备份与迷雾的差异分析、合并执行，附属数据改挂坐标键
备份（.rondbackup）与迷雾（.fwss）各加一层「先看差异再动手」： - 迷雾：上传先出差异页，说清叠加能补多少、整体替换会丢多少，并在真实底图上 用绿/灰/红标出会新增 / 不变 / 会丢的区域 - 备份：与当前数据集按 UUID 逐条比对（rond 每次导出都会重排 Z_PK，编号认不出人）， 新增合入、冲突逐条选、仅站内有逐条勾选删除，全在一个事务里执行； 合入的行写回 entity_raw，保证再传同一份备份时差异归零 - 地点按坐标对齐：站点的「补全地址」会把地点改名，拿名称当键会让一整个地点的 到访全变成假冲突 - 附属数据（备注/结论/访客票/图片）从 dataset_id+src_pk 换成坐标键，换数据集不再失联； 访客票没有 dataset_id，回填按活跃数据集认领 - 修正 contentHash 拿 Z_PK 当指纹的隐患，以及两处「src_pk 跨快照稳定」的错误注释 导出格式（.rondbackup / .fwss）保持原样，往返测试通过。
This commit is contained in:
tamakyi
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package fog
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import (
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"image"
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"math"
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)
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// 差异图的三种归属。改这里的色值要同步 templates/admin/fog-preview.html 的图例。
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var (
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DiffBothColor = [4]byte{126, 134, 146, 148} // 两边都有：导入不影响
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DiffGainColor = [4]byte{36, 164, 90, 205} // 只有快照有：叠加会新增
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DiffLostColor = [4]byte{214, 56, 56, 210} // 只有库里有：整体替换会丢
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)
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const (
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diffNone uint8 = iota
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diffBoth
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diffGain
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diffLost
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)
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// RenderDiffTile 渲染一张 256x256 的差异瓦片，供站长在真实底图上核对
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// 「这份快照能补上哪里」与「整体替换会丢掉哪里」。
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//
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// 与 RenderTile 一样按底图坐标系做 WGS84→GCJ02 纠偏；不做描边——三种颜色
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// 已经够区分，再描边在粗缩放级下会糊成一片。
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//
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// 逐格判定只在放大到「一格不小于一像素」（z>=14）时做；更粗的级别下一个块还不到
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// 一个像素，按块内占多数的类别整块涂色。绘制顺序 both→gain→lost，让「会新增」
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// 和「会丢」压住底色，重叠处不至于被灰吃掉。
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func RenderDiffTile(cur, inc map[[2]int][]byte, z, x, y int, crs string) *image.NRGBA {
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if z < 0 || z > 22 {
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return nil
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}
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step := WorldCells >> z
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scale := 256.0 / float64(step)
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ox, oy := float64(x*step), float64(y*step)
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layer := make([]uint8, 256*256)
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mark := func(px, py, w, h int, kind uint8) {
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for r := 0; r < h; r++ {
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yy := py + r
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if yy < 0 || yy > 255 {
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continue
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}
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row := yy * 256
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for c := 0; c < w; c++ {
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xx := px + c
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if xx < 0 || xx > 255 {
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continue
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}
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layer[row+xx] = kind
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}
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}
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}
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// 直接遍历块表而不是全球块范围：低缩放级下范围循环会膨胀到数十亿次空查询。
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// GCJ 偏移最多约 700m（74 格），粗裁剪时两侧各放宽两块保证不漏。
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const margin = 2 * BlockCells
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lo, hi := float64(x*step-margin), float64((x+1)*step+margin)
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rlo, rhi := float64(y*step-margin), float64((y+1)*step+margin)
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shift := crs == "gcj02"
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draw := func(pass uint8, gbx, gby int, cb, ib []byte) {
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bx, by := float64(gbx*BlockCells), float64(gby*BlockCells)
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if bx+BlockCells <= lo || bx >= hi || by+BlockCells <= rlo || by >= rhi {
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return
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}
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ddx, ddy := 0.0, 0.0
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if shift {
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ddx, ddy = blockShift(gbx, gby)
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}
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u0 := (bx + ddx - ox) * scale
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v0 := (by + ddy - oy) * scale
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if scale >= 1 {
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w := int(math.Ceil(scale))
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for row := 0; row < BlockCells; row++ {
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base := row * 8
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for colB := 0; colB < 8; colB++ {
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var cbv, ibv byte
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if cb != nil {
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cbv = cb[base+colB]
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}
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if ib != nil {
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ibv = ib[base+colB]
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}
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if cbv|ibv == 0 {
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continue
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}
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py := int(math.Floor(v0 + float64(row)*scale))
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for bit := 0; bit < 8; bit++ {
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m := byte(0x80 >> bit)
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kind := cellKind(cbv&m != 0, ibv&m != 0)
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if kind != pass {
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continue
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}
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px := int(math.Floor(u0 + float64(colB*8+bit)*scale))
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mark(px, py, w, w, kind)
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}
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}
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}
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return
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}
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// 一格不足一像素：整块按多数类涂，三种混在一起的块以数量最多的为准
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if dominantKind(blockDiffCounts(cb, ib)) != pass {
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return
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}
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u1 := (bx + BlockCells + ddx - ox) * scale
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v1 := (by + BlockCells + ddy - oy) * scale
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px0, py0 := int(math.Floor(u0)), int(math.Floor(v0))
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px1, py1 := int(math.Ceil(u1)), int(math.Ceil(v1))
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if px1-px0 < 1 {
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px1 = px0 + 1
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}
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if py1-py0 < 1 {
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py1 = py0 + 1
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}
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mark(px0, py0, px1-px0, py1-py0, pass)
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}
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for _, pass := range []uint8{diffBoth, diffGain, diffLost} {
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for key, cb := range cur {
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draw(pass, key[0], key[1], cb, inc[key])
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}
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// 只在快照里的块单独走一遍，两边都有的块不再重复画
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for key, ib := range inc {
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if _, dup := cur[key]; dup {
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continue
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}
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draw(pass, key[0], key[1], nil, ib)
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}
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}
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img := image.NewNRGBA(image.Rect(0, 0, 256, 256))
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for i, kind := range layer {
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if kind == diffNone {
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continue
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}
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var c [4]byte
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switch kind {
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case diffBoth:
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c = DiffBothColor
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case diffGain:
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c = DiffGainColor
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default:
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c = DiffLostColor
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}
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o := i * 4
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img.Pix[o], img.Pix[o+1], img.Pix[o+2], img.Pix[o+3] = c[0], c[1], c[2], c[3]
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}
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return img
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}
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func cellKind(inCur, inInc bool) uint8 {
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switch {
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case inCur && inInc:
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return diffBoth
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case inInc:
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return diffGain
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case inCur:
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return diffLost
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}
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return diffNone
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}
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// blockDiffCounts 数一个块里三类各占多少格。两个位图都可能缺失（只有一边有这个块）。
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func blockDiffCounts(cb, ib []byte) (both, gain, lost int) {
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n := len(cb)
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if len(ib) > n {
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n = len(ib)
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}
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for i := 0; i < n; i++ {
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var c, in byte
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if i < len(cb) {
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c = cb[i]
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}
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if i < len(ib) {
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in = ib[i]
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}
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both += int(popcount[c&in])
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gain += int(popcount[in&^c])
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lost += int(popcount[c&^in])
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}
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return
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}
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// dominantKind 取三类里占得最多的一类，用来在粗缩放级下决定整块的着色。
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func dominantKind(both, gain, lost int) uint8 {
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switch {
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case gain > 0 && gain >= both && gain >= lost:
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return diffGain
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case lost > 0 && lost >= both:
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return diffLost
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case both > 0:
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return diffBoth
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}
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return diffNone
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}
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