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）"""
@@ -951,6 +1113,17 @@ def api_categories():
     
     return jsonify(sorted(categories, key=lambda x: x.get('order', 0)))
 
+@app.route('/api/categories/<category_id>')
+def api_category_detail(category_id):
+    """获取单个分类详情"""
+    categories = load_data(CATEGORIES_FILE)
+    category = next((c for c in categories if c['id'] == category_id), None)
+    
+    if not category:
+        return jsonify({'error': 'Category not found'}), 404
+    
+    return jsonify(category)
+
 @app.route('/api/categories', methods=['POST'])
 def api_create_category():
     """创建新分类"""

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/config.json

@@ -6,9 +6,9 @@
   "copyright_year": "2026",
   "contact_email": "wlq@tphai.com",
   "github_url": "",
-  "updated_at": "2026-04-27 19:57:00",
-  "llm_base_url": "http://192.168.2.17:19007/v1",
-  "llm_api_key": "",
-  "llm_model": "auto",
-  "llm_vision_model": "gpt-4-vision-preview"
+  "llm_base_url": "https://open.bigmodel.cn/api/paas/v4",
+  "llm_api_key": "2259e33a1357460abe17919aaf81e73d.K44a8LPQTmFM5PKm",
+  "llm_model": "glm-4.5-air",
+  "llm_vision_model": "glm-4.6v",
+  "updated_at": "2026-04-27 23:58:26"
 }

data/cpus.json

@@ -1,10 +1,150 @@
 [
-  {"id": "epyc9654", "name": "AMD EPYC 9654", "manufacturer": "AMD", "architecture": "Zen 4", "cores": 96, "threads": 192, "base_clock_ghz": 2.4, "boost_clock_ghz": 3.7, "l3_cache_mb": 384, "tdp_watts": 360, "price_usd": 11000, "release_year": 2022, "description": "AMD顶级服务器CPU，96核心"},
-  {"id": "epyc9554", "name": "AMD EPYC 9554", "manufacturer": "AMD", "architecture": "Zen 4", "cores": 64, "threads": 128, "base_clock_ghz": 3.1, "boost_clock_ghz": 3.8, "l3_cache_mb": 256, "tdp_watts": 360, "price_usd": 6800, "release_year": 2022, "description": "64核心高性能服务器CPU"},
-  {"id": "epyc9454", "name": "AMD EPYC 9454", "manufacturer": "AMD", "architecture": "Zen 4", "cores": 48, "threads": 96, "base_clock_ghz": 2.75, "boost_clock_ghz": 3.8, "l3_cache_mb": 192, "tdp_watts": 290, "price_usd": 4100, "release_year": 2022, "description": "48核心服务器CPU"},
-  {"id": "xeonw9359x", "name": "Intel Xeon w9-3595X", "manufacturer": "Intel", "architecture": "Sapphire Rapids", "cores": 56, "threads": 112, "base_clock_ghz": 1.9, "boost_clock_ghz": 4.8, "l3_cache_mb": 105, "tdp_watts": 350, "price_usd": 6200, "release_year": 2023, "description": "Intel顶级工作站CPU"},
-  {"id": "xeonw5345", "name": "Intel Xeon w5-3435", "manufacturer": "Intel", "architecture": "Sapphire Rapids", "cores": 24, "threads": 48, "base_clock_ghz": 3.1, "boost_clock_ghz": 4.7, "l3_cache_mb": 45, "tdp_watts": 230, "price_usd": 950, "release_year": 2023, "description": "中端工作站CPU"},
-  {"id": "ryzen97950x", "name": "AMD Ryzen 9 7950X", "manufacturer": "AMD", "architecture": "Zen 4", "cores": 16, "threads": 32, "base_clock_ghz": 4.5, "boost_clock_ghz": 5.7, "l3_cache_mb": 64, "tdp_watts": 170, "price_usd": 550, "release_year": 2022, "description": "顶级消费级CPU，适合AI开发"},
-  {"id": "ryzen97950x3d", "name": "AMD Ryzen 9 7950X3D", "manufacturer": "AMD", "architecture": "Zen 4", "cores": 16, "threads": 32, "base_clock_ghz": 4.2, "boost_clock_ghz": 5.7, "l3_cache_mb": 144, "tdp_watts": 120, "price_usd": 700, "release_year": 2023, "description": "带3D V-Cache，游戏性能更强"},
-  {"id": "intel14900k", "name": "Intel Core i9-14900K", "manufacturer": "Intel", "architecture": "Raptor Lake Refresh", "cores": 24, "threads": 32, "base_clock_ghz": 3.2, "boost_clock_ghz": 6.0, "l3_cache_mb": 36, "tdp_watts": 125, "price_usd": 580, "release_year": 2023, "description": "Intel顶级消费级CPU"}
+  {
+    "id": "epyc9654",
+    "name": "AMD EPYC 9654",
+    "manufacturer": "AMD",
+    "architecture": "Zen 4",
+    "cores": 96,
+    "threads": 192,
+    "base_clock_ghz": 2.4,
+    "boost_clock_ghz": 3.7,
+    "l3_cache_mb": 384,
+    "tdp_watts": 360,
+    "price_usd": 11000,
+    "release_year": 2022,
+    "description": "AMD顶级服务器CPU，96核心",
+    "subcategory_id": "server"
+  },
+  {
+    "id": "epyc9554",
+    "name": "AMD EPYC 9554",
+    "manufacturer": "AMD",
+    "architecture": "Zen 4",
+    "cores": 64,
+    "threads": 128,
+    "base_clock_ghz": 3.1,
+    "boost_clock_ghz": 3.8,
+    "l3_cache_mb": 256,
+    "tdp_watts": 360,
+    "price_usd": 6800,
+    "release_year": 2022,
+    "description": "64核心高性能服务器CPU",
+    "subcategory_id": "server"
+  },
+  {
+    "id": "epyc9454",
+    "name": "AMD EPYC 9454",
+    "manufacturer": "AMD",
+    "architecture": "Zen 4",
+    "cores": 48,
+    "threads": 96,
+    "base_clock_ghz": 2.75,
+    "boost_clock_ghz": 3.8,
+    "l3_cache_mb": 192,
+    "tdp_watts": 290,
+    "price_usd": 4100,
+    "release_year": 2022,
+    "description": "48核心服务器CPU",
+    "subcategory_id": "server"
+  },
+  {
+    "id": "xeonw9359x",
+    "name": "Intel Xeon w9-3595X",
+    "manufacturer": "Intel",
+    "architecture": "Sapphire Rapids",
+    "cores": 56,
+    "threads": 112,
+    "base_clock_ghz": 1.9,
+    "boost_clock_ghz": 4.8,
+    "l3_cache_mb": 105,
+    "tdp_watts": 350,
+    "price_usd": 6200,
+    "release_year": 2023,
+    "description": "Intel顶级工作站CPU",
+    "subcategory_id": "server"
+  },
+  {
+    "id": "xeonw5345",
+    "name": "Intel Xeon w5-3435",
+    "manufacturer": "Intel",
+    "architecture": "Sapphire Rapids",
+    "cores": 24,
+    "threads": 48,
+    "base_clock_ghz": 3.1,
+    "boost_clock_ghz": 4.7,
+    "l3_cache_mb": 45,
+    "tdp_watts": 230,
+    "price_usd": 950,
+    "release_year": 2023,
+    "description": "中端工作站CPU",
+    "subcategory_id": "server"
+  },
+  {
+    "id": "ryzen97950x",
+    "name": "AMD Ryzen 9 7950X",
+    "manufacturer": "AMD",
+    "architecture": "Zen 4",
+    "cores": 16,
+    "threads": 32,
+    "base_clock_ghz": 4.5,
+    "boost_clock_ghz": 5.7,
+    "l3_cache_mb": 64,
+    "tdp_watts": 170,
+    "price_usd": 550,
+    "release_year": 2022,
+    "description": "顶级消费级CPU，适合AI开发",
+    "subcategory_id": "desktop"
+  },
+  {
+    "id": "ryzen97950x3d",
+    "name": "AMD Ryzen 9 7950X3D",
+    "manufacturer": "AMD",
+    "architecture": "Zen 4",
+    "cores": 16,
+    "threads": 32,
+    "base_clock_ghz": 4.2,
+    "boost_clock_ghz": 5.7,
+    "l3_cache_mb": 144,
+    "tdp_watts": 120,
+    "price_usd": 700,
+    "release_year": 2023,
+    "description": "带3D V-Cache，游戏性能更强",
+    "subcategory_id": "mobile"
+  },
+  {
+    "id": "intel14900k",
+    "name": "Intel Core i9-14900K",
+    "manufacturer": "Intel",
+    "architecture": "Raptor Lake Refresh",
+    "cores": 24,
+    "threads": 32,
+    "base_clock_ghz": 3.2,
+    "boost_clock_ghz": 6.0,
+    "l3_cache_mb": 36,
+    "tdp_watts": 125,
+    "price_usd": 580,
+    "release_year": 2023,
+    "description": "Intel顶级消费级CPU",
+    "subcategory_id": "desktop"
+  },
+  {
+    "name": "AMD 锐龙 AI 9 H 365",
+    "manufacturer": "AMD",
+    "architecture": "Zen 5, Zen 5c",
+    "cores": 10,
+    "threads": 20,
+    "base_clock_ghz": 2.0,
+    "boost_clock_ghz": 5.0,
+    "l3_cache_mb": 24,
+    "tdp_watts": 28,
+    "description": "AMD 锐龙 AI 处理器助力打造卓越 AI PC",
+    "id": "52af6cf2dc28",
+    "created_at": "2026-04-20 23:19:20",
+    "visible": true,
+    "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,
+    "subcategory_id": "mobile"
+  }
 ]

data/gpus.json

@@ -1,12 +1,207 @@
 [
-  {"id": "h100", "name": "NVIDIA H100", "manufacturer": "NVIDIA", "architecture": "Hopper", "cuda_cores": 16896, "tensor_cores": 528, "memory_gb": 80, "memory_bandwidth_gbs": 3352, "fp32_tflops": 67, "fp16_tflops": 1979, "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"},
-  {"id": "a100", "name": "NVIDIA A100", "manufacturer": "NVIDIA", "architecture": "Ampere", "cuda_cores": 6912, "tensor_cores": 432, "memory_gb": 80, "memory_bandwidth_gbs": 2039, "fp32_tflops": 19.5, "fp16_tflops": 312, "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"},
-  {"id": "a10040g", "name": "NVIDIA A100 40GB", "manufacturer": "NVIDIA", "architecture": "Ampere", "cuda_cores": 6912, "tensor_cores": 432, "memory_gb": 40, "memory_bandwidth_gbs": 1555, "fp32_tflops": 19.5, "fp16_tflops": 312, "int8_perf_tops": 624, "price_usd": 6000, "release_year": 2020, "description": "A100 40GB版本，性价比更高"},
-  {"id": "l40s", "name": "NVIDIA L40S", "manufacturer": "NVIDIA", "architecture": "Ada Lovelace", "cuda_cores": 18176, "tensor_cores": 568, "memory_gb": 48, "memory_bandwidth_gbs": 864, "fp32_tflops": 91.6, "fp16_tflops": 362, "int8_perf_tops": 724, "price_usd": 7000, "release_year": 2023, "description": "新一代数据中心GPU，推理优化"},
-  {"id": "rtx4090", "name": "NVIDIA RTX 4090", "manufacturer": "NVIDIA", "architecture": "Ada Lovelace", "cuda_cores": 16384, "tensor_cores": 512, "memory_gb": 24, "memory_bandwidth_gbs": 1008, "fp32_tflops": 82.6, "fp16_tflops": 330, "int8_perf_tops": 660, "price_usd": 1600, "release_year": 2022, "description": "消费级最强GPU，适合个人AI开发"},
-  {"id": "rtx4090d", "name": "NVIDIA RTX 4090D", "manufacturer": "NVIDIA", "architecture": "Ada Lovelace", "cuda_cores": 14592, "tensor_cores": 456, "memory_gb": 24, "memory_bandwidth_gbs": 1008, "fp32_tflops": 73.5, "fp16_tflops": 294, "int8_perf_tops": 588, "price_usd": 1400, "release_year": 2024, "description": "4090中国特供版，性能略降"},
-  {"id": "rtx3090", "name": "NVIDIA RTX 3090", "manufacturer": "NVIDIA", "architecture": "Ampere", "cuda_cores": 10496, "tensor_cores": 328, "memory_gb": 24, "memory_bandwidth_gbs": 936, "fp32_tflops": 35.6, "fp16_tflops": 142, "int8_perf_tops": 284, "price_usd": 1200, "release_year": 2020, "description": "上一代旗舰，性价比高"},
-  {"id": "rtx3080", "name": "NVIDIA RTX 3080", "manufacturer": "NVIDIA", "architecture": "Ampere", "cuda_cores": 8704, "tensor_cores": 272, "memory_gb": 10, "memory_bandwidth_gbs": 760, "fp32_tflops": 29.8, "fp16_tflops": 119, "int8_perf_tops": 238, "price_usd": 700, "release_year": 2020, "description": "中高端消费级GPU"},
-  {"id": "v100", "name": "NVIDIA V100", "manufacturer": "NVIDIA", "architecture": "Volta", "cuda_cores": 5120, "tensor_cores": 640, "memory_gb": 32, "memory_bandwidth_gbs": 900, "fp32_tflops": 14.8, "fp16_tflops": 118, "int8_perf_tops": 236, "price_usd": 4000, "release_year": 2017, "description": "上一代数据中心GPU，仍有价值"},
-  {"id": "mi300x", "name": "AMD MI300X", "manufacturer": "AMD", "architecture": "CDNA 3", "cuda_cores": 0, "tensor_cores": 304, "memory_gb": 192, "memory_bandwidth_gbs": 5300, "fp32_tflops": 81.7, "fp16_tflops": 1307, "int8_perf_tops": 2614, "price_usd": 15000, "release_year": 2023, "description": "AMD最强AI GPU，192GB显存"}
+  {
+    "id": "h100",
+    "name": "NVIDIA H100",
+    "manufacturer": "NVIDIA",
+    "architecture": "Hopper",
+    "cuda_cores": 16896,
+    "tensor_cores": 528,
+    "memory_gb": 80,
+    "memory_bandwidth_gbs": 3352,
+    "fp32_tflops": 67,
+    "fp16_tflops": 1979,
+    "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",
+    "subcategory_id": "datacenter"
+  },
+  {
+    "id": "a100",
+    "name": "NVIDIA A100",
+    "manufacturer": "NVIDIA",
+    "architecture": "Ampere",
+    "cuda_cores": 6912,
+    "tensor_cores": 432,
+    "memory_gb": 80,
+    "memory_bandwidth_gbs": 2039,
+    "fp32_tflops": 19.5,
+    "fp16_tflops": 312,
+    "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",
+    "subcategory_id": "datacenter"
+  },
+  {
+    "id": "a10040g",
+    "name": "NVIDIA A100 40GB",
+    "manufacturer": "NVIDIA",
+    "architecture": "Ampere",
+    "cuda_cores": 6912,
+    "tensor_cores": 432,
+    "memory_gb": 40,
+    "memory_bandwidth_gbs": 1555,
+    "fp32_tflops": 19.5,
+    "fp16_tflops": 312,
+    "int8_perf_tops": 624,
+    "price_usd": 6000,
+    "release_year": 2020,
+    "description": "A100 40GB版本，性价比更高",
+    "subcategory_id": "datacenter"
+  },
+  {
+    "id": "l40s",
+    "name": "NVIDIA L40S",
+    "manufacturer": "NVIDIA",
+    "architecture": "Ada Lovelace",
+    "cuda_cores": 18176,
+    "tensor_cores": 568,
+    "memory_gb": 48,
+    "memory_bandwidth_gbs": 864,
+    "fp32_tflops": 91.6,
+    "fp16_tflops": 362,
+    "int8_perf_tops": 724,
+    "price_usd": 7000,
+    "release_year": 2023,
+    "description": "新一代数据中心GPU，推理优化",
+    "subcategory_id": "datacenter"
+  },
+  {
+    "id": "rtx4090",
+    "name": "NVIDIA RTX 4090",
+    "manufacturer": "NVIDIA",
+    "architecture": "Ada Lovelace",
+    "cuda_cores": 16384,
+    "tensor_cores": 512,
+    "memory_gb": 24,
+    "memory_bandwidth_gbs": 1008,
+    "fp32_tflops": 82.6,
+    "fp16_tflops": 330,
+    "int8_perf_tops": 660,
+    "price_usd": 1600,
+    "release_year": 2022,
+    "description": "消费级最强GPU，适合个人AI开发",
+    "subcategory_id": "gaming"
+  },
+  {
+    "id": "rtx4090d",
+    "name": "NVIDIA RTX 4090D",
+    "manufacturer": "NVIDIA",
+    "architecture": "Ada Lovelace",
+    "cuda_cores": 14592,
+    "tensor_cores": 456,
+    "memory_gb": 24,
+    "memory_bandwidth_gbs": 1008,
+    "fp32_tflops": 73.5,
+    "fp16_tflops": 294,
+    "int8_perf_tops": 588,
+    "price_usd": 1400,
+    "release_year": 2024,
+    "description": "4090中国特供版，性能略降",
+    "subcategory_id": "gaming"
+  },
+  {
+    "id": "rtx3090",
+    "name": "NVIDIA RTX 3090",
+    "manufacturer": "NVIDIA",
+    "architecture": "Ampere",
+    "cuda_cores": 10496,
+    "tensor_cores": 328,
+    "memory_gb": 24,
+    "memory_bandwidth_gbs": 936,
+    "fp32_tflops": 35.6,
+    "fp16_tflops": 142,
+    "int8_perf_tops": 284,
+    "price_usd": 1200,
+    "release_year": 2020,
+    "description": "上一代旗舰，性价比高",
+    "subcategory_id": "gaming"
+  },
+  {
+    "id": "rtx3080",
+    "name": "NVIDIA RTX 3080",
+    "manufacturer": "NVIDIA",
+    "architecture": "Ampere",
+    "cuda_cores": 8704,
+    "tensor_cores": 272,
+    "memory_gb": 10,
+    "memory_bandwidth_gbs": 760,
+    "fp32_tflops": 29.8,
+    "fp16_tflops": 119,
+    "int8_perf_tops": 238,
+    "price_usd": 700,
+    "release_year": 2020,
+    "description": "中高端消费级GPU",
+    "subcategory_id": "gaming"
+  },
+  {
+    "id": "v100",
+    "name": "NVIDIA V100",
+    "manufacturer": "NVIDIA",
+    "architecture": "Volta",
+    "cuda_cores": 5120,
+    "tensor_cores": 640,
+    "memory_gb": 32,
+    "memory_bandwidth_gbs": 900,
+    "fp32_tflops": 14.8,
+    "fp16_tflops": 118,
+    "int8_perf_tops": 236,
+    "price_usd": 4000,
+    "release_year": 2017,
+    "description": "上一代数据中心GPU，仍有价值",
+    "subcategory_id": "datacenter"
+  },
+  {
+    "id": "mi300x",
+    "name": "AMD MI300X",
+    "manufacturer": "AMD",
+    "architecture": "CDNA 3",
+    "cuda_cores": 0,
+    "tensor_cores": 304,
+    "memory_gb": 192,
+    "memory_bandwidth_gbs": 5300,
+    "fp32_tflops": 81.7,
+    "fp16_tflops": 1307,
+    "int8_perf_tops": 2614,
+    "price_usd": 15000,
+    "release_year": 2023,
+    "description": "AMD最强AI GPU，192GB显存",
+    "subcategory_id": "datacenter"
+  },
+  {
+    "name": "RTX 6000D",
+    "manufacturer": "NVIDIA",
+    "memory_gb": 84,
+    "cuda_cores": 19968,
+    "description": "NVIDIA为中国市场定制的全新工作站显卡，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。显存总线为448位，核心频率为2430MHz，在Geekbench 6 OpenCL测试中获得390,656分。",
+    "id": "f56b2de6fac4",
+    "created_at": "2026-04-20 18:19:14",
+    "visible": true,
+    "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": "2026-04-28 11:56:48",
+    "subcategory_id": "professional",
+    "views": 0,
+    "images": []
+  },
+  {
+    "name": "RTX PRO 6000",
+    "description": "这款专业显卡基于 GB202 GPU，拥有 24064 个 CUDA 核心（188 个 SM），运行频率达 2,617 MHz，并配备 96 GB 支持 ECC 校验的 GDDR7 显存。\n\n相比之下，面向游戏市场的旗舰显卡 RTX 5090 虽同样基于 GB202 ，但其 CUDA 核心数量缩减至 21,760 个，频率为 2,410 MHz，显存容量为 32 GB。\n\n96G超大显存RTX PRO 6000Blackwell初次跑分，略逊于RTX 5090\n其测试平台采用了华硕 Pro WS WRX80E-SAGE SE WIFI 主板、AMD 锐龙 Threadripper PRO 3975WX 处理器、512 GB 内存。\n\n在 Geekbench 6.4.0 上，其测试平台 OpenCL 得分仅 368219 分，略低于 RTX 5090 的 376,858 分，差距约 2.3%，外媒认为这主要是由于 RTX PRO 6000 缺乏正式版驱动导致，且显卡功耗可能受限。\n\nRTX PRO 6000 系列将提供两种版本，分别为适用于紧凑型机箱，规格相同的Max-Q 工作站版，但TDP 功耗限制在 300W；以及支持最高600W TDP的标准版，可满足高强度计算需求。",
+    "id": "d246301f2032",
+    "created_at": "2026-04-20 18:21:00",
+    "visible": true,
+    "raw_text": "这款专业显卡基于 GB202 GPU，拥有 24064 个 CUDA 核心（188 个 SM），运行频率达 2,617 MHz，并配备 96 GB 支持 ECC 校验的 GDDR7 显存。\n\n相比之下，面向游戏市场的旗舰显卡 RTX 5090 虽同样基于 GB202 ，但其 CUDA 核心数量缩减至 21,760 个，频率为 2,410 MHz，显存容量为 32 GB。\n\n96G超大显存RTX PRO 6000Blackwell初次跑分，略逊于RTX 5090\n其测试平台采用了华硕 Pro WS WRX80E-SAGE SE WIFI 主板、AMD 锐龙 Threadripper PRO 3975WX 处理器、512 GB 内存。\n\n在 Geekbench 6.4.0 上，其测试平台 OpenCL 得分仅 368219 分，略低于 RTX 5090 的 376,858 分，差距约 2.3%，外媒认为这主要是由于 RTX PRO 6000 缺乏正式版驱动导致，且显卡功耗可能受限。\n\nRTX PRO 6000 系列将提供两种版本，分别为适用于紧凑型机箱，规格相同的Max-Q 工作站版，但TDP 功耗限制在 300W；以及支持最高600W TDP的标准版，可满足高强度计算需求。",
+    "architecture": "GB202",
+    "memory_gb": 96,
+    "cuda_cores": 24064,
+    "currency": "CNY",
+    "price_usd": 65000,
+    "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

@@ -0,0 +1,53 @@
+[
+  {
+    "name": "Osmo Pocket 4",
+    "brand": "DJI",
+    "price": 2999,
+    "specs": "[object Object]",
+    "id": "597e29af5937",
+    "category_id": "71fa2b4d818f",
+    "created_at": "2026-04-28 00:07:01",
+    "visible": true,
+    "raw_text": "",
+    "images": [],
+    "publish_date": "",
+    "views": 0,
+    "is_pinned": false,
+    "subcategory_id": "90ce312b560d",
+    "updated_at": "2026-04-28 12:32:38"
+  },
+  {
+    "name": "Osmo Pocket 3",
+    "brand": "DJI",
+    "price": 2799,
+    "specs": "[object Object]",
+    "id": "ad10ac80827b",
+    "category_id": "71fa2b4d818f",
+    "created_at": "2026-04-28 00:07:01",
+    "visible": true,
+    "raw_text": "",
+    "images": [],
+    "publish_date": "",
+    "views": 0,
+    "is_pinned": false,
+    "subcategory_id": "90ce312b560d",
+    "updated_at": "2026-04-28 12:32:43"
+  },
+  {
+    "name": "DJI Pocket 2",
+    "brand": "DJI",
+    "price": 1999,
+    "specs": "[object Object]",
+    "id": "0fde0f10ad96",
+    "category_id": "71fa2b4d818f",
+    "created_at": "2026-04-28 00:07:01",
+    "visible": true,
+    "raw_text": "",
+    "images": [],
+    "publish_date": "",
+    "views": 0,
+    "is_pinned": false,
+    "subcategory_id": "90ce312b560d",
+    "updated_at": "2026-04-28 12:32:50"
+  }
+]

data/knowledge.json

@@ -1,9 +1,66 @@
 [
-  {"id": "k001", "title": "什么是参数量？", "category": "ai-models", "icon": "ri-calculator-line", "content": "参数量（Parameters）是衡量大模型规模的指标，表示模型中权重参数的数量。例如 GPT-3 有 175B 参数，即约1750亿个参数。", "detail": "参数量决定了模型的容量和表达能力。一般来说，参数量越大，模型能力越强，但也需要更多计算资源。\n\n常见规模分类：\n- 小模型：<1B (适合边缘设备)\n- 中模型：1B-10B (消费级GPU可运行)\n- 大模型：10B-100B (需要多GPU)\n- 超大模型：>100B (需要数据中心)", "order": 1},
-  {"id": "k002", "title": "什么是上下文长度？", "category": "ai-models", "icon": "ri-text-wrap", "content": "上下文长度（Context Length）是模型能处理的输入文本最大长度。更长的上下文意味着模型可以理解更长的文档或对话历史。", "detail": "常见长度：\n- 4K：传统长度，适合简单对话\n- 32K：中等长度，适合长文档\n- 128K：超长上下文，如GPT-4 Turbo\n- 200K：Claude 3的极限长度", "order": 2},
-  {"id": "k003", "title": "什么是量化？", "category": "ai-models", "icon": "ri-scales-3-line", "content": "量化（Quantization）是将模型参数从高精度转换为低精度，减少显存占用和计算量。如FP16→INT8→INT4，精度损失可控，资源节省显著。", "detail": "量化效果：\n- FP32→FP16: 显存减半，精度基本不变\n- FP16→INT8: 显存再减半，精度略降\n- INT8→INT4: 显存再减半，需特殊技术\n\n推荐工具：llama.cpp、GPTQ、AWQ等", "order": 3},
-  {"id": "k004", "title": "什么是MMLU？", "category": "ai-models", "icon": "ri-bar-chart-box-line", "content": "MMLU（Massive Multitask Language Understanding）是评估大模型综合能力的标准测试集，覆盖57个学科领域。", "detail": "分数参考：\n- 60-70%：入门级，如GPT-3\n- 70-80%：中等水平，如Llama 2 70B\n- 80-90%：优秀水平，如GPT-4、Claude 3", "order": 4},
-  {"id": "k005", "title": "如何计算显存需求？", "category": "gpus", "icon": "ri-memory-line", "content": "模型显存需求 ≈ 参数量 × 每参数字节数 × 1.3（含KV Cache开销）", "detail": "计算公式：\n- FP32: 参数量 × 4字节 × 1.3\n- FP16: 参数量 × 2字节 × 1.3\n- INT8: 参数量 × 1字节 × 1.3\n- INT4: 参数量 × 0.5字节 × 1.3\n\n例如：7B模型FP16加载需要约 7 × 2 × 1.3 ≈ 18GB显存", "order": 1},
-  {"id": "k006", "title": "GPU架构演进", "category": "gpus", "icon": "ri-history-line", "content": "NVIDIA GPU架构从Fermi到Hopper，每一代都有显著提升。了解架构有助于选择合适的GPU。", "detail": "主要架构：\n- Volta (2017): V100, 引入Tensor Core\n- Turing (2018): RTX 20系列, RT Core\n- Ampere (2020): A100, RTX 30系列\n- Hopper (2022): H100, FP8支持\n- Ada Lovelace (2022): RTX 40系列, L40S", "order": 2},
-  {"id": "k007", "title": "CPU核心数选择", "category": "cpus", "icon": "ri-database-2-line", "content": "CPU核心数的选择取决于应用场景。更多核心适合并行任务，但单核性能也很重要。", "detail": "场景推荐：\n- 办公/日常：4-6核足够\n- 开发/编译：8-16核\n- 服务器/虚拟化：16-64核\n- 高性能计算：64核以上\n\n注意：AI训练主要依赖GPU，CPU主要用于数据预处理", "order": 1}
+  {
+    "id": "k001",
+    "title": "什么是参数量？",
+    "category": "ai-models",
+    "icon": "ri-calculator-line",
+    "content": "参数量（Parameters）是衡量大模型规模的指标，表示模型中权重参数的数量。例如 GPT-3 有 175B 参数，即约1750亿个参数。",
+    "detail": "参数量决定了模型的容量和表达能力。一般来说，参数量越大，模型能力越强，但也需要更多计算资源。\n\n常见规模分类：\n- 小模型：<1B (适合边缘设备)\n- 中模型：1B-10B (消费级GPU可运行)\n- 大模型：10B-100B (需要多GPU)\n- 超大模型：>100B (需要数据中心)",
+    "order": 1
+  },
+  {
+    "id": "k002",
+    "title": "什么是上下文长度？",
+    "category": "ai-models",
+    "icon": "ri-text-wrap",
+    "content": "上下文长度（Context Length）是模型能处理的输入文本最大长度。更长的上下文意味着模型可以理解更长的文档或对话历史。",
+    "detail": "常见长度：\n- 4K：传统长度，适合简单对话\n- 32K：中等长度，适合长文档\n- 128K：超长上下文，如GPT-4 Turbo\n- 200K：Claude 3的极限长度",
+    "order": 2
+  },
+  {
+    "id": "k003",
+    "title": "什么是量化？",
+    "category": "ai-models",
+    "icon": "ri-scales-3-line",
+    "content": "量化（Quantization）是将模型参数从高精度转换为低精度，减少显存占用和计算量。如FP16→INT8→INT4，精度损失可控，资源节省显著。",
+    "detail": "量化效果：\n- FP32→FP16: 显存减半，精度基本不变\n- FP16→INT8: 显存再减半，精度略降\n- INT8→INT4: 显存再减半，需特殊技术\n\n推荐工具：llama.cpp、GPTQ、AWQ等",
+    "order": 3
+  },
+  {
+    "id": "k004",
+    "title": "什么是MMLU？",
+    "category": "ai-models",
+    "icon": "ri-bar-chart-box-line",
+    "content": "MMLU（Massive Multitask Language Understanding）是评估大模型综合能力的标准测试集，覆盖57个学科领域。",
+    "detail": "分数参考：\n- 60-70%：入门级，如GPT-3\n- 70-80%：中等水平，如Llama 2 70B\n- 80-90%：优秀水平，如GPT-4、Claude 3",
+    "order": 4,
+    "visible": false
+  },
+  {
+    "id": "k005",
+    "title": "如何计算显存需求？",
+    "category": "gpus",
+    "icon": "ri-memory-line",
+    "content": "模型显存需求 ≈ 参数量 × 每参数字节数 × 1.3（含KV Cache开销）",
+    "detail": "计算公式：\n- FP32: 参数量 × 4字节 × 1.3\n- FP16: 参数量 × 2字节 × 1.3\n- INT8: 参数量 × 1字节 × 1.3\n- INT4: 参数量 × 0.5字节 × 1.3\n\n例如：7B模型FP16加载需要约 7 × 2 × 1.3 ≈ 18GB显存",
+    "order": 1
+  },
+  {
+    "id": "k006",
+    "title": "GPU架构演进",
+    "category": "gpus",
+    "icon": "ri-history-line",
+    "content": "NVIDIA GPU架构从Fermi到Hopper，每一代都有显著提升。了解架构有助于选择合适的GPU。",
+    "detail": "主要架构：\n- Volta (2017): V100, 引入Tensor Core\n- Turing (2018): RTX 20系列, RT Core\n- Ampere (2020): A100, RTX 30系列\n- Hopper (2022): H100, FP8支持\n- Ada Lovelace (2022): RTX 40系列, L40S",
+    "order": 2
+  },
+  {
+    "id": "k007",
+    "title": "CPU核心数选择",
+    "category": "cpus",
+    "icon": "ri-database-2-line",
+    "content": "CPU核心数的选择取决于应用场景。更多核心适合并行任务，但单核性能也很重要。",
+    "detail": "场景推荐：\n- 办公/日常：4-6核足够\n- 开发/编译：8-16核\n- 服务器/虚拟化：16-64核\n- 高性能计算：64核以上\n\n注意：AI训练主要依赖GPU，CPU主要用于数据预处理",
+    "order": 1
+  }
 ]

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": true
+    "visible": true,
+    "subcategory_id": "chat"
   }
 ]