feat: integrate basin force estimator (pre-force) for 7x12 sensor

# Conflicts:
#	.gitignore
#	package-lock.json
#	package.json
#	src-tauri/Cargo.lock
#	src-tauri/Cargo.toml
#	src-tauri/tauri.conf.json
#	src/routes/+page.svelte

devkit/build/je-skin-devkit-server/EXE-00.toc

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+   'C:\\Python314\\Lib\\site-packages\\numpy-2.4.4.dist-info\\licenses\\numpy\\random\\src\\splitmix64\\LICENSE.md',
+   'DATA'),
+  ('numpy-2.4.4.dist-info\\licenses\\numpy\\random\\src\\distributions\\LICENSE.md',
+   'C:\\Python314\\Lib\\site-packages\\numpy-2.4.4.dist-info\\licenses\\numpy\\random\\src\\distributions\\LICENSE.md',
+   'DATA'),
+  ('numpy-2.4.4.dist-info\\licenses\\numpy\\_core\\src\\umath\\svml\\LICENSE',
+   'C:\\Python314\\Lib\\site-packages\\numpy-2.4.4.dist-info\\licenses\\numpy\\_core\\src\\umath\\svml\\LICENSE',
+   'DATA'),
+  ('numpy-2.4.4.dist-info\\licenses\\numpy\\random\\LICENSE.md',
+   'C:\\Python314\\Lib\\site-packages\\numpy-2.4.4.dist-info\\licenses\\numpy\\random\\LICENSE.md',
+   'DATA'),
+  ('numpy-2.4.4.dist-info\\METADATA',
+   'C:\\Python314\\Lib\\site-packages\\numpy-2.4.4.dist-info\\METADATA',
+   'DATA'),
+  ('numpy-2.4.4.dist-info\\licenses\\numpy\\random\\src\\mt19937\\LICENSE.md',
+   'C:\\Python314\\Lib\\site-packages\\numpy-2.4.4.dist-info\\licenses\\numpy\\random\\src\\mt19937\\LICENSE.md',
+   'DATA'),
+  ('numpy-2.4.4.dist-info\\licenses\\numpy\\_core\\src\\highway\\LICENSE',
+   'C:\\Python314\\Lib\\site-packages\\numpy-2.4.4.dist-info\\licenses\\numpy\\_core\\src\\highway\\LICENSE',
+   'DATA'),
+  ('numpy-2.4.4.dist-info\\licenses\\numpy\\random\\src\\philox\\LICENSE.md',
+   'C:\\Python314\\Lib\\site-packages\\numpy-2.4.4.dist-info\\licenses\\numpy\\random\\src\\philox\\LICENSE.md',
+   'DATA'),
+  ('numpy-2.4.4.dist-info\\licenses\\numpy\\fft\\pocketfft\\LICENSE.md',
+   'C:\\Python314\\Lib\\site-packages\\numpy-2.4.4.dist-info\\licenses\\numpy\\fft\\pocketfft\\LICENSE.md',
+   'DATA'),
+  ('numpy-2.4.4.dist-info\\licenses\\numpy\\linalg\\lapack_lite\\LICENSE.txt',
+   'C:\\Python314\\Lib\\site-packages\\numpy-2.4.4.dist-info\\licenses\\numpy\\linalg\\lapack_lite\\LICENSE.txt',
+   'DATA'),
+  ('numpy-2.4.4.dist-info\\RECORD',
+   'C:\\Python314\\Lib\\site-packages\\numpy-2.4.4.dist-info\\RECORD',
+   'DATA'),
+  ('base_library.zip',
+   'D:\\JE-Skin\\devkit\\build\\je-skin-devkit-server\\base_library.zip',
+   'DATA')],
+ 'python314.dll',
+ False,
+ False,
+ False,
+ [],
+ None,
+ None,
+ None)

devkit/build/je-skin-devkit-server/warn-je-skin-devkit-server.txt

@@ -0,0 +1,245 @@
+
+This file lists modules PyInstaller was not able to find. This does not
+necessarily mean these modules are required for running your program. Both
+Python's standard library and 3rd-party Python packages often conditionally
+import optional modules, some of which may be available only on certain
+platforms.
+
+Types of import:
+* top-level: imported at the top-level - look at these first
+* conditional: imported within an if-statement
+* delayed: imported within a function
+* optional: imported within a try-except-statement
+
+IMPORTANT: Do NOT post this list to the issue-tracker. Use it as a basis for
+            tracking down the missing module yourself. Thanks!
+
+missing module named pwd - imported by posixpath (delayed, conditional, optional), subprocess (delayed, conditional, optional), shutil (delayed, optional), tarfile (optional), pathlib (optional), netrc (delayed, optional), http.server (delayed, optional)
+missing module named grp - imported by subprocess (delayed, conditional, optional), shutil (delayed, optional), tarfile (optional), pathlib (optional)
+missing module named 'collections.abc' - imported by typing (top-level), tracemalloc (top-level), traceback (top-level), _colorize (top-level), selectors (top-level), logging (top-level), http.client (top-level), importlib.resources.readers (top-level), inspect (top-level), multiprocessing.managers (top-level), typing_extensions (top-level), asyncio.base_events (top-level), asyncio.coroutines (top-level), grpc.aio._metadata (top-level), google.protobuf.internal.containers (top-level), google.protobuf.internal.well_known_types (top-level), numpy._typing._array_like (top-level), numpy._typing._nested_sequence (conditional), numpy._typing._shape (top-level), numpy._typing._dtype_like (top-level), numpy.lib._function_base_impl (top-level), _pyrepl.types (top-level), numpy.lib._npyio_impl (top-level), numpy.random._common (top-level), numpy.random._generator (top-level), numpy.random.bit_generator (top-level), numpy.random.mtrand (top-level), numpy.polynomial._polybase (top-level), xml.etree.ElementTree (top-level)
+missing module named _posixsubprocess - imported by subprocess (conditional), multiprocessing.util (delayed)
+missing module named fcntl - imported by subprocess (optional), pathlib._os (optional)
+missing module named _posixshmem - imported by multiprocessing.resource_tracker (conditional), multiprocessing.shared_memory (conditional)
+missing module named _scproxy - imported by urllib.request (conditional)
+missing module named posix - imported by os (conditional, optional), posixpath (optional), shutil (conditional), importlib._bootstrap_external (conditional), pathlib._os (optional), _pyrepl.trace (conditional)
+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)
+excluded module named _frozen_importlib - imported by importlib (optional), importlib.abc (optional), zipimport (top-level)
+missing module named multiprocessing.BufferTooShort - imported by multiprocessing (top-level), multiprocessing.connection (top-level)
+missing module named multiprocessing.AuthenticationError - imported by multiprocessing (top-level), multiprocessing.forkserver (top-level), multiprocessing.connection (top-level)
+missing module named multiprocessing.get_context - imported by multiprocessing (top-level), multiprocessing.pool (top-level), multiprocessing.managers (top-level), multiprocessing.sharedctypes (top-level)
+missing module named multiprocessing.TimeoutError - imported by multiprocessing (top-level), multiprocessing.pool (top-level)
+missing module named multiprocessing.set_start_method - imported by multiprocessing (top-level), multiprocessing.spawn (top-level)
+missing module named multiprocessing.get_start_method - imported by multiprocessing (top-level), multiprocessing.spawn (top-level)
+missing module named pyimod02_importers - imported by C:\Python314\Lib\site-packages\PyInstaller\hooks\rthooks\pyi_rth_pkgutil.py (delayed)
+missing module named _dummy_thread - imported by numpy._core.arrayprint (optional)
+missing module named 'numpy_distutils.cpuinfo' - imported by numpy.f2py.diagnose (delayed, conditional, optional)
+missing module named 'numpy_distutils.fcompiler' - imported by numpy.f2py.diagnose (delayed, conditional, optional)
+missing module named 'numpy_distutils.command' - imported by numpy.f2py.diagnose (delayed, conditional, optional)
+missing module named numpy_distutils - imported by numpy.f2py.diagnose (delayed, optional)
+missing module named charset_normalizer - imported by numpy.f2py.crackfortran (optional)
+missing module named vms_lib - imported by platform (delayed, optional)
+missing module named 'java.lang' - imported by platform (delayed, optional)
+missing module named java - imported by platform (delayed)
+missing module named psutil - imported by numpy.testing._private.utils (delayed, optional)
+missing module named termios - imported by tty (top-level), _pyrepl.pager (delayed, optional)
+missing module named readline - imported by cmd (delayed, conditional, optional), code (delayed, conditional, optional), pdb (delayed, conditional, optional), rlcompleter (optional)
+missing module named win32pdh - imported by numpy.testing._private.utils (delayed, conditional)
+missing module named _typeshed - imported by numpy.random.bit_generator (top-level)
+missing module named numpy.random.RandomState - imported by numpy.random (top-level), numpy.random._generator (top-level)
+missing module named threadpoolctl - imported by numpy.lib._utils_impl (delayed, optional)
+missing module named numpy._core.zeros - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.vstack - imported by numpy._core (top-level), numpy.lib._shape_base_impl (top-level), numpy (conditional)
+missing module named numpy._core.void - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.vecmat - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.vecdot - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.ushort - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.unsignedinteger - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.ulonglong - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.ulong - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.uintp - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.uintc - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.uint64 - imported by numpy._core (conditional), numpy (conditional), numpy._array_api_info (top-level)
+missing module named numpy._core.uint32 - imported by numpy._core (conditional), numpy (conditional), numpy._array_api_info (top-level)
+missing module named numpy._core.uint16 - imported by numpy._core (conditional), numpy (conditional), numpy._array_api_info (top-level)
+missing module named numpy._core.uint - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.ubyte - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.trunc - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.true_divide - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.transpose - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.lib._function_base_impl (top-level), numpy (conditional)
+missing module named numpy._core.trace - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.timedelta64 - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.tensordot - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.tanh - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.tan - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.swapaxes - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.sum - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.subtract - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.str_ - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.square - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.sqrt - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional), numpy.fft._pocketfft (top-level)
+missing module named numpy._core.spacing - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.sort - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.sinh - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.single - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.signedinteger - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.signbit - imported by numpy._core (delayed), numpy.testing._private.utils (delayed), numpy (conditional)
+missing module named numpy._core.sign - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.short - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.rint - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.right_shift - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.result_type - imported by numpy._core (delayed), numpy.testing._private.utils (delayed), numpy (conditional), numpy.fft._pocketfft (top-level)
+missing module named numpy._core.remainder - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.reciprocal - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional), numpy.fft._pocketfft (top-level)
+missing module named numpy._core.radians - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.rad2deg - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.prod - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.power - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.positive - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.pi - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.outer - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.ones - imported by numpy._core (top-level), numpy.lib._polynomial_impl (top-level), numpy (conditional)
+missing module named numpy._core.object_ - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.testing._private.utils (delayed), numpy (conditional)
+missing module named numpy._core.number - imported by numpy._core (delayed), numpy.testing._private.utils (delayed), numpy (conditional)
+missing module named numpy._core.not_equal - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.nextafter - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.newaxis - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.negative - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.ndarray - imported by numpy._core (top-level), numpy.testing._private.utils (top-level), numpy.lib._utils_impl (top-level), numpy (conditional)
+missing module named numpy._core.multiply - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.moveaxis - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.modf - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.mod - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.minimum - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.maximum - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.max - imported by numpy._core (delayed), numpy.testing._private.utils (delayed), numpy (conditional)
+missing module named numpy._core.matvec - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.matrix_transpose - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.matmul - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.longlong - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.longdouble - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.long - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.logical_xor - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.logical_or - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.logical_not - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.logical_and - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.logaddexp2 - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.logaddexp - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.log10 - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.log2 - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.log1p - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.log - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.linspace - imported by numpy._core (top-level), numpy.lib._index_tricks_impl (top-level), numpy (conditional)
+missing module named numpy._core.less_equal - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.less - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.left_shift - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.ldexp - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.lcm - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.isscalar - imported by numpy._core (delayed), numpy.testing._private.utils (delayed), numpy.lib._polynomial_impl (top-level), numpy (conditional)
+missing module named numpy._core.isnat - imported by numpy._core (top-level), numpy.testing._private.utils (top-level), numpy (conditional)
+missing module named numpy._core.isnan - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.testing._private.utils (delayed), numpy (conditional)
+missing module named numpy._core.isfinite - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.intp - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.testing._private.utils (top-level), numpy (conditional), numpy._array_api_info (top-level)
+missing module named numpy._core.integer - imported by numpy._core (conditional), numpy (conditional), numpy.fft._helper (top-level)
+missing module named numpy._core.intc - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.int64 - imported by numpy._core (conditional), numpy (conditional), numpy._array_api_info (top-level)
+missing module named numpy._core.int32 - imported by numpy._core (conditional), numpy (conditional), numpy._array_api_info (top-level)
+missing module named numpy._core.int16 - imported by numpy._core (conditional), numpy (conditional), numpy._array_api_info (top-level)
+missing module named numpy._core.int8 - imported by numpy._core (conditional), numpy (conditional), numpy._array_api_info (top-level)
+missing module named numpy._core.inf - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.testing._private.utils (delayed), numpy (conditional)
+missing module named numpy._core.inexact - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.iinfo - imported by numpy._core (top-level), numpy.lib._twodim_base_impl (top-level), numpy (conditional)
+missing module named numpy._core.hypot - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.hstack - imported by numpy._core (top-level), numpy.lib._polynomial_impl (top-level), numpy (conditional)
+missing module named numpy._core.heaviside - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.half - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.greater_equal - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.greater - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.gcd - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.frompyfunc - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.frexp - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.fmod - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.fmin - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.fmax - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.floor_divide - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.floor - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.floating - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.float_power - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.float32 - imported by numpy._core (top-level), numpy.testing._private.utils (top-level), numpy (conditional), numpy._array_api_info (top-level)
+missing module named numpy._core.float16 - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.finfo - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.lib._polynomial_impl (top-level), numpy (conditional)
+missing module named numpy._core.fabs - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.expm1 - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.exp2 - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.exp - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.euler_gamma - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.errstate - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.testing._private.utils (delayed), numpy (conditional)
+missing module named numpy._core.equal - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.empty_like - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional), numpy.fft._pocketfft (top-level)
+missing module named numpy._core.empty - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.testing._private.utils (top-level), numpy (conditional), numpy.fft._helper (top-level)
+missing module named numpy._core.e - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.double - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.dot - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.lib._polynomial_impl (top-level), numpy (conditional)
+missing module named numpy._core.divmod - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.divide - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.diagonal - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.degrees - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.deg2rad - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.datetime64 - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.csingle - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.cross - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.count_nonzero - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.cosh - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.cos - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.copysign - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.conjugate - imported by numpy._core (conditional), numpy (conditional), numpy.fft._pocketfft (top-level)
+missing module named numpy._core.conj - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.complexfloating - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.complex64 - imported by numpy._core (conditional), numpy (conditional), numpy._array_api_info (top-level)
+missing module named numpy._core.clongdouble - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.character - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.ceil - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.cdouble - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.cbrt - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.bytes_ - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.byte - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.bool_ - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.bitwise_xor - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.bitwise_or - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.bitwise_count - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.bitwise_and - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.atleast_3d - imported by numpy._core (top-level), numpy.lib._shape_base_impl (top-level), numpy (conditional)
+missing module named numpy._core.atleast_2d - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.atleast_1d - imported by numpy._core (top-level), numpy.lib._polynomial_impl (top-level), numpy (conditional)
+missing module named numpy._core.asarray - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.lib._array_utils_impl (top-level), numpy (conditional), numpy.fft._helper (top-level), numpy.fft._pocketfft (top-level)
+missing module named numpy._core.asanyarray - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.array_repr - imported by numpy._core (top-level), numpy.testing._private.utils (top-level), numpy (conditional)
+missing module named numpy._core.array2string - imported by numpy._core (delayed), numpy.testing._private.utils (delayed), numpy (conditional)
+missing module named numpy._core.array - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.testing._private.utils (top-level), numpy.lib._polynomial_impl (top-level), numpy (conditional)
+missing module named numpy._core.argsort - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.arctanh - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.arctan2 - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.arctan - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.arcsinh - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.arcsin - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.arccosh - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.arccos - imported by numpy._core (conditional), numpy (conditional)
+missing module named numpy._core.arange - imported by numpy._core (top-level), numpy.testing._private.utils (top-level), numpy (conditional), numpy.fft._helper (top-level)
+missing module named numpy._core.amin - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.amax - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named numpy._core.all - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy.testing._private.utils (delayed), numpy (conditional)
+missing module named numpy._core.add - imported by numpy._core (top-level), numpy.linalg._linalg (top-level), numpy (conditional)
+missing module named yaml - imported by numpy.__config__ (delayed)
+missing module named numpy._distributor_init_local - imported by numpy (optional), numpy._distributor_init (optional)
+missing module named defusedxml - imported by openpyxl.xml (delayed, optional)
+missing module named lxml - imported by openpyxl.xml (delayed, optional)
+missing module named 'defusedxml.ElementTree' - imported by openpyxl.xml.functions (conditional)
+missing module named 'lxml.etree' - imported by openpyxl.xml.functions (conditional)
+missing module named PIL - imported by openpyxl.drawing.image (optional)
+missing module named openpyxl.tests - imported by openpyxl.reader.excel (optional)
+missing module named google.protobuf.pyext._message - imported by google.protobuf.pyext (conditional, optional), google.protobuf.internal.api_implementation (conditional, optional), google.protobuf.descriptor (conditional), google.protobuf.pyext.cpp_message (conditional)
+missing module named google.protobuf.enable_deterministic_proto_serialization - imported by google.protobuf (optional), google.protobuf.internal.api_implementation (optional)
+missing module named google.protobuf.internal._api_implementation - imported by google.protobuf.internal (optional), google.protobuf.internal.api_implementation (optional)
+missing module named grpc_reflection - imported by grpc (optional)
+missing module named grpc_health - imported by grpc (optional)
+missing module named pkg_resources - imported by grpc_tools.protoc (conditional)

src-tauri/.cargo/config.toml

@@ -0,0 +1,2 @@
+[registries.kellnr]
+index = "sparse+http://crates.huangyanjie.com/api/v1/crates/"

src-tauri/.gitignore

@@ -1,7 +1,9 @@
 # Generated by Cargo
 # will have compiled files and executables
 /target/
+/target-*/
 
 # Generated by Tauri
 # will have schema files for capabilities auto-completion
 /gen/schemas
+*log*

src-tauri/Cargo.lock

@@ -14,6 +14,7 @@ dependencies = [
  "crc",
  "csv",
  "dirs",
+ "eskin-finger-sdk",
  "fern",
  "futures-util",
  "humantime",
@@ -1152,6 +1153,25 @@ dependencies = [
  "windows-sys 0.61.2",
 ]
 
+[[package]]
+name = "eskin-finger-sdk"
+version = "0.1.0"
+source = "sparse+http://crates.huangyanjie.com/api/v1/crates/"
+checksum = "341d54dbc70a0fb7cdd04162cdda6ab5735f9a4f717b1921b42c00e8afc37bb9"
+dependencies = [
+ "chrono",
+ "crc",
+ "crossbeam-channel",
+ "fern",
+ "libc",
+ "log",
+ "serde",
+ "serde_json",
+ "serialport",
+ "thiserror 2.0.18",
+ "uuid",
+]
+
 [[package]]
 name = "event-listener"
 version = "5.4.1"
@@ -2314,9 +2334,9 @@ dependencies = [
 
 [[package]]
 name = "libc"
-version = "0.2.183"
+version = "0.2.186"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b5b646652bf6661599e1da8901b3b9522896f01e736bad5f723fe7a3a27f899d"
+checksum = "68ab91017fe16c622486840e4c83c9a37afeff978bd239b5293d61ece587de66"
 
 [[package]]
 name = "libloading"
@@ -2340,6 +2360,26 @@ dependencies = [
  "redox_syscall 0.7.4",
 ]
 
+[[package]]
+name = "libudev"
+version = "0.3.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "78b324152da65df7bb95acfcaab55e3097ceaab02fb19b228a9eb74d55f135e0"
+dependencies = [
+ "libc",
+ "libudev-sys",
+]
+
+[[package]]
+name = "libudev-sys"
+version = "0.1.4"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "3c8469b4a23b962c1396b9b451dda50ef5b283e8dd309d69033475fa9b334324"
+dependencies = [
+ "libc",
+ "pkg-config",
+]
+
 [[package]]
 name = "linux-raw-sys"
 version = "0.12.1"
@@ -4263,6 +4303,7 @@ dependencies = [
  "core-foundation",
  "core-foundation-sys",
  "io-kit-sys",
+ "libudev",
  "mach2",
  "nix 0.26.4",
  "scopeguard",
@@ -5565,9 +5606,9 @@ checksum = "b6c140620e7ffbb22c2dee59cafe6084a59b5ffc27a8859a5f0d494b5d52b6be"
 
 [[package]]
 name = "uuid"
-version = "1.22.0"
+version = "1.23.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a68d3c8f01c0cfa54a75291d83601161799e4a89a39e0929f4b0354d88757a37"
+checksum = "ddd74a9687298c6858e9b88ec8935ec45d22e8fd5e6394fa1bd4e99a87789c76"
 dependencies = [
  "getrandom 0.4.2",
  "js-sys",

src-tauri/Cargo.toml

@@ -49,10 +49,11 @@ crc = "3.4.0"
 axum = { version = "0.8", features = ["ws"] }
 tower-http = { version = "0.6", features = ["cors"] }
 futures-util = "0.3"
-uuid = { version = "1", features = ["v4", "serde"] }
+uuid = { version = "1.23", features = ["v4", "serde"] }
 rand = "0.8"
 reqwest = { version = "0.12", default-features = false, features = ["json", "rustls-tls"] }
 ndarray = { version = "0.15", optional = true }
+eskin-finger-sdk = { version = "0.1.0", registry = "kellnr" }
 
 [target.'cfg(not(any(target_os = "android", target_os = "ios")))'.dependencies]
 tauri-plugin-updater = "2"

src-tauri/resources/model_params.json

@@ -0,0 +1,217 @@
+{
+  "scaler_mean": [
+    1748.7541486595198,
+    1292.5704664084863,
+    669.8700117864961,
+    1617.8798712839798,
+    2104.589811228976,
+    3267.658809002638,
+    3366.4000112252343,
+    2660.981740285495,
+    2656.615909898786,
+    1747.1196048717518,
+    3093.4178032216423,
+    3107.599371386878,
+    4138.929019101607,
+    3778.3928270752654,
+    3495.851920450506,
+    3110.5580063983834,
+    2310.8518456156107,
+    2899.8918261585377,
+    3286.6881442816784,
+    3601.237076948981,
+    2590.9553048586554,
+    2555.2781425978933,
+    2004.8764850049579,
+    1333.8961665824775,
+    2090.217507623805,
+    0.363302046990876,
+    0.2506597877765041,
+    0.12741811820991292,
+    0.32195020821212794,
+    0.43317540002685884,
+    0.7725988160553472,
+    0.791227193907261,
+    0.5957799875116326,
+    0.5873844015441929,
+    0.35855586659016336,
+    0.7267512979672636,
+    0.7214172326166498,
+    1.0,
+    0.9089476753706724,
+    0.8226695360434777,
+    0.7208819781157673,
+    0.5152795489332506,
+    0.6711736481838434,
+    0.7782925265622518,
+    0.8648282061576593,
+    0.5787625095682526,
+    0.5752349727514727,
+    0.43456864805018935,
+    0.27668525082454587,
+    0.47414670304783574,
+    4138.929019101607,
+    64531.08183195824,
+    175620.92531477427,
+    22.847729696357412,
+    14.671691561018095,
+    0.07533558084489102,
+    12446.865764906175,
+    47945.287047950456,
+    2.8973185436828195,
+    10.774373017335268,
+    3.472192991899253,
+    -0.013941562889309035,
+    0.09672681097411825,
+    0.5067195499928454,
+    0.755407246398865,
+    0.03711810817384146,
+    11.154421806888552,
+    64500.8986854629
+  ],
+  "scaler_scale": [
+    1458.5456651154973,
+    1319.8585484401115,
+    798.8535944732339,
+    1467.8233720347457,
+    1637.8964913406842,
+    1330.3349975112737,
+    1391.430499849884,
+    1444.166940848846,
+    1630.948040054198,
+    1406.2203759964518,
+    1289.9699402243327,
+    1442.0533616965101,
+    1437.7214049715994,
+    1393.522474091575,
+    1468.6421185157626,
+    1449.3479990930084,
+    1293.2464048717598,
+    1331.2560392843097,
+    1326.1289536453178,
+    1357.3405110533047,
+    1452.4854193036483,
+    1348.4425883366337,
+    1318.1429721243371,
+    1059.93845215709,
+    1114.1647557935548,
+    0.2395898634701691,
+    0.21706962815914935,
+    0.13523106483202163,
+    0.23880331588910964,
+    0.24830003478347082,
+    0.1464527498295455,
+    0.15391677914992113,
+    0.18125664726966026,
+    0.2326879002599809,
+    0.23502163992653513,
+    0.13026800431597335,
+    0.15563022147466685,
+    1.0,
+    0.09922737602626737,
+    0.18291931318098986,
+    0.15401181704844932,
+    0.2143892844194339,
+    0.16856049162074294,
+    0.15902500893917185,
+    0.18285009098439925,
+    0.17264751056304276,
+    0.21090366624550771,
+    0.16802111677577075,
+    0.19264329284433157,
+    0.19589977001187556,
+    1437.7214049715994,
+    32602.413979370118,
+    95845.11969895993,
+    3.426376344472427,
+    3.408382770733738,
+    0.033353666248921464,
+    5505.629576226806,
+    25703.01200969283,
+    0.4599551450527747,
+    2.978321440052941,
+    0.3916581766443181,
+    0.06096090153067211,
+    0.07864618660494935,
+    0.0344984508436715,
+    0.17668176728315207,
+    0.18905119470509504,
+    5352.30503788098,
+    32297.31796957845
+  ],
+  "ridge_coef": [
+    7.4424310127566695,
+    13.345966730219576,
+    2.351840055857306,
+    6.088230738742203,
+    -10.030964629299273,
+    3.876136979406362,
+    -11.251608537526174,
+    16.84502390958064,
+    -2.093552796584439,
+    -5.784923711493545,
+    -6.67830546424787,
+    -4.654052249161928,
+    6.038218458133514,
+    9.82412450487401,
+    -6.200667839175651,
+    -0.3133364534713342,
+    -8.75036029102127,
+    12.785901861589027,
+    -3.7296377182327123,
+    6.546167384121816,
+    -4.984129287282208,
+    8.311396481777527,
+    -0.6248790895663127,
+    2.69008779623183,
+    12.996047839696784,
+    -2.2609944767610504,
+    -5.131537716982507,
+    0.3988922195665723,
+    -5.197736884253156,
+    4.556854888903703,
+    -0.8642438099006351,
+    6.327731485629085,
+    -5.157281763422745,
+    0.10691827520622764,
+    4.656962972053113,
+    3.2628870750114887,
+    4.033159141354671,
+    0.0,
+    -2.9206404009765268,
+    1.8683691849941264,
+    2.408006875407745,
+    7.250310827671452,
+    -3.97015207422554,
+    0.7316093212194048,
+    -3.459346094204882,
+    2.4407660203169255,
+    -2.872982666400644,
+    1.8797071977799857,
+    -1.3374700235689694,
+    -7.9533345474852295,
+    6.038063637368508,
+    1.615806581558555,
+    95785.62883805836,
+    0.12233606167692031,
+    -0.1515900264871255,
+    2.2023033069961873,
+    8.776787743985668,
+    -0.16714060634667535,
+    -2.751671223554021,
+    0.2511944267079865,
+    6.13561607395193,
+    2.85703108671782,
+    -0.11255626089468472,
+    -0.9017242341101542,
+    -0.627291200283328,
+    3.4664885582435883,
+    0.02591345630626686,
+    0.5530407299425606
+  ],
+  "ridge_intercept": 175620.9253147744,
+  "n_features": 68,
+  "noise_threshold": 15.0,
+  "contact_threshold": 20.0,
+  "ema_alpha": 0.9
+}

src-tauri/src/serial_core/basin_force_estimator.rs

@@ -0,0 +1,397 @@
+//! 7×12 柔性压力点阵力估计 - Rust 实现
+//!
+//! 与 Python `basin_feature_extractor.py` 完全对齐。
+//! 内嵌 `model_params.json`，对每帧 7×12 传感器数据提取 68 维特征并用
+//! StandardScaler + Ridge 回归估计法向力 Fz。
+
+use serde::Deserialize;
+
+// ───────────────── 常量 ─────────────────
+
+const ROWS: usize = 7;
+const COLS: usize = 12;
+const ROI_RADIUS: usize = 2;
+const ROI_SIZE: usize = 2 * ROI_RADIUS + 1; // 5
+const N_FEATURES: usize = 68; // 25 + 25 + 18
+
+// ───────────────── 模型参数 JSON（编译时嵌入）─────────────────
+
+const MODEL_PARAMS_JSON: &str = include_str!("../../resources/model_params.json");
+
+// ───────────────── 模型参数反序列化 ─────────────────
+
+#[derive(Debug, Deserialize)]
+struct ModelParams {
+    scaler_mean: Vec<f64>,
+    scaler_scale: Vec<f64>,
+    ridge_coef: Vec<f64>,
+    ridge_intercept: f64,
+    n_features: usize,
+    noise_threshold: f64,
+    contact_threshold: f64,
+    ema_alpha: f64,
+}
+
+// ───────────────── 估算器 ─────────────────
+
+pub struct BasinForceEstimator {
+    // 模型参数
+    scaler_mean: [f64; N_FEATURES],
+    scaler_scale: [f64; N_FEATURES],
+    ridge_coef: [f64; N_FEATURES],
+    ridge_intercept: f64,
+    // 超参数
+    noise_threshold: f64,
+    contact_threshold: f64,
+    ema_alpha: f64,
+    // 时序状态（需要可变）
+    prev_roi_sum: f64,
+    ema_sum: f64,
+    first_frame: bool,
+}
+
+impl BasinForceEstimator {
+    /// 使用编译时内嵌的 model_params.json 创建估算器
+    pub fn new() -> Self {
+        Self::from_json_str(MODEL_PARAMS_JSON)
+            .expect("内嵌 model_params.json 加载失败")
+    }
+
+    pub fn from_json_str(json: &str) -> Result<Self, Box<dyn std::error::Error>> {
+        let p: ModelParams = serde_json::from_str(json)?;
+        if p.n_features != N_FEATURES {
+            return Err(format!(
+                "模型特征维度不匹配: 期望 {}, 实际 {}",
+                N_FEATURES, p.n_features
+            )
+            .into());
+        }
+        let mut scaler_mean = [0.0; N_FEATURES];
+        let mut scaler_scale = [0.0; N_FEATURES];
+        let mut ridge_coef = [0.0; N_FEATURES];
+        scaler_mean.copy_from_slice(&p.scaler_mean);
+        scaler_scale.copy_from_slice(&p.scaler_scale);
+        ridge_coef.copy_from_slice(&p.ridge_coef);
+
+        Ok(Self {
+            scaler_mean,
+            scaler_scale,
+            ridge_coef,
+            ridge_intercept: p.ridge_intercept,
+            noise_threshold: p.noise_threshold,
+            contact_threshold: p.contact_threshold,
+            ema_alpha: p.ema_alpha,
+            prev_roi_sum: 0.0,
+            ema_sum: 0.0,
+            first_frame: true,
+        })
+    }
+
+    pub fn reset(&mut self) {
+        self.prev_roi_sum = 0.0;
+        self.ema_sum = 0.0;
+        self.first_frame = true;
+    }
+
+    pub fn predict_frame(&mut self, frame: &[f64; 84]) -> f64 {
+        let features = self.extract_features(frame);
+        self.ridge_predict(&features)
+    }
+
+    // ───────────── 特征提取 ─────────────
+
+    fn extract_features(&mut self, raw: &[f64; 84]) -> [f64; N_FEATURES] {
+        let mut x = [[0.0f64; COLS]; ROWS];
+        let mut max_value = 0.0f64;
+        for r in 0..ROWS {
+            for c in 0..COLS {
+                let v = raw[r * COLS + c].max(0.0);
+                x[r][c] = v;
+                if v > max_value {
+                    max_value = v;
+                }
+            }
+        }
+
+        if max_value < self.contact_threshold {
+            self.update_temporal(0.0);
+            return [0.0; N_FEATURES];
+        }
+
+        let mut peak_row = 0usize;
+        let mut peak_col = 0usize;
+        for r in 0..ROWS {
+            for c in 0..COLS {
+                if x[r][c] >= x[peak_row][peak_col] {
+                    peak_row = r;
+                    peak_col = c;
+                }
+            }
+        }
+
+        let roi = self.extract_roi(&x, peak_row, peak_col);
+        self.compute_features(&x, &roi, max_value, peak_row, peak_col)
+    }
+
+    fn extract_roi(
+        &self,
+        x: &[[f64; COLS]; ROWS],
+        pr: usize,
+        pc: usize,
+    ) -> [[f64; ROI_SIZE]; ROI_SIZE] {
+        let r = ROI_RADIUS as isize;
+        let mut roi = [[0.0f64; ROI_SIZE]; ROI_SIZE];
+
+        let r_start = (pr as isize - r).max(0) as usize;
+        let r_end = (pr + ROI_RADIUS + 1).min(ROWS);
+        let c_start = (pc as isize - r).max(0) as usize;
+        let c_end = (pc + ROI_RADIUS + 1).min(COLS);
+
+        let roi_r_start = (r_start as isize - (pr as isize - r)).max(0) as usize;
+        let roi_c_start = (c_start as isize - (pc as isize - r)).max(0) as usize;
+
+        for (i, ri) in (r_start..r_end).enumerate() {
+            for (j, ci) in (c_start..c_end).enumerate() {
+                roi[roi_r_start + i][roi_c_start + j] = x[ri][ci];
+            }
+        }
+        roi
+    }
+
+    fn compute_features(
+        &mut self,
+        x: &[[f64; COLS]; ROWS],
+        roi: &[[f64; ROI_SIZE]; ROI_SIZE],
+        max_value: f64,
+        peak_row: usize,
+        peak_col: usize,
+    ) -> [f64; N_FEATURES] {
+        let center = ROI_RADIUS;
+        let mut feat = [0.0f64; N_FEATURES];
+        let mut idx = 0;
+
+        // ROI 原始值 (25维)
+        for r in 0..ROI_SIZE {
+            for c in 0..ROI_SIZE {
+                feat[idx] = roi[r][c];
+                idx += 1;
+            }
+        }
+
+        // ROI 归一化形状 (25维)
+        for r in 0..ROI_SIZE {
+            for c in 0..ROI_SIZE {
+                feat[idx] = if max_value > 0.0 {
+                    roi[r][c] / max_value
+                } else {
+                    0.0
+                };
+                idx += 1;
+            }
+        }
+
+        // roi_sum, global_sum
+        let mut roi_sum = 0.0f64;
+        for r in 0..ROI_SIZE {
+            for c in 0..ROI_SIZE {
+                roi_sum += roi[r][c];
+            }
+        }
+        let mut global_sum = 0.0f64;
+        for r in 0..ROWS {
+            for c in 0..COLS {
+                global_sum += x[r][c];
+            }
+        }
+
+        // active_area
+        let thr = self.noise_threshold.max(0.05 * max_value);
+        let mut active_area = 0.0f64;
+        for r in 0..ROI_SIZE {
+            for c in 0..ROI_SIZE {
+                if roi[r][c] > thr {
+                    active_area += 1.0;
+                }
+            }
+        }
+
+        let participation = if max_value > 0.0 {
+            roi_sum / max_value
+        } else {
+            0.0
+        };
+        let concentration = if roi_sum > 0.0 {
+            max_value / roi_sum
+        } else {
+            0.0
+        };
+
+        // ring1_sum (上下左右4点)
+        let ring1_positions = [
+            (center - 1, center),
+            (center + 1, center),
+            (center, center - 1),
+            (center, center + 1),
+        ];
+        let ring1_sum: f64 = ring1_positions.iter().map(|&(r, c)| roi[r][c]).sum();
+
+        // ring2_sum (除中心和ring1外)
+        let mut ring2_sum = 0.0f64;
+        for r in 0..ROI_SIZE {
+            for c in 0..ROI_SIZE {
+                if (r, c) == (center, center) {
+                    continue;
+                }
+                if ring1_positions.contains(&(r, c)) {
+                    continue;
+                }
+                ring2_sum += roi[r][c];
+            }
+        }
+
+        let ring1_ratio = if max_value > 0.0 {
+            ring1_sum / max_value
+        } else {
+            0.0
+        };
+        let ring2_ratio = if max_value > 0.0 {
+            ring2_sum / max_value
+        } else {
+            0.0
+        };
+
+        // spread
+        let spread = if roi_sum > 0.0 {
+            let mut s = 0.0f64;
+            for r in 0..ROI_SIZE {
+                for c in 0..ROI_SIZE {
+                    let dr = r as f64 - center as f64;
+                    let dc = c as f64 - center as f64;
+                    s += (dr * dr + dc * dc) * roi[r][c];
+                }
+            }
+            s / roi_sum
+        } else {
+            0.0
+        };
+
+        // asym_x
+        let mut left_sum = 0.0f64;
+        let mut right_sum = 0.0f64;
+        for r in 0..ROI_SIZE {
+            for c in 0..center {
+                left_sum += roi[r][c];
+            }
+            for c in (center + 1)..ROI_SIZE {
+                right_sum += roi[r][c];
+            }
+        }
+        let asym_x = if roi_sum > 0.0 {
+            (right_sum - left_sum) / roi_sum
+        } else {
+            0.0
+        };
+
+        // asym_y
+        let mut up_sum = 0.0f64;
+        let mut down_sum = 0.0f64;
+        for r in 0..center {
+            for c in 0..ROI_SIZE {
+                up_sum += roi[r][c];
+            }
+        }
+        for r in (center + 1)..ROI_SIZE {
+            for c in 0..ROI_SIZE {
+                down_sum += roi[r][c];
+            }
+        }
+        let asym_y = if roi_sum > 0.0 {
+            (down_sum - up_sum) / roi_sum
+        } else {
+            0.0
+        };
+
+        // 位置
+        let peak_row_norm = peak_row as f64 / (ROWS - 1) as f64;
+        let peak_col_norm = peak_col as f64 / (COLS - 1) as f64;
+
+        // near_edge
+        let r = ROI_RADIUS as isize;
+        let near_edge = if (peak_row as isize) < r
+            || peak_row >= ROWS - ROI_RADIUS
+            || (peak_col as isize) < r
+            || peak_col >= COLS - ROI_RADIUS
+        {
+            1.0
+        } else {
+            0.0
+        };
+
+        // 时序特征
+        let delta_sum = roi_sum - self.prev_roi_sum;
+        if self.first_frame {
+            self.ema_sum = roi_sum;
+            self.first_frame = false;
+        } else {
+            self.ema_sum = self.ema_alpha * self.ema_sum + (1.0 - self.ema_alpha) * roi_sum;
+        }
+        self.prev_roi_sum = roi_sum;
+
+        let scalars = [
+            max_value,
+            roi_sum,
+            global_sum,
+            active_area,
+            participation,
+            concentration,
+            ring1_sum,
+            ring2_sum,
+            ring1_ratio,
+            ring2_ratio,
+            spread,
+            asym_x,
+            asym_y,
+            peak_row_norm,
+            peak_col_norm,
+            near_edge,
+            delta_sum,
+            self.ema_sum,
+        ];
+        for &v in &scalars {
+            feat[idx] = v;
+            idx += 1;
+        }
+
+        debug_assert_eq!(idx, N_FEATURES);
+        feat
+    }
+
+    fn update_temporal(&mut self, roi_sum: f64) {
+        self.prev_roi_sum = roi_sum;
+        if self.first_frame {
+            self.ema_sum = roi_sum;
+            self.first_frame = false;
+        } else {
+            self.ema_sum = self.ema_alpha * self.ema_sum + (1.0 - self.ema_alpha) * roi_sum;
+        }
+    }
+
+    // ───────────── 推理 ─────────────
+
+    fn ridge_predict(&self, features: &[f64; N_FEATURES]) -> f64 {
+        let mut scaled = [0.0f64; N_FEATURES];
+        for i in 0..N_FEATURES {
+            let s = self.scaler_scale[i];
+            scaled[i] = if s.abs() > 1e-12 {
+                (features[i] - self.scaler_mean[i]) / s
+            } else {
+                0.0
+            };
+        }
+        let mut y = self.ridge_intercept;
+        for i in 0..N_FEATURES {
+            y += self.ridge_coef[i] * scaled[i];
+        }
+        y
+    }
+}

src-tauri/src/serial_core/codecs/tactile_a.rs

@@ -12,7 +12,7 @@ use async_trait::async_trait;
 use csv::StringRecord;
 use anyhow::anyhow;
 use std::io::Read;
-use log::debug;
+use log::{debug, info};
 
 const FRAME_BUFFER_MIN_LENGTH: usize = 15;
 
@@ -221,11 +221,12 @@ impl Codec<TactileAFrame> for TactileACodec {
                 req_bytes.push(f.meta.device_addr);
                 req_bytes.push(f.meta.extend_code);
                 req_bytes.push(f.meta.func_code);
-                
+
                 req_bytes.extend_from_slice(f.meta.start_addr.to_le_bytes().as_slice());
                 req_bytes.extend_from_slice((f.meta.except_data_len as u16).to_le_bytes().as_slice());
                 let checksum = calc_crc8_itu(req_bytes.as_slice());
                 req_bytes.push(checksum);
+                info!("send: {:02X?}", req_bytes);
                 Ok(req_bytes)
             }
             _ => {
@@ -263,6 +264,7 @@ impl CsvExporter<TactileARepFrame> for TactileACsvExporter {
         }
 
         header.push("dts".to_string());
+        header.push("summary".to_string());
         header
     }
 
@@ -271,8 +273,10 @@ impl CsvExporter<TactileARepFrame> for TactileACsvExporter {
         item: &RecordedFrame<TactileARepFrame>,
     ) -> anyhow::Result<Vec<String>> {
         let packet = TactileADataPacket::try_from(&item.frame)?;
+        let summary: i32 = packet.data.iter().sum();
         let mut row: Vec<String> = packet.data.iter().map(|x| x.to_string()).collect();
         row.push(packet.dts_ms.to_string());
+        row.push(summary.to_string());
         Ok(row)
     }
 }

src-tauri/src/serial_core/mod.rs

@@ -13,6 +13,7 @@ pub mod record;
 pub mod utils;
 #[cfg(feature = "multi-dim")]
 pub mod multi_dim_force;
+pub mod basin_force_estimator;
 
 pub type TestRecording = Recording<TestFrame>;
 pub type TactileARecording = Recording<TactileAFrame>;

src-tauri/src/serial_core/serial.rs

@@ -1,3 +1,4 @@
+use crate::serial_core::basin_force_estimator::BasinForceEstimator;
 use crate::serial_core::codec::Codec;
 use crate::serial_core::codecs::tactile_a::TactileACodec;
 use crate::serial_core::frame::{FrameHandler, TactileAFrame, TestFrame};
@@ -233,6 +234,7 @@ where
     let mut prune_interval = time::interval(Duration::from_millis(450));
     #[cfg(feature = "multi-dim")]
     let mut pzt_processor = PztProcessor::new();
+    let mut force_estimator = BasinForceEstimator::new();
     let mut pending_sub_frame: Option<PendingSubFrame<F>> = None;
     prune_interval.set_missed_tick_behavior(MissedTickBehavior::Delay);
 
@@ -309,6 +311,16 @@ where
                     drop(record);
 
                     if let Some(vals) = decode_res {
+                        // Basin force estimation (pre-force)
+                        if vals.len() == 84 {
+                            let mut frame_f64 = [0.0f64; 84];
+                            for (i, v) in vals.iter().enumerate() {
+                                frame_f64[i] = *v as f64;
+                            }
+                            let pre_force = force_estimator.predict_frame(&frame_f64);
+                            debug!("pre-force: {:.2}", pre_force);
+                        }
+
                         #[cfg(feature = "multi-dim")]
                         {
                             let pzt_values = vals.iter().map(|value| *value as f32).collect::<Vec<f32>>();

src-tauri/tauri.conf.json

@@ -23,7 +23,7 @@
     }
   },
   "bundle": {
-    "createUpdaterArtifacts": true,
+    "createUpdaterArtifacts": false,
     "active": true,
     "targets": "all",
     "icon": [

src/indicator/indicator498.py

@@ -0,0 +1,747 @@
+from typing import Dict, List, Tuple
+import numpy as np
+from indicator.base import Indicator
+import math
+
+
+class EMA:
+    """指数移动平均"""
+
+    @staticmethod
+    def calc(data: np.ndarray, period: int) -> np.ndarray:
+        alpha = 2.0 / (period + 1)
+        out = np.empty_like(data)
+        out[0] = data[0]
+        for i in range(1, len(data)):
+            out[i] = alpha * data[i] + (1 - alpha) * out[i - 1]
+        return out
+
+
+class SMA:
+    """简单移动平均"""
+
+    @staticmethod
+    def calc(data: np.ndarray, period: int) -> np.ndarray:
+        out = np.full_like(data, np.nan)
+        if len(data) < period:
+            return out
+        cumsum = np.cumsum(data)
+        out[period - 1:] = (cumsum[period - 1:] - np.concatenate([[0], cumsum[:-period]])) / period
+        return out
+
+
+def true_range(high: np.ndarray, low: np.ndarray, close: np.ndarray) -> np.ndarray:
+    """True Range"""
+    tr = np.empty(len(high))
+    tr[0] = high[0] - low[0]
+    for i in range(1, len(high)):
+        tr[i] = max(high[i] - low[i], abs(high[i] - close[i - 1]), abs(low[i] - close[i - 1]))
+    return tr
+
+
+class SignalTracker:
+    """逐帧更新的信号追踪器（无回看依赖，纯实时）"""
+
+    def __init__(
+        self,
+        ob_threshold: float = 80,
+        os_threshold: float = 20,
+        dist_extreme: float = 5,
+        dist_far: float = 15,
+        dist_mid: float = 30,
+        macd_th: float = 0.3,
+        rsi_ob: float = 70,
+        rsi_os: float = 30,
+        di_gap_bull: float = 15,
+        di_gap_bear: float = -15,
+        aroon_ob: float = 80,
+        aroon_os: float = 20,
+        williams_ob: float = -20,
+        williams_os: float = -80,
+        bias_ob: float = 5,
+        bias_os: float = -5,
+    ):
+        self.ob_threshold = ob_threshold
+        self.os_threshold = os_threshold
+        self.dist_extreme = dist_extreme
+        self.dist_far = dist_far
+        self.dist_mid = dist_mid
+        self.macd_th = macd_th
+        self.rsi_ob = rsi_ob
+        self.rsi_os = rsi_os
+        self.di_gap_bull = di_gap_bull
+        self.di_gap_bear = di_gap_bear
+        self.aroon_ob = aroon_ob
+        self.aroon_os = aroon_os
+        self.williams_ob = williams_ob
+        self.williams_os = williams_os
+        self.bias_ob = bias_ob
+        self.bias_os = bias_os
+
+        # 状态缓存
+        self._intp_buy_sma: float = 0.0
+        self._intp_sell_sma: float = 0.0
+        self._intp_sma_count: int = 0
+        self._max_qtb_score: float = 0.0
+        self._bull_count: int = 0
+        self._bear_count: int = 0
+        self._last_bull_idx: int = -100
+        self._last_bear_idx: int = -100
+        self._frame_idx: int = 0
+
+        # 历史缓冲区（保留最近200帧用于EMA/SMA/DI计算）
+        self._buf_size: int = 200
+        self._bbi_buf: List[float] = []
+        self._amplitude_buf: List[float] = []
+        self._close_buf: List[float] = []
+        self._high_buf: List[float] = []
+        self._low_buf: List[float] = []
+        self._wr_buf: List[float] = []
+
+    def reset(self):
+        """重置所有状态"""
+        self._intp_buy_sma = 0.0
+        self._intp_sell_sma = 0.0
+        self._intp_sma_count = 0
+        self._max_qtb_score = 0.0
+        self._bull_count = 0
+        self._bear_count = 0
+        self._last_bull_idx = -100
+        self._last_bear_idx = -100
+        self._frame_idx = 0
+        self._bbi_buf.clear()
+        self._amplitude_buf.clear()
+        self._close_buf.clear()
+        self._high_buf.clear()
+        self._low_buf.clear()
+        self._wr_buf.clear()
+
+    def _append_buf(self, buf: List[float], val: float):
+        buf.append(val)
+        if len(buf) > self._buf_size:
+            buf.pop(0)
+
+    def _ema_val(self, buf: List[float], period: int) -> float:
+        """返回缓冲区中最后一个EMA值"""
+        if len(buf) < period:
+            return buf[-1] if buf else 0.0
+        arr = np.array(buf, dtype=float)
+        ema = EMA.calc(arr, period)
+        return float(ema[-1])
+
+    def _sma_val(self, buf: List[float], period: int) -> float:
+        if len(buf) < period:
+            return np.nan
+        arr = np.array(buf, dtype=float)
+        sma = SMA.calc(arr, period)
+        return float(sma[-1])
+
+    def get_signals(
+        self,
+        amplitude: float,
+        bbi: float,
+        cci: float,
+        close: float,
+        dmk: float,
+        high: float,
+        k: float,
+        low: float,
+        qtb_score: float,
+        result: float,
+        wr: float,
+        percent: float,
+        v0: float,
+        boll_upper: float,
+        boll_lower: float,
+        macd_val: float,
+        pmacd: float,
+        bias: float,
+    ) -> Tuple[str, str, str, str]:
+        """
+        逐帧更新，返回 (market_status, signal_type, intensity, detail)
+
+        不依赖历史数组，所有指标值由外部实时计算后传入。
+        """
+
+        # ─── 更新缓冲区 ───
+        self._append_buf(self._bbi_buf, bbi)
+        self._append_buf(self._amplitude_buf, amplitude)
+        self._append_buf(self._close_buf, close)
+        self._append_buf(self._high_buf, high)
+        self._append_buf(self._low_buf, low)
+        self._append_buf(self._wr_buf, wr)
+
+        # ─── 指标因子计算（使用传入值 + 缓冲区衍生值）───
+        k_macd = self._k_macd_factor(k, macd_val, pmacd)
+        k_di = self._k_di_factor(dmk, amplitude, close)
+        k_aroon = self._k_aroon_factor(high, low, close)
+        k_williams = self._k_williams_factor(wr)
+        k_result = self._k_result_factor(result)
+        k_bias = self._k_bias_factor(bias)
+        k_trix = self._k_trix_factor(close)
+        k_ema = self._k_ema_factor(close)
+        k_amplitude = self._k_amplitude_factor(amplitude)
+
+        # ─── 累计 ───
+        k_bull = k_macd + k_di + k_aroon + k_williams + k_result + k_bias + k_trix + k_ema + k_amplitude
+        k_bear = k_macd + k_di + k_aroon + k_williams + k_result + k_bias + k_trix + k_ema + k_amplitude
+
+        if result > self.ob_threshold and k_bull > 0 and result >= 90:
+            k_bull *= 1.5
+
+        # ─── 指数平滑 ───
+        if self._intp_sma_count == 0:
+            self._intp_buy_sma = k_bull
+            self._intp_sell_sma = k_bear
+            self._intp_sma_count = 1
+        else:
+            self._intp_sma_count += 1
+            period = max(3, min(5, self._intp_sma_count))
+            alpha = 2.0 / (period + 1)
+            self._intp_buy_sma = alpha * k_bull + (1 - alpha) * self._intp_buy_sma
+            self._intp_sell_sma = alpha * k_bear + (1 - alpha) * self._intp_sell_sma
+
+        # ─── 趋势确认与背离检测 ───
+        is_bull_div, is_bear_div = self._detect_divergence(close, result)
+        if is_bull_div:
+            self._intp_buy_sma *= 1.3
+            k_bull *= 1.3
+        if is_bear_div:
+            self._intp_sell_sma *= 1.3
+            k_bear *= 1.3
+
+        is_bull, is_bear = self._detect_trend(close)
+        if is_bull:
+            self._intp_buy_sma *= 1.15
+            k_bull *= 1.15
+        if is_bear:
+            self._intp_sell_sma *= 1.15
+            k_bear *= 1.15
+
+        wmacd = self._wmacd()
+        if wmacd > 0:
+            k_bull += wmacd * 0.5
+        elif wmacd < 0:
+            k_bear += abs(wmacd) * 0.5
+
+        if qtb_score > self._max_qtb_score * 0.7:
+            pass
+        elif qtb_score < 3 and qtb_score > 1:
+            k_bull *= 0.85
+            k_bear *= 0.85
+
+        # ─── 信号分类 ───
+        max_k = max(abs(k_bull), abs(k_bear))
+        if max_k > 0:
+            buy_score = ((k_bull + max_k) / (2 * max_k)) * 100
+            sell_score = ((k_bear + max_k) / (2 * max_k)) * 100
+        else:
+            buy_score = sell_score = 50
+
+        bull_t = buy_score > 70 and result < 90
+        bear_t = sell_score > 70 and result > 10
+        k_cci = cci / 300
+        boll_mid = self._boll_mid()
+
+        is_bull_t = (
+            bull_t
+            and k_bull > k_cci
+            and close < boll_mid
+            and (
+                (result > 65 and result < 85 and k_bull > 0 and k_bear > 0 and (k_bear - k_bull < 1.5 or result > 75))
+                or (result < 35 and result > 15 and k_bear > 0 and k_bull > 0 and (k_bull - k_bear < 1.5 or result < 25))
+            )
+        )
+        is_bear_t = (
+            bear_t
+            and k_bear > k_cci
+            and close > boll_mid
+            and (
+                (result > 65 and result < 85 and k_bull > 0 and k_bear > 0 and (k_bear - k_bull < 1.5 or result > 75))
+                or (result < 35 and result > 15 and k_bear > 0 and k_bull > 0 and (k_bull - k_bear < 1.5 or result < 25))
+            )
+        )
+
+        if is_bull_t:
+            self._bull_count += 1
+            self._bear_count = 0
+            self._last_bull_idx = self._frame_idx
+        elif is_bear_t:
+            self._bear_count += 1
+            self._bull_count = 0
+            self._last_bear_idx = self._frame_idx
+        else:
+            self._bull_count = max(self._bull_count - 1, 0)
+            self._bear_count = max(self._bear_count - 1, 0)
+
+        # ─── 信号输出 ───
+        sig_strength = 1.0 + max(0, (self._bull_count - 3) * 0.1) + max(0, (self._bear_count - 3) * 0.1)
+
+        strength = (
+            "极强" if sig_strength >= 1.7
+            else "强" if sig_strength >= 1.4
+            else "中" if sig_strength >= 1.1
+            else "弱"
+        )
+
+        if buy_score >= sell_score:
+            signal = f"买{strength}"
+        else:
+            signal = f"卖{strength}"
+
+        # ─── 市场状态 ───
+        if abs(buy_score - sell_score) < 10:
+            status = "中性震荡"
+        elif buy_score > sell_score:
+            if self._bull_count >= 3:
+                status = "强势上涨"
+            elif result > self.ob_threshold:
+                status = "高位企稳"
+            else:
+                status = "温和上涨"
+        else:
+            if self._bear_count >= 3:
+                status = "强势下跌"
+            elif result < self.os_threshold:
+                status = "低位企稳"
+            else:
+                status = "温和下跌"
+
+        # ─── 距离 ───
+        boll_len = boll_upper - boll_lower
+        if boll_len > 0:
+            dist_ratio = (close - boll_lower) / boll_len * 100
+        else:
+            dist_ratio = 50
+
+        if dist_ratio < 50 - self.dist_extreme:
+            dist = "极端超卖"
+        elif dist_ratio < 50 - self.dist_far:
+            dist = "远离"
+        elif dist_ratio < 50 - self.dist_mid:
+            dist = "偏离"
+        elif dist_ratio < 50 + self.dist_mid:
+            dist = "接近"
+        elif dist_ratio < 50 + self.dist_far:
+            dist = "靠近"
+        elif dist_ratio < 50 + self.dist_extreme:
+            dist = "远超"
+        else:
+            dist = "极端超买"
+
+        # ─── 强度 ───
+        max_score = max(buy_score, sell_score)
+        intensity = (
+            "超强" if max_score >= 90
+            else "强" if max_score >= 80
+            else "中" if max_score >= 65
+            else "弱" if max_score >= 55
+            else "极弱"
+        )
+
+        # ─── 详情 ───
+        bias_val = self._ema_bias()
+        bbp_val = self._boll_pct_b(close, boll_upper, boll_lower)
+        detail = (
+            f"前量:{percent:.1f} 数量:{int(amplitude):03d} 百分比:{bbp_val:.1f} "
+            f"正:{k_bull:.1f} 负:{k_bear:.1f} 连买:{self._bull_count} 连卖:{self._bear_count} "
+            f"误差:{bias_val:.1f}"
+        )
+
+        self._frame_idx += 1
+        return status, signal, intensity, detail
+
+    # ═══════════════════════════════════════════════════
+    #  指标因子（全部基于实时数据，无回看窗口）
+    # ═══════════════════════════════════════════════════
+
+    def _k_macd_factor(self, k: float, macd_val: float, pmacd: float) -> float:
+        is_ob = k > self.ob_threshold
+        is_os = k < self.os_threshold
+        k_macd = 0.0
+        if is_ob or is_os:
+            if macd_val < 0:
+                if is_os and k < 30 and pmacd > 0 and pmacd <= 10 and macd_val > 1.5:
+                    k_macd = 3.5
+                elif is_ob and k > 80 and macd_val > 3 and abs(pmacd) < 5:
+                    k_macd = -3.5
+        elif pmacd < 0 and macd_val < 0:
+            if abs(pmacd) > 15 and macd_val >= -0.5:
+                k_macd = 3.5
+            elif 5 < abs(pmacd) < 15 and macd_val > 0.7:
+                k_macd = 3.5
+        elif pmacd > 0 and macd_val > 0:
+            if pmacd >= 15 and macd_val < 0.5:
+                k_macd = -3.5
+            elif 5 < pmacd < 15 and macd_val < -0.7:
+                k_macd = -3.5
+        elif macd_val >= 3:
+            k_macd = macd_val * 1.5
+        elif macd_val <= -3:
+            k_macd = macd_val * 1.5
+        return k_macd
+
+    def _k_di_factor(self, dmk: float, amplitude: float, close: float) -> float:
+        if len(self._close_buf) < 20:
+            return 0.0
+        k_close = np.array(self._close_buf, dtype=float)
+        period = 14
+        if len(k_close) < period + 1:
+            return 0.0
+        dx_list = []
+        for i in range(1, min(period + 1, len(k_close))):
+            diff = k_close[-i] - k_close[-i - 1]
+            dx_list.append(diff)
+        if not dx_list:
+            return 0.0
+        last_diff = dx_list[0]
+        adx_val = abs(dmk) * 0.5
+
+        k_di = 0.0
+        if adx_val < 20:
+            return 0.0
+        if dmk > 0 and len(dx_list) > 5:
+            gains = [d for d in dx_list[:5] if d > 0]
+            if len(gains) >= 3 and last_diff > 0:
+                k_di = min(adx_val / 5, 6.0)
+        elif dmk < 0 and len(dx_list) > 5:
+            losses = [d for d in dx_list[:5] if d < 0]
+            if len(losses) >= 3 and last_diff < 0:
+                k_di = -min(adx_val / 5, 6.0)
+        return k_di
+
+    def _k_aroon_factor(self, high: float, low: float, close: float) -> float:
+        period = 14
+        if len(self._high_buf) < period:
+            return 0.0
+        highs = self._high_buf[-period:]
+        lows = self._low_buf[-period:]
+        highest = max(highs)
+        lowest = min(lows)
+        rng = highest - lowest
+        if rng == 0:
+            return 0.0
+        k_aroon = 0.0
+        pct = (close - lowest) / rng * 100
+        if pct >= self.aroon_ob:
+            if close >= highest * 0.995:
+                k_aroon = -2.0
+            else:
+                k_aroon = 2.0
+        elif pct <= self.aroon_os:
+            if close <= lowest * 1.005:
+                k_aroon = 2.0
+            else:
+                k_aroon = -2.0
+        else:
+            k_aroon = (pct - 50) / 50 * 1.2
+        return k_aroon
+
+    def _k_williams_factor(self, wr: float) -> float:
+        k_wr = 0.0
+        if wr > self.williams_ob:
+            if wr > -10:
+                k_wr = -1.2
+            else:
+                k_wr = -0.8
+        elif wr < self.williams_os:
+            if wr < -90:
+                k_wr = 1.2
+            else:
+                k_wr = 0.8
+        return k_wr
+
+    def _k_result_factor(self, result: float) -> float:
+        k_result = 0.0
+        is_ob = result > self.ob_threshold
+        is_os = result < self.os_threshold
+        if is_os and result < 10:
+            k_result = 1.5
+        elif is_ob and result > 90:
+            k_result = -1.5
+        return k_result
+
+    def _k_bias_factor(self, bias: float) -> float:
+        k_bias = 0.0
+        if bias < self.bias_os and bias < -3:
+            k_bias = min(abs(bias) / 5, 2.5)
+        elif bias > self.bias_ob and bias > 3:
+            k_bias = -min(abs(bias) / 5, 2.5)
+        return k_bias
+
+    def _k_trix_factor(self, close: float) -> float:
+        ema3 = self._ema_val(self._close_buf, 3)
+        ema9 = self._ema_val(self._close_buf, 9)
+        if ema9 == 0:
+            return 0.0
+        trix = (ema3 - ema9) / ema9 * 100
+        k_trix = 0.0
+        if trix > 0:
+            k_trix = min(trix / 2, 3.0)
+        elif trix < 0:
+            k_trix = max(trix / 2, -3.0)
+        return k_trix
+
+    def _k_ema_factor(self, close: float) -> float:
+        ema9 = self._ema_val(self._close_buf, 9)
+        if ema9 == 0:
+            return 0.0
+        bias = (close - ema9) / ema9 * 100
+        k_ema = 0.0
+        if bias < -2:
+            k_ema = min(abs(bias) * 0.3, 2.0)
+        elif bias > 2:
+            k_ema = -min(abs(bias) * 0.3, 2.0)
+        return k_ema
+
+    def _k_amplitude_factor(self, amplitude: float) -> float:
+        if len(self._amplitude_buf) < 10:
+            return 0.0
+        buf = self._amplitude_buf[-10:]
+        mean = sum(buf) / len(buf)
+        if mean == 0:
+            return 0.0
+        ratio = (amplitude - mean) / mean
+        k_amp = 0.0
+        if ratio > 0.5:
+            k_amp = min(ratio * 1.5, 3.0)
+        elif ratio < -0.3:
+            k_amp = max(ratio * 1.5, -3.0)
+        return k_amp
+
+    def _detect_divergence(self, close: float, result: float) -> Tuple[bool, bool]:
+        """简化背离检测（基于累计计数）"""
+        is_bull_div = False
+        is_bear_div = False
+        if len(self._close_buf) > 30:
+            c30 = self._close_buf[-30]
+            if close > c30 * 1.05 and result < 50:
+                is_bull_div = True
+            elif close < c30 * 0.95 and result > 50:
+                is_bear_div = True
+        return is_bull_div, is_bear_div
+
+    def _detect_trend(self, close: float) -> Tuple[bool, bool]:
+        """简化趋势确认"""
+        is_bull = False
+        is_bear = False
+        if len(self._close_buf) > 20:
+            c20 = self._close_buf[-20]
+            if close > c20 * 1.05:
+                is_bull = True
+            elif close < c20 * 0.95:
+                is_bear = True
+        return is_bull, is_bear
+
+    def _wmacd(self) -> float:
+        """简化加权MACD（基于缓冲区）"""
+        if len(self._close_buf) < 12:
+            return 0.0
+        ema12 = self._ema_val(self._close_buf, 12)
+        ema26 = self._ema_val(self._close_buf, 26)
+        return ema12 - ema26
+
+    def _boll_mid(self) -> float:
+        if len(self._close_buf) < 20:
+            return self._close_buf[-1] if self._close_buf else 0.0
+        return sum(self._close_buf[-20:]) / 20
+
+    def _ema_bias(self) -> float:
+        if not self._close_buf:
+            return 0.0
+        ema5 = self._ema_val(self._close_buf, 5)
+        ema10 = self._ema_val(self._close_buf, 10)
+        if ema10 == 0:
+            return 0.0
+        return (ema5 - ema10) / ema10 * 1000
+
+    def _boll_pct_b(self, close: float, upper: float, lower: float) -> float:
+        boll_len = upper - lower
+        if boll_len == 0:
+            return 50.0
+        return (close - lower) / boll_len * 100
+
+
+def get_signals(indicator: Indicator) -> Tuple[str, str, str, str]:
+    """
+    基于技术指标生成交易信号（逐帧调用版）
+
+    参数：
+        indicator: Indicator 对象，包含所有实时指标值
+
+    返回：
+        tuple: (market_status, signal_type, intensity, detail)
+    """
+    # ─── 指标提取 ───
+    bbi = indicator.BBI
+    amplitude = indicator.AMPLITUDE
+    cci = indicator.CCI
+    close = indicator.CLOSE
+    dmk = indicator.DMK
+    high = indicator.HIGH
+    k = indicator.K
+    low = indicator.LOW
+    macd_val = indicator.MACD
+    ob = indicator.OB
+    os_ = indicator.OS
+    ovs = indicator.OVS
+    ovc = indicator.OVC
+    result = indicator.RESULT
+    wr = indicator.WR
+    percent = indicator.PERCENT
+    v0 = indicator.V0
+    boll_upper = indicator.BOLL_UP
+    boll_lower = indicator.BOLL_LO
+    bias = indicator.BIAS
+
+    # ─── 指标阈值 ───
+    ob_threshold = ob if ob > 0 else 80
+    os_threshold = os_ if os_ > 0 else 20
+    dist_extreme = ovc if ovc > 0 else 5
+    dist_far = ovs if ovs > 0 else 15
+    dist_mid = 30
+    macd_th = 0.3
+    rsi_ob = ovc if ovc > 0 else 70
+    rsi_os = ovs if ovs > 0 else 30
+    di_gap_bull = ovc if ovc > 0 else 15
+    di_gap_bear = -di_gap_bull
+    aroon_ob = ob_threshold
+    aroon_os = os_threshold
+    williams_ob = -20
+    williams_os = -80
+    bias_ob = 5
+    bias_os = -5
+
+    # ─── 指标因子 ───
+    pmacd = getattr(indicator, 'PMACD', macd_val)
+
+    k_macd = _k_macd_factor(ob_threshold, os_threshold, k, macd_val, pmacd)
+    k_di = _k_di_factor(dmk, amplitude, close)
+    k_aroon = _k_aroon_factor(aroon_ob, aroon_os, high, low, close)
+    k_williams = _k_williams_factor(williams_ob, williams_os, wr)
+    k_result = _k_result_factor(ob_threshold, os_threshold, result)
+    k_bias = _k_bias_factor(bias_ob, bias_os, bias)
+
+    # ─── K值累计 ───
+    k_bull = k_macd + k_di + k_aroon + k_williams + k_result + k_bias
+    k_bear = k_macd + k_di + k_aroon + k_williams + k_result + k_bias
+
+    if result > ob_threshold and k_bull > 0 and result >= 90:
+        k_bull *= 1.5
+
+    # ─── 强度计算 ───
+    max_k = max(abs(k_bull), abs(k_bear))
+    if max_k > 0:
+        buy_score = ((k_bull + max_k) / (2 * max_k)) * 100
+        sell_score = ((k_bear + max_k) / (2 * max_k)) * 100
+    else:
+        buy_score = sell_score = 50
+
+    # ─── 信号判断 ───
+    bull_t = buy_score > 70 and result < 90
+    bear_t = sell_score > 70 and result > 10
+
+    k_cci = cci / 300
+    boll_mid = indicator.BOLL_MID if hasattr(indicator, 'BOLL_MID') else (boll_upper + boll_lower) / 2
+    boll_len = boll_upper - boll_lower
+
+    is_bull_t = (
+        bull_t
+        and k_bull > k_cci
+        and close < boll_mid
+        and (
+            (result > 65 and result < 85 and k_bull > 0 and k_bear > 0 and (k_bear - k_bull < 1.5 or result > 75))
+            or (result < 35 and result > 15 and k_bear > 0 and k_bull > 0 and (k_bull - k_bear < 1.5 or result < 25))
+        )
+    )
+    is_bear_t = (
+        bear_t
+        and k_bear > k_cci
+        and close > boll_mid
+        and (
+            (result > 65 and result < 85 and k_bull > 0 and k_bear > 0 and (k_bear - k_bull < 1.5 or result > 75))
+            or (result < 35 and result > 15 and k_bear > 0 and k_bull > 0 and (k_bull - k_bear < 1.5 or result < 25))
+        )
+    )
+
+    # ─── 信号强度 ───
+    sig_strength = 1.0
+    if is_bull_t:
+        sig_strength += 0.3 + max(0, (result - 70) / 30)
+    elif is_bear_t:
+        sig_strength += 0.3 + max(0, (30 - result) / 30)
+
+    strength = (
+        "极强" if sig_strength >= 1.7
+        else "强" if sig_strength >= 1.4
+        else "中" if sig_strength >= 1.1
+        else "弱"
+    )
+
+    # ─── 信号类型 ───
+    if is_bull_t:
+        signal = f"买{strength}"
+    elif is_bear_t:
+        signal = f"卖{strength}"
+    else:
+        signal = "观望"
+
+    # ─── 市场状态 ───
+    if abs(buy_score - sell_score) < 10:
+        status = "中性震荡"
+    elif buy_score > sell_score:
+        if result > ob_threshold:
+            status = "高位企稳"
+        else:
+            status = "温和上涨"
+    else:
+        if result < os_threshold:
+            status = "低位企稳"
+        else:
+            status = "温和下跌"
+
+    # ─── 距离 ───
+    if boll_len > 0:
+        dist_ratio = (close - boll_lower) / boll_len * 100
+    else:
+        dist_ratio = 50
+
+    if dist_ratio < 50 - dist_extreme:
+        dist = "极端超卖"
+    elif dist_ratio < 50 - dist_far:
+        dist = "远离"
+    elif dist_ratio < 50 - dist_mid:
+        dist = "偏离"
+    elif dist_ratio < 50 + dist_mid:
+        dist = "接近"
+    elif dist_ratio < 50 + dist_far:
+        dist = "靠近"
+    elif dist_ratio < 50 + dist_extreme:
+        dist = "远超"
+    else:
+        dist = "极端超买"
+
+    # ─── 强度标签 ───
+    max_score = max(buy_score, sell_score)
+    intensity = (
+        "超强" if max_score >= 90
+        else "强" if max_score >= 80
+        else "中" if max_score >= 65
+        else "弱" if max_score >= 55
+        else "极弱"
+    )
+
+    # ─── 详情 ───
+    ema5 = indicator.EMA5 if hasattr(indicator, 'EMA5') else close
+    ema10 = indicator.EMA10 if hasattr(indicator, 'EMA10') else close
+    ema20 = indicator.EMA20 if hasattr(indicator, 'EMA20') else close
+    bias_val = (ema5 - ema10) / ema10 * 1000 if ema10 != 0 else 0
+
+    bbp_val = (close - boll_lower) / boll_len * 100 if boll_len > 0 else 50
+
+    detail = (
+        f"前量:{percent:.1f} 数量:{int(amplitude):03d} 百分比:{bbp_val:.1f} "
+        f"正:{k_bull:.1f} 负:{k_bear:.1f}"
+    )
+
+    return status, signal, intensity, detail