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Refactor local InSAR asset and production workflows

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Harmon
2026-06-21 12:30:21 +08:00
parent 65a8cc4eac
commit 71c524967c
88 changed files with 11165 additions and 3017 deletions
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backend/app/ai_service.py
+77 -24
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@@ -13,6 +13,7 @@ from sklearn.preprocessing import StandardScaler
from scipy.stats import entropy
from concurrent.futures import ProcessPoolExecutor
import functools
from typing import List, Optional
from .config import settings
@@ -224,25 +225,74 @@ def get_model_info() -> dict:
OLLAMA_BASE_URL = settings.OLLAMA_BASE_URL
OLLAMA_API_URL = settings.OLLAMA_API_URL
DEFAULT_VLM_MODEL = settings.DEFAULT_VLM_MODEL
VLM_MODEL_MARKERS = (
"qwen3-vl",
"qwen2-vl",
"minicpm-v",
"llama3.2-vision",
"llava",
"vision",
"-vl",
"_vl",
)
async def _get_available_vlm_model() -> str:
def _normalize_model_name(model_name: Optional[str]) -> Optional[str]:
normalized = str(model_name or "").strip()
return normalized or None
def is_likely_vlm_model(model_name: Optional[str]) -> bool:
lower = str(model_name or "").strip().lower()
return bool(lower and any(marker in lower for marker in VLM_MODEL_MARKERS))
async def get_ollama_models(timeout: float = 2.0) -> List[str]:
"""Return model names reported by the local Ollama service."""
async with httpx.AsyncClient(timeout=timeout) as client:
resp = await client.get(f"{OLLAMA_BASE_URL.rstrip('/')}/api/tags")
resp.raise_for_status()
return [
str(model.get("name") or "").strip()
for model in resp.json().get("models", [])
if str(model.get("name") or "").strip()
]
async def get_ollama_vlm_models(timeout: float = 2.0) -> List[str]:
"""Return installed Ollama models whose names indicate image-input support."""
return [
model_name
for model_name in await get_ollama_models(timeout=timeout)
if is_likely_vlm_model(model_name)
]
async def _get_available_vlm_model(preferred_model: Optional[str] = None) -> str:
"""自动检测本地可用的 VLM 模型"""
preferred = _normalize_model_name(preferred_model)
try:
async with httpx.AsyncClient(timeout=2.0) as client:
resp = await client.get(f"{OLLAMA_BASE_URL}/api/tags")
if resp.status_code == 200:
models = [m['name'] for m in resp.json().get('models', [])]
# 优先级：qwen3-vl > qwen2-vl > minicpm-v > 任何包含 vl 的模型
for target in ["qwen3-vl:8b", "qwen2-vl", "minicpm-v"]:
for m in models:
if target in m: return m
for m in models:
if "vl" in m.lower(): return m
except:
models = await get_ollama_models(timeout=2.0)
if preferred and is_likely_vlm_model(preferred):
if preferred in models:
return preferred
for model in models:
if is_likely_vlm_model(model) and (model.startswith(preferred) or preferred in model):
return model
# 优先级：qwen3-vl > qwen2-vl > minicpm-v > 任何包含 vl/vision/llava 的模型
for target in ["qwen3-vl", "qwen2-vl", "minicpm-v", "llama3.2-vision", "llava"]:
for model in models:
if target in model.lower():
return model
for model in models:
if is_likely_vlm_model(model):
return model
except Exception:
pass
return DEFAULT_VLM_MODEL
async def analyze_map_with_vlm(images_base64: list, prompt: str, progress_callback=None) -> str:
async def analyze_map_with_vlm(
images_base64: list,
prompt: str,
progress_callback=None,
model_name: Optional[str] = None,
raise_on_error: bool = False,
) -> str:
"""
使用本地 Ollama 部署的多模态大模型分析地图截图。
已改为一次性返回模式，以提高连接稳定性。
@@ -250,10 +300,10 @@ async def analyze_map_with_vlm(images_base64: list, prompt: str, progress_callba
if not images_base64:
return "未接收到有效的地图截图。"
model_name = await _get_available_vlm_model()
resolved_model_name = await _get_available_vlm_model(model_name)
payload = {
"model": model_name,
"model": resolved_model_name,
"prompt": prompt,
"images": [img.split(",")[1] if "," in img else img for img in images_base64],
"stream": False, # 关闭流式传输，改为一次性返回
@@ -282,9 +332,11 @@ async def analyze_map_with_vlm(images_base64: list, prompt: str, progress_callba
final_output += f"> [!NOTE] 思考过程\n> {full_thinking}\n\n"
final_output += full_response
return final_output.strip() if final_output else f"模型 ({model_name}) 未返回任何内容。"
return final_output.strip() if final_output else f"模型 ({resolved_model_name}) 未返回任何内容。"
except Exception as e:
if raise_on_error:
raise RuntimeError(f"Ollama VLM request failed: {str(e)}") from e
return f"AI 分析过程中发生错误: {str(e)}"
async def generate_dinsar_diagnosis(
@@ -293,12 +345,13 @@ async def generate_dinsar_diagnosis(
date_str: str,
quality_context: str,
hazard_info: str,
progress_callback=None
progress_callback=None,
model_name: Optional[str] = None,
) -> str:
"""
针对 VLM 优化的 D-InSAR 专家诊断逻辑。
"""
model_name = await _get_available_vlm_model()
resolved_model_name = await _get_available_vlm_model(model_name)
prompt = (
f"你是一位拥有 20 年经验的资深 InSAR 地质灾害解译专家。请根据提供的 D-InSAR 形变图及背景信息，撰写一份专业的诊断报告。\n\n"
@@ -320,22 +373,22 @@ async def generate_dinsar_diagnosis(
f"- 使用 Markdown 格式，语言严谨、专业，严禁幻觉。\n"
f"- 报告末尾必须包含以下加粗文字：\n"
f"**--- 免责声明 ---**\n"
f"**本报告由 AI 自动生成（模型：{model_name}），仅供科研参考，不具备法律效力。**"
f"**本报告由 AI 自动生成（模型：{resolved_model_name}），仅供科研参考，不具备法律效力。**"
)
return await analyze_map_with_vlm(images_base64, prompt, progress_callback=progress_callback)
return await analyze_map_with_vlm(images_base64, prompt, progress_callback=progress_callback, model_name=resolved_model_name)
async def warm_up_vlm() -> bool:
async def warm_up_vlm(model_name: Optional[str] = None) -> bool:
"""
预热 VLM 模型，将其加载至显存。
发送一个轻量级请求以触发模型冷启动。
返回 True 表示成功，False 表示失败。
"""
# 预热时直接使用探测到的模型
model_name = await _get_available_vlm_model()
# 预热时直接使用指定模型或探测到的模型
resolved_model_name = await _get_available_vlm_model(model_name)
payload = {
"model": model_name,
"model": resolved_model_name,
"prompt": "hi",
"stream": False,
"keep_alive": "30m"