tamakyi
30c502ce7b
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。
91 lines
2.9 KiB
Go
91 lines
2.9 KiB
Go
package fog
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// Diff 是一份待导入快照与当前迷雾之间的差异画像。
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//
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// 导入有两种语义完全不同的模式：叠加只做按位或、不可能丢数据；整体替换会先清空，
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// 快照里没有的块一律消失。以前两种模式在界面上只是一个下拉框，站长在下手之前
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// 看不到「这次能补多少」和「这次会丢多少」，实测里整体替换静默丢掉过 14% 的已探索区域。
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type Diff struct {
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CurBlocks, CurCells int // 库里现有
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IncBlocks, IncCells int // 快照本身
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// GainBlocks/GainCells 是叠加模式真正新增的部分：快照里当前完全没有的整块，
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// 加上重叠块里补亮的格。
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GainBlocks int
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GainCells int
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// OverlapBlocks 是与现有块坐标相同的块数，其中 SameBlocks 是一格都不会变的。
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OverlapBlocks int
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SameBlocks int
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// LostBlocks/LostCells 是快照里完全没有、只有整体替换才会消失的部分。
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LostBlocks int
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LostCells int
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curAreaM2, incAreaM2, gainAreaM2, lostAreaM2 float64
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}
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// DiffBlocks 比对当前块集与一份待导入快照。入参块集不要求有序。
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func DiffBlocks(cur, inc []Block) Diff {
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var d Diff
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d.CurBlocks, d.IncBlocks = len(cur), len(inc)
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curMap := make(map[[2]int][]byte, len(cur))
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for _, b := range cur {
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curMap[[2]int{b.GX, b.GY}] = b.Bitmap
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n := popcountSum(b.Bitmap)
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d.CurCells += n
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d.curAreaM2 += float64(n) * cellAreaM2(b.GX, b.GY)
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}
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incSet := make(map[[2]int]struct{}, len(inc))
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for _, b := range inc {
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incSet[[2]int{b.GX, b.GY}] = struct{}{}
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n := popcountSum(b.Bitmap)
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d.IncCells += n
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d.incAreaM2 += float64(n) * cellAreaM2(b.GX, b.GY)
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old, ok := curMap[[2]int{b.GX, b.GY}]
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if !ok {
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d.GainBlocks++
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d.GainCells += n
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d.gainAreaM2 += float64(n) * cellAreaM2(b.GX, b.GY)
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continue
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}
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d.OverlapBlocks++
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gain := 0
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for i := range b.Bitmap {
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gain += int(popcount[b.Bitmap[i]&^old[i]])
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}
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if gain == 0 {
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d.SameBlocks++
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continue
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}
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d.GainCells += gain
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d.gainAreaM2 += float64(gain) * cellAreaM2(b.GX, b.GY)
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}
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for _, b := range cur {
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if _, ok := incSet[[2]int{b.GX, b.GY}]; ok {
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continue
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}
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n := popcountSum(b.Bitmap)
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d.LostBlocks++
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d.LostCells += n
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d.lostAreaM2 += float64(n) * cellAreaM2(b.GX, b.GY)
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}
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return d
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}
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func (d Diff) CurAreaKm2() float64 { return d.curAreaM2 / 1e6 }
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func (d Diff) IncAreaKm2() float64 { return d.incAreaM2 / 1e6 }
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func (d Diff) GainAreaKm2() float64 { return d.gainAreaM2 / 1e6 }
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func (d Diff) LostAreaKm2() float64 { return d.lostAreaM2 / 1e6 }
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// MergeAreaKm2 是叠加后的总量，CoverAreaKm2 是整体替换后的总量。
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func (d Diff) MergeAreaKm2() float64 { return (d.curAreaM2 + d.gainAreaM2) / 1e6 }
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func (d Diff) CoverAreaKm2() float64 { return d.incAreaM2 / 1e6 }
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// Changed 报告叠加是否会让地图有任何变化——为假说明这份快照已经被完全覆盖过。
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func (d Diff) Changed() bool { return d.GainCells > 0 }
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