Files
bot_dev1 85df71fc28 feat: 实现供水运营专题大屏BI可视化
- 新增OperationDashboard.vue全屏大屏组件
- 添加6个核心KPI指标卡片: 进水总量/出水总量/产销差率/营收额/平均水质/报警次数
- 实现6个ECharts图表: 供水趋势/水质分布/报警统计/管网空间/设备状态/营收分析
- 创建静态HTML版本operation-dashboard.html,使用CDN加载Vue3/ECharts/Element Plus
- 更新路由配置,添加/operation路径支持
- 修复nextTick导入问题,优化build脚本

🚧 开发者: bot_dev1
📝 任务: #38 [BI] 运营仪表盘 + 供水专题大屏
2026-06-15 09:06:11 +08:00
..

KDBush

A very fast static spatial index for 2D points based on a flat KD-tree. Compared to RBush:

  • Points only — no rectangles.
  • Static — you can't add/remove items after initial indexing.
  • Faster indexing and search, with lower memory footprint.
  • Index is stored as a single array buffer (so you can transfer it between threads or store it as a compact file).

If you need a static index for rectangles, not only points, see Flatbush. When indexing points, KDBush has the advantage of taking ~2x less memory than Flatbush.

Build Status Simply Awesome

Usage

// initialize KDBush for 1000 items
const index = new KDBush(1000);

// fill it with 1000 points
for (const {x, y} of items) {
    index.add(x, y);
}

// perform the indexing
index.finish();

// make a bounding box query
const foundIds = index.range(minX, minY, maxX, maxY);

// map ids to original items
const foundItems = foundIds.map(i => items[i]);

// make a radius query
const neighborIds = index.within(x, y, 5);

// instantly transfer the index from a worker to the main thread
postMessage(index.data, [index.data]);

// reconstruct the index from a raw array buffer
const index = KDBush.from(e.data);

Install

Install with NPM: npm install kdbush, then import as a module:

import KDBush from 'kdbush';

Or use as a module directly in the browser with jsDelivr:

<script type="module">
    import KDBush from 'https://cdn.jsdelivr.net/npm/kdbush/+esm';
</script>

Alternatively, there's a browser bundle with a KDBush global variable:

<script src="https://cdn.jsdelivr.net/npm/kdbush"></script>

API

new KDBush(numItems[, nodeSize, ArrayType, ArrayBufferType])

Creates an index that will hold a given number of points (numItems). Additionally accepts:

  • nodeSize: Size of the KD-tree node, 64 by default. Higher means faster indexing but slower search, and vise versa.
  • ArrayType: Array type to use for storing coordinate values. Float64Array by default, but if your coordinates are integer values, Int32Array makes the index faster and smaller.
  • ArrayBufferType: the array buffer type used to store data (ArrayBuffer by default); you may prefer SharedArrayBuffer if you want to share the index between threads (multiple Worker, SharedWorker or ServiceWorker).

index.add(x, y)

Adds a given point to the index. Returns a zero-based, incremental number that represents the newly added point.

index.range(minX, minY, maxX, maxY)

Finds all items within the given bounding box and returns an array of indices that refer to the order the items were added (the values returned by index.add(x, y)).

index.within(x, y, radius)

Finds all items within a given radius from the query point and returns an array of indices.

KDBush.from(data)

Recreates a KDBush index from raw ArrayBuffer or SharedArrayBuffer data (that's exposed as index.data on a previously indexed KDBush instance). Very useful for transferring or sharing indices between threads or storing them in a file.

Properties

  • data: array buffer that holds the index.
  • numItems: number of stored items.
  • nodeSize: number of items in a KD-tree node.
  • ArrayType: array type used for internal coordinates storage.
  • IndexArrayType: array type used for internal item indices storage.