feat: 图片改为参数截图，支持多次解析并记录解析来源历史

- 添加子类别编辑弹框（subcategoryModal）
- 子类别列表可视化显示（卡片样式）
- 支持添加、编辑、删除子类别
- 表单输入关键特性字段和标签
- 替换原来的JSON文本编辑方式

data/config.json

@@ -0,0 +1,14 @@
+{
+  "site_name": "ParamHub",
+  "site_subtitle": "参数百科",
+  "footer_text": "ParamHub - 模型与硬件参数速查平台",
+  "icp_number": "",
+  "copyright_year": "2026",
+  "contact_email": "wlq@tphai.com",
+  "github_url": "",
+  "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,
+    "description": "AMD顶级服务器CPU，96核心",
+    "subcategory_id": "server",
+    "publish_date": "2022-01-01"
+  },
+  {
+    "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,
+    "description": "64核心高性能服务器CPU",
+    "subcategory_id": "server",
+    "publish_date": "2022-01-01"
+  },
+  {
+    "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,
+    "description": "48核心服务器CPU",
+    "subcategory_id": "server",
+    "publish_date": "2022-01-01"
+  },
+  {
+    "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,
+    "description": "Intel顶级工作站CPU",
+    "subcategory_id": "server",
+    "publish_date": "2023-01-01"
+  },
+  {
+    "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,
+    "description": "中端工作站CPU",
+    "subcategory_id": "server",
+    "publish_date": "2023-01-01"
+  },
+  {
+    "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,
+    "description": "顶级消费级CPU，适合AI开发",
+    "subcategory_id": "desktop",
+    "publish_date": "2022-01-01"
+  },
+  {
+    "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,
+    "description": "带3D V-Cache，游戏性能更强",
+    "subcategory_id": "mobile",
+    "publish_date": "2023-01-01"
+  },
+  {
+    "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,
+    "description": "Intel顶级消费级CPU",
+    "subcategory_id": "desktop",
+    "publish_date": "2023-01-01"
+  },
+  {
+    "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": "2022-01-01",
+    "views": 0,
+    "is_pinned": false,
+    "subcategory_id": "mobile"
+  }
 ]

data/gpus.json

@@ -1,12 +1,209 @@
 [
-  {"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,
+    "description": "数据中心顶级GPU，专为AI训练设计",
+    "subcategory_id": "datacenter",
+    "publish_date": "2022-01-01"
+  },
+  {
+    "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,
+    "description": "数据中心主力GPU，AI训练推理通用",
+    "subcategory_id": "datacenter",
+    "publish_date": "2020-01-01"
+  },
+  {
+    "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,
+    "description": "A100 40GB版本，性价比更高",
+    "subcategory_id": "datacenter",
+    "publish_date": "2020-01-01"
+  },
+  {
+    "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,
+    "description": "新一代数据中心GPU，推理优化",
+    "subcategory_id": "datacenter",
+    "publish_date": "2023-01-01"
+  },
+  {
+    "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,
+    "description": "消费级最强GPU，适合个人AI开发",
+    "subcategory_id": "gaming",
+    "publish_date": "2022-01-01"
+  },
+  {
+    "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,
+    "description": "4090中国特供版，性能略降",
+    "subcategory_id": "gaming",
+    "publish_date": "2024-01-01"
+  },
+  {
+    "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,
+    "description": "上一代旗舰，性价比高",
+    "subcategory_id": "gaming",
+    "publish_date": "2020-01-01"
+  },
+  {
+    "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,
+    "description": "中高端消费级GPU",
+    "subcategory_id": "gaming",
+    "publish_date": "2020-01-01"
+  },
+  {
+    "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,
+    "description": "上一代数据中心GPU，仍有价值",
+    "subcategory_id": "datacenter",
+    "publish_date": "2017-01-01"
+  },
+  {
+    "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,
+    "description": "AMD最强AI GPU，192GB显存",
+    "subcategory_id": "datacenter",
+    "publish_date": "2023-01-01"
+  },
+  {
+    "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": [],
+    "publish_date": "2024-01-01"
+  },
+  {
+    "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": [],
+    "publish_date": "2020-01-01"
+  }
 ]

data/items_021dc76d36be.json

@@ -2,11 +2,16 @@
   {
     "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",
+    "publish_date": "2021-01-01"
   },
   {
     "name": "秦PLUS",
@@ -29,6 +34,8 @@
     "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",
+    "publish_date": "2023-01-01"
   }
 ]

data/items_71fa2b4d818f.json

@@ -0,0 +1,102 @@
+[
+  {
+    "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": "2023-01-01",
+    "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": "2023-01-01",
+    "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": "2023-01-01",
+    "views": 0,
+    "is_pinned": false,
+    "subcategory_id": "90ce312b560d",
+    "updated_at": "2026-04-28 12:32:50"
+  },
+  {
+    "name": "EOS R7",
+    "brand": "佳能 (Canon)",
+    "year": 2022,
+    "specs": {
+      "品牌": "佳能 (Canon)",
+      "商品编号": "10090975539899",
+      "店铺": "佳能 (Canon) 数码旗舰店",
+      "外接电源": "支持外接电源",
+      "电池类型": "锂离子电池",
+      "接口": "Wi-Fi 蓝牙 HDMI",
+      "高清摄像": "4K超高清视频",
+      "焦点数量": "5915个",
+      "型号": "EOS R7",
+      "有效像素": "3250万",
+      "传感器类型": "CMOS",
+      "上市时间": "2022-06",
+      "取景器类型": "电子取景器",
+      "液晶屏像素": "162万",
+      "液晶屏尺寸": "3.2英寸",
+      "液晶屏类型": "侧翻屏 旋转屏",
+      "最大光圈": "F3.5",
+      "标准ISO感光度": "ISO 100-32000",
+      "连拍速度": "电子最高约30张/秒，机械最高约15张/秒",
+      "存储介质": "SD卡 SDHC卡 SDXC卡",
+      "功能": "Wi-Fi 4K视频 5轴防抖 高速连拍 翻转自拍",
+      "滤镜直径": "55mm",
+      "视频拍摄能力": "4K 60P",
+      "传感器尺寸": "APS-C",
+      "视频采样": "4:2:2",
+      "像素": "3000-4000万",
+      "镜头卡口": "佳能RF卡口",
+      "RAW照片输出": "14bit",
+      "适用对象": "入门级",
+      "类型": "机身"
+    },
+    "description": "入门级机身",
+    "id": "c8c3f124b2ce",
+    "category_id": "71fa2b4d818f",
+    "created_at": "2026-04-28 16:38:03",
+    "visible": true,
+    "raw_text": "",
+    "images": [
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+    ],
+    "publish_date": "2022-01-01",
+    "views": 0,
+    "is_pinned": false
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+]

data/items_phones.json

@@ -0,0 +1,81 @@
+[
+  {
+    "name": "华为Pura X Max",
+    "brand": "华为",
+    "processor": "麒麟9030 Pro",
+    "screen_size": "7.6",
+    "year": 2026,
+    "description": "全球首款横向阔折叠屏手机，内屏7.6英寸（WQHD+分辨率），外屏5.5英寸，搭载麒麟9030 Pro芯片和鸿蒙6系统，支持AI眼动翻页和手写笔功能，素皮版重约210g",
+    "id": "5ffe89899549",
+    "category_id": "phones",
+    "created_at": "2026-04-28 18:20:59",
+    "visible": true,
+    "raw_text": "华为Pura X Max‌：全球首款横向阔折叠屏手机，内屏7.6英寸（WQHD+分辨率），外屏5.5英寸，搭载麒麟9030 Pro芯片和鸿蒙6系统，支持AI眼动翻页和手写笔功能，素皮版重约210g，2026年4月20日上市。\n华为 Pura X Max 是华为最新推出的大阔折叠屏手机，官方起售价10999 元，提供多种存储版本及配色选择，已在华为商城等渠道正式开售 。更多详情可访问 [华为官网](https://consumer.huawei.com/cn/phones/pura-x-max/specs/) 或 [华为商城](https://item.vmall.com/product/comdetail/index.html?prdId=10086621059876&sbomCode=2601010615007) 。\n版本价格与发售信息\n\n1. 发售时间：于 2026 年 4 月 20 日正式发布，4 月 25 日 10:08 正式开售 。\n2. 官方定价：\n   - 12GB+256GB：10999 元。\n   - 12GB+512GB：11999 元。\n   - 16GB+512GB 典藏版：12999 元。\n   - 16GB+1TB 典藏版：13999 元。\n3. 购买渠道：可通过华为官网及华为商城等官方渠道购买，部分第三方平台价格可能存在波动，建议以官方定价为准 。\n核心硬件配置\n\n1. 屏幕显示：\n   - 内屏：7.7 英寸折叠柔性 OLED，支持 1-120Hz LTPO 2.0 自适应刷新率，分辨率 2584×1828 像素 。\n   - 外屏：5.4 英寸 OLED，支持 1-120Hz LTPO 2.0 自适应刷新率，分辨率 1848×1264 像素 。\n   - 亮度：外屏峰值亮度 3500 尼特，内屏峰值亮度 3000 尼特，户外强光下清晰可见 。\n2. 性能系统：\n   - 处理器：搭载麒麟 9030 Pro 芯片，整机性能提升 30% 。\n   - 操作系统：预装 HarmonyOS 6.1，支持多设备协同 。\n3. 影像系统：\n   - 后置：5000 万像素超光变主摄（F1.4-F4.0）+ 1250 万像素超广角 + 5000 万像素潜望长焦 + 第二代红枫原色摄像头 。\n   - 前置：内外屏均配备 800 万像素摄像头，支持外屏自拍 。\n4. 续航充电：\n   - 电池：5300mAh 典型值，支持 66W 有线超级快充及 50W 无线超级快充 。\n折叠形态与 AI 体验\n\n1. 阔折叠设计：\n   - 采用√2:1 黄金比例设计，内外屏比例一致，接近 A4 纸对折比例，提升阅读和办公体验 。\n   - 机身重量约 229 克，折叠态厚度 11.2mm，展开态厚度 5.2mm，便携性较好 。\n2. AI 功能：\n   - 支持小艺伴随式 AI、AI 灵感妙创、AI 眼动翻页等功能，提升交互效率 。\n   - 首发支持华为 M-Pen 3 Mini 手写笔，适配“天生会画”App，支持动态照片手绘 。\n3. 配色材质：\n   - 提供幻夜黑、橄榄金、星际蓝、活力橙、零度白 5 款配色 。\n   - 外屏采用第二代昆仑玻璃，支持 IP58+IP59 级防尘防水，耐用性增强 。",
+    "images": [],
+    "subcategory_id": "",
+    "publish_date": "2026-01-01",
+    "views": 0,
+    "is_pinned": false,
+    "price": 10999,
+    "specs": {
+      "screen": {
+        "inner": {
+          "size": 7.7,
+          "type": "折叠柔性OLED",
+          "refreshRate": "1-120Hz LTPO 2.0自适应刷新率",
+          "resolution": "2584×1828像素",
+          "brightness": 3000
+        },
+        "outer": {
+          "size": 5.4,
+          "type": "OLED",
+          "refreshRate": "1-120Hz LTPO 2.0自适应刷新率",
+          "resolution": "1848×1264像素",
+          "brightness": 3500
+        }
+      },
+      "performance": {
+        "processor": "麒麟9030 Pro芯片",
+        "os": "HarmonyOS 6.1"
+      },
+      "memory": {
+        "ram": [
+          "12GB",
+          "16GB"
+        ],
+        "storage": [
+          "256GB",
+          "512GB",
+          "1TB"
+        ]
+      },
+      "camera": {
+        "rear": "5000万像素超光变主摄 + 1250万像素超广角 + 5000万像素潜望长焦 + 第二代红枫原色摄像头",
+        "front": "800万像素"
+      },
+      "battery": {
+        "capacity": 5300,
+        "charging": {
+          "wired": 66,
+          "wireless": 50
+        }
+      },
+      "design": {
+        "weight": 229,
+        "thickness": {
+          "folded": 11.2,
+          "unfolded": 5.2
+        },
+        "waterResistance": "IP58+IP59"
+      },
+      "colors": [
+        "幻夜黑",
+        "橄榄金",
+        "星际蓝",
+        "活力橙",
+        "零度白"
+      ]
+    },
+    "updated_at": "2026-04-28 18:29:08"
+  }
+]

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 +9,17 @@
     "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": [],
+    "publish_date": "2023-03-14"
   },
   {
     "id": "gpt4turbo",
@@ -29,7 +35,9 @@
     "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",
+    "publish_date": "2023-11-06"
   },
   {
     "id": "gpt35",
@@ -45,7 +53,9 @@
     "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",
+    "publish_date": "2023-03-01"
   },
   {
     "id": "claude3opus",
@@ -61,7 +71,9 @@
     "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",
+    "publish_date": "2024-03-04"
   },
   {
     "id": "claude3sonnet",
@@ -77,7 +89,9 @@
     "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",
+    "publish_date": "2024-03-04"
   },
   {
     "id": "llama270b",
@@ -93,7 +107,9 @@
     "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",
+    "publish_date": "2023-07-18"
   },
   {
     "id": "llama3",
@@ -109,7 +125,9 @@
     "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",
+    "publish_date": "2024-04-18"
   },
   {
     "id": "mistral7b",
@@ -125,7 +143,9 @@
     "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",
+    "publish_date": "2023-09-27"
   },
   {
     "id": "mixtral8x7b",
@@ -141,7 +161,9 @@
     "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",
+    "publish_date": "2023-12-11"
   },
   {
     "id": "qwen72b",
@@ -157,7 +179,9 @@
     "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",
+    "publish_date": "2024-02-05"
   },
   {
     "id": "deepseekv3",
@@ -173,7 +197,9 @@
     "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",
+    "publish_date": "2024-12-26"
   },
   {
     "id": "glm4",
@@ -190,6 +216,8 @@
     "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",
+    "publish_date": "2024-01-01"
   }
 ]

templates/category.html

@@ -51,9 +51,16 @@
                         class="w-full pl-12 pr-4 py-2 border border-gray-200 rounded-lg focus:outline-none focus:border-indigo-400"
                         onkeyup="filterItems()">
                 </div>
-                <select id="sortSelect" onchange="sortItems()" class="px-4 py-2 border border-gray-200 rounded-lg focus:outline-none">
+                <select id="sortBy" onchange="loadItems()" class="px-4 py-2 border border-gray-200 rounded-lg focus:outline-none">
+                    <option value="default">默认排序（置顶优先）</option>
+                    <option value="publish_date">按发布日期</option>
+                    <option value="views">按热度</option>
                     <option value="name">按名称</option>
-                    <option value="created_at">按时间</option>
+                    <option value="created_at">按创建时间</option>
+                </select>
+                <select id="sortOrder" onchange="loadItems()" class="px-4 py-2 border border-gray-200 rounded-lg focus:outline-none">
+                    <option value="desc">降序</option>
+                    <option value="asc">升序</option>
                 </select>
             </div>
         </div>
@@ -116,7 +123,9 @@
 
         // 加载数据
         async function loadItems() {
-            const res = await fetch(`/api/items/${categoryId}`);
+            const sortBy = document.getElementById('sortBy').value;
+            const sortOrder = document.getElementById('sortOrder').value;
+            const res = await fetch(`/api/items/${categoryId}?sort=${sortBy}&order=${sortOrder}`);
             allItems = await res.json();
             
             document.getElementById('itemCount').textContent = allItems.length;
@@ -137,22 +146,28 @@
 
             document.getElementById('itemsList').innerHTML = items.map(item => {
                 const fields = Object.entries(item)
-                    .filter(([key, val]) => !['id', 'category_id', 'created_at', 'updated_at'].includes(key) && val)
+                    .filter(([key, val]) => !['id', 'category_id', 'created_at', 'updated_at', 'visible', 'is_pinned', 'views', 'publish_date'].includes(key) && val)
                     .slice(0, 5)
                     .map(([key, val]) => `<span class="text-gray-500 text-sm">${key}: ${val}</span>`)
                     .join('<br>');
 
                 return `
-                    <div class="border border-gray-200 rounded-lg p-4 hover:shadow-md transition group">
+                    <div class="border border-gray-200 rounded-lg p-4 hover:shadow-md transition group ${item.is_pinned ? 'bg-yellow-50 border-yellow-300' : ''}">
                         <div class="flex items-start justify-between">
                             <div>
-                                <h3 class="font-medium text-gray-800 group-hover:text-indigo-600">${item.name || item.title || '未命名'}</h3>
+                                <h3 class="font-medium text-gray-800 group-hover:text-indigo-600 flex items-center gap-2">
+                                    ${item.is_pinned ? '<i class="ri-pushpin-fill text-yellow-500" title="置顶"></i>' : ''}
+                                    ${item.name || item.title || '未命名'}
+                                </h3>
                                 <div class="mt-2 space-y-1">
                                     ${fields}
                                 </div>
                             </div>
-                            <div class="text-xs text-gray-400">
-                                ${item.created_at ? item.created_at.split(' ')[0] : ''}
+                            <div class="text-right">
+                                <div class="text-xs text-gray-400">
+                                    ${item.publish_date || (item.created_at ? item.created_at.split(' ')[0] : '')}
+                                </div>
+                                ${item.views ? `<div class="text-xs text-gray-400 mt-1"><i class="ri-eye-line"></i> ${item.views}</div>` : ''}
                             </div>
                         </div>
                     </div>
@@ -176,19 +191,6 @@
             renderItems(filtered);
         }
 
-        // 排序
-        function sortItems() {
-            const sortBy = document.getElementById('sortSelect').value;
-            const sorted = [...allItems].sort((a, b) => {
-                if (sortBy === 'name') {
-                    return (a.name || a.title || '').localeCompare(b.name || b.title || '');
-                } else {
-                    return (b.created_at || '').localeCompare(a.created_at || '');
-                }
-            });
-            renderItems(sorted);
-        }
-
         // 初始化
         loadNav();
         loadItems();

templates/models.html

@@ -32,6 +32,19 @@
             <p class="text-gray-500 mt-1">AI大模型参数规格一览</p>
         </div>
 
+        <!-- 子类别选择器 -->
+        <div class="bg-white rounded-xl shadow-sm p-4 mb-4">
+            <div class="flex items-center gap-2 mb-2">
+                <span class="text-sm text-gray-600"><i class="ri-folder-line mr-1"></i>子类别：</span>
+            </div>
+            <div class="flex gap-2" id="subcategoryTabs">
+                <button onclick="selectSubcategory('')" class="px-4 py-2 bg-indigo-600 text-white rounded-lg text-sm" id="subcat-all">
+                    <i class="ri-apps-line mr-1"></i>全部
+                </button>
+                <!-- 动态加载子类别 -->
+            </div>
+        </div>
+
         <!-- 搜索和筛选 -->
         <div class="bg-white rounded-xl shadow-sm p-4 mb-6">
             <div class="flex gap-4 items-center">
@@ -42,14 +55,18 @@
                         oninput="loadModels()">
                 </div>
                 <select id="sortBy" class="px-4 py-2 border border-gray-200 rounded-lg" onchange="loadModels()">
+                    <option value="default">默认排序（置顶优先）</option>
+                    <option value="publish_date">按发布日期</option>
+                    <option value="views">按热度</option>
                     <option value="name">按名称</option>
                     <option value="parameters">按参数量</option>
                     <option value="mmlu">按MMLU分数</option>
                     <option value="context_length">按上下文长度</option>
+                    <option value="created_at">按创建时间</option>
                 </select>
                 <select id="sortOrder" class="px-4 py-2 border border-gray-200 rounded-lg" onchange="loadModels()">
-                    <option value="asc">升序</option>
                     <option value="desc">降序</option>
+                    <option value="asc">升序</option>
                 </select>
                 <select id="filterType" class="px-4 py-2 border border-gray-200 rounded-lg" onchange="loadModels()">
                     <option value="all">全部</option>
@@ -97,12 +114,38 @@
     <script>
         let allModels = [];
         let categories = [];
+        let currentCategory = null;
+        let currentSubcategory = '';
+        
+        // 子类别默认特性配置
+        const DEFAULT_KEY_FEATURES = {
+            'chat': ['context_length', 'mmlu', 'input_price', 'output_price'],
+            'code': ['humaneval', 'context_length', 'input_price'],
+            'reasoning': ['mmlu', 'context_length', 'parameters'],
+            'vision': ['context_length', 'mmlu', 'input_price']
+        };
+        
+        const FEATURE_LABELS = {
+            'context_length': '上下文',
+            'mmlu': 'MMLU',
+            'humaneval': 'HumanEval',
+            'input_price': '输入价',
+            'output_price': '输出价',
+            'parameters': '参数量',
+            'reasoning_capability': '推理',
+            'vision_capability': '视觉',
+            'multimodal': '多模态'
+        };
 
         // 加载导航栏
         async function loadNav() {
             const res = await fetch('/api/categories');
             categories = await res.json();
             
+            // 获取当前类别的子类别
+            currentCategory = categories.find(c => c.id === 'ai-models');
+            renderSubcategoryTabs();
+            
             const builtinPages = [
                 {name: '首页', href: '/'},
                 {name: '工具', href: '/tools'},
@@ -130,6 +173,49 @@
             
             document.getElementById('topNav').innerHTML = navHtml;
         }
+        
+        // 渲染子类别选择器
+        function renderSubcategoryTabs() {
+            const container = document.getElementById('subcategoryTabs');
+            if (!currentCategory || !currentCategory.subcategories) {
+                container.innerHTML = '';
+                return;
+            }
+            
+            let html = `<button onclick="selectSubcategory('')" class="px-4 py-2 ${currentSubcategory === '' ? 'bg-indigo-600 text-white' : 'bg-gray-100 text-gray-600 hover:bg-gray-200'} rounded-lg text-sm" id="subcat-all">
+                <i class="ri-apps-line mr-1"></i>全部
+            </button>`;
+            
+            currentCategory.subcategories.forEach(sub => {
+                const isActive = currentSubcategory === sub.id;
+                html += `<button onclick="selectSubcategory('${sub.id}')" class="px-4 py-2 ${isActive ? 'bg-indigo-600 text-white' : 'bg-gray-100 text-gray-600 hover:bg-gray-200'} rounded-lg text-sm" id="subcat-${sub.id}">
+                    <i class="${sub.icon || 'ri-folder-line'} mr-1"></i>${sub.name}
+                </button>`;
+            });
+            
+            container.innerHTML = html;
+        }
+        
+        // 选择子类别
+        function selectSubcategory(subId) {
+            currentSubcategory = subId;
+            renderSubcategoryTabs();
+            loadModels();
+        }
+        
+        // 获取当前子类别的关键特性
+        function getKeyFeatures() {
+            if (!currentSubcategory || !currentCategory || !currentCategory.subcategories) {
+                return ['parameters', 'context_length', 'mmlu', 'input_price'];
+            }
+            
+            const subcat = currentCategory.subcategories.find(s => s.id === currentSubcategory);
+            if (subcat && subcat.key_features) {
+                return subcat.key_features;
+            }
+            
+            return ['parameters', 'context_length', 'mmlu', 'input_price'];
+        }
 
         async function loadModels() {
             const keyword = document.getElementById('searchInput').value.trim();
@@ -151,6 +237,21 @@
                 models = models.filter(m => !m.is_open_source);
             }
             
+            // 子类别过滤（通过模型名称/描述中的关键词判断）
+            if (currentSubcategory && currentCategory && currentCategory.subcategories) {
+                const subcat = currentCategory.subcategories.find(s => s.id === currentSubcategory);
+                if (subcat) {
+                    // 简化过滤：根据子类别关键词匹配
+                    // 实际应该有 subcategory_id 字段，这里暂时用名称匹配
+                    // 用户可以在后台编辑时指定子类别
+                    models = models.filter(m => {
+                        const subcatField = m.subcategory || m.subcategory_id;
+                        if (subcatField) return subcatField === currentSubcategory;
+                        return true; // 暂时显示全部，等后台支持子类别字段后再过滤
+                    });
+                }
+            }
+            
             renderModels(models);
         }
 
@@ -162,38 +263,93 @@
                 return;
             }
             
-            const html = models.map(m => `
-                <tr class="border-b hover:bg-gray-50 transition">
+            // 动态获取关键特性
+            const keyFeatures = getKeyFeatures();
+            
+            // 动态表头
+            let headerHtml = `
+                <tr>
+                    <th class="px-4 py-3 text-left text-sm font-medium text-gray-600">模型名称</th>
+                    <th class="px-4 py-3 text-left text-sm font-medium text-gray-600">厂商</th>
+            `;
+            
+            keyFeatures.forEach(f => {
+                headerHtml += `<th class="px-4 py-3 text-left text-sm font-medium text-gray-600">${FEATURE_LABELS[f] || f}</th>`;
+            });
+            
+            headerHtml += `
+                    <th class="px-4 py-3 text-left text-sm font-medium text-gray-600">类型</th>
+                    <th class="px-4 py-3 text-center text-sm font-medium text-gray-600">操作</th>
+                </tr>
+            `;
+            
+            document.querySelector('#modelsTable thead').innerHTML = headerHtml;
+            
+            // 动态内容
+            const html = models.map(m => {
+                let rowHtml = `
+                <tr class="border-b hover:bg-gray-50 transition ${m.is_pinned ? 'bg-yellow-50' : ''}">
                     <td class="px-4 py-3">
-                        <div class="font-medium text-gray-800">${m.name}</div>
-                        <div class="text-xs text-gray-500">${m.architecture || ''}</div>
+                        <div class="flex items-center gap-2">
+                            ${m.is_pinned ? '<i class="ri-pushpin-fill text-yellow-500" title="置顶"></i>' : ''}
+                            <div>
+                                <div class="font-medium text-gray-800">${m.name}</div>
+                                <div class="text-xs text-gray-500">${m.architecture || ''}</div>
+                            </div>
+                        </div>
                     </td>
                     <td class="px-4 py-3 text-gray-600">${m.organization}</td>
-                    <td class="px-4 py-3">
-                        <span class="px-2 py-1 bg-blue-100 text-blue-700 rounded text-sm">${m.parameters}B</span>
-                    </td>
-                    <td class="px-4 py-3 text-gray-600">${formatContext(m.context_length)}</td>
-                    <td class="px-4 py-3">
-                        <span class="px-2 py-1 bg-green-100 text-green-700 rounded text-sm">${m.mmlu || '-'}%</span>
-                    </td>
+                `;
+                
+                // 关键特性列
+                keyFeatures.forEach(f => {
+                    const value = formatFeatureValue(f, m);
+                    rowHtml += `<td class="px-4 py-3">${value}</td>`;
+                });
+                
+                rowHtml += `
                     <td class="px-4 py-3">
                         ${m.is_open_source 
                             ? '<span class="px-2 py-1 bg-emerald-100 text-emerald-700 rounded text-sm">开源</span>'
                             : '<span class="px-2 py-1 bg-gray-100 text-gray-700 rounded text-sm">商业</span>'}
                     </td>
-                    <td class="px-4 py-3 text-sm text-gray-600">
-                        ${m.input_price ? `$${m.input_price}/$${m.output_price}` : '免费'}
-                    </td>
                     <td class="px-4 py-3 text-center">
                         <button onclick="showDetail('${m.id}')" class="text-indigo-600 hover:text-indigo-800 text-sm">
                             <i class="ri-eye-line mr-1"></i>详情
                         </button>
                     </td>
                 </tr>
-            `).join('');
+                `;
+                
+                return rowHtml;
+            }).join('');
             
             document.getElementById('modelsTable').innerHTML = html;
         }
+        
+        // 格式化特性值
+        function formatFeatureValue(feature, model) {
+            const value = model[feature];
+            
+            if (value === null || value === undefined) return '<span class="text-gray-400">-</span>';
+            
+            switch (feature) {
+                case 'parameters':
+                    return `<span class="px-2 py-1 bg-blue-100 text-blue-700 rounded text-sm">${value}B</span>`;
+                case 'context_length':
+                    return `<span class="text-gray-600">${formatContext(value)}</span>`;
+                case 'mmlu':
+                    return `<span class="px-2 py-1 bg-green-100 text-green-700 rounded text-sm">${value}%</span>`;
+                case 'humaneval':
+                    return `<span class="px-2 py-1 bg-purple-100 text-purple-700 rounded text-sm">${value}%</span>`;
+                case 'input_price':
+                    return `<span class="text-sm text-gray-600">$${value || 0}</span>`;
+                case 'output_price':
+                    return `<span class="text-sm text-gray-600">$${value || 0}</span>`;
+                default:
+                    return `<span class="text-gray-600">${value}</span>`;
+            }
+        }
 
         function formatContext(len) {
             if (!len) return '-';