docs(pbf): archive Full production provenance and engineering history
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OpenAI Codex
222
navsea_build_kyushu_core_sqlite.py
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222
navsea_build_kyushu_core_sqlite.py
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from __future__ import annotations
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import datetime as dt
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import sqlite3
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from pathlib import Path
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SOURCE_DB = Path("out/navsea_kyushu_semantic.sqlite")
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OUT = Path("out/navsea_kyushu_core.sqlite")
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KEEP_GROUNDING = ("干潮帯", "0-2m", "0-5m", "0-10m", "2-5m", "2-10m", "5-10m")
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def open_db() -> sqlite3.Connection:
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if OUT.exists():
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OUT.unlink()
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OUT.parent.mkdir(parents=True, exist_ok=True)
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conn = sqlite3.connect(OUT)
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conn.execute("PRAGMA journal_mode=OFF")
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conn.execute("PRAGMA synchronous=OFF")
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conn.execute("PRAGMA temp_store=MEMORY")
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conn.execute("PRAGMA cache_size=150000")
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conn.execute("PRAGMA locking_mode=EXCLUSIVE")
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return conn
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def create_schema(conn: sqlite3.Connection) -> None:
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conn.executescript(
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"""
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CREATE TABLE meta (
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key TEXT PRIMARY KEY,
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value TEXT NOT NULL
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);
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CREATE TABLE semantic_object (
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feature_id INTEGER PRIMARY KEY,
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fid TEXT,
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source_layer TEXT,
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canonical_object_type TEXT NOT NULL,
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object_family TEXT NOT NULL,
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detection_class TEXT NOT NULL,
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capability TEXT,
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z INTEGER,
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x INTEGER,
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y INTEGER,
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geom_type TEXT,
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is_land INTEGER NOT NULL DEFAULT 0,
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is_hole INTEGER NOT NULL DEFAULT 0,
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is_collision INTEGER NOT NULL DEFAULT 0,
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is_grounding INTEGER NOT NULL DEFAULT 0
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);
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CREATE INDEX idx_semantic_object_fid ON semantic_object(fid);
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CREATE INDEX idx_semantic_object_tile ON semantic_object(z, x, y);
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CREATE INDEX idx_semantic_object_capability ON semantic_object(capability, canonical_object_type);
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CREATE TABLE grid_cell (
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tile_key TEXT PRIMARY KEY,
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z INTEGER NOT NULL,
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x INTEGER NOT NULL,
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y INTEGER NOT NULL,
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land_count INTEGER NOT NULL DEFAULT 0,
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hole_count INTEGER NOT NULL DEFAULT 0,
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collision_count INTEGER NOT NULL DEFAULT 0,
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grounding_count INTEGER NOT NULL DEFAULT 0,
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blocked INTEGER NOT NULL DEFAULT 0,
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blocked_reason TEXT
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);
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CREATE INDEX idx_grid_cell_zxy ON grid_cell(z, x, y);
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CREATE INDEX idx_grid_cell_blocked ON grid_cell(blocked, z);
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"""
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)
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conn.executemany(
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"INSERT INTO meta(key, value) VALUES (?, ?)",
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[
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("source_db", str(SOURCE_DB)),
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("source_scope", "九州"),
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("build_time", dt.datetime.now().isoformat(timespec="seconds")),
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("notes", "九州核心版语义索引库，只保留陆地、碰撞物、浅水搁浅层"),
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("keep_grounding", ",".join(KEEP_GROUNDING)),
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],
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)
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conn.commit()
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def main() -> None:
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if not SOURCE_DB.exists():
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raise SystemExit(f"missing source db: {SOURCE_DB}")
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src = sqlite3.connect(SOURCE_DB)
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src.row_factory = sqlite3.Row
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conn = open_db()
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create_schema(conn)
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placeholders = ",".join("?" for _ in KEEP_GROUNDING)
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sql = f"""
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SELECT
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feature_id, fid, source_layer, canonical_object_type, object_family, detection_class,
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capability, z, x, y, geom_type, is_land, is_hole, is_collision, is_grounding
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FROM semantic_object
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WHERE is_land = 1
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OR is_hole = 1
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OR is_collision = 1
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OR (is_grounding = 1 AND canonical_object_type IN ({placeholders}))
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ORDER BY z, x, y, feature_id
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"""
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batch = []
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grid = {}
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count = 0
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batch_size = 4000
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for row in src.execute(sql, KEEP_GROUNDING):
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row = dict(row)
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batch.append(
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(
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row["feature_id"],
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row["fid"],
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row["source_layer"],
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row["canonical_object_type"],
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row["object_family"],
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row["detection_class"],
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row["capability"],
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row["z"],
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row["x"],
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row["y"],
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row["geom_type"],
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row["is_land"],
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row["is_hole"],
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row["is_collision"],
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row["is_grounding"],
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)
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)
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tile_key = f'{row["z"]}/{row["x"]}/{row["y"]}'
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c = grid.get(tile_key)
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if c is None:
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c = {"z": row["z"], "x": row["x"], "y": row["y"], "land": 0, "hole": 0, "collision": 0, "grounding": 0}
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grid[tile_key] = c
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c["land"] += int(row["is_land"])
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c["hole"] += int(row["is_hole"])
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c["collision"] += int(row["is_collision"])
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c["grounding"] += int(row["is_grounding"])
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count += 1
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if len(batch) >= batch_size:
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conn.executemany(
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"""
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INSERT INTO semantic_object(
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feature_id, fid, source_layer, canonical_object_type, object_family, detection_class,
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capability, z, x, y, geom_type, is_land, is_hole, is_collision, is_grounding
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) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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""",
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batch,
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)
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conn.commit()
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batch.clear()
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print(f"已写入 {count} 条核心对象")
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if batch:
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conn.executemany(
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"""
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INSERT INTO semantic_object(
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feature_id, fid, source_layer, canonical_object_type, object_family, detection_class,
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capability, z, x, y, geom_type, is_land, is_hole, is_collision, is_grounding
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) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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""",
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batch,
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)
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conn.commit()
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cell_rows = []
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for tile_key, c in grid.items():
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blocked = 0
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reason = None
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if c["land"] > c["hole"] and c["land"] > 0:
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blocked = 1
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reason = "land"
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elif c["collision"] > 0:
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blocked = 1
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reason = "collision"
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elif c["grounding"] > 0:
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blocked = 1
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reason = "grounding"
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cell_rows.append(
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(
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tile_key,
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c["z"],
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c["x"],
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c["y"],
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c["land"],
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c["hole"],
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c["collision"],
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c["grounding"],
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blocked,
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reason,
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)
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)
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conn.executemany(
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"""
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INSERT INTO grid_cell(
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tile_key, z, x, y, land_count, hole_count, collision_count, grounding_count,
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blocked, blocked_reason
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) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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""",
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cell_rows,
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)
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conn.commit()
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conn.execute("ANALYZE")
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conn.commit()
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obj_count = conn.execute("SELECT COUNT(*) FROM semantic_object").fetchone()[0]
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cell_count = conn.execute("SELECT COUNT(*) FROM grid_cell").fetchone()[0]
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conn.close()
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src.close()
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print(f"完成: objects={obj_count}, cells={cell_count}")
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print(f"输出文件: {OUT.resolve()}")
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if __name__ == "__main__":
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main()
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