diff --git a/build/GUI应用/Analysis-00.toc b/build/GUI应用/Analysis-00.toc index ee7db62..d5731c9 100644 --- a/build/GUI应用/Analysis-00.toc +++ b/build/GUI应用/Analysis-00.toc @@ -4541,53 +4541,32 @@ [('python313.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\python313.dll', 'BINARY'), - ('numpy.libs\\libscipy_openblas64_-9e3e5a4229c1ca39f10dc82bba9e2b2b.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\numpy.libs\\libscipy_openblas64_-9e3e5a4229c1ca39f10dc82bba9e2b2b.dll', - 'BINARY'), ('numpy.libs\\msvcp140-a4c2229bdc2a2a630acdc095b4d86008.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\numpy.libs\\msvcp140-a4c2229bdc2a2a630acdc095b4d86008.dll', 'BINARY'), + ('numpy.libs\\libscipy_openblas64_-9e3e5a4229c1ca39f10dc82bba9e2b2b.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\numpy.libs\\libscipy_openblas64_-9e3e5a4229c1ca39f10dc82bba9e2b2b.dll', + 'BINARY'), ('PyQt5\\Qt5\\bin\\libGLESv2.dll', 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'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\_weakrefset.py', + 'PYMODULE'), + ('enum', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\enum.py', 'PYMODULE'), + ('copyreg', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\copyreg.py', + 'PYMODULE'), + ('sre_compile', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\sre_compile.py', 'PYMODULE'), - ('io', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\io.py', 'PYMODULE'), ('_collections_abc', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\_collections_abc.py', 'PYMODULE'), - ('abc', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\abc.py', 'PYMODULE'), - ('weakref', - 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\weakref.py', + ('codecs', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\codecs.py', 'PYMODULE'), + ('types', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\types.py', 'PYMODULE'), + ('genericpath', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\genericpath.py', + 'PYMODULE'), + ('abc', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\abc.py', 'PYMODULE'), + ('reprlib', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\reprlib.py', + 'PYMODULE'), + ('io', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\io.py', 'PYMODULE'), ('traceback', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\traceback.py', 'PYMODULE'), diff --git a/build/GUI应用/EXE-00.toc b/build/GUI应用/EXE-00.toc index d2fceb7..0f67fb5 100644 --- a/build/GUI应用/EXE-00.toc +++ b/build/GUI应用/EXE-00.toc @@ -77,53 +77,32 @@ ('python313.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\python313.dll', 'BINARY'), - ('numpy.libs\\libscipy_openblas64_-9e3e5a4229c1ca39f10dc82bba9e2b2b.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\numpy.libs\\libscipy_openblas64_-9e3e5a4229c1ca39f10dc82bba9e2b2b.dll', - 'BINARY'), ('numpy.libs\\msvcp140-a4c2229bdc2a2a630acdc095b4d86008.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\numpy.libs\\msvcp140-a4c2229bdc2a2a630acdc095b4d86008.dll', 'BINARY'), + ('numpy.libs\\libscipy_openblas64_-9e3e5a4229c1ca39f10dc82bba9e2b2b.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\numpy.libs\\libscipy_openblas64_-9e3e5a4229c1ca39f10dc82bba9e2b2b.dll', + 'BINARY'), ('PyQt5\\Qt5\\bin\\libGLESv2.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\bin\\libGLESv2.dll', 'BINARY'), ('PyQt5\\Qt5\\bin\\d3dcompiler_47.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\bin\\d3dcompiler_47.dll', 'BINARY'), - ('PyQt5\\Qt5\\bin\\opengl32sw.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\bin\\opengl32sw.dll', - 'BINARY'), ('PyQt5\\Qt5\\bin\\libEGL.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\bin\\libEGL.dll', 'BINARY'), + ('PyQt5\\Qt5\\bin\\opengl32sw.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\bin\\opengl32sw.dll', + 'BINARY'), ('pywin32_system32\\pywintypes313.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\pywin32_system32\\pywintypes313.dll', 'BINARY'), ('pywin32_system32\\pythoncom313.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\pywin32_system32\\pythoncom313.dll', 'BINARY'), - ('PyQt5\\Qt5\\plugins\\platforms\\qwindows.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\platforms\\qwindows.dll', - 'BINARY'), - ('PyQt5\\Qt5\\plugins\\imageformats\\qjpeg.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\imageformats\\qjpeg.dll', - 'BINARY'), - ('PyQt5\\Qt5\\plugins\\generic\\qtuiotouchplugin.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\generic\\qtuiotouchplugin.dll', - 'BINARY'), - ('PyQt5\\Qt5\\plugins\\imageformats\\qtiff.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\imageformats\\qtiff.dll', - 'BINARY'), - ('PyQt5\\Qt5\\plugins\\imageformats\\qgif.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\imageformats\\qgif.dll', - 'BINARY'), - ('PyQt5\\Qt5\\plugins\\imageformats\\qwebp.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\imageformats\\qwebp.dll', - 'BINARY'), - ('PyQt5\\Qt5\\plugins\\imageformats\\qsvg.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\imageformats\\qsvg.dll', - 'BINARY'), - ('PyQt5\\Qt5\\plugins\\imageformats\\qtga.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\imageformats\\qtga.dll', + ('PyQt5\\Qt5\\plugins\\platforms\\qwebgl.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\platforms\\qwebgl.dll', 'BINARY'), ('PyQt5\\Qt5\\plugins\\imageformats\\qicns.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\imageformats\\qicns.dll', @@ -131,24 +110,45 @@ ('PyQt5\\Qt5\\plugins\\iconengines\\qsvgicon.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\iconengines\\qsvgicon.dll', 'BINARY'), + ('PyQt5\\Qt5\\plugins\\imageformats\\qgif.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\imageformats\\qgif.dll', + 'BINARY'), + ('PyQt5\\Qt5\\plugins\\imageformats\\qjpeg.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\imageformats\\qjpeg.dll', + 'BINARY'), + ('PyQt5\\Qt5\\plugins\\imageformats\\qsvg.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\imageformats\\qsvg.dll', + 'BINARY'), ('PyQt5\\Qt5\\plugins\\platformthemes\\qxdgdesktopportal.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\platformthemes\\qxdgdesktopportal.dll', 'BINARY'), - ('PyQt5\\Qt5\\plugins\\imageformats\\qwbmp.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\imageformats\\qwbmp.dll', + ('PyQt5\\Qt5\\plugins\\imageformats\\qtga.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\imageformats\\qtga.dll', + 'BINARY'), + ('PyQt5\\Qt5\\plugins\\generic\\qtuiotouchplugin.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\generic\\qtuiotouchplugin.dll', + 'BINARY'), + ('PyQt5\\Qt5\\plugins\\platforms\\qminimal.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\platforms\\qminimal.dll', 'BINARY'), ('PyQt5\\Qt5\\plugins\\platforms\\qoffscreen.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\platforms\\qoffscreen.dll', 'BINARY'), + ('PyQt5\\Qt5\\plugins\\imageformats\\qwbmp.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\imageformats\\qwbmp.dll', + 'BINARY'), + ('PyQt5\\Qt5\\plugins\\imageformats\\qwebp.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\imageformats\\qwebp.dll', + 'BINARY'), + ('PyQt5\\Qt5\\plugins\\imageformats\\qtiff.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\imageformats\\qtiff.dll', + 'BINARY'), + ('PyQt5\\Qt5\\plugins\\platforms\\qwindows.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\platforms\\qwindows.dll', + 'BINARY'), ('PyQt5\\Qt5\\plugins\\imageformats\\qico.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\imageformats\\qico.dll', 'BINARY'), - ('PyQt5\\Qt5\\plugins\\platforms\\qwebgl.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\platforms\\qwebgl.dll', - 'BINARY'), - ('PyQt5\\Qt5\\plugins\\platforms\\qminimal.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\platforms\\qminimal.dll', - 'BINARY'), ('PyQt5\\Qt5\\plugins\\styles\\qwindowsvistastyle.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\plugins\\styles\\qwindowsvistastyle.dll', 'BINARY'), @@ -464,57 +464,60 @@ ('_zoneinfo.pyd', 'C:\\Users\\27942\\.conda\\envs\\haha\\DLLs\\_zoneinfo.pyd', 'EXTENSION'), - ('api-ms-win-crt-environment-l1-1-0.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-environment-l1-1-0.dll', - 'BINARY'), - ('api-ms-win-crt-locale-l1-1-0.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-locale-l1-1-0.dll', - 'BINARY'), ('api-ms-win-crt-math-l1-1-0.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-math-l1-1-0.dll', 'BINARY'), - ('zlib.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\zlib.dll', 'BINARY'), - ('api-ms-win-crt-string-l1-1-0.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-string-l1-1-0.dll', - 'BINARY'), - ('api-ms-win-crt-filesystem-l1-1-0.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-filesystem-l1-1-0.dll', - 'BINARY'), ('VCRUNTIME140.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\VCRUNTIME140.dll', 'BINARY'), - ('api-ms-win-crt-runtime-l1-1-0.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-runtime-l1-1-0.dll', - 'BINARY'), - ('api-ms-win-crt-process-l1-1-0.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-process-l1-1-0.dll', - 'BINARY'), ('api-ms-win-crt-time-l1-1-0.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-time-l1-1-0.dll', 'BINARY'), - ('api-ms-win-crt-stdio-l1-1-0.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-stdio-l1-1-0.dll', - 'BINARY'), - ('api-ms-win-crt-conio-l1-1-0.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-conio-l1-1-0.dll', + ('api-ms-win-crt-locale-l1-1-0.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-locale-l1-1-0.dll', 'BINARY'), ('api-ms-win-crt-heap-l1-1-0.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-heap-l1-1-0.dll', 'BINARY'), - ('api-ms-win-core-path-l1-1-0.dll', - 'C:\\Windows\\system32\\api-ms-win-core-path-l1-1-0.dll', + ('api-ms-win-crt-runtime-l1-1-0.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-runtime-l1-1-0.dll', + 'BINARY'), + ('api-ms-win-crt-stdio-l1-1-0.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-stdio-l1-1-0.dll', + 'BINARY'), + ('api-ms-win-crt-process-l1-1-0.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-process-l1-1-0.dll', + 'BINARY'), + ('api-ms-win-crt-environment-l1-1-0.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-environment-l1-1-0.dll', + 'BINARY'), + ('api-ms-win-crt-filesystem-l1-1-0.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-filesystem-l1-1-0.dll', + 'BINARY'), + ('api-ms-win-crt-conio-l1-1-0.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-conio-l1-1-0.dll', 'BINARY'), ('api-ms-win-crt-convert-l1-1-0.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-convert-l1-1-0.dll', 'BINARY'), - ('api-ms-win-crt-private-l1-1-0.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-private-l1-1-0.dll', + ('api-ms-win-core-path-l1-1-0.dll', + 'C:\\Windows\\system32\\api-ms-win-core-path-l1-1-0.dll', + 'BINARY'), + ('api-ms-win-crt-string-l1-1-0.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-string-l1-1-0.dll', + 'BINARY'), + ('zlib.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\zlib.dll', 'BINARY'), + ('VCRUNTIME140_1.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\VCRUNTIME140_1.dll', 'BINARY'), ('api-ms-win-crt-utility-l1-1-0.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-utility-l1-1-0.dll', 'BINARY'), - ('VCRUNTIME140_1.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\VCRUNTIME140_1.dll', + ('api-ms-win-crt-private-l1-1-0.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\api-ms-win-crt-private-l1-1-0.dll', + 'BINARY'), + ('PyQt5\\Qt5\\bin\\MSVCP140.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\bin\\MSVCP140.dll', 'BINARY'), ('PyQt5\\Qt5\\bin\\VCRUNTIME140_1.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\bin\\VCRUNTIME140_1.dll', @@ -522,20 +525,23 @@ ('PyQt5\\Qt5\\bin\\VCRUNTIME140.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\bin\\VCRUNTIME140.dll', 'BINARY'), - ('PyQt5\\Qt5\\bin\\MSVCP140.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\bin\\MSVCP140.dll', + ('PyQt5\\Qt5\\bin\\Qt5Network.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\bin\\Qt5Network.dll', 'BINARY'), - ('PyQt5\\Qt5\\bin\\Qt5Gui.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\bin\\Qt5Gui.dll', - 'BINARY'), - ('MSVCP140.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\MSVCP140.dll', + ('PyQt5\\Qt5\\bin\\Qt5WebSockets.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\bin\\Qt5WebSockets.dll', 'BINARY'), ('PyQt5\\Qt5\\bin\\Qt5Core.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\bin\\Qt5Core.dll', 'BINARY'), - ('PyQt5\\Qt5\\bin\\Qt5Network.dll', - 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\bin\\Qt5Network.dll', + ('PyQt5\\Qt5\\bin\\Qt5Quick.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\bin\\Qt5Quick.dll', + 'BINARY'), + ('MSVCP140.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\MSVCP140.dll', + 'BINARY'), + ('PyQt5\\Qt5\\bin\\Qt5Gui.dll', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\bin\\Qt5Gui.dll', 'BINARY'), ('PyQt5\\Qt5\\bin\\Qt5Svg.dll', 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\PyQt5\\Qt5\\bin\\Qt5Svg.dll', @@ -543,12 +549,6 @@ ('PyQt5\\Qt5\\bin\\Qt5DBus.dll', 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('numpy-2.4.1.dist-info\\licenses\\numpy\\ma\\LICENSE', - 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\numpy-2.4.1.dist-info\\licenses\\numpy\\ma\\LICENSE', + ('numpy-2.4.1.dist-info\\licenses\\numpy\\random\\src\\philox\\LICENSE.md', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\numpy-2.4.1.dist-info\\licenses\\numpy\\random\\src\\philox\\LICENSE.md', + 'DATA'), + ('numpy-2.4.1.dist-info\\licenses\\numpy\\_core\\src\\npysort\\x86-simd-sort\\LICENSE.md', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\numpy-2.4.1.dist-info\\licenses\\numpy\\_core\\src\\npysort\\x86-simd-sort\\LICENSE.md', + 'DATA'), + ('numpy-2.4.1.dist-info\\licenses\\LICENSE.txt', + 'C:\\Users\\27942\\.conda\\envs\\haha\\Lib\\site-packages\\numpy-2.4.1.dist-info\\licenses\\LICENSE.txt', 'DATA'), ('base_library.zip', 'C:\\Users\\27942\\Desktop\\codes\\haha\\build\\GUI应用\\base_library.zip', diff --git a/build/GUI应用/PYZ-00.pyz b/build/GUI应用/PYZ-00.pyz index 463c0a9..768238e 100644 Binary files a/build/GUI应用/PYZ-00.pyz and b/build/GUI应用/PYZ-00.pyz differ diff --git a/build/GUI应用/base_library.zip b/build/GUI应用/base_library.zip index e3ec5d5..33a6473 100644 Binary files a/build/GUI应用/base_library.zip and b/build/GUI应用/base_library.zip differ diff --git a/build/GUI应用/warn-GUI应用.txt b/build/GUI应用/warn-GUI应用.txt index d03d5e6..c2a2ab1 100644 --- a/build/GUI应用/warn-GUI应用.txt +++ b/build/GUI应用/warn-GUI应用.txt @@ -18,7 +18,7 @@ missing module named urllib.urlopen - imported by urllib (delayed, optional), lx missing module named urllib.urlencode - imported by urllib (delayed, optional), lxml.html (delayed, optional) missing module named pwd - imported by posixpath (delayed, conditional, optional), shutil (delayed, optional), tarfile (optional), pathlib._local (optional), subprocess (delayed, conditional, optional), http.server (delayed, optional), psutil (optional), netrc (delayed, optional), getpass (delayed, optional), setuptools._distutils.util (delayed, conditional, optional), setuptools._vendor.backports.tarfile (optional), setuptools._distutils.archive_util (optional) missing module named grp - imported by shutil (delayed, optional), tarfile (optional), pathlib._local (optional), subprocess (delayed, conditional, optional), setuptools._vendor.backports.tarfile (optional), setuptools._distutils.archive_util (optional) -missing module named 'collections.abc' - imported by traceback (top-level), typing (top-level), inspect (top-level), logging (top-level), importlib.resources.readers (top-level), selectors (top-level), tracemalloc (top-level), numpy._typing._array_like (top-level), numpy._typing._nested_sequence (conditional), numpy._typing._shape (top-level), numpy._typing._dtype_like (top-level), asyncio.base_events (top-level), http.client (top-level), asyncio.coroutines (top-level), numpy.lib._function_base_impl (top-level), numpy.lib._npyio_impl (top-level), numpy.random._common (top-level), numpy.random._generator (top-level), numpy.random.bit_generator (top-level), typing_extensions (top-level), numpy.random.mtrand (top-level), numpy.polynomial._polybase (top-level), pandas._typing (top-level), pandas._libs.tslibs.offsets (top-level), pandas._libs.tslibs.timezones (top-level), pandas._libs.tslibs.tzconversion (top-level), pandas.util._exceptions (conditional), pandas._config.config (conditional), pandas._libs.lib (top-level), pandas._libs.properties (top-level), pandas.util.version (top-level), pandas.util._decorators (conditional), pandas._libs.hashtable (top-level), pandas.core.dtypes.inference (conditional), pandas.core.dtypes.common (conditional), pandas._libs.arrays (top-level), pandas.util._validators (top-level), pandas._libs.internals (top-level), pandas.core.common (top-level), pandas.core.col (top-level), pandas.core.construction (conditional), pandas.core.dtypes.concat (conditional), pandas._libs.ops (top-level), pandas.core.ops.invalid (conditional), pandas.core.ops.common (conditional), 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(conditional), pandas._testing._warnings (conditional), pandas._libs.testing (top-level), pandas._testing.contexts (conditional), pandas._testing (conditional), pandas._version (top-level), pandas.io.html (conditional), lxml.html (top-level), lxml.html._setmixin (optional), cssselect.parser (conditional), cssselect.xpath (conditional), pandas.io.spss (conditional), curl_cffi._asyncio_selector (top-level), curl_cffi.requests.cookies (top-level), curl_cffi.requests.headers (top-level), curl_cffi.requests.models (top-level), curl_cffi.requests.session (top-level), curl_cffi.requests.utils (top-level), curl_cffi.requests.websockets (top-level), setuptools (top-level), setuptools._distutils.filelist (top-level), setuptools._distutils.util (top-level), setuptools._vendor.jaraco.functools (top-level), setuptools._vendor.more_itertools.more (top-level), setuptools._distutils._modified (top-level), setuptools._distutils.compat (top-level), setuptools._distutils.spawn (top-level), setuptools._distutils.compilers.C.base (top-level), setuptools._distutils.fancy_getopt (top-level), setuptools._reqs (top-level), setuptools._vendor.jaraco.context (top-level), setuptools.discovery (top-level), setuptools.dist (top-level), setuptools._vendor.importlib_metadata (top-level), setuptools._vendor.importlib_metadata._meta (top-level), setuptools._distutils.command.bdist (top-level), setuptools._distutils.core (top-level), setuptools._distutils.cmd (top-level), setuptools._distutils.dist (top-level), configparser (top-level), setuptools._distutils.extension (top-level), setuptools.config.setupcfg (top-level), setuptools.config.expand (top-level), setuptools.config.pyprojecttoml (top-level), setuptools.config._apply_pyprojecttoml (top-level), setuptools.extension (top-level), tomllib._parser (top-level), setuptools._vendor.tomli._parser (conditional), setuptools.wheel (top-level), setuptools.command.egg_info (top-level), setuptools.command.bdist_egg (top-level), setuptools.command.sdist (top-level), setuptools._distutils.command.build (top-level), setuptools._distutils.command.sdist (top-level), setuptools.glob (top-level), setuptools.command._requirestxt (top-level), setuptools.command.bdist_wheel (top-level), requests.compat (top-level), tldextract.tldextract (top-level), tldextract.cache (top-level), filelock.asyncio (conditional), tldextract.suffix_list (top-level), setuptools._distutils.command.build_ext (top-level), _pyrepl.types (top-level), _pyrepl.readline (top-level), setuptools._distutils.compilers.C.msvc (top-level) +missing module named 'collections.abc' - imported by logging (top-level), inspect (top-level), typing (top-level), importlib.resources.readers (top-level), selectors (top-level), tracemalloc (top-level), traceback (top-level), numpy._typing._array_like (top-level), numpy._typing._nested_sequence (conditional), numpy._typing._shape (top-level), numpy._typing._dtype_like (top-level), asyncio.base_events (top-level), http.client (top-level), asyncio.coroutines (top-level), numpy.lib._function_base_impl (top-level), numpy.lib._npyio_impl (top-level), numpy.random._common (top-level), numpy.random._generator (top-level), numpy.random.bit_generator (top-level), typing_extensions (top-level), numpy.random.mtrand (top-level), numpy.polynomial._polybase (top-level), pandas._typing (top-level), pandas._libs.tslibs.offsets (top-level), pandas._libs.tslibs.timezones (top-level), pandas._libs.tslibs.tzconversion (top-level), pandas.util._exceptions (conditional), pandas._config.config (conditional), pandas._libs.lib (top-level), pandas._libs.properties (top-level), pandas.util.version (top-level), pandas.util._decorators (conditional), pandas._libs.hashtable (top-level), pandas.core.dtypes.inference (conditional), pandas.core.dtypes.common (conditional), pandas._libs.arrays (top-level), pandas.util._validators (top-level), pandas._libs.internals (top-level), pandas.core.common (top-level), pandas.core.col (top-level), pandas.core.construction (conditional), pandas.core.dtypes.concat (conditional), pandas._libs.ops (top-level), pandas.core.ops.invalid (conditional), pandas.core.ops.common (conditional), pandas.core.frame (top-level), pandas.core.nanops (conditional), pandas.core.accessor (conditional), pandas.core.indexes.extension (conditional), pandas.core.sorting (conditional), pandas.core.arrays.base (conditional), pandas.io.formats.format (top-level), pandas.core.base (conditional), pandas.core.array_algos.datetimelike_accumulations (conditional), pandas.core.arrays._arrow_string_mixins (conditional), pandas.core.array_algos.masked_accumulations (conditional), pandas.core.array_algos.masked_reductions (conditional), pandas.core.arrays.masked (conditional), pandas.core.groupby.generic (top-level), pandas.core.apply (top-level), pandas.core._numba.executor (conditional), pandas.core.util.numba_ (conditional), pandas.core.groupby.groupby (top-level), pandas.core.arrays.numeric (conditional), pandas.core.arrays.floating (conditional), pandas.core.arrays.integer (conditional), pandas.core.strings.object_array (conditional), pandas.core.arrays.string_arrow (conditional), pandas.core.groupby.base (conditional), pandas.core.groupby.numba_ (conditional), pandas.core.series (top-level), pandas.io.formats.printing (top-level), pandas.core.arrays.sparse.array (conditional), pandas.core.arrays.sparse.scipy_sparse (conditional), pandas.core.arrays.timedeltas (conditional), pandas.core.indexes.range (top-level), pandas.core.tools.timedeltas (conditional), pandas.core.indexes.datetimelike (conditional), pandas.core.indexes.multi (top-level), pandas.core.methods.selectn (top-level), pandas.core.reshape.concat (conditional), pandas.core.strings.accessor (conditional), pandas.core.tools.datetimes (conditional), pandas.io.formats.info (conditional), pandas.plotting._core (conditional), pandas.plotting._misc (conditional), pandas.core.groupby.grouper (conditional), pandas.core.groupby.ops (conditional), pandas.core.groupby.indexing (top-level), pandas.core.arrays.interval (conditional), pandas.core.indexes.interval (conditional), pandas.core.arrays.period (conditional), pandas.core.indexes.period (conditional), pandas._libs.window.aggregations (top-level), pandas.core.window.numba_ (conditional), pandas.core.window.rolling (conditional), pandas.core.window.expanding (conditional), pandas.core.resample (conditional), pandas.core.reshape.merge (top-level), pandas.core.reshape.tile (conditional), pandas.io._util (conditional), pandas.core.arrays.arrow.array (conditional), pandas.core.arrays.datetimelike (conditional), pandas.core.arrays.datetimes (conditional), pandas.core.indexes.datetimes (conditional), pandas.io.common (top-level), pandas.io.formats.html (conditional), pandas.io.formats.string (conditional), pandas.io.formats.csvs (top-level), pandas.io.formats.style_render (top-level), pandas.core.interchange.dataframe_protocol (conditional), pandas._libs.json (top-level), pandas.io.json._normalize (conditional), pandas._libs.parsers (top-level), pandas.io.parsers.base_parser (conditional), pandas.io.parsers.c_parser_wrapper (conditional), pandas.io.parsers.python_parser (conditional), pandas.io.parsers.readers (conditional), pandas.io.json._json (conditional), pandas.io.sas.sasreader (top-level), pandas.io.stata (conditional), pandas.io.formats.style (conditional), pandas.io.formats.excel (top-level), pandas.io.formats.css (conditional), pandas.io.excel._base (top-level), pandas.io.excel._util (top-level), xml.etree.ElementTree (top-level), pandas.core.arrays._mixins (conditional), pandas.core.indexes.category (conditional), pandas.core.indexing (conditional), pandas.core.methods.describe (conditional), pandas.core.generic (conditional), pandas.core.computation.parsing (conditional), pandas.compat.pickle_compat (conditional), pandas.core.computation.ops (conditional), pandas.core.computation.align (conditional), pandas.core.computation.expr (conditional), pandas.io.pytables (conditional), pandas.core.internals.managers (top-level), pandas.core.internals.ops (conditional), pandas.core.internals.construction (conditional), pandas.io.sql (conditional), sqlite3.dbapi2 (top-level), pandas.core.reshape.melt (conditional), pandas.core.interchange.dataframe (conditional), pandas.core.methods.to_dict (conditional), pandas.io.feather_format (conditional), pandas.io.xml (conditional), pandas.core.reshape.pivot (conditional), pandas.core.arrays.categorical (conditional), pandas.core.internals.blocks (conditional), pandas.core.internals.concat (conditional), pandas.core.indexes.base (conditional), pandas.core.arrays.numpy_ (conditional), pandas.core.dtypes.cast (conditional), pandas.core.missing (conditional), pandas.core.arrays.string_ (conditional), pandas.core.arrays.arrow.accessors (conditional), pandas.core.dtypes.dtypes (conditional), pandas.tseries.holiday (conditional), pandas.core.util.hashing (conditional), pandas.core.config_init (top-level), pandas.core.reshape.encoding (top-level), pandas._config.localization (conditional), pandas._testing._io (conditional), pandas._testing._warnings (conditional), pandas._libs.testing (top-level), pandas._testing.contexts (conditional), pandas._testing (conditional), pandas._version (top-level), pandas.io.html (conditional), lxml.html (top-level), lxml.html._setmixin (optional), cssselect.parser (conditional), cssselect.xpath (conditional), pandas.io.spss (conditional), curl_cffi._asyncio_selector (top-level), curl_cffi.requests.cookies (top-level), curl_cffi.requests.headers (top-level), curl_cffi.requests.models (top-level), curl_cffi.requests.session (top-level), curl_cffi.requests.utils (top-level), curl_cffi.requests.websockets (top-level), setuptools (top-level), setuptools._distutils.filelist (top-level), setuptools._distutils.util (top-level), setuptools._vendor.jaraco.functools (top-level), setuptools._vendor.more_itertools.more (top-level), setuptools._distutils._modified (top-level), setuptools._distutils.compat (top-level), setuptools._distutils.spawn (top-level), setuptools._distutils.compilers.C.base (top-level), setuptools._distutils.fancy_getopt (top-level), setuptools._reqs (top-level), setuptools._vendor.jaraco.context (top-level), setuptools.discovery (top-level), setuptools.dist (top-level), setuptools._vendor.importlib_metadata (top-level), setuptools._vendor.importlib_metadata._meta (top-level), setuptools._distutils.command.bdist (top-level), setuptools._distutils.core (top-level), setuptools._distutils.cmd (top-level), setuptools._distutils.dist (top-level), configparser (top-level), setuptools._distutils.extension (top-level), setuptools.config.setupcfg (top-level), setuptools.config.expand (top-level), setuptools.config.pyprojecttoml (top-level), setuptools.config._apply_pyprojecttoml (top-level), setuptools.extension (top-level), tomllib._parser (top-level), setuptools._vendor.tomli._parser (conditional), setuptools.wheel (top-level), setuptools.command.egg_info (top-level), setuptools.command.bdist_egg (top-level), setuptools.command.sdist (top-level), setuptools._distutils.command.build (top-level), setuptools._distutils.command.sdist (top-level), setuptools.glob (top-level), setuptools.command._requirestxt (top-level), setuptools.command.bdist_wheel (top-level), requests.compat (top-level), tldextract.tldextract (top-level), tldextract.cache (top-level), filelock.asyncio (conditional), tldextract.suffix_list (top-level), setuptools._distutils.command.build_ext (top-level), _pyrepl.types (top-level), _pyrepl.readline (top-level), setuptools._distutils.compilers.C.msvc (top-level) missing module named posix - imported by os (conditional, optional), posixpath (optional), shutil (conditional), importlib._bootstrap_external (conditional), _pyrepl.unix_console (delayed, optional) missing module named resource - imported by posix (top-level) missing module named _frozen_importlib_external - imported by importlib._bootstrap (delayed), importlib (optional), importlib.abc (optional), zipimport (top-level) diff --git a/dist/多多发文助手.exe b/dist/多多自动化发文.exe similarity index 99% rename from dist/多多发文助手.exe rename to dist/多多自动化发文.exe index 9e96e61..2b6e186 100644 Binary files a/dist/多多发文助手.exe and b/dist/多多自动化发文.exe differ diff --git a/gui_app.py b/gui_app.py index 28cb653..ce9be63 100644 --- a/gui_app.py +++ b/gui_app.py @@ -384,9 +384,20 @@ class MainWindow(QMainWindow): self.update_status_label.setStyleSheet("color: #666; font-size: 10px;") update_row.addWidget(self.update_status_label) update_row.addStretch() - self.batch_upload_checkbox = CheckBox("批量上传（如果文件夹中有多个视频，将使用批量上传模式）") + self.batch_upload_checkbox = CheckBox("批量上传") self.batch_upload_checkbox.setChecked(False) + self.batch_upload_checkbox.setToolTip("勾选后，同一多多ID的多个视频将批量上传（按序号顺序处理）") update_row.addWidget(self.batch_upload_checkbox) + # 最大批量数输入框 + batch_limit_label = QLabel("最大批量数:") + batch_limit_label.setStyleSheet("color: #666; font-size: 10px; margin-left: 8px;") + update_row.addWidget(batch_limit_label) + self.batch_limit_input = LineEdit() + self.batch_limit_input.setPlaceholderText("5") + self.batch_limit_input.setText("5") + self.batch_limit_input.setFixedWidth(50) + self.batch_limit_input.setToolTip("每次批量上传的最大视频数量，超过则分批上传") + update_row.addWidget(self.batch_limit_input) update_row_widget = QWidget() update_row_widget.setLayout(update_row) grid.addWidget(update_row_widget, 2, 0, 1, 4) @@ -1755,6 +1766,33 @@ class MainWindow(QMainWindow): except Exception: return None + def _is_schedule_time_expired(self, time_start): + """检查定时发布时间是否已过期（早于当前时间） + + Args: + time_start: 定时发布时间字符串，如 "2026-01-15 09:30:00" + + Returns: + bool: 如果时间已过期返回True，否则返回False。如果时间为空或无法解析，返回False（不跳过） + """ + if not time_start: + return False + + try: + # 解析时间字符串 + schedule_dt = self._parse_schedule_time(time_start) + if not schedule_dt: + return False + + # 与当前时间比较 + now = datetime.now() + if schedule_dt < now: + return True + return False + except Exception as e: + logger.warning(f"检查定时发布时间是否过期时出错: {e}") + return False + def _parse_interval_seconds(self, interval_value): """解析间隔时间（默认按分钟），支持秒/分钟/小时（如: 30, 10m, 2h, 10分钟, 2小时） @@ -3294,8 +3332,8 @@ class MainWindow(QMainWindow): self.batch_failed_count = existing_failed self.set_status_cards(pending=existing_pending, success=existing_success, failed=existing_failed) - # 应用定时发布 + 间隔时间逻辑（跨分页全局应用） - self._apply_schedule_intervals(configs_to_process) + # 注意：不再在此处调用 _apply_schedule_intervals + # 间隔时间计算已在"更新数据"按钮中完成，用户可能在之后手动修改了时间，应保留用户修改 self.log_text.append("=" * 50) self.log_text.append(f"开始分析配置，准备批量上传...") @@ -3368,6 +3406,18 @@ class MainWindow(QMainWindow): # 获取用户是否勾选了"批量上传"复选框 use_batch_upload = self.batch_upload_checkbox.isChecked() self.log_text.append(f"批量上传模式: {'已勾选' if use_batch_upload else '未勾选'}") + + # 获取最大批量数 + batch_limit = 5 # 默认值 + try: + batch_limit_text = self.batch_limit_input.text().strip() + if batch_limit_text: + batch_limit = int(batch_limit_text) + if batch_limit < 1: + batch_limit = 1 + except (ValueError, AttributeError): + batch_limit = 5 + self.log_text.append(f"最大批量数: {batch_limit}") for user_id, items in grouped_by_user_id.items(): all_files = [] @@ -3377,52 +3427,149 @@ class MainWindow(QMainWindow): if item['config_index'] not in related_config_indices: related_config_indices.append(item['config_index']) - video_files = [f for f in all_files if f['path'].is_file() and any(f['path'].suffix.lower() == ext for ext in video_extensions)] - image_folders = [f for f in all_files if f['path'].is_dir()] + # 按序号排序所有文件 + def get_sort_key(f): + try: + return int(f.get('index', '0')) + except (ValueError, TypeError): + return 0 + all_files_sorted = sorted(all_files, key=get_sort_key) # 准备工作线程需要的配置（同一个多多ID共用基准配置） first_config = items[0].get('config', {}) - # 根据用户是否勾选批量上传来决定处理方式 - if use_batch_upload and len(video_files) > 1: - # 勾选了批量上传且有多个视频：使用 action1 批量上传 - self.batch_task_queue.append({ - 'type': 'batch_video', - 'config': first_config, - 'files': video_files, - 'user_id': user_id, - 'count': len(video_files), - 'config_indices': related_config_indices - }) - else: - # 未勾选批量上传 或 只有1个视频：逐个使用 action 方法上传 - for video_file in video_files: - vid_index = video_file.get('index', '') - matching_idx = next((it['config_index'] for it in items if it['config'].get('序号') == vid_index), related_config_indices[0] if related_config_indices else 0) - matching_config = next((it['config'] for it in items if it['config'].get('序号') == vid_index), first_config) + # 按序号顺序处理，遇到视频时收集连续视频进行批量上传 + i = 0 + while i < len(all_files_sorted): + current_file = all_files_sorted[i] + is_video = current_file['path'].is_file() and any( + current_file['path'].suffix.lower() == ext for ext in video_extensions) + + if is_video and use_batch_upload: + # 收集从当前位置开始的连续视频（按序号顺序，最多batch_limit个） + video_batch = [] + batch_config_indices = [] + j = i + while j < len(all_files_sorted) and len(video_batch) < batch_limit: + f = all_files_sorted[j] + f_is_video = f['path'].is_file() and any( + f['path'].suffix.lower() == ext for ext in video_extensions) + if f_is_video: + # 检查定时发布时间是否已过期 + f_time_start = f.get('time_start', '') + if self._is_schedule_time_expired(f_time_start): + f_index = f.get('index', '') + self.log_text.append(f" 跳过序号 {f_index}：定时发布时间已过期 ({f_time_start})") + # 更新表格状态为"已跳过" + matching_idx = next( + (it['config_index'] for it in items if it['config'].get('序号') == f_index), + None) + if matching_idx is not None: + self._update_table_status(matching_idx, "已跳过(时间过期)", is_config_index=True) + self.configs[matching_idx]['情况'] = '已跳过(时间过期)' + j += 1 + continue + video_batch.append(f) + f_index = f.get('index', '') + matching_idx = next( + (it['config_index'] for it in items if it['config'].get('序号') == f_index), + related_config_indices[0] if related_config_indices else 0) + if matching_idx not in batch_config_indices: + batch_config_indices.append(matching_idx) + j += 1 + else: + # 遇到非视频文件，停止收集 + break + + if video_batch: + if len(video_batch) > 1: + # 批量上传多个视频 + self.batch_task_queue.append({ + 'type': 'batch_video', + 'config': first_config, + 'files': video_batch, + 'user_id': user_id, + 'count': len(video_batch), + 'config_indices': batch_config_indices + }) + else: + # 只有1个视频，单独上传 + vid_index = video_batch[0].get('index', '') + matching_config = next( + (it['config'] for it in items if it['config'].get('序号') == vid_index), + first_config) + self.batch_task_queue.append({ + 'type': 'single_video', + 'config': matching_config, + 'files': video_batch, + 'user_id': user_id, + 'count': 1, + 'config_indices': batch_config_indices + }) + i = j + elif is_video: + # 未勾选批量上传，逐个上传视频 + vid_index = current_file.get('index', '') + # 检查定时发布时间是否已过期 + f_time_start = current_file.get('time_start', '') + if self._is_schedule_time_expired(f_time_start): + self.log_text.append(f" 跳过序号 {vid_index}：定时发布时间已过期 ({f_time_start})") + matching_idx = next( + (it['config_index'] for it in items if it['config'].get('序号') == vid_index), + None) + if matching_idx is not None: + self._update_table_status(matching_idx, "已跳过(时间过期)", is_config_index=True) + self.configs[matching_idx]['情况'] = '已跳过(时间过期)' + i += 1 + continue + + matching_idx = next( + (it['config_index'] for it in items if it['config'].get('序号') == vid_index), + related_config_indices[0] if related_config_indices else 0) + matching_config = next( + (it['config'] for it in items if it['config'].get('序号') == vid_index), + first_config) self.batch_task_queue.append({ 'type': 'single_video', 'config': matching_config, - 'files': [video_file], + 'files': [current_file], 'user_id': user_id, 'count': 1, 'config_indices': [matching_idx] }) - - # 2. 图片文件夹任务 - for img_folder in image_folders: - f_idx = img_folder.get('index', '') - matching_idx = next((it['config_index'] for it in items if it['config'].get('序号') == f_idx), related_config_indices[0]) - matching_config = next((it['config'] for it in items if it['config'].get('序号') == f_idx), first_config) - - self.batch_task_queue.append({ - 'type': 'image_folder', - 'config': matching_config, - 'files': [img_folder], - 'user_id': user_id, - 'count': 1, - 'config_indices': [matching_idx] - }) + i += 1 + else: + # 图片文件夹 + f_idx = current_file.get('index', '') + # 检查定时发布时间是否已过期 + f_time_start = current_file.get('time_start', '') + if self._is_schedule_time_expired(f_time_start): + self.log_text.append(f" 跳过序号 {f_idx}：定时发布时间已过期 ({f_time_start})") + matching_idx = next( + (it['config_index'] for it in items if it['config'].get('序号') == f_idx), + None) + if matching_idx is not None: + self._update_table_status(matching_idx, "已跳过(时间过期)", is_config_index=True) + self.configs[matching_idx]['情况'] = '已跳过(时间过期)' + i += 1 + continue + + matching_idx = next( + (it['config_index'] for it in items if it['config'].get('序号') == f_idx), + related_config_indices[0]) + matching_config = next( + (it['config'] for it in items if it['config'].get('序号') == f_idx), + first_config) + + self.batch_task_queue.append({ + 'type': 'image_folder', + 'config': matching_config, + 'files': [current_file], + 'user_id': user_id, + 'count': 1, + 'config_indices': [matching_idx] + }) + i += 1 if self.batch_task_queue: self._process_next_batch_task() diff --git a/main.py b/main.py index 7f84ae3..1ad818b 100644 --- a/main.py +++ b/main.py @@ -613,7 +613,7 @@ class Pdd: if immediate_publish: immediate_publish.click() logger.info(" ✓ 已点击'立即发布'选项") - time.sleep(0.5) + time.sleep(3) else: logger.warning(" ✗ 未找到'立即发布'选项") @@ -1284,21 +1284,21 @@ class Pdd: else: logger.info(" - 未找到'我已阅读并同意'复选框，可能已默认勾选") - one_key_publish = creator_tab.ele('x://*[text()="一键发布"]', timeout=3) - if one_key_publish: - logger.info(" ✓ 找到'一键发布'按钮") - one_key_publish.click() - logger.info(" ✓ 已点击一键发布按钮") - - logger.info(" 正在验证一键发布是否成功...") - ok, reason = self._verify_publish_success(creator_tab=creator_tab, video_container=None) - if ok: - logger.info(f" ✓ 一键发布校验通过：{reason}") - else: - logger.warning(f" ✗ 一键发布校验失败：{reason}") - time.sleep(2) - else: - logger.warning(" ✗ 未找到'一键发布'按钮") + # one_key_publish = creator_tab.ele('x://*[text()="一键发布"]', timeout=3) + # if one_key_publish: + # logger.info(" ✓ 找到'一键发布'按钮") + # one_key_publish.click() + # logger.info(" ✓ 已点击一键发布按钮") + # + # logger.info(" 正在验证一键发布是否成功...") + # ok, reason = self._verify_publish_success(creator_tab=creator_tab, video_container=None) + # if ok: + # logger.info(f" ✓ 一键发布校验通过：{reason}") + # else: + # logger.warning(f" ✗ 一键发布校验失败：{reason}") + # time.sleep(2) + # else: + # logger.warning(" ✗ 未找到'一键发布'按钮") except Exception as e: logger.warning(f" ✗ 一键发布失败: {e}") import traceback diff --git a/user/user_data/ActorSafetyLists/8.5866.7803/_metadata/verified_contents.json b/user/user_data/ActorSafetyLists/8.5866.7803/_metadata/verified_contents.json deleted file mode 100644 index 1ded5b7..0000000 --- a/user/user_data/ActorSafetyLists/8.5866.7803/_metadata/verified_contents.json +++ /dev/null @@ -1 +0,0 @@ -[{"description":"treehash per file","signed_content":{"payload":"eyJjb250ZW50X2hhc2hlcyI6W3siYmxvY2tfc2l6ZSI6NDA5NiwiZGlnZXN0Ijoic2hhMjU2IiwiZmlsZXMiOlt7InBhdGgiOiJsaXN0ZGF0YS5qc29uIiwicm9vdF9oYXNoIjoidzdCdWtiRnV5NU1lNk1Ndzc5SUszWEloX2R0VzB0NUk0RkRnUVZldVF1ZyJ9LHsicGF0aCI6Im1hbmlmZXN0Lmpzb24iLCJyb290X2hhc2giOiJRVjJld2Npb0hsVkJaMW1tek5YSTdtYjd0SXRjSjQyUXphNFFPUHJKTi1RIn1dLCJmb3JtYXQiOiJ0cmVlaGFzaCIsImhhc2hfYmxvY2tfc2l6ZSI6NDA5Nn1dLCJpdGVtX2lkIjoibmlub2RhYmNlanBlZ2xmamJraGRwbGFvZ2xwY2JmZmoiLCJpdGVtX3ZlcnNpb24iOiI4LjU4NjYuNzgwMyIsInByb3RvY29sX3ZlcnNpb24iOjF9","signatures":[{"header":{"kid":"publisher"},"protected":"eyJhbGciOiJSUzI1NiJ9","signature":"Bdid54WHxcNszKj6PE6yfwhavYWsiEQ_K3MHyir-5CUwHUvfczQK4_3CBA1IMCSc2oRu4hyt8pnWnHbtNkVDr9p-9xoTriZfD1LV9K9yAAg7jRJZwTgUVo54WSt77HuxyGnUaMZJCLWHUs27vjbmhu7iWFs8S3u78g31XOqtOTgAJf3RRLhrUMYsBwbWAzv4v_U7SqfFL2JCAqZNEZG9kKxPN4N0nO5Job72vb7GFywIo_vjJdE_2tSBzERIM90S9IgJR9ZpKomdT9Jza5ublrIVjZ-pOgQCmSO0PamODK66dvaoQyYYePQEsjDZWgao62u2rAI_ytuUzh8BfVqiNlUiTKVhZrpymrPfkhXR5PAPzbYt12rOSbHRqpVzFI_pgPr2QYjCXRl0vKG2uwfBFvSjE6mq-b5k81-0YXK7xgqfoQw4zMd3rFK6ab5t6nKLXKOTIz8625IUpqV3ujEjqlBjX9u5kowKrU72t6YTqR2lUsSPMa6a0xz1cKCZC7esrZsh73j8TyTU18VszOduDgLurTYzlUXiBvAPCkidqPF9RbHNYMJ9O5hMbhqcZGEf-MTE4YrDEH3E5vq6p3yVTLave6xHcktmf9XwnEcFH7XXjh419RkDBpdxpCLgBwFrpoSMH8hqbnr_vycEPEdbkJdr5p59A5Ypt3e9a1-oik4"},{"header":{"kid":"webstore"},"protected":"eyJhbGciOiJSUzI1NiJ9","signature":"QnYXTj9TwuDFoZTf8j6oGfpC4ltPysivi1I0_IqwMzaMSP6_SlMjqSTnsz7_yZmeqBeJ0QfkzDiAaEi8_lpkcpHh6-zliJzc7HhE38s64w7m1bx-LUlJWZU2TPg9Te1EV3Ye4_qnLdgmNC07JP-Uo8re1ldtIXMSLZIrIdxNIJ7DxD_1mOduynbPwa1CyFWpIHi-0tr0G-1x6SDPAICtt6-FU1M4HkDlg57CC6CCwUFLSaJbEiHu2N0l1qw7q8qmfY1aAcNA1EFHG-230KijoTPkCLRpFGal8gMYIn0d9iao4VtrSe2aCBpuijk1kjV_RMfT0ks-8lCy7b4FQ_-lyQ"}]}}] 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a/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/d87757fb952668c2_0 b/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/5101a7cb9e0e7faf_0 similarity index 97% rename from user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/d87757fb952668c2_0 rename to user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/5101a7cb9e0e7faf_0 index 72cc1a5..b792c44 100644 Binary files a/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/d87757fb952668c2_0 and b/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/5101a7cb9e0e7faf_0 differ diff --git a/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/d87757fb952668c2_1 b/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/5101a7cb9e0e7faf_1 similarity index 86% rename from user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/d87757fb952668c2_1 rename to user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/5101a7cb9e0e7faf_1 index a89c755..151031e 100644 Binary files a/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/d87757fb952668c2_1 and b/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/5101a7cb9e0e7faf_1 differ diff --git a/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/93be6d71af4f294c_0 b/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/549d7f4055495109_0 similarity index 97% rename from user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/93be6d71af4f294c_0 rename to user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/549d7f4055495109_0 index 1bce824..5155e20 100644 Binary files a/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/93be6d71af4f294c_0 and b/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/549d7f4055495109_0 differ diff --git a/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/93be6d71af4f294c_1 b/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/549d7f4055495109_1 similarity index 85% rename from user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/93be6d71af4f294c_1 rename to user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/549d7f4055495109_1 index d77247a..8e4e0cc 100644 Binary files a/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/93be6d71af4f294c_1 and b/user/user_data/Default/Service 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to user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/7ecbd28ec0fb00ff_0 index 5e0cba2..0985cec 100644 Binary files a/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/ce3bccd483224e80_0 and b/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/7ecbd28ec0fb00ff_0 differ diff --git a/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/ce3bccd483224e80_1 b/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/7ecbd28ec0fb00ff_1 similarity index 88% rename from user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/ce3bccd483224e80_1 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to user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/955dafca41da3abc_0 index ec36e9e..8523802 100644 Binary files a/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/068bdbf6aacb6ff3_0 and b/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/955dafca41da3abc_0 differ diff --git a/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/068bdbf6aacb6ff3_1 b/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/955dafca41da3abc_1 similarity index 83% rename from user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/068bdbf6aacb6ff3_1 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to user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/bbd3deca5f319369_0 index 0342705..4ff1c24 100644 Binary files a/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/95ff5d6903763fb7_0 and b/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/bbd3deca5f319369_0 differ diff --git a/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/95ff5d6903763fb7_1 b/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/bbd3deca5f319369_1 similarity index 84% rename from user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/95ff5d6903763fb7_1 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Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/9c8cc7ea12121c51_0 rename to user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/bee61fdef038c50a_0 index 9e205ec..b9095e9 100644 Binary files a/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/9c8cc7ea12121c51_0 and b/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/bee61fdef038c50a_0 differ diff --git a/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/9c8cc7ea12121c51_1 b/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/bee61fdef038c50a_1 similarity index 84% rename from 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from user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/ddbc862cb3543a51_0 rename to user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/bf3cfc5af5362cdc_0 index b253373..1b5198c 100644 Binary files a/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/ddbc862cb3543a51_0 and b/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/bf3cfc5af5362cdc_0 differ diff --git a/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/05119aae69136ec8_0 b/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/c29eccd4b5e9e517_0 similarity index 94% rename from user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/05119aae69136ec8_0 rename to user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/c29eccd4b5e9e517_0 index 67b1b3c..2e97747 100644 Binary files a/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/05119aae69136ec8_0 and b/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/c29eccd4b5e9e517_0 differ diff --git a/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/05119aae69136ec8_1 b/user/user_data/Default/Service Worker/CacheStorage/987935aee542b816248034eca833fe60a4d902b0/80f34c7e-30be-44fa-bc19-123b3b28bc6a/c29eccd4b5e9e517_1 similarity index 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C:\Users\27942\Desktop\codes\haha\user\user_data\Default\Service Worker\Database/000003.log diff --git a/user/user_data/Default/Service Worker/Database/LOG.old b/user/user_data/Default/Service Worker/Database/LOG.old index b0c8056..e1ec359 100644 --- a/user/user_data/Default/Service Worker/Database/LOG.old +++ b/user/user_data/Default/Service Worker/Database/LOG.old @@ -1,3 +1,3 @@ -2026/02/03-00:14:54.289 5168 Reusing MANIFEST C:\Users\27942\Desktop\codes\haha\user\user_data\Default\Service Worker\Database/MANIFEST-000001 -2026/02/03-00:14:54.290 5168 Recovering log #3 -2026/02/03-00:14:54.291 5168 Reusing old log C:\Users\27942\Desktop\codes\haha\user\user_data\Default\Service Worker\Database/000003.log +2026/02/04-20:35:40.683 d0c Reusing MANIFEST C:\Users\27942\Desktop\codes\haha\user\user_data\Default\Service Worker\Database/MANIFEST-000001 +2026/02/04-20:35:40.683 d0c Recovering log #3 +2026/02/04-20:35:40.684 d0c Reusing old log 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Storage/LOG.old +++ b/user/user_data/Default/Session Storage/LOG.old @@ -1,3 +1,3 @@ -2026/02/03-00:14:54.474 918 Reusing MANIFEST C:\Users\27942\Desktop\codes\haha\user\user_data\Default\Session Storage/MANIFEST-000001 -2026/02/03-00:14:54.475 918 Recovering log #10 -2026/02/03-00:14:54.480 918 Reusing old log C:\Users\27942\Desktop\codes\haha\user\user_data\Default\Session Storage/000010.log +2026/02/04-20:35:40.851 33c Reusing MANIFEST C:\Users\27942\Desktop\codes\haha\user\user_data\Default\Session Storage/MANIFEST-000001 +2026/02/04-20:35:40.852 33c Recovering log #15 +2026/02/04-20:35:40.856 33c Reusing old log C:\Users\27942\Desktop\codes\haha\user\user_data\Default\Session Storage/000015.log diff --git a/user/user_data/Default/Session Storage/MANIFEST-000001 b/user/user_data/Default/Session Storage/MANIFEST-000001 index 03229dc..307645d 100644 Binary files a/user/user_data/Default/Session Storage/MANIFEST-000001 and b/user/user_data/Default/Session Storage/MANIFEST-000001 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Characteristics Database/000009.log +2026/02/04-20:45:34.729 11fc Reusing MANIFEST C:\Users\27942\Desktop\codes\haha\user\user_data\Default\Site Characteristics Database/MANIFEST-000001 +2026/02/04-20:45:34.730 11fc Recovering log #9 +2026/02/04-20:45:34.731 11fc Reusing old log C:\Users\27942\Desktop\codes\haha\user\user_data\Default\Site Characteristics Database/000009.log diff --git a/user/user_data/Default/Site Characteristics Database/LOG.old b/user/user_data/Default/Site Characteristics Database/LOG.old index 4af8ca4..287f807 100644 --- a/user/user_data/Default/Site Characteristics Database/LOG.old +++ b/user/user_data/Default/Site Characteristics Database/LOG.old @@ -1,3 +1,3 @@ -2026/02/03-00:14:54.300 1150 Reusing MANIFEST C:\Users\27942\Desktop\codes\haha\user\user_data\Default\Site Characteristics Database/MANIFEST-000001 -2026/02/03-00:14:54.300 1150 Recovering log #9 -2026/02/03-00:14:54.301 1150 Reusing old log C:\Users\27942\Desktop\codes\haha\user\user_data\Default\Site 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","variations_safe_seed_date":"13414682141000000","variations_safe_seed_fetch_time":"13414682735388840","variations_safe_seed_locale":"zh-CN","variations_safe_seed_milestone":144,"variations_safe_seed_permanent_consistency_country":"cn","variations_safe_seed_session_consistency_country":"cn","variations_safe_seed_signature":"MEQCIGWXttOXAg8fpVecKdXgp1+EId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'Tiger2027h2'|MFkwEwYHKoZIzj0CAQYIKoZIzj0DAQcDQgAEb0AgkemhsPmYe1goCSy5ncf2lG9vtK6f+SzODKJMYEgPOT+z93cUEKM1EaTuo09rozfdqhjeihIl25y9A3JhyQ==,A4AqwmL24F4D+Lxve5hRMk/Xaj31t1lRdeIi+46b1fY= *#https://tiger2027h2.ct.sectigo.com/2 -B -J -SectigoRsectigo_tiger2027h2https://crbug.com/3991246092 -Let's Encrypt 'Oak2025h2'|MFkwEwYHKoZIzj0CAQYIKoZIzj0DAQcDQgAEtXYwB63GyNLkS9L1vqKNnP10+jrW+lldthxg090fY4eG40Xg1RvANWqrJ5GVydc9u8H3cYZp9LNfkAmqrr2NqQ==,DeHyMCvTDcFAYhIJ6lUu/Ed0fLHX6TDvDkIetH5OqjQ= *&https://oak.ct.letsencrypt.org/2025h2/2 -B -J - Let's EncryptRletsencrypt_oak2025h24,fn06m+bnTrDRl01hT1F1TdZPYfxciFZZn7NAayeGOVQ=https://crbug.com/9636932 -Let's Encrypt 'Oak2026h1'|MFkwEwYHKoZIzj0CAQYIKoZIzj0DAQcDQgAEmdRhcCL6d5MNs8eAliJRvyV5sQFC6UF7iwzHsmVaifT64gJG1IrHzBAHESdFSJAjQN56TYky+9cK616MovH2SQ==,GYbUxyiqb/66A294Kk0BkarOLXIxD67OXXBBLSVMx9Q= *&https://oak.ct.letsencrypt.org/2026h1/2 -ΗB -J - Let's EncryptRletsencrypt_oak2026h14Ÿ,deSRNfTNPgd9wfzoXIznvi+QUTxuK0R+daC6JGKGK3Q=https://crbug.com/414591432 -Let's Encrypt 'Oak2026h2'|MFkwEwYHKoZIzj0CAQYIKoZIzj0DAQcDQgAEanCds5bj7IU2lcNPnIvZfMnVkSmu69aH3AS8O/Y0D/bbCPdSqYjvuz9Z1tT29PxcqYxf+w1g5CwPFuwqsm3rFQ==,rKswcGzr7IQx9BPS9JFfER5CJEOx8qaMTzwrO6ceAsM= *&https://oak.ct.letsencrypt.org/2026h2/2 -B -J - Let's EncryptRletsencrypt_oak2026h23̭>,uTgg1k3DUbSFFdXewyyxbsQuCc9RupplMphTwtXqvf4=https://crbug.com/414591432 -TrustAsia 'log2026a'|MFkwEwYHKoZIzj0CAQYIKoZIzj0DAQcDQgAEp056yaYH+f907JjLSeEAJLNZLoP9wHA1M0xjynSDwDxbU0B8MR81pF8P5O5PiRfoWy7FrAAFyXY3RZcDFf9gWQ==,dNudWPfUfp39eHoWKpkcGM9pjafHKZGMmhiwRQ26RLw= *(https://ct2026-a.trustasia.com/log2026a/2 -ڬ΀B -J - TrustAsiaٲRtrustasia_log2026acrbug.com/409178532 -TrustAsia 'log2026b'|MFkwEwYHKoZIzj0CAQYIKoZIzj0DAQcDQgAEDxKMqebj7GLu31jIUOYmcHYQtwQ5s6f4THM7wzhaEgBM4NoOFopFMgoxqiLHnX0FU8eelOqbV0a/T6R++9/6hQ==,Jbfv3qETAZPtkweXcKoyKiZiDeNayKp8dRl94LGp4GU= *(https://ct2026-b.trustasia.com/log2026b/2 -ڬ΀B -J - TrustAsiaٲRtrustasia_log2026bcrbug.com/409178532 -TrustAsia 'HETU2027'|MFkwEwYHKoZIzj0CAQYIKoZIzj0DAQcDQgAE14jG8D9suqIVWPtTNOL33uXKZ4mUnnOMrIwOWeZU7GtoDRCWIXfy/9/SC8lTAbtP2NOP4wjIufAk6f64sY4DWg==,7drrgVxjITRJtHvlB3kFq9DZMUfCesUUazvFjkPptsc= *(https://hetu2027.trustasia.com/hetu2027/2 -B -J - TrustAsiaRtrustasia_hetu2027https://crbug.com/409178532 -Let's Encrypt 'Sycamore2026h1'|MFkwEwYHKoZIzj0CAQYIKoZIzj0DAQcDQgAEfEEe0JZknA91/c6eNl1aexgeKzuGQUMvRCXPXg9L227O5I4Pi++Abcpq6qxlVUKPYafAJelAnMfGzv3lHCc8gA==,pcl4kl1XRheChw3YiWYLXFVki30AQPLsB2hR0YhpGfc= <*/https://log.sycamore.ct.letsencrypt.org/2026h1/2 -B -J - Let's EncryptRletsencrypt_sycamore2026h1Z/https://mon.sycamore.ct.letsencrypt.org/2026h1/https://crbug.com/414591432 -Let's Encrypt 'Sycamore2026h2'|MFkwEwYHKoZIzj0CAQYIKoZIzj0DAQcDQgAEwR1FtiiMbpvxR+sIeiZ5JSCIDIdTAPh7OrpdchcrCcyNVDvNUq358pqJx2qdyrOI+EjGxZ7UiPcN3bL3Q99FqA==,bP5QGUOoXqkWvFLRM+TcyR7xQRx9JYQg0XOAnhgY6zo= <*/https://log.sycamore.ct.letsencrypt.org/2026h2/2 -̌B -J - Let's EncryptRletsencrypt_sycamore2026h2Z/https://mon.sycamore.ct.letsencrypt.org/2026h2/https://crbug.com/414591432 -Let's Encrypt 'Sycamore2027h1'|MFkwEwYHKoZIzj0CAQYIKoZIzj0DAQcDQgAEWrGdYyZYB7teCS4K/oKIsbV0yVBSgjlOwO22OOCoA6Y252QhFzC8Wg7oVXVKqfkWaSaM/n+3pfCBf4BAkpdx8g==,jspHC6zeavOiBrCkeoS3Rv4fxr+VPiXmm07kAkjzxug= <*/https://log.sycamore.ct.letsencrypt.org/2027h1/2 -̌B -J - Let's EncryptRletsencrypt_sycamore2027h1Z/https://mon.sycamore.ct.letsencrypt.org/2027h1/https://crbug.com/414591432 -Let's Encrypt 'Sycamore2027h2'|MFkwEwYHKoZIzj0CAQYIKoZIzj0DAQcDQgAEK+2zy2UWRMIyC2jU46+rj8UsyMjLsQIr1Y/6ClbdpWGthUb8y3Maf4zfAZTWW+AH9wAWPLRL5vmtz7Zkh2f2nA==,5eNiR9ku9K2jhYO1NZHbcp/C8ArktnRRdNPd/GqiU4g= <*/https://log.sycamore.ct.letsencrypt.org/2027h2/2 -B -J - Let's EncryptRletsencrypt_sycamore2027h2Z/https://mon.sycamore.ct.letsencrypt.org/2027h2/https://crbug.com/414591432 -Let's Encrypt 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EncryptRletsencrypt_willow2027h1Z-https://mon.willow.ct.letsencrypt.org/2027h1/https://crbug.com/414591432 -Let's Encrypt 'Willow2027h2'|MFkwEwYHKoZIzj0CAQYIKoZIzj0DAQcDQgAEYbMDg0qQEEYjsTttdDlouTKhg3fRiMJYNE+Epr/2bXyeQdQOHKQNKv5sbIKxjtE/5Vqo9YjQbnaOeH4Wm4PhdQ==,ppWirZJtb5lujvxJAUJX2LvwRqfWJYm4jcLXh2x45S8= <*-https://log.willow.ct.letsencrypt.org/2027h2/2 -B -J - Let's EncryptRletsencrypt_willow2027h2Z-https://mon.willow.ct.letsencrypt.org/2027h2/https://crbug.com/414591432 -Geomys 'Tuscolo2026h1'|MFkwEwYHKoZIzj0CAQYIKoZIzj0DAQcDQgAEflxzMg2Ajjg7h1+ZIvQ9LV6yFvdj6uRi9YbvtRnSCgS2SamkH56WcPRaBTRYARPDIr5JwLqgJAVA/NvDxdJXOw==,cX6V88I4im2x44RJPTHhWqliCHYtQgDgBQzQZ7WmYeI= <**https://tuscolo2026h1.sunlight.geomys.org/2 -B -J -GeomysRgeomys_tuscolo2026h1Z*https://tuscolo2026h1.skylight.geomys.org/https://crbug.com/4166913302 -Geomys 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Ou:b ax'b7RW(ӧ KwA- 4{ꅺмw< %([ ,?G/xlt;ppR͌] RDŕO-t'jåk)2uO ,TIпQ5'za=_jWdR+ e ~qȣR6 +9SFG{r okʑ5Yt8[؈=8S$ Ӯ E6G2:6Tj9 \ No newline at end of file diff --git a/user/user_data/PKIMetadata/1566/kp_pinslist.pb b/user/user_data/PKIMetadata/1572/kp_pinslist.pb similarity index 99% rename from user/user_data/PKIMetadata/1566/kp_pinslist.pb rename to user/user_data/PKIMetadata/1572/kp_pinslist.pb index 900b604..7b48ae1 100644 Binary files a/user/user_data/PKIMetadata/1566/kp_pinslist.pb and b/user/user_data/PKIMetadata/1572/kp_pinslist.pb differ diff --git a/user/user_data/PKIMetadata/1566/manifest.json b/user/user_data/PKIMetadata/1572/manifest.json similarity index 72% rename from user/user_data/PKIMetadata/1566/manifest.json rename to user/user_data/PKIMetadata/1572/manifest.json index 2fe6709..54045d7 100644 --- a/user/user_data/PKIMetadata/1566/manifest.json +++ b/user/user_data/PKIMetadata/1572/manifest.json @@ -1,5 +1,5 @@ { "manifest_version": 2, "name": "pkiMetadata", - "version": "1566" + "version": "1572" } \ No newline at end of file diff --git a/user/user_data/Safe Browsing/ChromeExtMalware.store.32_13414679585378031 b/user/user_data/Safe Browsing/ChromeExtMalware.store.32_13414679585378031 new file mode 100644 index 0000000..fc7aee0 --- /dev/null +++ b/user/user_data/Safe Browsing/ChromeExtMalware.store.32_13414679585378031 @@ -0,0 +1 @@ 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