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feat(radar): 三维雷达接入设计 + Mala 转换器原型(已过架构评审) 

- tools/radar_convert: RADAR_TYPE_MALAMIRA 转换插件原型(info/convert/probe)
  .rad→.head / .rd3→.data(原样) / .pos→.cor；probe 用真实数据确诊数据体
  主序 = position-major reshape(K,M,N)，X=道/Y=通道/Z=采样，无需轴置换
- docs/spec: 2026-06-29 三维雷达体渲染+切片+异常接入设计
  核心论点=只换最内层 reader、下游渲染/切片/异常链复用；含已验证数据事实、
  内存实算、插件化转换层、双数据集测试策略；已过 opus 架构评审(PASS-WITH-CONCERNS,
  HIGH-1 填体逻辑须抽共享helper / HIGH-2 明星路 in-app 渲染属首次非已验证 已并入)
- samples/radar/malamira_南同大道: 6 线 .head/.cor 入库(大 .data gitignore,附重生成命令)
- 记录客户文档 §3.3 BITS 公式 bug(漏 SAMPLES 维)，须同步后端
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# ---- Radar sample data: keep .head/.cor (small text), ignore big .data binaries ----
samples/**/*.data

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# 三维雷达 DS 接入设计：规范化格式 → 体渲染 + 切片 + 异常 — 2026-06-29
> 负责人范围：**仅三维雷达的体渲染 + 切片 + 异常标注**（不含 2D 波列图详情页、不含二维雷达）。
> 本 spec 基于精读现有架构（HEAD `main`）+ Explore 代理交叉验证 + 用真实 Mala 数据实测确诊。
> 所有"数据事实"均已用 `D:/Downloads/MALA南同大道_rSlicer` 6 条测线核对，**无未验证假设**。
---
## 1. 背景与锁定决策
客户新增雷达数据类型，模型层级沿用 `项目 → GS → TM(TmObject) → DS列表`。三维雷达 TM 携带
`头信息(.head)` + DS 列表 `= 雷达RTK轨迹(.cor,备份+快照) + 每频率 雷达3D-频率N(打标.index + 通道1..N data.data)`。
数据体 = **K 切片(沿运动) × M 通道(sweep) × N 采样(深度)**（与 IDS/OpenGPR 手册一致）。
四项锁定决策（用户 2026-06-29 确认）：
| 维度 | 决策 |
|---|---|
| 职责范围 | 仅三维体 + 切片 + 异常；不做 2D 波列图详情、不做二维雷达 |
| 数据来源 | 后端未就绪 → 先读**本地已转换好的目录**（后端就绪后切按 DS 下载） |
| 输入格式 | 客户端**规范化 `.head/.data/.cor/.index`**（新写 reader） |
| 渲染规模 | 先**单线 / 下采样稠密体**打通 |
---
## 2. 已验证的数据事实（★C++ reader 必须遵守，零假设）
实测工具：`tools/radar_convert/malamira.py probe`（B-scan/C-scan 主序核对）。
1. **数据体主序 = position-major**：磁盘扫描顺序 = `(道0:通道0..M-1)(道1:通道0..M-1)…`，每 sweep 内 N 采样连续。
即 `flat.reshape(K, M, N)[道][通道][采样]`。probe 确诊：position-major 的 B-scan 出现连续直达波 +
地层 + 双曲线绕射；channel-major 为竖条乱码（已排除）。
2. **直接对应 geopro 体轴** `X=道(nx=K)` / `Y=通道(ny=M)` / `Z=采样(nz=N)`，**无需轴置换**——与现有
`Gpr3dvVolumeBridge` 轴映射完全一致。⚠️ 但**磁盘主序(采样最快→通道→道)与体内存布局(道最快，`((k·ny+j)·nx+i)`，
`ScalarVolumeI16.hpp:58`)相反**，新 reader 必须**逐体素散射重排填值**（照 bridge 的 `vol.at(to,j,s)=`），
**严禁 memcpy/整块拷贝**。"无需转置"指轴语义，不是字节布局。
3. **维度推导**：`M=NUMBER_OF_CH`、`N=SAMPLES`、`K=LAST_TRACE/NUMBER_OF_CH`（`.head` 的 `LAST_TRACE`
是**总扫描数**=K×M，不是道数）。
4. **数据类型**：int16 小端(`.rd3`/`bits=16`) / int32 小端(`.rd7`/`bits=32`)。`-32768` 是直达波饱和真实值，**非空值哨兵**。
5. **`BITS` 公式 bug**：客户文档 §3.3 `BITS = 文件大小/LAST_TRACE/NUMBER_OF_CH×8` **漏了 SAMPLES 维、量纲错**。
正确：`bytes = 文件大小/(LAST_TRACE×SAMPLES)`，并与扩展名交叉校验。**须同步后端**（见 `tools/radar_convert/README.md`）。
---
## 3. 架构论点：只换最内层 reader，下游 100% 复用
现有真实雷达体链路是分层的，**只有最内层 reader 绑定厂商格式**：
```
Api3dRepository::createGprVolume [api/Api3dRepository.cpp:128] 注册 dd_voxel/灰度色阶/预填 cachedGrid
└ data::createGprVolumeGrid [data/GprVolumeRepository.cpp:38]
├ io::gpr::buildLineVolumeFromGpr3dv ← 仅此层绑定厂商 .iprb/.ord（读 Impulse 原始）
└ data::builtI16ToVolumeGrid [data/GprVolumeRepository.cpp:11] int16→float 稠密体
→ 勾选 → VtkSceneController.loadVolume → VtkSceneView.addVolume → render::buildVoxel(GPU RayCast)
→ SliceTool / InteractionManager / AnomalyDrawTool （切片 B/C-scan + 点线面异常，全现成）
```
**新增一个产 `core::BuiltI16`（同轴映射 X=道/Y=通道/Z=采样）的规范化 reader**，则 `builtI16ToVolumeGrid`
往下的渲染/切片/异常链**零改动**。这正是 `GprVolumeRepository.cpp:14` 注释写明的"数据层方案 A"。
### 完全复用、不碰的部分
`render::buildVoxel`（GPU 体绘制）、三级树 voxel 段（`CategoryConfig.hpp:23` `dd_voxel`）、勾选→`loadVolume`→`addVolume`、
切片（`SliceTool` updown 深度 C-scan / leftright·frontback B-scan + `InteractionManager`）、
异常（`AnomalyDrawTool` 点/线/面 → `dd_anomaly` 挂体）、跨视图色阶联动、`builtI16ToVolumeGrid` 适配器。
---
## 4. 插件化转换层（已落地 Mala）
转换 = "按雷达型号的插件"，本地 Python 工具 = 未来服务端下发插件的原型，契约不变：
```
plugin_id : RADAR_TYPE_MALAMIRA # 对齐文档 §2.1 型号列表
supports(fileset) -> bool
convert(lineDir, prefix, outDir) -> {head, data, cor} # 厂商原始 → 规范化
```
- **`RADAR_TYPE_MALAMIRA`（已完成）**：`tools/radar_convert/malamira.py`。`.rad→.head`(§3.3)、
`.rd3→.data`(§3.5 原样拷贝)、`.pos→.cor`(§2.2.2)。`info/convert/probe` 三命令。6 条线全部转换 + 校验通过。
与文档偏差 5 处见 `tools/radar_convert/README.md`（含 BITS 公式修正）。
- **`RADAR_TYPE_IMPLUSE`（规划，暂不做）**：见 §7。
客户端职责（未来）：设备对接/原始导入 → 按型号拉插件 → `convert` → 喂规范化 reader。
**契约两点待补（登记）**：(a) `convert` 返回 `{head,data,cor}` **未含 `.index`(打标)**，有打标的型号需扩约定；
(b) 规范化 `.data` 无扩展名，C++ reader 解析字节宽(2/4) **单点依赖 `.head` 的 `BITS` 字段** → 保证转换器 BITS 永远正确是隐性前提（`malamira.py` 的 `filesize==LAST_TRACE×SAMPLES×bytes` 断言已守住，见 §2.5）。
---
## 5. 新增 / 改动清单（C++ 侧，待实现）
| # | 文件 | 动作 |
|---|---|---|
| ① | `src/io/gpr/NormalizedRadarReader.{hpp,cpp}` **新** | 纯函数：`parseHead(.head)→RadarHeader`；`readDataCube(.data,header)→cube`（按 `BITS`+`ENDIAN_TYPE` 解二进制，`reshape(K,M,N)` 主序已定）；`parseCor(.cor)→vector<TracePos>`（先解析，世界配准后置） |
| ② | `src/io/gpr/NormalizedRadarVolumeBridge.{hpp,cpp}` **新** | `buildLineVolumeFromNormalized(lineDir,prefix,metrics,coarse,targetDy)→core::BuiltI16`。⚠️ **DRY**：`Gpr3dvVolumeBridge.cpp:133-197` 的值域扫描/`Quant`构造/逐体素填值/spacing 当前内联且耦合 Impulse 模型——动工时**先把这段抽成共享 helper**（签名接受抽象立方体访问器 `(c,t,s)→short` + 通道偏移/几何），两个 bridge 同调；`planChannelInterpolation`/`depthOfSample` 已是共享纯函数。**不要复制填体循环**（否则两份独立漂移） |
| ③ | `src/data/GprVolumeRepository.{hpp,cpp}` 改 | 加 `createRadarVolumeGrid(lineDir,prefix,coarse,targetDy)`（调②，复用 `builtI16ToVolumeGrid`）。**不动**现有 `createGprVolumeGrid` |
| ④ | `src/data/api/Api3dRepository.{cpp,hpp}` 改 | 加 `createRadarVolume(...)`（≈`createGprVolume` 复制换调③），仍注册 `dd_voxel` |
| ⑤ | `src/app/main.cpp` 改 | 本地导入入口「导入三维雷达测线目录」：选目录+前缀 → `createRadarVolume` → `refreshAnalysis` → 自动勾选渲染（后端就绪后换按 DS 下载） |
| ⑥ | 测试 | `tests/io/gpr/test_normalized_radar_reader.cpp`（喂 `tests/data/radar/` 截断样例，断言维度/字节序/通道插值/主序） |
### 几何映射（关键算法，纯函数易测）
- **X(沿线 spacing)** = `DISTANCE_INTERVAL`（距离模式，本期唯一支持）。
- **Y(通道横向)** = 复用 `GprGeometry::planChannelInterpolation(chXOffsets, targetDy)`（`GprGeometry.hpp:27`）——
通道偏移取自 `.head` 的 `CH_X_OFFSETS`（不读 `.ord`），线内插值、**绝不跨线**（默认 2.5cm）。
- **Z(深度 spacing)** = `TIMEWINDOW/(SAMPLES-1) × 波速/2`；波速由 `DIELECTRIC` 求，`.rad` 无该字段时用默认波速。
- **量化** → `core::BuiltI16`（复用 `core/algo/GprVolumeBuilder.hpp`）。
- **`.cor`/`.index`** 本期只解析不深用：单线放原点沿 +X，切片/异常即可工作。逐道 GPS 世界配准 + 打标 → 多线阶段。
---
## 6. 分期
- **P0（本期）**：①–⑥，单线规范化体打通。**验收**：导入 `samples/radar/malamira_南同大道`（如 000 线）→
树出现 `dd_voxel` → 勾选渲染 → 切 updown/leftright/frontback → 画点/线/面异常并挂体下。
- **P1**：`.cor` 逐道 GPS 世界配准 + 高程、`.index` 打标、多线合一场景。
- **P2**：大体量 → 把 `gpr_poc` 的 `ChunkedVolumeStore`+`*VolumeSource` 核外/LOD 接进 app（碰 controller/view，单独立项）。
---
## 7. 双数据集测试策略 + Impulse 转换器决策
已有完整 POC 基于**另一套雷达数据 明星路（Impulse）**，交接见 `.superpowers/sdd/HANDOFF-2026-06-27-gpr-lod-slice.md`。
明星路 数据在本地（`D:/Downloads/明星路`，14G，20 测线）。
**关键差异**：明星路 是 `RADAR_TYPE_IMPLUSE`——每线 **14 通道为 14 个独立 `.iprb` 二进制**（`_A01.._A14`，各 ~74MB）+
逐通道 `.iprh` + `.ord` + `.gps/.time/.cor/.mrk`。Mala 则把 16 通道打进**一个** position-major `.rd3`。
现有 C++ P1 链（`buildLineVolumeFromGpr3dv`）**能直读 `.iprb`**。
> ⚠️ **校正（评审 HIGH-2）**：明星路真正"经验证"的渲染是 **`gpr_poc` CLI 的核外 LOD 多线**路径，**不是** app 内
> `createGprVolume→builtI16ToVolumeGrid→loadVolume→addVolume` 的**稠密单体**路径——后者的 **app 导入 UI 触发从未接**
> （HANDOFF-2026-06-27 §6 第 3 条："数据层 `createGprVolume` 已就绪，但 UI 触发未接"）。故 app 内稠密渲染对**两套数据都是首次**。
**决策：本期不建 Impulse→规范化 转换器。** 理由：
1. Impulse `.iprb` 原始二进制远比 Mala "改名+字段映射"复杂（14 个独立通道文件、需逆向二进制布局）；且 P1 链含**处理**
（gain/filter），而规范化 `.data` 是**原始**——raw vs processed 语义需先厘清。高成本、低边际价值。
2. 明星路 的 `.iprb` 已有现成 in-app 数据层入口（`createGprVolume`，只差接 UI 触发），复用成本低。
**双数据集测试（校正后表述）**：
- **Mala（16ch，规范化 reader 新路径）** + **明星路（14ch，现有 `.iprb` P1 路径）** 在 app 内**首次**稠密渲染，
二者喂**同一条下游 render/slice/anomaly 链** → 互证下游对两种通道数/几何无关。**前置任务**：明星路 in-app 导入入口
同样需接（复用现成 `createGprVolume`，与清单⑤同形）。
- 以 **`gpr_poc` CLI 的明星路成像作为离线对照基线**（那才是已验证的），不声称 in-app 已验证。
- ⚠️ 注意：明星路是**处理后**数据、Mala 是**原始**数据 → 双测只验"管路通 + 几何/通道数无关"，**不验"成像一致"**（二者有无增益滤波，视觉不可比）。
- 把 **`RADAR_TYPE_IMPLUSE` 转换器列为规划的第二插件**，待规范化管线需吞并 Impulse（多半与服务端插件同步）时再做，
届时一并解决 raw/processed 取舍。当前"插件产规范化→reader 吃规范化"契约**仅对 Mala 一家成立，属过渡态**。
---
## 8. 风险 / 待确认
1. **`.cor` 坐标语义**：`.pos` 是本地投影坐标（米），按文档直映入经纬度列、N/E/M 占位、解状态=4。
真实 CRS 未知；单线渲染不依赖 `.cor` 配准，多线阶段需后端给 `CRS_CODE`。
2. **波速默认值**：Mala `.rad` 无 `DIELECTRIC` → Z 深度 spacing 用默认波速（建议介电常数≈9 / v≈0.1 m·ns⁻¹），
仅影响深度标尺，不影响体结构。需确认默认值。
3. **采集模式**：本期只支持**距离模式**（X 用 `DISTANCE_INTERVAL`）；时间模式（`SCAN_SECOND`）推迟。
4. **大体量 / 内存实算**：app 侧 `ScalarVolume` 是 `std::vector<double>`（8 字节/体素，**比 int16 体放大 4×**，是真正天花板）。
单线最大线（K=3778, ny≈49@2.5cm, N=516）：coarse=4 → nx=945 → 体素 23.9M → **≈182 MiB**（+中间 BuiltI16 48MB +VTK 副本，单线峰值 <0.5GB，**P0 安全**）；
coarse=1 全分辨率 → **≈764 MB** 仅体（单线勉强，非 P0 默认）。**多线/全分辨率必走 P2 核外**（`ChunkedVolumeStore`，已有 gpr_poc 实现）。
---
## 9. 测试计划
- **转换器（已验证）**：`info` 维度校验 6/6 通过；`convert` 产物 `.data` 与源 `.rd3` 字节一致；`probe` 主序确诊。
- **C++ reader 单测**：`tests/data/radar/` 放截断样例（position-major 可按字节前截 K' 道得合法小体）；
断言 K/M/N、字节序、`planChannelInterpolation` 行数、主序填值正确。
- **bridge/repo 单测**：`createRadarVolumeGrid` 产 `VolumeGrid` 维度/spacing/vmin-vmax 合理；NaN 空值语义。
- **联调（人工）**：`build.bat app` → 导入样例 → 渲染 + 三向切片 + 三类异常；对照 明星路 现有路径同场景。
- 失败排查看桌面日志 `%LOCALAPPDATA%/Geomative/Geopro3/logs/geopro_*.log`。
---
## 10. 文件地图（现有锚点）
- 数据体模型 `src/data/repo/I3dSceneRepository.hpp:19`（`VolumeGrid` 稠密 float）
- 渲染入口 `src/controller/I3dSceneView.hpp:45`（`addVolume`）/ `src/app/VtkSceneView.cpp:173`
- 体素工厂 `src/render/actors/VoxelActor.hpp:29/47`（`buildVoxel`/`buildVoxelI16`）
- GPR IO `src/io/gpr/Gpr3dvVolumeBridge.hpp:47`（轴映射范本）/ `GprGeometry.hpp:27,32`（通道插值/深度）
- 体进 app `src/data/api/Api3dRepository.cpp:128`（`createGprVolume`）/ `src/data/GprVolumeRepository.cpp:11,38`
- 切片/异常 `src/render/interact/SliceTool.hpp` / `InteractionManager.hpp:52,57` / `AnomalyDrawTool.hpp:33`
- 三级树 `src/app/panels/columns/CategoryAnalysisTab.hpp:28` / `CategoryConfig.hpp:23`
- 转换器 `tools/radar_convert/malamira.py` + `README.md`；样例 `samples/radar/malamira_南同大道/`
- 详情视图扩展（如未来做 2D 波列图，非本期）`docs/superpowers/specs/2026-06-28-dataset-detail-view-architecture-and-extension-guide.md`

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# 样例：MALA南同大道（RADAR_TYPE_MALAMIRA → 规范化格式）
三维雷达联调/单测固定样例。由 `tools/radar_convert/malamira.py` 从原始 Mala Mira
数据转换而来（16 通道、516 采样、距离模式，6 条测线）。
- `*.head` / `*.cor`：**纳入 git**（小文本）。
- `*.data`：**.gitignore 忽略**（每条 ~40–62MB，共 ~277MB，过大不入库）。需要时按下方命令重生成。
## 重新生成 .data（及全部规范化档）
```bash
python tools/radar_convert/malamira.py convert \
"D:/Downloads/MALA南同大道_rSlicer" \
--out "samples/radar/malamira_南同大道"
```
原始数据来源：`D:/Downloads/MALA南同大道_rSlicer`（厂商 Mala rSlicer 导出，`.rad/.rd3/_G01.pos`）。
## 已验证数据事实（见 tools/radar_convert/README.md）
- `.data` 主序 = **position-major** `(K道, M通道, N采样)`，int16 小端，无需转置。
- 维度：K∈[2333,3778]，M=16，N=516；`K = LAST_TRACE / NUMBER_OF_CH`。

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VERSION:1
1 317.179340 N 472.759046 E 49.980000 M 4
12 317.201303 N 472.700649 E 51.040000 M 4
17 317.384393 N 473.183925 E 51.140000 M 4
23 317.597198 N 473.749575 E 51.200000 M 4
29 317.792284 N 474.255457 E 51.240000 M 4
35 318.046986 N 474.867687 E 51.280000 M 4
41 318.273818 N 475.403025 E 51.310000 M 4
48 318.543100 N 475.999329 E 51.340000 M 4
55 318.830654 N 476.625089 E 51.370000 M 4
60 319.046043 N 477.118469 E 51.390000 M 4
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34
samples/radar/malamira_南同大道/南同大道_000.head Normal file
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@ -0,0 +1,34 @@
DATE:2022-03-10
START_TIME:10:46
STOP_TIME:
UNITS:m
MODE:距离模式
ANTENNAS:200 MHz shielded
FREQUENCY:5351.611816
STACKS:2
LAST_TRACE:60448
POSITIVE_DIRECTION:1
SAMPLES:516
TIME_INTERVAL:0.000000
TIMEWINDOW:96.419553
DEPTH:
ZERO_POSITION:
DIELECTRIC:
SOIL_TYPE:
BITS:16
MARK:
DISTANCE_INTERVAL:0.099194
START_POSITION:0.000000
STOP_POSITION:374.656262
WHEEL_GPS:
WHEEL_CALIBRATION:84.4270000000
SCAN_SECOND:
NUMBER_OF_CH:16
CH_X_OFFSETS:0.080 0.160 0.240 0.320 0.400 0.480 0.560 0.640 0.720 0.800 0.880 0.960 1.040 1.120 1.200 1.280
RTK_X_OFFSET:
RTK_Y_OFFSET:0.350
RTK_Z_OFFSET:
GAIN:
FILTER:
SMOOTH:
ENDIAN_TYPE:1

335
samples/radar/malamira_南同大道/南同大道_001.cor Normal file
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34
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@ -0,0 +1,34 @@
DATE:2022-03-10
START_TIME:10:48
STOP_TIME:
UNITS:m
MODE:距离模式
ANTENNAS:200 MHz shielded
FREQUENCY:5351.611816
STACKS:2
LAST_TRACE:41456
POSITIVE_DIRECTION:1
SAMPLES:516
TIME_INTERVAL:0.000000
TIMEWINDOW:96.419553
DEPTH:
ZERO_POSITION:
DIELECTRIC:
SOIL_TYPE:
BITS:16
MARK:
DISTANCE_INTERVAL:0.097424
START_POSITION:0.000000
STOP_POSITION:252.327351
WHEEL_GPS:
WHEEL_CALIBRATION:84.4270000000
SCAN_SECOND:
NUMBER_OF_CH:16
CH_X_OFFSETS:0.080 0.160 0.240 0.320 0.400 0.480 0.560 0.640 0.720 0.800 0.880 0.960 1.040 1.120 1.200 1.280
RTK_X_OFFSET:
RTK_Y_OFFSET:0.350
RTK_Z_OFFSET:
GAIN:
FILTER:
SMOOTH:
ENDIAN_TYPE:1

346
samples/radar/malamira_南同大道/南同大道_002.cor Normal file
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34
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245
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34
samples/radar/malamira_南同大道/南同大道_003.head Normal file
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@ -0,0 +1,34 @@
DATE:2022-03-10
START_TIME:10:52
STOP_TIME:
UNITS:m
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LAST_TRACE:38144
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SOIL_TYPE:
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MARK:
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313
samples/radar/malamira_南同大道/南同大道_004.cor Normal file
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VERSION:1
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34
samples/radar/malamira_南同大道/南同大道_004.head Normal file
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@ -0,0 +1,34 @@
DATE:2022-03-10
START_TIME:10:54
STOP_TIME:
UNITS:m
MODE:距离模式
ANTENNAS:200 MHz shielded
FREQUENCY:5351.611816
STACKS:2
LAST_TRACE:52496
POSITIVE_DIRECTION:1
SAMPLES:516
TIME_INTERVAL:0.000000
TIMEWINDOW:96.419553
DEPTH:
ZERO_POSITION:
DIELECTRIC:
SOIL_TYPE:
BITS:16
MARK:
DISTANCE_INTERVAL:0.095304
START_POSITION:0.000000
STOP_POSITION:312.597339
WHEEL_GPS:
WHEEL_CALIBRATION:84.4270000000
SCAN_SECOND:
NUMBER_OF_CH:16
CH_X_OFFSETS:0.080 0.160 0.240 0.320 0.400 0.480 0.560 0.640 0.720 0.800 0.880 0.960 1.040 1.120 1.200 1.280
RTK_X_OFFSET:
RTK_Y_OFFSET:0.350
RTK_Z_OFFSET:
GAIN:
FILTER:
SMOOTH:
ENDIAN_TYPE:1

342
samples/radar/malamira_南同大道/南同大道_005.cor Normal file
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@ -0,0 +1,342 @@
VERSION:1
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34
samples/radar/malamira_南同大道/南同大道_005.head Normal file
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@ -0,0 +1,34 @@
DATE:2022-03-10
START_TIME:11:02
STOP_TIME:
UNITS:m
MODE:距离模式
ANTENNAS:200 MHz shielded
FREQUENCY:5351.611816
STACKS:2
LAST_TRACE:37328
POSITIVE_DIRECTION:1
SAMPLES:516
TIME_INTERVAL:0.000000
TIMEWINDOW:96.419553
DEPTH:
ZERO_POSITION:
DIELECTRIC:
SOIL_TYPE:
BITS:16
MARK:
DISTANCE_INTERVAL:0.095987
START_POSITION:0.000000
STOP_POSITION:223.841500
WHEEL_GPS:
WHEEL_CALIBRATION:84.4270000000
SCAN_SECOND:
NUMBER_OF_CH:16
CH_X_OFFSETS:0.080 0.160 0.240 0.320 0.400 0.480 0.560 0.640 0.720 0.800 0.880 0.960 1.040 1.120 1.200 1.280
RTK_X_OFFSET:
RTK_Y_OFFSET:0.350
RTK_Z_OFFSET:
GAIN:
FILTER:
SMOOTH:
ENDIAN_TYPE:1

71
tools/radar_convert/README.md Normal file
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@ -0,0 +1,71 @@
# radar_convert — 雷达原始数据 → 规范化格式 转换插件（本地原型）
把厂商原始雷达数据转换成客户端**规范化格式** `.head / .data / .cor (/.index)`，供三维雷达
渲染器消费。本目录是**未来"服务端下发转换插件"的本地原型**：今天的 Python 工具实现的
`convert` 契约，将来由客户端按设备型号拉取对应插件执行，接口不变。
当前实现型号：**`RADAR_TYPE_MALAMIRA`**（Mala Mira rSlicer，`.rad + .rd3|.rd7 + _G01.pos`）。
---
## 插件契约（本地工具 = 未来插件，接口一致）
```
plugin_id : RADAR_TYPE_MALAMIRA
supports(fileset) -> bool # 据文件组成判断是否本插件可处理
convert(lineDir, prefix, outDir) -> {head,data,cor}
```
- **输入**：一条测线三件套 `{prefix}.rad` + `{prefix}.rd3|.rd7` + `{prefix}_G01.pos`（轨迹可选）。
- **输出**：规范化目录 `{prefix}.head` + `{prefix}.data` + `{prefix}.cor`。
- 映射规则源自客户《雷达业务开发说明》§3.3（.rad→.head）/ §3.5（.rd3→.data）/ §2.2.2（.pos→.cor）。
## 用法
```bash
# 1) 列出目录内测线 + 维度一致性校验（不写文件）
python tools/radar_convert/malamira.py info <lineDir>
# 2) 转换（--prefix 省略=全部测线）
python tools/radar_convert/malamira.py convert <lineDir> --out <outDir> [--prefix 南同大道_000]
# 3) probe：出图核对 .rd3 数据体主序（写一张 PNG 到 --out）
python tools/radar_convert/malamira.py probe <lineDir> --prefix 南同大道_000 --out <dir>
```
---
## 数据体维度与排列（★渲染器必读，已用真实数据核对）
- 体维度：`K`(道/切片，沿运动) × `M`(通道) × `N`(采样/深度)。
- `M = NUMBER_OF_CH`，`N = SAMPLES`，`K = LAST_TRACE / NUMBER_OF_CH`。
- `.rad` 的 `LAST TRACE` 是**总扫描数**（=K×M），不是道数 K。`.head` 原样透传该值，
渲染器按 `K = LAST_TRACE / NUMBER_OF_CH` 求道数。
- **`.data` 主序 = position-major（已 probe 核对，MALA南同大道_000）**：
磁盘扫描顺序 = `(道0: 通道0..M-1)(道1: 通道0..M-1)…`，每个 sweep 内 `N` 个采样连续。
即 `flat.reshape(K, M, N)[道][通道][采样]`。
- 直接对应 geopro 体轴 `X=道(nx=K)`、`Y=通道(ny=M)`、`Z=采样(nz=N)`，**无需转置**。
- 反例（错误主序）：channel-major `reshape(M,K,N)` 的 B-scan 呈竖条乱码——probe 已排除。
- 数据类型：`int16` 小端（`.rd3`）/ `int32` 小端（`.rd7`）。`.rd3` 中出现 `-32768`
为直达波饱和的真实值，非空值哨兵。
---
## 与客户文档的偏差（实现时的取舍，便于后端对齐）
| # | 项 | 文档 | 本工具 | 理由 |
|---|---|---|---|---|
| 1 | `BITS` 计算 | `文件大小/LAST_TRACE/NUMBER_OF_CH×8` | `bytes = 文件大小/(LAST_TRACE×SAMPLES)`，×8；并与扩展名(.rd3→16/.rd7→32)交叉校验 | 文档公式漏了 SAMPLES 维、量纲不符；本式对 6 条线均得 16，且 `文件大小==LAST_TRACE×SAMPLES×bytes` 严格成立 |
| 2 | `ENDIAN_TYPE` | §3.3 表："留空" | 填 `1`（小端） | 同节要点 1 明确"通常小端即 1"；渲染需确定字节序 |
| 3 | `WHEEL_CALIBRATION` | §3.3 表："留空" | 透传 `.rad` 实际值 | `.rad` 有该字段，透传更忠实（不影响渲染） |
| 4 | `.cor` 坐标 | 北→纬度 / 东→经度 | 同文档直映，N/E/M 为占位标识，解状态=4 | `.pos` 是本地投影坐标(米)、非经纬度；CRS 未知。单线渲染不依赖 .cor 配准，多线阶段再处理 |
| 5 | `.index` | 可选打标文件 | 本数据集无打标 → 不产出 | 该目录仅有 FCODES.TXT(编码字典)，无 .index 源 |
---
## 已验证（MALA南同大道_rSlicer，6 条测线，2026-06-29）
- `info`：6 条线全部通过 `文件大小 == LAST_TRACE×SAMPLES×bytes` 校验。
K∈[2333,3778]，M=16，N=516，bits=16，距离模式，dx≈0.095–0.099m，时窗 96.42ns，均含轨迹。
- `convert`：6 条线 `.head/.data/.cor` 全部产出；`.data` 与源 `.rd3` 字节数严格一致。
- `probe`：position-major B-scan 出现连续直达波 + 地层 + 双曲线绕射 → 主序确认。

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
RADAR_TYPE_MALAMIRA 转换插件（本地原型）。
把 Mala Mira rSlicer 原始三件套（.rad + .rd3|.rd7 + _G01.pos）转换成客户端规范化
格式（.head + .data + .cor），并提供 probe 子命令出图核对 .rd3 数据体主序。
本工具实现的 convert 契约 = 未来"服务端下发插件"的接口：
plugin_id : RADAR_TYPE_MALAMIRA
supports(fileset) -> bool
convert(lineDir, prefix, outDir) -> {head, data, cor}
字段映射规则见客户《雷达业务开发说明》§3.3 / §2.2.2 / §3.5。与文档的少量偏差见 README.md。
"""
import argparse
import os
import shutil
import sys
import numpy as np
PLUGIN_ID = "RADAR_TYPE_MALAMIRA"
# 规范化 .head 字段顺序（三维雷达，文档 §1.2.2）。
HEAD_FIELD_ORDER = [
"DATE", "START_TIME", "STOP_TIME", "UNITS", "MODE", "ANTENNAS", "FREQUENCY",
"STACKS", "LAST_TRACE", "POSITIVE_DIRECTION", "SAMPLES", "TIME_INTERVAL",
"TIMEWINDOW", "DEPTH", "ZERO_POSITION", "DIELECTRIC", "SOIL_TYPE", "BITS",
"MARK", "DISTANCE_INTERVAL", "START_POSITION", "STOP_POSITION", "WHEEL_GPS",
"WHEEL_CALIBRATION", "SCAN_SECOND", "NUMBER_OF_CH", "CH_X_OFFSETS",
"RTK_X_OFFSET", "RTK_Y_OFFSET", "RTK_Z_OFFSET", "GAIN", "FILTER", "SMOOTH",
"ENDIAN_TYPE",
]
# ---------------------------------------------------------------------------
# 解析 .rad
# ---------------------------------------------------------------------------
def parse_rad(rad_path):
"""读 Mala .rad（ASCII，KEY:VALUE 行）→ dict（保留原始键，值去首尾空白）。"""
raw = {}
with open(rad_path, "r", encoding="utf-8", errors="replace") as f:
for line in f:
if ":" not in line:
continue
key, _, val = line.partition(":")
raw[key.strip()] = val.strip()
return raw
def _f(raw, key, default=None):
v = raw.get(key, "")
if v == "" or v is None:
return default
try:
return float(v)
except ValueError:
return default
def _i(raw, key, default=None):
v = _f(raw, key, None)
return int(round(v)) if v is not None else default
def compute_dims(raw, data_path):
"""从 .rad + 数据文件大小推导体维度并做一致性校验。
返回 dict：positions(K)=道/切片数, channels(M), samples(N),
last_trace(总扫描数=K*M), bits, bytes_per_sample。
"""
samples = _i(raw, "SAMPLES")
channels = _i(raw, "NUMBER_OF_CH")
last_trace = _i(raw, "LAST TRACE") # Mala 中 = 总扫描数(道 * 通道)
if not samples or not channels or not last_trace:
raise ValueError(
"缺少 SAMPLES / NUMBER_OF_CH / LAST TRACE，无法推导维度: %s" % data_path)
ext = os.path.splitext(data_path)[1].lower()
bytes_per_sample = 2 if ext == ".rd3" else 4 if ext == ".rd7" else None
if bytes_per_sample is None:
raise ValueError("未知数据扩展名(仅 .rd3/.rd7): %s" % data_path)
filesize = os.path.getsize(data_path)
expect = last_trace * samples * bytes_per_sample
if filesize != expect:
raise ValueError(
"数据体大小不符: %s 实际 %d 字节, 期望 LAST_TRACE(%d)*SAMPLES(%d)*%d = %d"
% (data_path, filesize, last_trace, samples, bytes_per_sample, expect))
if last_trace % channels != 0:
raise ValueError(
"LAST_TRACE(%d) 不能被 NUMBER_OF_CH(%d) 整除，无法切分道/通道"
% (last_trace, channels))
positions = last_trace // channels
return {
"positions": positions,
"channels": channels,
"samples": samples,
"last_trace": last_trace,
"bits": bytes_per_sample * 8,
"bytes_per_sample": bytes_per_sample,
"filesize": filesize,
}
# ---------------------------------------------------------------------------
# .rad -> .head
# ---------------------------------------------------------------------------
def build_head(raw, dims):
"""按 §3.3 把 .rad 映射成规范化 .head 字段 dict。无对应字段留空。"""
ch_y = raw.get("CH_Y_OFFSETS", "").split()
head = {k: "" for k in HEAD_FIELD_ORDER}
head.update({
"DATE": raw.get("DATE", ""),
"START_TIME": raw.get("TIME", ""),
"UNITS": raw.get("UNITS", ""),
"MODE": "距离模式", # Mala 默认距离模式(§3.3 要点 4)
"ANTENNAS": raw.get("ANTENNAS", ""),
"FREQUENCY": raw.get("FREQUENCY", ""),
"STACKS": raw.get("STACKS", ""),
"LAST_TRACE": str(dims["last_trace"]),
"POSITIVE_DIRECTION": raw.get("POSITIVE DIRECTION", ""),
"SAMPLES": str(dims["samples"]),
"TIME_INTERVAL": raw.get("TIME INTERVAL", ""),
"TIMEWINDOW": raw.get("TIMEWINDOW", ""),
"BITS": str(dims["bits"]),
"DISTANCE_INTERVAL": raw.get("DISTANCE INTERVAL", ""),
"START_POSITION": raw.get("START POSITION", ""),
"STOP_POSITION": raw.get("STOP POSITION", ""),
"WHEEL_CALIBRATION": raw.get("WHEEL CALIBRATION", ""),
"NUMBER_OF_CH": str(dims["channels"]),
"CH_X_OFFSETS": raw.get("CH_X_OFFSETS", "").strip(),
"RTK_Y_OFFSET": ch_y[0] if ch_y else "", # §3.3：取 CH_Y_OFFSETS 首元素
"ENDIAN_TYPE": "1", # Mala rd3 小端(§3.3 要点 1)
})
return head
def write_head(head, out_path):
with open(out_path, "w", encoding="utf-8", newline="\n") as f:
for k in HEAD_FIELD_ORDER:
f.write("%s:%s\n" % (k, head.get(k, "")))
# ---------------------------------------------------------------------------
# .pos -> .cor (§2.2.2 场景二)
# ---------------------------------------------------------------------------
def convert_pos_to_cor(pos_path, cor_path):
""".pos(本地坐标: 序号 北 东 高程) → .cor(序号 纬度 N 经度 E 高程 M 解状态=4)。
注：.pos 为本地投影坐标(米)，按文档直接映射 北→纬度 / 东→经度；N/E/M 为占位标识，
解状态固定填 4(RTK Fixed)。单线渲染不依赖 .cor 做世界配准，多线阶段再用。
"""
rows = []
with open(pos_path, "r", encoding="utf-8", errors="replace") as f:
for line in f:
s = line.strip()
if not s or s.upper().startswith("UNITS"):
continue
parts = s.split()
if len(parts) < 4:
continue
idx = int(float(parts[0]))
north, east, elev = float(parts[1]), float(parts[2]), float(parts[3])
rows.append((idx, north, east, elev))
with open(cor_path, "w", encoding="utf-8", newline="\n") as f:
f.write("VERSION:1\n")
for idx, north, east, elev in rows:
f.write("%d\t%.6f\tN\t%.6f\tE\t%.6f\tM\t4\n" % (idx, north, east, elev))
return len(rows)
# ---------------------------------------------------------------------------
# 测线发现
# ---------------------------------------------------------------------------
def find_lines(line_dir):
"""遍历目录，返回有效测线 [(prefix, rad, data, pos|None)]（§3.2 抽取规则）。"""
out = []
for name in sorted(os.listdir(line_dir)):
if not name.lower().endswith(".rad"):
continue
prefix = name[:-4]
rad = os.path.join(line_dir, name)
data = None
for ext in (".rd3", ".rd7"):
cand = os.path.join(line_dir, prefix + ext)
if os.path.exists(cand):
data = cand
break
if data is None:
print(" [跳过] %s 缺 .rd3/.rd7 数据文件" % prefix, file=sys.stderr)
continue
pos = os.path.join(line_dir, prefix + "_G01.pos")
out.append((prefix, rad, data, pos if os.path.exists(pos) else None))
return out
# ---------------------------------------------------------------------------
# convert
# ---------------------------------------------------------------------------
def convert_line(prefix, rad, data, pos, out_dir):
raw = parse_rad(rad)
dims = compute_dims(raw, data)
os.makedirs(out_dir, exist_ok=True)
head = build_head(raw, dims)
write_head(head, os.path.join(out_dir, prefix + ".head"))
shutil.copyfile(data, os.path.join(out_dir, prefix + ".data")) # §3.5 原样拷贝
cor_n = convert_pos_to_cor(pos, os.path.join(out_dir, prefix + ".cor")) if pos else 0
print("[%s] 道(K)=%d 通道(M)=%d 采样(N)=%d bits=%d .data=%.1fMB .cor=%d点%s"
% (prefix, dims["positions"], dims["channels"], dims["samples"],
dims["bits"], dims["filesize"] / 1e6, cor_n,
"" if pos else " (无轨迹)"))
return dims
def cmd_convert(args):
if args.prefix:
rad = os.path.join(args.line_dir, args.prefix + ".rad")
data = None
for ext in (".rd3", ".rd7"):
if os.path.exists(os.path.join(args.line_dir, args.prefix + ext)):
data = os.path.join(args.line_dir, args.prefix + ext)
pos = os.path.join(args.line_dir, args.prefix + "_G01.pos")
convert_line(args.prefix, rad, data, pos if os.path.exists(pos) else None,
args.out)
else:
lines = find_lines(args.line_dir)
print("发现 %d 条测线，输出 → %s" % (len(lines), args.out))
for prefix, rad, data, pos in lines:
convert_line(prefix, rad, data, pos, args.out)
# ---------------------------------------------------------------------------
# probe：核对 .rd3 数据体主序
# ---------------------------------------------------------------------------
def load_flat(data_path, dims, endian="<"):
dt = np.dtype("%si%d" % (endian, dims["bytes_per_sample"]))
flat = np.fromfile(data_path, dtype=dt)
n = dims["last_trace"] * dims["samples"]
if flat.size != n:
raise ValueError("读到 %d 个样本，期望 %d" % (flat.size, n))
return flat.astype(np.float32)
def _clip(img):
"""按 99 分位绝对值裁剪对比度，返回 (img, vmax)。"""
v = np.percentile(np.abs(img), 99) or 1.0
return img, v
def cmd_probe(args):
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
raw = parse_rad(os.path.join(args.line_dir, args.prefix + ".rad"))
data = None
for ext in (".rd3", ".rd7"):
cand = os.path.join(args.line_dir, args.prefix + ext)
if os.path.exists(cand):
data = cand
dims = compute_dims(raw, data)
K, M, N = dims["positions"], dims["channels"], dims["samples"]
flat = load_flat(data, dims, "<" if args.endian == "little" else ">")
os.makedirs(args.out, exist_ok=True)
print("[probe] %s K(道)=%d M(通道)=%d N(采样)=%d amp[min=%.0f max=%.0f mean|.|=%.1f]"
% (args.prefix, K, M, N, flat.min(), flat.max(), np.abs(flat).mean()))
ch = args.channel
# H1: position-major sweeps 顺序 = (pos0:ch0..chM-1)(pos1:..) → reshape(K,M,N)
h1 = flat.reshape(K, M, N)
bscan_h1 = h1[:, ch, :].T # (N 采样 × K 道)
# H2: channel-major sweeps 顺序 = (ch0:pos0..posK-1)(ch1:..) → reshape(M,K,N)
h2 = flat.reshape(M, K, N)
bscan_h2 = h2[ch, :, :].T # (N 采样 × K 道)
# C-scan（H1 主序下某采样深度的 道×通道 平面）
cscan_h1 = h1[:, :, args.depth] # (K × M)
panels = [
("H1 position-major B-scan ch%d" % ch, bscan_h1, "trace (K)", "sample (N)"),
("H2 channel-major B-scan ch%d" % ch, bscan_h2, "trace (K)", "sample (N)"),
("H1 C-scan @sample %d" % args.depth, cscan_h1, "channel (M)", "trace (K)"),
]
fig, axes = plt.subplots(1, 3, figsize=(18, 6))
for axp, (title, img, xl, yl) in zip(axes, panels):
_, vmax = _clip(img)
axp.imshow(img, aspect="auto", cmap="gray", vmin=-vmax, vmax=vmax,
interpolation="nearest")
axp.set_title(title)
axp.set_xlabel(xl)
axp.set_ylabel(yl)
fig.suptitle("%s %s -- main-order check: the coherent B-scan (layers/hyperbolas) is correct"
% (PLUGIN_ID, args.prefix), fontsize=12)
fig.tight_layout()
out_png = os.path.join(args.out, "probe_%s.png" % args.prefix)
fig.savefig(out_png, dpi=110)
print("[probe] 出图 → %s" % out_png)
# ---------------------------------------------------------------------------
def cmd_info(args):
lines = find_lines(args.line_dir)
print("目录 %s 发现 %d 条测线 (plugin=%s)" % (args.line_dir, len(lines), PLUGIN_ID))
for prefix, rad, data, pos in lines:
raw = parse_rad(rad)
dims = compute_dims(raw, data)
print(" %-18s K=%-5d M=%-3d N=%-4d bits=%d dx=%s tw=%sns ch_x=%d个 轨迹=%s"
% (prefix, dims["positions"], dims["channels"], dims["samples"],
dims["bits"], raw.get("DISTANCE INTERVAL", "?"),
raw.get("TIMEWINDOW", "?"),
len(raw.get("CH_X_OFFSETS", "").split()),
"有" if pos else "无"))
def main():
ap = argparse.ArgumentParser(description="RADAR_TYPE_MALAMIRA 转换插件(本地原型)")
sub = ap.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("info", help="列出目录内测线 + 维度校验")
p.add_argument("line_dir")
p.set_defaults(func=cmd_info)
p = sub.add_parser("convert", help="转换为规范化 .head/.data/.cor")
p.add_argument("line_dir")
p.add_argument("--prefix", default=None, help="只转某条线(默认全部)")
p.add_argument("--out", required=True, help="输出目录")
p.set_defaults(func=cmd_convert)
p = sub.add_parser("probe", help="出图核对 .rd3 数据体主序")
p.add_argument("line_dir")
p.add_argument("--prefix", required=True)
p.add_argument("--out", required=True)
p.add_argument("--channel", type=int, default=0)
p.add_argument("--depth", type=int, default=200, help="C-scan 取的采样深度")
p.add_argument("--endian", choices=["little", "big"], default="little")
p.set_defaults(func=cmd_probe)
args = ap.parse_args()
args.func(args)
if __name__ == "__main__":
main()