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base: yanjie:display
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yanjie:main yanjie:customer-demo yanjie:silicone yanjie:android-main yanjie:sdk-dev yanjie:pre-module-dev yanjie:display yanjie:cli yanjie:dev
yanjie:0.4.0 yanjie:0.3.1 yanjie:0.3.0 yanjie:0.2.0 yanjie:0.1.0
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compare: yanjie:pre-module-dev
Branches Tags
yanjie:customer-demo yanjie:main yanjie:silicone yanjie:android-main yanjie:sdk-dev yanjie:pre-module-dev yanjie:display yanjie:cli yanjie:dev
yanjie:0.4.0 yanjie:0.3.1 yanjie:0.3.0 yanjie:0.2.0 yanjie:0.1.0

10 Commits
display ... pre-module

Author SHA1 Message Date
lenn
0833694e1b feat: integrate basin force estimator (pre-force) for 7x12 sensor 2026-05-11 17:04:47 +08:00
lenn
83832139a8 Merge branch 'display' 
# Conflicts:
#	.gitignore
#	package-lock.json
#	package.json
#	src-tauri/Cargo.lock
#	src-tauri/Cargo.toml
#	src-tauri/tauri.conf.json
#	src/routes/+page.svelte
 2026-04-29 16:08:46 +08:00
lenn
50700e73df update .gitignore 2026-04-20 09:32:43 +08:00
lenn
7e047daf00 remove log file 2026-04-15 09:50:29 +08:00
lenn
842d4980d9 remove target dirs 2026-04-15 09:49:33 +08:00
lenn
04a0c3e46b remove unnecessary dotfiles 2026-04-15 09:46:19 +08:00
lenn
d4e1da6219 更新软件版本号 2026-04-15 09:44:54 +08:00
lenn
d1c9be56ec Merge branch 'main' of https://gitea.e-skin.top/E-Skin/JE-Skin 2026-04-15 09:28:09 +08:00
lenn
2e30fa388a 导出数据添加summary列 2026-04-15 09:20:56 +08:00
Lenn
4b2203e008 合并请求 #2 
feat:增加点和数字切换，减小点最大尺寸，增加range配色方案
 2026-04-09 01:18:56 +00:00
30 changed files with 33881 additions and 8 deletions
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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)

26695
devkit/build/je-skin-devkit-server/xref-je-skin-devkit-server.html Normal file
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BIN
devkit/dist/je-skin-devkit-server.exe vendored Normal file
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2
src-tauri/.cargo/config.toml Normal file
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@@ -0,0 +1,2 @@
[registries.kellnr]
index = "sparse+http://crates.huangyanjie.com/api/v1/crates/"

2
src-tauri/.gitignore vendored
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@@ -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*

49
src-tauri/Cargo.lock generated
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@@ -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",

3
src-tauri/Cargo.toml
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@@ -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"

1021
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217
src-tauri/resources/model_params.json Normal file
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@@ -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
}

397
src-tauri/src/serial_core/basin_force_estimator.rs Normal file
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@@ -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
}
}

6
src-tauri/src/serial_core/codecs/tactile_a.rs
View File

@@ -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;
@@ -226,6 +226,7 @@ impl Codec<TactileAFrame> for TactileACodec {
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)
}
}

1
src-tauri/src/serial_core/mod.rs
View File

@@ -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>;

12
src-tauri/src/serial_core/serial.rs
View File

@@ -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>>();

2
src-tauri/tauri.conf.json
View File

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

747
src/indicator/indicator498.py Normal file
View File

@@ -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