         "id": "midrange",
         "name": "中端手机",
         "icon": "ri-price-tag-3-line",
-        "key_features": ["processor", "ram_gb", "battery_mah", "price"],
+        "key_features": [
+          "processor",
+          "ram_gb",
+          "battery_mah",
+          "price"
+        ],
         "feature_labels": {
           "processor": "处理器",
           "ram_gb": "内存",
@@ -193,7 +250,12 @@
         "id": "gaming-laptop",
         "name": "游戏笔记本",
         "icon": "ri-gamepad-line",
-        "key_features": ["processor", "gpu", "ram_gb", "price"],
+        "key_features": [
+          "processor",
+          "gpu",
+          "ram_gb",
+          "price"
+        ],
         "feature_labels": {
           "processor": "处理器",
           "gpu": "显卡",
@@ -205,7 +267,12 @@
         "id": "business-laptop",
         "name": "商务笔记本",
         "icon": "ri-briefcase-line",
-        "key_features": ["processor", "ram_gb", "weight_kg", "price"],
+        "key_features": [
+          "processor",
+          "ram_gb",
+          "weight_kg",
+          "price"
+        ],
         "feature_labels": {
           "processor": "处理器",
           "ram_gb": "内存",
@@ -228,7 +295,11 @@
         "id": "sedan",
         "name": "轿车",
         "icon": "ri-car-line",
-        "key_features": ["engine", "power_kw", "price"],
+        "key_features": [
+          "engine",
+          "power_kw",
+          "price"
+        ],
         "feature_labels": {
           "engine": "发动机",
           "power_kw": "功率",
@@ -239,7 +310,11 @@
         "id": "suv",
         "name": "SUV",
         "icon": "ri-truck-line",
-        "key_features": ["engine", "seats", "price"],
+        "key_features": [
+          "engine",
+          "seats",
+          "price"
+        ],
         "feature_labels": {
           "engine": "发动机",
           "seats": "座位数",
@@ -253,35 +328,53 @@
     "name": "摄像",
     "icon": "ri-camera-line",
     "color": "blue",
-    "order": 0,
+    "order": 9,
     "visible": true,
     "description": "相机、摄像机等",
     "created_at": "2026-04-25 16:38:47",
     "subcategories": [
       {
-        "id": "mirrorless",
-        "name": "无反相机",
-        "icon": "ri-camera-line",
-        "key_features": ["sensor", "megapixels", "video_resolution", "price"],
         "feature_labels": {
-          "sensor": "传感器",
           "megapixels": "像素",
-          "video_resolution": "视频",
-          "price": "价格"
-        }
+          "price": "价格",
+          "sensor": "传感器",
+          "video_resolution": "视频"
+        },
+        "icon": "ri-camera-line",
+        "id": "mirrorless",
+        "key_features": [
+          "sensor",
+          "megapixels",
+          "video_resolution",
+          "price"
+        ],
+        "name": "无反相机"
       },
       {
-        "id": "dslr",
-        "name": "单反相机",
-        "icon": "ri-camera-2-line",
-        "key_features": ["sensor", "megapixels", "lens_mount", "price"],
         "feature_labels": {
-          "sensor": "传感器",
-          "megapixels": "像素",
           "lens_mount": "卡口",
-          "price": "价格"
-        }
+          "megapixels": "像素",
+          "price": "价格",
+          "sensor": "传感器"
+        },
+        "icon": "ri-camera-2-line",
+        "id": "dslr",
+        "key_features": [
+          "sensor",
+          "megapixels",
+          "lens_mount",
+          "price"
+        ],
+        "name": "单反相机"
+      },
+      {
+        "id": "90ce312b560d",
+        "name": "口袋云台相机",
+        "icon": "ri-folder-line",
+        "key_features": [],
+        "feature_labels": {}
       }
-    ]
+    ],
+    "updated_at": "2026-04-28 10:55:02"
   }
 ]

data/cpus.json

@@ -12,7 +12,8 @@
     "tdp_watts": 360,
     "price_usd": 11000,
     "release_year": 2022,
-    "description": "AMD顶级服务器CPU，96核心"
+    "description": "AMD顶级服务器CPU，96核心",
+    "subcategory_id": "server"
   },
   {
     "id": "epyc9554",
@@ -27,7 +28,8 @@
     "tdp_watts": 360,
     "price_usd": 6800,
     "release_year": 2022,
-    "description": "64核心高性能服务器CPU"
+    "description": "64核心高性能服务器CPU",
+    "subcategory_id": "server"
   },
   {
     "id": "epyc9454",
@@ -42,7 +44,8 @@
     "tdp_watts": 290,
     "price_usd": 4100,
     "release_year": 2022,
-    "description": "48核心服务器CPU"
+    "description": "48核心服务器CPU",
+    "subcategory_id": "server"
   },
   {
     "id": "xeonw9359x",
@@ -57,7 +60,8 @@
     "tdp_watts": 350,
     "price_usd": 6200,
     "release_year": 2023,
-    "description": "Intel顶级工作站CPU"
+    "description": "Intel顶级工作站CPU",
+    "subcategory_id": "server"
   },
   {
     "id": "xeonw5345",
@@ -72,7 +76,8 @@
     "tdp_watts": 230,
     "price_usd": 950,
     "release_year": 2023,
-    "description": "中端工作站CPU"
+    "description": "中端工作站CPU",
+    "subcategory_id": "server"
   },
   {
     "id": "ryzen97950x",
@@ -87,7 +92,8 @@
     "tdp_watts": 170,
     "price_usd": 550,
     "release_year": 2022,
-    "description": "顶级消费级CPU，适合AI开发"
+    "description": "顶级消费级CPU，适合AI开发",
+    "subcategory_id": "desktop"
   },
   {
     "id": "ryzen97950x3d",
@@ -102,7 +108,8 @@
     "tdp_watts": 120,
     "price_usd": 700,
     "release_year": 2023,
-    "description": "带3D V-Cache，游戏性能更强"
+    "description": "带3D V-Cache，游戏性能更强",
+    "subcategory_id": "mobile"
   },
   {
     "id": "intel14900k",
@@ -117,7 +124,8 @@
     "tdp_watts": 125,
     "price_usd": 580,
     "release_year": 2023,
-    "description": "Intel顶级消费级CPU"
+    "description": "Intel顶级消费级CPU",
+    "subcategory_id": "desktop"
   },
   {
     "name": "AMD 锐龙 AI 9 H 365",
@@ -136,6 +144,7 @@
     "raw_text": "AMD 锐龙 AI 9 H 365\nAMD 锐龙 AI 处理器助力打造卓越 AI PC\n\n \n全部折叠\n一般规格\n名称\nAMD 锐龙 AI 9 H 365\n产品系列\n锐龙\n系列\n锐龙 AI 300 系列\n外形规格\n笔记本电脑 , 台式机\nAMD PRO 技术\n否\n区域供货状况\n中国\n原代号\nStrix Point\n处理器架构\n4x Zen 5 , 6x Zen 5c\nCPU 核心数\n10\n多线程 (SMT)\n是\n线程数\n20\n最高加速时钟频率 \n最高可达 5 GHz\nMax Zen5c Clock \n最高可达 3.3 GHz\n基准时钟频率 \n2 GHz\nZen5 Base Clock\n2 GHz\nZen5c Base Clock\n2 GHz\nL2 高速缓存\n10 MB\nL3 高速缓存\n24 MB\n默认热设计功耗 (TDP)\n28W\nAMD 可配置热设计功耗 (cTDP)\n15-54W\nCPU 核心的处理器工艺\nTSMC 4nm FinFET\n封装芯片计数\n1\nAMD EXPO™ 内存超频技术\n是\n精准频率提升 (PBO)\n是\n曲线优化器电压偏移\n是\nCPU 平台\nFP8\n支持的扩展\nAES , AMD-V , AVX , AVX2 , AVX512 , FMA3 , MMX-plus , SHA , SSE , SSE2 , SSE3 , SSE4.1 , SSE4.2 , SSE4A , SSSE3 , x86-64\n最高工作温度 (Tjmax)\n100°C\n*支持的操作系统\nWindows 11 - 64-Bit Edition , RHEL x86 64-Bit , Ubuntu x86 64-Bit\n连接\nNative USB 4 (40Gbps)\n2\nNative USB 3.2 Gen 2 (10Gbps)\n2\nNative USB 2.0 (480Mbps)\n4\nPCI Express® Version\nPCIe® 4.0\n原生 PCIe® 通道 (总共/可用)\n16 , 16\nNVMe 支持\nBoot , RAID0 , RAID1\n系统内存类型\nDDR5 (FP8) , LPDDR5X (FP8)\n内存通道数\n2\n最大内存\n256 GB\n最高内存速度\n2x2R\tDDR5-5600, LPDDR5x-8000\n支持 ECC\n否\n显卡功能\n显卡型号\nAMD Radeon™ 880M\n显卡核心数\n12\n显卡频率\n2900 MHz\nDirectX® 版本\n12\nDisplayPort™ 版本\n2.1\nDisplayPort 扩展功能\nAdaptive-Sync , HDR Metadata , UHBR10\nDisplayPort 最高刷新率 (SDR)\n7680x4320 @ 60Hz , 3840x2160 @ 240Hz , 3440x1440 @ 360Hz , 2560x1440 @ 480Hz , 1920x1080 @ 600Hz\nDisplayPort 最高刷新率 (HDR)\n7680x4320 @ 60Hz , 3840x2160 @ 240Hz , 3440x1440 @ 360Hz , 2560x1440 @ 480Hz , 1920x1080 @ 600Hz\nHDMI® 版本\n2.1\n支持的 HDCP 版本\n2.3\nUSB Type-C® DisplayPort™ 备用模式\n是\n支持多个显示器\n是\n显示器个数上限\n4\nAMD FreeSync™\n是\n无线显示\nMiracast\n最大视频编码带宽 (SDR)\n1080p630 8bpc H.264, 1440p373 8bpc H.264, 2160p175 8bpc H.264, 1080p630 8bpc H.265, 1440p373 8bpc H.265, 2160p175 8bpc H.265, 4320p43 8bpc H.265, 1080p864 8/10bpc AV1, 1440p513 8/10bpc AV1, 2160p240 8/10bpc AV1, 4320p60 8/10bpc AV1\n\n最大视频解码带宽\n1080p60 8bpc MPEG2, 1080p60 8bpc VC1, 1080p786 8/10bpc VP9, 2160p196 8/10bpc VP9, 4320p49 8/10bpc VP9, 1080p1200 8bpc H.264, 2160p300 8bpc H.264, 4320p75 8bpc H.264, 1080p786 8/10bpc H.265, 2160p196 8/10bpc H.265, 4320p49 8/10bpc H.265, 1080p960 8/10bpc\n\nAMD SmartShift MAX\n是\nAMD 显存智取技术\n支持\nAI 引擎性能\nAMD Ryzen™ AI\n支持\nOverall TOPS\n最高可达 73 TOPS\nNPU TOPS\n最高可达 50 TOPS\n产品 ID\nTray 产品 ID\n100-000001530 (FP8)\n安全\nAMD 增强病毒防护 (NX bit)\n是",
     "publish_date": "",
     "views": 0,
-    "is_pinned": false
+    "is_pinned": false,
+    "subcategory_id": "mobile"
   }
 ]

data/gpus.json

@@ -13,7 +13,8 @@
     "int8_perf_tops": 3958,
     "price_usd": 30000,
     "release_year": 2022,
-    "description": "数据中心顶级GPU，专为AI训练设计"
+    "description": "数据中心顶级GPU，专为AI训练设计",
+    "subcategory_id": "datacenter"
   },
   {
     "id": "a100",
@@ -29,7 +30,8 @@
     "int8_perf_tops": 624,
     "price_usd": 10000,
     "release_year": 2020,
-    "description": "数据中心主力GPU，AI训练推理通用"
+    "description": "数据中心主力GPU，AI训练推理通用",
+    "subcategory_id": "datacenter"
   },
   {
     "id": "a10040g",
@@ -45,7 +47,8 @@
     "int8_perf_tops": 624,
     "price_usd": 6000,
     "release_year": 2020,
-    "description": "A100 40GB版本，性价比更高"
+    "description": "A100 40GB版本，性价比更高",
+    "subcategory_id": "datacenter"
   },
   {
     "id": "l40s",
@@ -61,7 +64,8 @@
     "int8_perf_tops": 724,
     "price_usd": 7000,
     "release_year": 2023,
-    "description": "新一代数据中心GPU，推理优化"
+    "description": "新一代数据中心GPU，推理优化",
+    "subcategory_id": "datacenter"
   },
   {
     "id": "rtx4090",
@@ -77,7 +81,8 @@
     "int8_perf_tops": 660,
     "price_usd": 1600,
     "release_year": 2022,
-    "description": "消费级最强GPU，适合个人AI开发"
+    "description": "消费级最强GPU，适合个人AI开发",
+    "subcategory_id": "gaming"
   },
   {
     "id": "rtx4090d",
@@ -93,7 +98,8 @@
     "int8_perf_tops": 588,
     "price_usd": 1400,
     "release_year": 2024,
-    "description": "4090中国特供版，性能略降"
+    "description": "4090中国特供版，性能略降",
+    "subcategory_id": "gaming"
   },
   {
     "id": "rtx3090",
@@ -109,7 +115,8 @@
     "int8_perf_tops": 284,
     "price_usd": 1200,
     "release_year": 2020,
-    "description": "上一代旗舰，性价比高"
+    "description": "上一代旗舰，性价比高",
+    "subcategory_id": "gaming"
   },
   {
     "id": "rtx3080",
@@ -125,7 +132,8 @@
     "int8_perf_tops": 238,
     "price_usd": 700,
     "release_year": 2020,
-    "description": "中高端消费级GPU"
+    "description": "中高端消费级GPU",
+    "subcategory_id": "gaming"
   },
   {
     "id": "v100",
@@ -141,7 +149,8 @@
     "int8_perf_tops": 236,
     "price_usd": 4000,
     "release_year": 2017,
-    "description": "上一代数据中心GPU，仍有价值"
+    "description": "上一代数据中心GPU，仍有价值",
+    "subcategory_id": "datacenter"
   },
   {
     "id": "mi300x",
@@ -157,7 +166,8 @@
     "int8_perf_tops": 2614,
     "price_usd": 15000,
     "release_year": 2023,
-    "description": "AMD最强AI GPU，192GB显存"
+    "description": "AMD最强AI GPU，192GB显存",
+    "subcategory_id": "datacenter"
   },
   {
     "name": "RTX 6000D",
@@ -171,7 +181,10 @@
     "raw_text": "据tweaktown报道，NVIDIA为中国市场定制的全新工作站显卡RTX 6000D近日迎来首度拆解。该卡搭载84GB GDDR7显存、19968个CUDA核心，采用被动散热设计，专为服务器机箱风道优化。\n\n\n相较于满血RTX PRO 6000（96GB GDDR7/512-bit），中国特供版RTX 6000D在规格上进行了多处调整。国内团队“技数犬”发布了拆解视频。\n\n据了解，RTX 6000D为无风扇被动散热设计，完全依靠机箱气流降温。\n\nRTX 6000D搭载28颗VRAM模块，总计84GB GDDR7显存，显存总线为448位（相比RTX PRO 6000的96GB/512位有所减少）。\n\nRTX 6000D GPU 核心为156 SM单元，19,968个CUDA核心，比RTX PRO 6000少约17%。\n\nRTX 6000D核心频率为2430MHz（RTX PRO 6000为2600MHz），TDP暂未公布。性能方面，RTX 6000D在Geekbench 6 OpenCL测试中获得390,656分，低于RTX PRO 6000的45–50万分。",
     "currency": "CNY",
     "price_usd": 45000,
-    "updated_at": "2026-04-20 18:28:10"
+    "updated_at": "2026-04-28 11:56:48",
+    "subcategory_id": "professional",
+    "views": 0,
+    "images": []
   },
   {
     "name": "RTX PRO 6000",
@@ -185,7 +198,10 @@
     "cuda_cores": 24064,
     "currency": "CNY",
     "price_usd": 65000,
-    "updated_at": "2026-04-20 18:28:23",
-    "manufacturer": "NVIDIA"
+    "updated_at": "2026-04-28 11:56:38",
+    "manufacturer": "NVIDIA",
+    "subcategory_id": "professional",
+    "views": 0,
+    "images": []
   }
 ]

data/items_021dc76d36be.json

@@ -2,11 +2,15 @@
   {
     "name": "比亚迪宋plus dmi 2021款",
     "brand": "比亚迪",
-    "price": "18.87",
+    "price": 18.87,
     "year": "2021",
     "category_id": "021dc76d36be",
     "id": "3d20dbcd4bdd",
-    "created_at": "2026-04-09 10:09:56"
+    "created_at": "2026-04-09 10:09:56",
+    "subcategory_id": "suv",
+    "views": 0,
+    "images": [],
+    "updated_at": "2026-04-28 12:32:13"
   },
   {
     "name": "秦PLUS",
@@ -29,6 +33,7 @@
     "category_id": "021dc76d36be",
     "created_at": "2026-04-11 02:03:45",
     "visible": true,
-    "raw_text": "秦PLUS的外观设计极具现代感和运动气息，前脸采用了家族化设计语言，标志性的大尺寸进气格栅占据了前脸的大部分空间，搭配锐利的LED大灯组，营造出强烈的视觉冲击力。车身线条流畅，腰线从车头贯穿至车尾，增强了整车的运动感。车尾部分，简洁大方的设计与前脸相呼应，整体风格时尚而不失稳重。\n\n上海：秦PLUS优惠促销，最新报价5.98万！轻松开新车\n\n秦PLUS拥有4780*1837*1515mm的长宽高尺寸和2718mm的轴距，赋予其宽敞的内部空间。车侧线条流畅且动感十足，从前轮距1580mm到后轮距1590mm，车轮布局合理，增强了车辆的稳定性和操控性。配备的225/60 R16轮胎规格，匹配独特风格的轮圈，为车辆增添了一抹动感与时尚的气息。\n\n上海：秦PLUS优惠促销，最新报价5.98万！轻松开新车\n\n秦PLUS的内饰风格简洁大气，给人以科技感和舒适感。中控台布局合理，配备了10.1英寸的中控屏幕，支持语音识别控制系统，可轻松操作多媒体系统、导航、电话和空调等功能。方向盘采用皮质材料，手感舒适，支持手动上下和前后调节，方便驾驶员调整到最佳驾驶姿势。座椅采用仿皮材质，主驾驶座椅具备前后调节、靠背调节和高低调节功能，而副驾驶座椅则支持前后调节和靠背调节，确保了乘客的舒适度。后排座椅可以按比例放倒，增加储物空间，同时，车内还配备了USB和Type-C接口，方便乘客为电子设备充电。\n\n上海：秦PLUS优惠促销，最新报价5.98万！轻松开新车\n\n秦PLUS搭载了一台1.5L 101马力的L4发动机，最大功率为74kW，最大扭矩为126N·m。与之匹配的是E-CVT无级变速器，这使得车辆在提供平稳的动力输出的同时，还能有效降低油耗。\n\n汽车之家车主@天艺风云 表示，外观设计是他当初选择秦PLUS的原因之一。他赞赏整体造型时尚大气，龙脸设计搭配犀利的大灯，辨识度极高。车身线条流畅，溜背式造型增添了几分运动感。全新的“龙鳞辉熠”格栅，精致又霸气，每次停车都有人问这是什么车，外观确实很吸引人。"
+    "raw_text": "秦PLUS的外观设计极具现代感和运动气息，前脸采用了家族化设计语言，标志性的大尺寸进气格栅占据了前脸的大部分空间，搭配锐利的LED大灯组，营造出强烈的视觉冲击力。车身线条流畅，腰线从车头贯穿至车尾，增强了整车的运动感。车尾部分，简洁大方的设计与前脸相呼应，整体风格时尚而不失稳重。\n\n上海：秦PLUS优惠促销，最新报价5.98万！轻松开新车\n\n秦PLUS拥有4780*1837*1515mm的长宽高尺寸和2718mm的轴距，赋予其宽敞的内部空间。车侧线条流畅且动感十足，从前轮距1580mm到后轮距1590mm，车轮布局合理，增强了车辆的稳定性和操控性。配备的225/60 R16轮胎规格，匹配独特风格的轮圈，为车辆增添了一抹动感与时尚的气息。\n\n上海：秦PLUS优惠促销，最新报价5.98万！轻松开新车\n\n秦PLUS的内饰风格简洁大气，给人以科技感和舒适感。中控台布局合理，配备了10.1英寸的中控屏幕，支持语音识别控制系统，可轻松操作多媒体系统、导航、电话和空调等功能。方向盘采用皮质材料，手感舒适，支持手动上下和前后调节，方便驾驶员调整到最佳驾驶姿势。座椅采用仿皮材质，主驾驶座椅具备前后调节、靠背调节和高低调节功能，而副驾驶座椅则支持前后调节和靠背调节，确保了乘客的舒适度。后排座椅可以按比例放倒，增加储物空间，同时，车内还配备了USB和Type-C接口，方便乘客为电子设备充电。\n\n上海：秦PLUS优惠促销，最新报价5.98万！轻松开新车\n\n秦PLUS搭载了一台1.5L 101马力的L4发动机，最大功率为74kW，最大扭矩为126N·m。与之匹配的是E-CVT无级变速器，这使得车辆在提供平稳的动力输出的同时，还能有效降低油耗。\n\n汽车之家车主@天艺风云 表示，外观设计是他当初选择秦PLUS的原因之一。他赞赏整体造型时尚大气，龙脸设计搭配犀利的大灯，辨识度极高。车身线条流畅，溜背式造型增添了几分运动感。全新的“龙鳞辉熠”格栅，精致又霸气，每次停车都有人问这是什么车，外观确实很吸引人。",
+    "subcategory_id": "sedan"
   }
 ]

data/items_71fa2b4d818f.json

@@ -3,75 +3,51 @@
     "name": "Osmo Pocket 4",
     "brand": "DJI",
     "price": 2999,
-    "specs": {
-      "传感器类型": "1英寸CMOS",
-      "镜头": "20mm, f/2.0",
-      "ISO范围": "50-12800",
-      "视频分辨率": "4K 60fps",
-      "照片最大分辨率": "5472×3648",
-      "电池容量": "1545mAh",
-      "工作温度": "0°C至40°C"
-    },
+    "specs": "[object Object]",
     "id": "597e29af5937",
     "category_id": "71fa2b4d818f",
     "created_at": "2026-04-28 00:07:01",
     "visible": true,
     "raw_text": "",
-    "images": [
-      "/static/uploads/1ad784e0b3c6_1777305525.png"
-    ],
+    "images": [],
     "publish_date": "",
     "views": 0,
-    "is_pinned": false
+    "is_pinned": false,
+    "subcategory_id": "90ce312b560d",
+    "updated_at": "2026-04-28 12:32:38"
   },
   {
     "name": "Osmo Pocket 3",
     "brand": "DJI",
     "price": 2799,
-    "specs": {
-      "传感器类型": "1英寸CMOS",
-      "镜头": "20mm, f/2.0",
-      "ISO范围": "50-6400",
-      "视频分辨率": "4K 60fps",
-      "照片最大分辨率": "5472×3648",
-      "电池容量": "1300mAh",
-      "工作温度": "0°C至40°C"
-    },
+    "specs": "[object Object]",
     "id": "ad10ac80827b",
     "category_id": "71fa2b4d818f",
     "created_at": "2026-04-28 00:07:01",
     "visible": true,
     "raw_text": "",
-    "images": [
-      "/static/uploads/1ad784e0b3c6_1777305525.png"
-    ],
+    "images": [],
     "publish_date": "",
     "views": 0,
-    "is_pinned": false
+    "is_pinned": false,
+    "subcategory_id": "90ce312b560d",
+    "updated_at": "2026-04-28 12:32:43"
   },
   {
     "name": "DJI Pocket 2",
     "brand": "DJI",
     "price": 1999,
-    "specs": {
-      "传感器类型": "1/1.7英寸CMOS",
-      "镜头": "20mm, f/1.8",
-      "ISO范围": "100-3200",
-      "视频分辨率": "4K 60fps",
-      "照片最大分辨率": "6272×4680",
-      "电池容量": "875mAh",
-      "工作温度": "0°C至40°C"
-    },
+    "specs": "[object Object]",
     "id": "0fde0f10ad96",
     "category_id": "71fa2b4d818f",
     "created_at": "2026-04-28 00:07:01",
     "visible": true,
     "raw_text": "",
-    "images": [
-      "/static/uploads/1ad784e0b3c6_1777305525.png"
-    ],
+    "images": [],
     "publish_date": "",
     "views": 0,
-    "is_pinned": false
+    "is_pinned": false,
+    "subcategory_id": "90ce312b560d",
+    "updated_at": "2026-04-28 12:32:50"
   }
 ]

data/models.json

@@ -9,11 +9,16 @@
     "input_price": 0.03,
     "output_price": 0.06,
     "mmlu": 86.4,
-    "humaneval": 67.0,
+    "humaneval": 67,
     "is_open_source": false,
     "license": "Proprietary",
     "description": "OpenAI最强大的多模态大模型",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "updated_at": "2026-04-28 11:57:02",
+    "raw_text": "\nGPT-4 Turbo version with 128K context length, price is $10 per 1M input tokens",
+    "subcategory_id": "chat",
+    "views": 0,
+    "images": []
   },
   {
     "id": "gpt4turbo",
@@ -29,7 +34,8 @@
     "is_open_source": false,
     "license": "Proprietary",
     "description": "GPT-4增强版，128K上下文",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "subcategory_id": "chat"
   },
   {
     "id": "gpt35",
@@ -45,7 +51,8 @@
     "is_open_source": false,
     "license": "Proprietary",
     "description": "性价比高的通用模型",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "subcategory_id": "chat"
   },
   {
     "id": "claude3opus",
@@ -61,7 +68,8 @@
     "is_open_source": false,
     "license": "Proprietary",
     "description": "Anthropic最强模型，200K上下文",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "subcategory_id": "code"
   },
   {
     "id": "claude3sonnet",
@@ -77,7 +85,8 @@
     "is_open_source": false,
     "license": "Proprietary",
     "description": "平衡性能与成本",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "subcategory_id": "chat"
   },
   {
     "id": "llama270b",
@@ -93,7 +102,8 @@
     "is_open_source": true,
     "license": "Llama 2 Community",
     "description": "Meta开源大模型，70B参数",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "subcategory_id": "chat"
   },
   {
     "id": "llama3",
@@ -109,7 +119,8 @@
     "is_open_source": true,
     "license": "Llama 3 Community",
     "description": "Meta最新开源模型，性能接近GPT-4",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "subcategory_id": "code"
   },
   {
     "id": "mistral7b",
@@ -125,7 +136,8 @@
     "is_open_source": true,
     "license": "Apache 2.0",
     "description": "小巧高效的开源模型",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "subcategory_id": "chat"
   },
   {
     "id": "mixtral8x7b",
@@ -141,7 +153,8 @@
     "is_open_source": true,
     "license": "Apache 2.0",
     "description": "MoE架构，高效推理",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "subcategory_id": "chat"
   },
   {
     "id": "qwen72b",
@@ -157,7 +170,8 @@
     "is_open_source": true,
     "license": "Apache 2.0",
     "description": "阿里开源大模型，中文能力强",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "subcategory_id": "chat"
   },
   {
     "id": "deepseekv3",
@@ -173,7 +187,8 @@
     "is_open_source": true,
     "license": "MIT",
     "description": "DeepSeek最新模型，性价比极高",
-    "created_at": "2024-01-01"
+    "created_at": "2024-01-01",
+    "subcategory_id": "code"
   },
   {
     "id": "glm4",
@@ -190,6 +205,7 @@
     "license": "Proprietary",
     "description": "智谱AI大模型，中文能力强",
     "created_at": "2024-01-01",
-    "visible": false
+    "visible": true,
+    "subcategory_id": "chat"
   }
 ]