v1.9.0-rc.3 - performance optimization

2026-02-02 18:14:52 +08:00
parent a879fe01f7
commit 8148969e1e
23 changed files with 42688 additions and 495 deletions
@@ -0,0 +1,88 @@
+import fs from 'fs'
+import path from 'path'
+import { fileURLToPath } from 'url'
+
+const __filename = fileURLToPath(import.meta.url)
+const __dirname = path.dirname(__filename)
+
+// 生成大数据集：100个资源，每个资源20-30个任务，总计约2500个任务
+// 用于测试资源视图的垂直滚动性能和区间树算法优化效果
+function generateLargeResourceData() {
+  const resources = []
+  const departments = ['研发部', '临床部', '质量部', '注册部', '生产部', '市场部', '工程部', '测试部']
+  const types = ['研究员', '试验主任', '数据分析师', '质量专员', '注册专员', '项目经理', '工程师', '测试工程师']
+  const colors = ['#2196f3', '#4caf50', '#ff9800', '#f44336', '#9c27b0', '#00bcd4', '#8bc34a', '#ffc107', '#e91e63', '#3f51b5']
+
+  for (let i = 1; i <= 100; i++) {
+    const resourceId = `resource-${i.toString().padStart(3, '0')}`
+    const taskCount = 20 + Math.floor(Math.random() * 11) // 20-30个任务
+    const tasks = []
+    const taskIdStart = i * 1000
+
+    // 为每个资源生成任务
+    for (let j = 0; j < taskCount; j++) {
+      const taskId = taskIdStart + j
+      const startDay = Math.floor(Math.random() * 365) // 365天内随机开始
+      const duration = 3 + Math.floor(Math.random() * 28) // 3-30天
+
+      const startDate = new Date(2025, 0, 1)
+      startDate.setDate(startDate.getDate() + startDay)
+      const endDate = new Date(startDate)
+      endDate.setDate(endDate.getDate() + duration)
+
+      const percent = [25, 50, 75, 100][Math.floor(Math.random() * 4)]
+
+      tasks.push({
+        id: taskId,
+        name: `任务${taskId} - ${['需求分析', '方案设计', '实施开发', '测试验证', '文档编写', '评审会议'][j % 6]}`,
+        startDate: startDate.toISOString().split('T')[0],
+        endDate: endDate.toISOString().split('T')[0],
+        progress: Math.floor(Math.random() * 100),
+        type: ['task', 'story', 'bug', 'milestone'][Math.floor(Math.random() * 4)],
+        resources: [
+          {
+            id: resourceId,
+            name: `资源${i}`,
+            percent,
+          },
+        ],
+      })
+    }
+
+    resources.push({
+      id: resourceId,
+      name: `资源${i} - ${types[i % types.length]}`,
+      type: types[i % types.length],
+      avatar: `https://i.pravatar.cc/150?img=${i}`,
+      department: departments[i % departments.length],
+      skills: ['技能A', '技能B', '技能C'],
+      utilization: 60 + Math.floor(Math.random() * 40),
+      color: colors[i % colors.length],
+      tasks,
+    })
+  }
+
+  return {
+    resources,
+    meta: {
+      generated: new Date().toISOString(),
+      resourceCount: resources.length,
+      taskCount: resources.reduce((sum, r) => sum + r.tasks.length, 0),
+      description: '性能测试用大数据集 - 100个资源，约2500个任务，用于测试垂直滚动性能和区间树算法优化效果',
+    },
+  }
+}
+
+const data = generateLargeResourceData()
+const outputPath = path.join(__dirname, '..', 'demo', 'data-resources-large.json')
+
+fs.writeFileSync(
+  outputPath,
+  JSON.stringify(data, null, 2),
+  'utf-8',
+)
+
+console.log('✅ 已生成大数据集:')
+console.log(`  - 资源数: ${data.meta.resourceCount}`)
+console.log(`  - 任务数: ${data.meta.taskCount}`)
+console.log('  - 文件: demo/data-resources-large.json')