从pdf获取到的时间是当前用户的时区，需要转换为0时区和小包当前节点进行比较

ccs_base/wizard/batch_get_pod_info_wizard.py

@@ -94,7 +94,7 @@ class BatchGetPodInfoWizard(models.TransientModel):
             
             # 如果启用了涂抹文字，进行处理
             if self.remove_specified_text and processed_files:
-                processed_files = self._remove_specified_text(processed_files, debug_mode=False)
+                # processed_files = self._remove_specified_text(processed_files, debug_mode=False)
                 # 合并PDF并保存到pdf_file字段
                 self._merge_pdf_files(processed_files)
             

ccs_base/wizard/image-to-coordinate(1).py

-import os
-from openai import OpenAI
-import requests
-import mimetypes
-import base64
-import fitz  # PyMuPDF
-import json
-from PIL import Image, ImageDraw
-
-client = OpenAI(
-    # 若没有配置环境变量，请用阿里云百炼API Key将下行替换为：api_key="sk-xxx",
-    # 新加坡和北京地域的API Key不同。获取API Key：https://help.aliyun.com/zh/model-studio/get-api-key
-    api_key='sk-e41914f0d9c94035a5ae1322e9a61fb1',
-    # 以下是北京地域base_url，如果使用新加坡地域的模型，需要将base_url替换为：https://dashscope-intl.aliyuncs.com/compatible-mode/v1
-    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
-)
-
-pdf_path = "C:/Users/Administrator/Desktop/43610236590 (3).pdf"
-
-def pdf_to_images(pdf_path, output_dir='./pdf_pages', dpi=150):
-    """
-    将PDF文件逐页转为PNG图片
-    :param pdf_path: PDF文件路径
-    :param output_dir: 输出目录，默认当前目录下pdf_pages文件夹
-    :param dpi: 渲染分辨率，默认150
-    :return: 生成的图片路径列表
-    """
-    os.makedirs(output_dir, exist_ok=True)
-    doc = fitz.open(pdf_path)
-    image_paths = []
-    for page_num in range(len(doc)):
-        page = doc.load_page(page_num)
-        mat = fitz.Matrix(dpi / 72, dpi / 72)
-        pix = page.get_pixmap(matrix=mat)
-        output_path = os.path.join(output_dir, f"page_{page_num + 1}.png")
-        pix.save(output_path)
-        image_paths.append(output_path)
-    doc.close()
-    return image_paths
-
-
-def download_image(image_url, save_path='output.png'):
-    try:
-        response = requests.get(image_url, stream=True, timeout=300)  # 设置超时
-        response.raise_for_status()  # 如果HTTP状态码不是200，则引发异常
-        with open(save_path, 'wb') as f:
-            for chunk in response.iter_content(chunk_size=8192):
-                f.write(chunk)
-        print(f"图像已成功下载到: {save_path}")
-
-    except requests.exceptions.RequestException as e:
-        print(f"图像下载失败: {e}")
-    
-def encode_file(file_path):
-    mime_type, _ = mimetypes.guess_type(file_path)
-    if not mime_type or not mime_type.startswith("image/"):
-        raise ValueError("不支持或无法识别的图像格式")
-    with open(file_path, "rb") as image_file:
-        encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
-    return f"data:{mime_type};base64,{encoded_string}"
-
-image_paths = pdf_to_images(pdf_path)
-image_base64 = encode_file(image_paths[0])
-# 获取图片分辨率，并在提示中要求模型按比例（相对宽高的0-1浮点数）返回坐标
-img_w, img_h = Image.open(image_paths[0]).size
-print(f"页面尺寸: {img_w}x{img_h} 像素")
-
-def safe_extract_json(text: str):
-    """从模型返回文本中尽可能鲁棒地提取JSON对象。"""
-    # 直接尝试解析
-    try:
-        return json.loads(text)
-    except Exception:
-        pass
-
-    # 尝试提取首尾花括号之间的内容
-    start = text.find('{')
-    end = text.rfind('}')
-    if start != -1 and end != -1 and end > start:
-        candidate = text[start:end+1]
-        try:
-            return json.loads(candidate)
-        except Exception:
-            # 将单引号替换为双引号再试一次（有些模型会返回单引号JSON样式）
-            candidate2 = candidate.replace("'", '"')
-            try:
-                return json.loads(candidate2)
-            except Exception:
-                return None
-    return None
-
-def normalize_bbox(bbox, img_w, img_h):
-    """
-    规范化bbox为像素坐标 [x1,y1,x2,y2]，并保证边界有效。
-    支持：
-    - 像素坐标；
-    - 归一化坐标（0-1）；
-    - [x1,y1,w,h] 形式（若x2<=x1或y2<=y1则按宽高处理）。
-    """
-    if not isinstance(bbox, (list, tuple)) or len(bbox) != 4:
-        return None
-    x1, y1, x2, y2 = bbox
-
-    # 如果可能是归一化坐标
-    if 0 <= x1 <= 1 and 0 <= y1 <= 1 and 0 <= x2 <= 1 and 0 <= y2 <= 1:
-        x1 = int(round(x1 * img_w))
-        y1 = int(round(y1 * img_h))
-        x2 = int(round(x2 * img_w))
-        y2 = int(round(y2 * img_h))
-    else:
-        # 像素坐标或 [x1,y1,w,h]
-        x1 = int(round(x1))
-        y1 = int(round(y1))
-        x2 = int(round(x2))
-        y2 = int(round(y2))
-
-        # 如果x2<=x1或y2<=y1，尝试将其视作宽高
-        if x2 <= x1 or y2 <= y1:
-            x2 = x1 + max(1, x2)
-            y2 = y1 + max(1, y2)
-
-    # 修正边界并加上少量边距
-    if x1 > x2:
-        x1, x2 = x2, x1
-    if y1 > y2:
-        y1, y2 = y2, y1
-
-    # 边距按短边的2%（最少5px，最多30px）
-    short_side = min(img_w, img_h)
-    margin = max(5, min(30, int(0.02 * short_side)))
-    x1 = max(0, x1 - margin)
-    y1 = max(0, y1 - margin)
-    x2 = min(img_w - 1, x2 + margin)
-    y2 = min(img_h - 1, y2 + margin)
-    return [x1, y1, x2, y2]
-
-def erase_regions_on_image(image_path: str, coords_map: dict, save_path: str):
-    img = Image.open(image_path).convert('RGB')
-    draw = ImageDraw.Draw(img)
-    w, h = img.size
-
-    for key, bbox in coords_map.items():
-        nb = normalize_bbox(bbox, w, h)
-        if nb is None:
-            print(f"坐标格式错误，跳过 {key}: {bbox}")
-            continue
-        x1, y1, x2, y2 = nb
-        draw.rectangle([x1, y1, x2, y2], fill=(255, 255, 255))
-        print(f"已抹除 {key} 区域: {nb}")
-
-    os.makedirs(os.path.dirname(save_path), exist_ok=True)
-    img.save(save_path)
-    print(f"清理后的图片已保存: {save_path}")
-
-def draw_debug_boxes(image_path: str, coords_map: dict, save_path: str):
-    """在原图上绘制预测的矩形框用于人工核对。"""
-    img = Image.open(image_path).convert('RGB')
-    draw = ImageDraw.Draw(img)
-    w, h = img.size
-    for key, bbox in coords_map.items():
-        nb = normalize_bbox(bbox, w, h)
-        if nb is None:
-            print(f"跳过无法解析的坐标: {key} -> {bbox}")
-            continue
-        x1, y1, x2, y2 = nb
-        draw.rectangle([x1, y1, x2, y2], outline=(255, 0, 0), width=3)
-        draw.text((x1, max(0, y1 - 16)), key, fill=(255, 0, 0))
-    os.makedirs(os.path.dirname(save_path), exist_ok=True)
-    img.save(save_path)
-    print(f"调试框已生成: {save_path}")
-
-def images_to_pdf(image_paths, output_pdf):
-    os.makedirs(os.path.dirname(output_pdf), exist_ok=True)
-    pil_images = [Image.open(p).convert('RGB') for p in image_paths]
-    if not pil_images:
-        raise RuntimeError("没有需要写入PDF的图片")
-    first = pil_images[0]
-    rest = pil_images[1:]
-    first.save(output_pdf, save_all=True, append_images=rest)
-    print(f"已生成PDF: {output_pdf}")
-
-completion = client.chat.completions.create(
-    model="qwen3-vl-plus", # 此处以qwen3-vl-plus为例，可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/models
-    messages=[
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "image_url",
-                    "image_url": {
-                        "url": image_base64
-                    },
-                },
-                {"type": "text", "text": f"图像分辨率为{img_w}x{img_h}像素。坐标系定义：以原始图像左上角为原点(0,0)，x向右增加，y向下增加；不要使用任何预处理(缩放或加黑边)产生的坐标。请仅返回这两个文本的矩形框坐标，且必须是归一化到[0,1]的浮点数(相对于原始图像宽高)，返回格式严格为压缩JSON、无任何解释：{{\"AGN\": [x1_rel, y1_rel, x2_rel, y2_rel], \"UCLINK LOGISITICS LTD\": [x3_rel, y3_rel, x4_rel, y4_rel]}}。"},
-            ],
-        },
-    ],
-    temperature=0.1,
-)
-
-raw_text = completion.choices[0].message.content
-print(raw_text)
-result = safe_extract_json(raw_text)
-if result is None or not isinstance(result, dict):
-    raise RuntimeError("模型返回内容无法解析为JSON坐标，请检查返回格式。")
-
-# 只处理第一页：将抹除后的图片写入 output/cleaned_page_1.png，然后重新生成PDF
-cleaned_dir = os.path.join("./output")
-cleaned_first = os.path.join(cleaned_dir, "cleaned_page_1.png")
-debug_first = os.path.join(cleaned_dir, "debug_page_1.png")
-draw_debug_boxes(image_paths[0], result, debug_first)
-erase_regions_on_image(image_paths[0], result, cleaned_first)
-
-# 合成PDF：第一页使用清理后的图片，其余页沿用原图
-final_images = [cleaned_first] + image_paths[1:]
-images_to_pdf(final_images, os.path.join(cleaned_dir, "cleaned.pdf"))
-

ccs_base/wizard/image-to-coordinate.py

@@ -16,7 +16,7 @@ client = OpenAI(
     base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
 )
 
-pdf_path = "./43610272216.pdf"
+pdf_path = "C:/Users/Administrator/Desktop/43610281036.pdf"
 
 def pdf_to_images(pdf_path, output_dir='./pdf_pages', dpi=150):
     """
@@ -279,11 +279,13 @@ text = f"""（仅归一化坐标，严格 JSON）
 - 返回坐标为相对宽高的归一化浮点数，范围 [0,1]，保留 4 位小数；保证 0 ≤ x1 < x2 ≤ 1，0 ≤ y1 < y2 ≤ 1。
 - 禁止任何图片预处理（裁剪、缩放、加边距、重采样）；坐标必须对应原始图像。
 - 严格只输出下面的压缩的 JSON，不要附加解释或其他文本。
-- JSON中不要出现不在实例中的参数,例如bbox_2d
+- JSON中不要出现不在实例中的参数,例如bbox_2d，确保bbox_norm中有且仅有x1,y1,x2,y2四个参数。
 输出 JSON 格式（示例为格式演示，实际数值请识别后填充）："""
 
 text += '[{"text":"AGN","bbox_norm":{"x1":0.0000,"y1":0.0000,"x2":0.0000,"y2":0.0000}},{"text":"UCLINK","bbox_norm":{"x1":0.0000,"y1":0.0000,"x2":0.0000,"y2":0.0000}},{"text":"LOGISITICS","bbox_norm":{"x1":0.0000,"y1":0.0000,"x2":0.0000,"y2":0.0000}},{"text":"LTD","bbox_norm":{"x1":0.0000,"y1":0.0000,"x2":0.0000,"y2":0.0000}}]'
 
+# 记录AI处理开始时间
+ai_start_time = time.time()
 completion = client.chat.completions.create(
     model="qwen3-vl-plus", # 此处以qwen3-vl-plus为例，可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/models
     messages=[
@@ -302,6 +304,9 @@ completion = client.chat.completions.create(
     ],
     temperature=0.1,
 )
+# 记录AI处理结束时间
+ai_end_time = time.time()
+ai_processing_time = ai_end_time - ai_start_time
 
 raw_text = completion.choices[0].message.content
 # raw_text = '```json[{"bbox_norm": {"x1": 0.1028, "y1": 0.1934, "x2": 0.1325, "y2": 0.2006}, "text": "AGN", "occurrence_index": 0},{"bbox_norm": {"x1": 0.1028, "y1": 0.2057, "x2": 0.1608, "y2": 0.2165}, "text": "UCLINK", "occurrence_index": 0},{"bbox_norm": {"x1": 0.1677, "y1": 0.2057, "x2": 0.2657, "y2": 0.2165}, "text": "LOGISITICS", "occurrence_index": 0},{"bbox_norm": {"x1": 0.2726, "y1": 0.2057, "x2": 0.3023, "y2": 0.2165}, "text": "LTD", "occurrence_index": 0}]```'
@@ -325,5 +330,7 @@ erase_regions_on_image(image_paths[0], coords_map, cleaned_first)
 final_images = [cleaned_first] + image_paths[1:]
 images_to_pdf(final_images, os.path.join(cleaned_dir, "cleaned.pdf"))
 end_time = time.time()
-print(f"耗时: {end_time - begin_time} 秒")
+total_time = end_time - begin_time
+print(f"总耗时: {total_time:.2f} 秒")
+print(f"AI处理耗时: {ai_processing_time:.2f} 秒（AI API调用时间）")
 

ccs_connect_tiktok/wizard/batch_get_pod_info_wizard.py

@@ -2,6 +2,8 @@
 # License AGPL-3.0 or later (http://www.gnu.org/licenses/agpl.html).
 import logging
 from odoo import models, fields, api, _
+import pytz
+from datetime import datetime
 _logger = logging.getLogger(__name__)
 
 
@@ -37,9 +39,13 @@ class BatchGetPodInfoWizard(models.TransientModel):
                     _logger.warning(f"提单 {bl.bl_no} 未提取到时间信息，跳过节点推送")
                     continue
                 
-                # 取最早时间作为提取时间
-                extract_time = min(extracted_times)
-                _logger.info(f"提单 {bl.bl_no} 最早提取时间: {extract_time}")
+                # 取最早时间作为提取时间（用户时区的本地时间）
+                extract_time_local = min(extracted_times)
+                _logger.info(f"提单 {bl.bl_no} 最早提取时间（本地）: {extract_time_local}")
+                
+                # 将提取时间从用户时区转换为UTC（0时区）
+                extract_time = self._convert_local_time_to_utc(extract_time_local)
+                _logger.info(f"提单 {bl.bl_no} 最早提取时间（UTC）: {extract_time}")
                 
                 # 获取小包信息
                 ship_packages = bl.ship_package_ids
@@ -217,11 +223,15 @@ class BatchGetPodInfoWizard(models.TransientModel):
                 if interval_time:
                     # 计算提取时间减去小包当前节点的操作时间
                     time_diff = extract_time - current_node_operation_time
-                    if time_diff < interval_time:
-                        _logger.warning(f"小包 {package.id} 时间差 {time_diff} < 前序间隔时间 {interval_time}，不满足补推条件")
+                    # 将时间差转换为分钟数
+                    time_diff_minutes = time_diff.total_seconds() / 60
+                    # 将间隔时间转换为分钟数
+                    interval_minutes = interval_time.total_seconds() / 60
+                    if time_diff_minutes < interval_minutes:
+                        _logger.warning(f"小包 {package.id} 时间差 {time_diff_minutes:.1f} 分钟 < 前序间隔时间 {interval_minutes:.1f} 分钟，不满足补推条件")
                         return False
                     else:
-                        _logger.info(f"小包 {package.id} 时间差 {time_diff} >= 前序间隔时间 {interval_time}，满足补推条件")
+                        _logger.info(f"小包 {package.id} 时间差 {time_diff_minutes:.1f} 分钟 >= 前序间隔时间 {interval_minutes:.1f} 分钟，满足补推条件")
             
             _logger.info(f"小包 {package.id} 满足推送条件")
             return True
@@ -271,3 +281,40 @@ class BatchGetPodInfoWizard(models.TransientModel):
             _logger.error(f"获取小包 {package.id} 前序间隔时间失败: {str(e)}")
             return None
 
+    def _convert_local_time_to_utc(self, local_time):
+        """
+        将本地时间（用户时区）转换为UTC时间（0时区）
+        :param local_time: 本地时间的datetime对象（naive或aware）
+        :return: UTC时间的datetime对象（naive，用于数据库存储）
+        """
+        try:
+            # 获取用户时区，如果没有则使用系统默认时区（通常为Asia/Shanghai或UTC）
+            user_tz_name = self.env.user.tz
+            if not user_tz_name:
+                # 尝试从系统配置获取默认时区，如果没有则使用UTC
+                user_tz_name = self.env['ir.config_parameter'].sudo().get_param('timezone') or 'UTC'
+            
+            user_tz = pytz.timezone(user_tz_name)
+            
+            # 如果local_time是naive datetime（没有时区信息），假设它是用户时区的时间
+            if local_time.tzinfo is None:
+                # 将naive datetime标记为用户时区
+                local_time_aware = user_tz.localize(local_time)
+            else:
+                # 如果已经是aware datetime，先转换到用户时区，再转换到UTC
+                local_time_aware = local_time.astimezone(user_tz)
+            
+            # 转换为UTC时间
+            utc_time = local_time_aware.astimezone(pytz.UTC)
+            
+            # 返回naive UTC datetime（Odoo数据库通常存储naive UTC datetime）
+            utc_time_naive = utc_time.replace(tzinfo=None)
+            
+            _logger.info(f"时区转换: 本地时间 {local_time} (用户时区: {user_tz_name}) -> UTC时间 {utc_time_naive}")
+            return utc_time_naive
+            
+        except Exception as e:
+            _logger.error(f"时区转换失败: {str(e)}，使用原时间（假设已经是UTC）")
+            # 如果转换失败，返回原时间并移除时区信息（假设已经是UTC）
+            return local_time.replace(tzinfo=None) if hasattr(local_time, 'tzinfo') and local_time.tzinfo else local_time
+