CN105997104A - In-vivo real-time noninvasive physiological parameter collecting method and device based on multiple optical fiber bundles - Google Patents

Abstract

The invention provides a real-time non-invasive physiological parameter acquisition method and device based on multi-fiber bundles, including an optical module and an electrical module, the optical module mainly consists of a fiber bundle group, a monochromatic light source group, a photoelectric sensor and a mechanical structure that plays a fixed role composition. The optical fiber bundle group acquires reflected and transmitted light intensity for physiological parameters. The electrical module includes a dual-channel preamplifier unit, a dual-channel filter unit, a dual-channel numerical control amplifier unit, a processor unit, a display and input unit, a voltage follower unit, an analog selection unit, and a voltage-current conversion unit. The processor unit calculates the number of physiological parameters to measure the wavelength and the ratio of transmitted and reflected light intensity over time to extract the characteristic parameter value from the signal, determine the total number of obtained characteristic parameter values, according to the partial least square method and high-precision reference voltage The unit calculates the expression of the physiological parameter, and the invention can effectively improve the measurement accuracy of the physiological parameter of the human body, and is a non-invasive detection of the human body, with high measurement accuracy and convenient use.

Description

A method and device for in vivo real-time non-invasive physiological parameter acquisition based on multi-fiber bundles

technical field

The patent of the present invention relates to the field of biomedical signal processing, in particular to an in vivo real-time non-invasive physiological parameter acquisition method and device based on multi-fiber bundles.

Background technique

At present, there have been many instruments or devices that use optical methods to achieve non-invasive acquisition of physiological parameters. For example, the blood oxygen saturation measuring instrument uses two single-wavelength lights with wavelengths of 940nm and 660nm to irradiate the fingertips in turn, and calculates the corresponding blood oxygen saturation value by detecting the change of light intensity after transmission, but the two The optical path of the irradiation wavelength through the finger is different, so the accuracy of the measurement will be affected; in addition, this method cannot test the magnitude of the reflected light intensity. If the reflected light intensity can be measured, the measurement accuracy will be further increased. There is also the measurement of photoelectric volumetric wave, which is generally irradiated by infrared light to ensure that the intensity of the irradiated light is basically unchanged, and the light intensity after passing through the finger or earlobe is measured. Similarly, if the reflected light can be measured, a part of the incident light can be eliminated by means of the reflected light. The effect of photoinstability on volumetric waves. Therefore, the present invention proposes an in vivo real-time non-invasive physiological parameter acquisition method and device based on multi-fiber bundles, which can well overcome the above-mentioned shortcomings.

Contents of the invention

The present invention proposes an in vivo real-time non-invasive physiological parameter acquisition method based on multi-fiber bundles, which includes the following steps:

Step 1: Determine the wavelength and number of detection waves of various physiological parameters;

The second step: measure a certain part of the human body multiple times, measure the body temperature of the measured person through a temperature sensor, and measure the value of the corresponding physiological parameter through other equipment. According to the wavelength and number of detection waves determined in the first step, for the same parameter , in the case of different wavelengths, obtain the ratio of the transmitted light intensity to the reflected light intensity and the time change, and record the total number N of wavelengths used in the actual measurement;

Step 3: Using the above-mentioned ratio of transmitted light intensity to reflected light intensity and the change over time, calculate the average value of the maximum value and the average value of the minimum value of the ratio of transmitted and reflected light intensity within the set pulsation period, And extract the characteristic parameter value from the time-varying signal of the ratio of transmitted and reflected light intensity, and determine the total number M of the obtained characteristic parameter values;

Step 4: Use personalized fitting for each measured physiological parameter, assuming that the value of a certain physiological parameter to be measured is y*, then its expression is expressed as:

$\begin{array}{c}\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; *</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; i</span>}}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; i</span>}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}\mathrm{<span\; class="notranslate">\; i</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\\ <span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; l</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; l</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; i</span>}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}\mathrm{<span\; class="notranslate">\; i</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; T</span>}\mathrm{<span\; class="notranslate">\; b</span>}}{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; +</span>{{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; T</span>}}}^{<span\; class="notranslate">\; \&prime;</span>}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\end{array}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

In formula (1), M is the total number of acquired characteristic parameter values, N is the total number of wavelengths used in actual measurement, c ₀ represents the fitting calculation coefficient, and c _2i-1 represents the calculation coefficient of the i-th characteristic parameter , P _i represents the value of the i-th characteristic parameter, R _{max i} and R _{min i} represent the average value of the maximum value and the average value of the minimum value of the ratio of the i-th wavelength of transmitted light and reflected light in the pulsation cycle, respectively, k _2i-1 , k _2i , l _2i-1 , and l _2i represent the calculation coefficients of the first power and the second power of R _{max i} and R _{min i} respectively, T _b represents the body temperature at the time of measurement, and c _Tb represents the response to the above body temperature T The calculation coefficient of the first power of _b , c _Tb ' represents the calculation coefficient of the second power of the above-mentioned body temperature T _b , and the above-mentioned coefficients can be obtained by the partial least square method.

Further, the first step of the step also includes scanning human whole blood with an ultraviolet-visible-near-infrared spectrophotometer, and according to the absorption peaks of various groups, functional groups or molecules in chemistry, it can be determined that the physiological Parameter-related and irrelevant wavelengths or wavelength ranges; for the above two types of wavelengths or wavelength ranges, select 1 to 4 types respectively to determine the number of detection wavelengths.

Further, in the fourth step of the step, the partial least square method is used to obtain the coefficients of the physiological parameter values to be measured in the expression y*, and the specific method is: set x ₀ =1, x _2i-1 =R _i , x _2i ＝(R _i ) ² , x _2i-1+2M ＝R _{max i} , x _2i+2M ＝R _{max i} ² , x _2i-1+2(M+N) ＝R _{min i} , x _{2i+2 (M+N)} ＝R _{min i} ² , x _2(M+2N)+1 ＝T _b , x _2(M+2N)+2 ＝T _b ² , the previous corresponding coefficients are represented by a _i in turn, then Formula (1) can be replaced by:

$\begin{array}{c}\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; *</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; )</span>}\\ <span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\end{array}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

Let L=2(M+2N), then formula (2) can be further abbreviated as: Assume that the value of the corresponding physiological parameter measured by other equipment or methods is represented by y, and the same person has been measured K times in total, and K>L, and y _m represents the mth value of the corresponding parameter measured by other equipment or methods, y _m * represents the fitting value of the mth data, then its difference δ _m =|y _m -y _m *|, according to formula (3), y _m * can be expressed as:

${\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; *</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; L</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

According to the principle of partial least squares method, to make minimum, ie

minimum. Then there are:

$\frac{<span\; class="notranslate">\; \&part;</span><span\; class="notranslate">\; \&lsqb;</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; -</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; L</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&rsqb;</span>}{<span\; class="notranslate">\; \&part;</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

Arranging formula (5), we can get: According to the formula (6), the various coefficients a _i in the formula (2) can be solved, and a _i can be brought into the formula (1), and the complete expression of the physiological parameter value to be measured can be obtained as y*.

An in vivo real-time non-invasive physiological parameter acquisition device based on multi-fiber bundles, including an optical module and an electrical module, the optical module includes a fiber bundle group, a monochromatic light source group that provides corresponding light wavelengths and required irradiation light intensity for measurement, A photoelectric sensor that converts changing optical signals into electrical signals. The optical fiber bundle group gathers various monochromatic lights in a small range, and obtains reflected and transmitted light from a small range and passes the light through the photoelectric sensor. It is converted into an electrical signal and sent to the electrical module for processing; the electrical module includes a dual-channel preamplifier unit that converts the changes in the transmitted light intensity and reflected light intensity obtained by the photoelectric sensor into a voltage proportional to the respective light intensity , convert the voltage converted by the change of transmitted light intensity and reflected light intensity into a corresponding digital value, provide a high-precision reference voltage to the outside, and a processor unit that controls other units of the device, and convert the high-precision voltage into a current input to the voltage-current conversion unit of the monochromatic light source group.

Further, the optical module also includes a mechanical structure for fixing the above-mentioned optical fiber bundle group, monochromatic light source group, and photoelectric sensor together.

Further, the monochromatic light source group is composed of LEDs with various monochromatic wavelengths.

Further, the electrical unit also includes a dual-channel filter unit located between the dual-channel preamplifier unit and the processor unit, and is provided with a dual-channel filter unit for filtering voltage signals converted from changes in transmitted light intensity and reflected light intensity, and selecting an appropriate multiple to perform An amplified dual-channel numerical control amplifying unit; located between the voltage-current conversion unit and the processor unit, an analog selection unit controlled by the processor unit and capable of selecting the output of the corresponding channel, the analog selection unit and the processor A voltage follower unit is arranged between the units; the electrical unit also includes a display and input unit.

Further, the dual-channel pre-amplification unit, dual-channel filter unit, dual-channel numerical control amplification unit, processor unit, display and input unit, voltage follower unit, analog selection unit, and voltage-current conversion unit are converted by DC-DC voltage unit powered.

Further, the processor unit includes D/A conversion and A/D conversion; the processor unit is set with reference values and linear correction values under different magnifications.

Effective gain: The present invention proposes a new optical path structure that can detect reflected light intensity and transmitted light intensity at the same time, which can effectively improve the measurement accuracy of human physiological parameters, and is non-invasive detection of human body, with high measurement accuracy and convenient use. The optical module of the invention is designed with an optical fiber bundle group, which can effectively reduce the influence of stray light and is convenient for assembly.

Description of drawings:

Fig. 1 is a hardware principle block diagram of the present invention;

Figure 2 is a schematic diagram of real-time data acquisition by the optical module.

detailed description

The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

The present invention proposes an in vivo real-time non-invasive physiological parameter acquisition method based on multi-fiber bundles, which includes the following steps:

Step 1: Determine the wavelength and number of detection waves of various physiological parameters

Use the UV-Vis-NIR spectrophotometer to scan human whole blood in the range of 185nm-3300nm, analyze the absorption peak and relatively flat wavelength of the whole blood, and compare and analyze various groups, functional groups or Analyze the absorption peaks of molecules, etc., analyze the reasons for the appearance of each absorption peak, determine the wavelength or wavelength range that is closely related to the physiological parameters to be measured, and determine the wavelength or wavelength that has little or no change with the physiological parameters to be measured scope. In this embodiment, if the correlation with the wavelength or wavelength range to be measured is less than 5%, it is regarded as a wavelength or wavelength range with little change with the physiological parameter to be measured.

The number of measurement wavelengths is determined according to the acquired data and the number of physiological parameters to be measured. The specific principle is: ensure that 1 to 4 wavelengths are selected from the wavelengths or wavelength ranges closely related to each physiological parameter to be measured, and 1 to 4 are selected from wavelengths or wavelength ranges that have little or no change with each physiological parameter to be measured For different physiological parameters, the same wavelength can be selected as much as possible to reduce the total number of selected wavelengths.

The second step is to measure a certain part of the human body multiple times, measure the body temperature of the measured person through a temperature sensor, and measure the value of the corresponding physiological parameter through other equipment. According to the wavelength and number of detection waves determined in the first step, for the same parameter , in the case of different wavelengths, obtain the ratio of transmitted light intensity to reflected light intensity and the time change, and determine the total number N of wavelengths used for actual measurement. The principle of determination is to ensure the measurement accuracy of each physiological parameter to be measured Under the premise, try to use as few wavelengths as possible.

Step 3: Using the above-mentioned ratio of transmitted light intensity to reflected light intensity and the change over time, calculate the average value of the maximum value and the average value of the minimum value of the ratio of transmitted and reflected light intensity within the set pulsation period, The characteristic parameter values are extracted from the time-varying signal of the ratio of transmitted and reflected light intensity, and the total number M of acquired characteristic parameter values is determined. In this embodiment, the set pulsation period can be selected from 2 to 100.

Step 4: Individualized fitting is used for each measured physiological parameter, and data fitting is performed using the data obtained above and the partial least square method. The specific method is:

Assuming that the value of a certain physiological parameter to be measured is y*, its expression is expressed as:

$\begin{array}{c}\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; *</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; i</span>}}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; i</span>}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}\mathrm{<span\; class="notranslate">\; i</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\\ <span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; l</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; l</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; i</span>}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}\mathrm{<span\; class="notranslate">\; i</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; T</span>}\mathrm{<span\; class="notranslate">\; b</span>}}{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; +</span>{{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; T</span>}}}^{<span\; class="notranslate">\; \&prime;</span>}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\end{array}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

In formula (1), M is the total number of acquired characteristic parameter values, N is the total number of wavelengths used in actual measurement, c ₀ represents the fitting calculation coefficient, and c _2i-1 represents the calculation coefficient of the i-th characteristic parameter , P _i represents the value of the i-th characteristic parameter, R _{max i} and R _{min i} represent the average value of the maximum value and the average value of the minimum value of the ratio of the i-th wavelength of transmitted light and reflected light in the pulsation cycle, respectively, k _2i-1 , k _2i , l _2i-1 , and l _2i represent the calculation coefficients of the first power and the second power of R _{max i} and R _{min i} respectively, T _b represents the body temperature at the time of measurement, and c _Tb represents the response to the above body temperature T The calculation coefficient of the first power of _b , c _Tb ' represents the calculation coefficient of the second power of the above-mentioned body temperature T _b .

Use the partial least square method to obtain the coefficients of the expression of the physiological parameter value y* to be measured. The specific method is: set x ₀ =1, x _2i-1 =R _i , x _2i =(R _i ) ² , x _2i-1+2M = R _{max i} , x _2i+2M = R _{max i} ² , x _2i-1+2(M+N) = R _{min i} , x _2i+2(M+N) = R _{min i} ² , x _2(M+2N)+1 ＝T _b , x _2(M+2N)+2 ＝T _b ² , the corresponding coefficients before it are represented by a _i in turn, then the formula (1) can be replaced by:

$\begin{array}{c}\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; *</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; )</span>}\\ <span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\end{array}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

Let L=2(M+2N), then formula (2) can be further abbreviated as:

Assume that the value of the corresponding physiological parameter measured by other equipment or methods is represented by y, and the same person has been measured K times in total, and K>L, and y _m represents the mth value of the corresponding parameter measured by other equipment or methods, y _m * represents the fitting value of the mth data, then its difference δ _m =|y _m -y _m *|, according to formula (3), y _m * can be expressed as:

${\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; *</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; L</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

According to the principle of partial least squares method, to make minimum, ie

minimum. Then there are:

$\frac{<span\; class="notranslate">\; \&part;</span><span\; class="notranslate">\; \&lsqb;</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; -</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; L</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&rsqb;</span>}{<span\; class="notranslate">\; \&part;</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

Arranging formula (5), we can get:

After rearranging formula (6), we have:

where (k=0,Λ,L)(7)

because So formula (7) can be organized as:

$\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; L</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span>$

Among them (k=0, Λ, L) formula (8) can also be written as:

$\left\{\begin{array}{c}\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}\mathrm{<span\; class="notranslate">\; m</span>}}\\ \underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; m</span>}}\\ <span\; class="notranslate">\; ...</span>\\ \underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; m</span>}}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 9</span>}<span\; class="notranslate">\; )</span>$

The same physiological parameter data measured by N other devices measured, and the data of the expression y* measured in the method of the present invention, are brought into the L+1 equations in the formula (9), and solved a ₀ ,a ₁ ,…,a _L , then the complete multivariate fitting function formula (3) can be obtained, from the above-mentioned x ₀ =1, x _2i-1 =R _i , x _2i =(R _i ) ² , x _2i-1+2M ＝R _{max i} , x _2i+2M ＝R _{max i} ² , x _2i-1+2(M+N) ＝R _{min i} , x _2i+2(M+N) ＝R _{min i} ² , x _2(M+2N)+1 ＝T _b , x _2(M+2N)+2 ＝T _b ² , put into formula (1) to get the complete expression of y*.

An in vivo real-time non-invasive physiological parameter acquisition device based on multi-fiber bundles, as shown in Figure 1, includes an optical module and an electrical module. The core part of the electrical module is a processor unit, and the processor unit in this embodiment is 32 bits. On the one hand, the electrical module provides a high-precision reference voltage through the D/A conversion inside the 32-bit processor unit, and after the voltage follower unit, under the control of the 32-bit processor unit, selects the corresponding voltage through the analog selection unit. The output of the channel is converted into a high-precision current by the subsequent voltage-current conversion unit and input to the monochromatic light source group in the optical module. In this way, the electrical module can realize high-precision control of the brightness of the LEDs that constitute the monochromatic light source group; the other On the one hand, the dual-channel pre-amplification unit is used to convert the changes in the transmitted light intensity and reflected light intensity obtained by the photoelectric sensor in the optical module into voltages proportional to their respective light intensities, and various interference signals are filtered out through the dual-channel filter unit Finally, under the control of the 32-bit microprocessor unit, select the appropriate magnification through the dual-channel numerical control amplifier unit and send it to the 12-bit A/D conversion in the 32-bit microprocessor unit to realize its voltage conversion For the corresponding digital value, at the same time, use the A/D converted value of the voltage provided by the external high-precision reference voltage unit, and the reference value and linear correction value under different magnifications set by the microprocessor unit to further improve the voltage. Measurement accuracy, that is, to improve the accuracy of the transmitted light intensity and reflected light intensity, and at the same time obtain the corresponding values of the physiological parameters required to be measured through corresponding calculations, and display them through the display and input unit. The DC-DC voltage conversion unit mainly provides the learned power for each unit of the device.

The optical module is mainly composed of optical fiber bundle group, monochromatic light source group, photoelectric sensor and mechanical structure. When the optical module collects real-time data, as shown in Figure 2, put your finger or earlobe on the optical module. Among them, the optical fiber bundle group gathers various monochromatic lights in a small area, and obtains reflected and transmitted light from a small area, converts the light into electrical signals through photoelectric sensors and sends them to the electrical module for processing, and at the same time makes each monochromatic light It can evenly irradiate the corresponding parts of the measurement to improve the overall measurement accuracy. The practical fiber bundle group can also effectively reduce the influence of stray light, and it is easy to assemble. The monochromatic light source group is mainly composed of LEDs with various monochromatic wavelengths, which provide corresponding light wavelengths and required irradiation light intensity for measurement. The photoelectric sensor is mainly to convert the changing optical signal into an electrical signal and transmit it to the electrical module. The mechanical structure mainly fixes the above-mentioned optical fiber bundle group, monochromatic light source group, and photoelectric sensor in one body to facilitate the measurement of human physiological parameters.

The technical solutions provided by the embodiments of the invention have been introduced in detail above, and specific examples have been used in this paper to illustrate the principles and implementation methods of the embodiments of the present invention. The descriptions of the above embodiments are only applicable to help understand the embodiments of the present invention At the same time, for those of ordinary skill in the art, according to the embodiment of the present invention, there will be changes in the specific implementation and application scope. In summary, the content of this specification should not be construed as limiting the present invention.

Claims (9)

1. A real-time non-invasive physiological parameter acquisition method in vivo based on multi-fiber bundle, is characterized in that, comprises the following steps: Step 1: Determine the wavelength and number of detection waves of various physiological parameters; The second step: measure a certain part of the human body multiple times, measure the body temperature of the measured person through a temperature sensor, and measure the value of the corresponding physiological parameter through other equipment. According to the wavelength and number of detection waves determined in the first step, for the same parameter , in the case of different wavelengths, obtain the ratio of the transmitted light intensity to the reflected light intensity and the time change, and record the total number N of wavelengths used in the actual measurement; Step 3: Using the above-mentioned ratio of transmitted light intensity to reflected light intensity and the change over time, calculate the average value of the maximum value and the average value of the minimum value of the ratio of transmitted and reflected light intensity within the set pulsation period, And extract the characteristic parameter value from the time-varying signal of the ratio of transmitted and reflected light intensity, and determine the total number M of the obtained characteristic parameter values; Step 4: Use personalized fitting for each measured physiological parameter, assuming that the value of a certain physiological parameter to be measured is y*, then its expression is expressed as: $\begin{array}{c}\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; *</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; i</span>}}{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; i</span>}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}\mathrm{<span\; class="notranslate">\; i</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\\ <span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; l</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; l</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; i</span>}}{{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}\mathrm{<span\; class="notranslate">\; i</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; T</span>}\mathrm{<span\; class="notranslate">\; b</span>}}{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}<span\; class="notranslate">\; +</span>{{\mathrm{<span\; class="notranslate">\; c</span>}}_{\mathrm{<span\; class="notranslate">\; T</span>}}}^{<span\; class="notranslate">\; \&prime;</span>}{{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; b</span>}}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\end{array}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$ In formula (1), M is the total number of acquired characteristic parameter values, N is the total number of wavelengths used in actual measurement, c ₀ represents the fitting calculation coefficient, and c _2i-1 represents the calculation coefficient of the i-th characteristic parameter , P _i represents the value of the i-th characteristic parameter, R _maxi and R _mini represent the average value of the maximum value and the average value of the minimum value of the ratio of transmitted light and reflected light at the i-th wavelength in the pulsation cycle, respectively, k _{2i -1} , k _2i , l _2i-1 , and l _2i respectively represent the calculation coefficients of the first power and the second power of R _maxi and R _mini , T _b represents the body temperature at the time of measurement, and c _Tb represents the first power of the above body temperature T _b The calculation coefficient of c _Tb ' represents the calculation coefficient of the square of the above-mentioned body temperature T _b , and the above-mentioned coefficients can be obtained by the partial least square method. 2. a kind of in vivo real-time non-invasive physiological parameter collection method based on multi-fiber bundle according to claim 1, is characterized in that, described step the first step also comprises, adopts ultraviolet-visible-near-infrared spectrophotometer to human body Whole blood is scanned, and according to the absorption peaks of various groups, functional groups or molecules in chemistry, determine the relevant and irrelevant wavelengths or wavelength ranges that need to be measured for physiological parameters; for the above two types of wavelengths or wavelength ranges, select 1 to 4 kinds respectively , to determine the number of detection wavelengths. 3. a kind of in vivo real-time non-invasive physiological parameter collection method based on multi-fiber bundle according to claim 1, is characterized in that, described step 4th step, adopts partial least squares method to obtain the physiological parameter value that needs to measure y *The various coefficients of the expression, the specific method is: set x ₀ =1, x _2i-1 =R _i , x _2i =(R _i ) ² , x _2i-1+2M =R _maxi , x _2i+2M = R _maxi ² , x _2i-1+2(M+N) ＝R _mini , x _2i+2(M+N) ＝R _mini ² , x _2(M+2N)+1 ＝T _b , x _{2(M +2N)+2} ＝T _b ² , and its corresponding coefficients are represented by a _i in turn, then the formula (1) can be replaced as: $\begin{array}{c}\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; *</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; )</span>}\\ <span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; )</span>}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}\end{array}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$ Let _L=2(M+2N) , then formula (2) can be further abbreviated as: Assume that the value of the corresponding physiological parameter measured by other equipment or methods is represented by y, and the same person has been measured K times in total, and K>L, and y _m represents the mth value of the corresponding parameter measured by other equipment or methods, y _m * represents the fitting value of the mth data, then its difference δ _m =|y _m -y _m *|, according to formula (3), y _m * can be expressed as: ${\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; *</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; L</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$ According to the principle of partial least squares method, to make minimum, ie minimum. Then there are: $\frac{<span\; class="notranslate">\; \&part;</span><span\; class="notranslate">\; \&lsqb;</span>\underset{\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; -</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; L</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&rsqb;</span>}{<span\; class="notranslate">\; \&part;</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Lambda;</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$ Arranging formula (5), we can get: According to the formula (6), the various coefficients a _i in the formula (2) can be solved, and a _i can be brought into the formula (1), and the complete expression of the physiological parameter value to be measured can be obtained as y*. 4. An in vivo real-time non-invasive physiological parameter acquisition device based on multi-fiber bundles, characterized in that it includes an optical module and an electrical module, the optical module includes an optical fiber bundle group, provides corresponding light wavelengths and required irradiation light intensity for measurement The monochromatic light source group, the photoelectric sensor that converts the changing optical signal into an electrical signal, the optical fiber bundle group gathers various monochromatic light in a small range, and obtains reflected and transmitted light from a small range and passes through The photoelectric sensor converts light into an electrical signal and sends it to the electrical module for processing; the electrical module includes a circuit that converts the changes in the transmitted light intensity and reflected light intensity obtained by the photoelectric sensor into voltages proportional to the respective light intensities. The double-channel preamplifier unit converts the voltage converted by the change of the transmitted light intensity and reflected light intensity into a corresponding digital value, provides a high-precision reference voltage to the outside, and a processor unit that controls other units of the device. The high-precision voltage is converted into a current input to the voltage-current conversion unit of the monochromatic light source group. 5. A kind of in vivo real-time noninvasive physiological parameter acquisition device based on multi-fiber bundles according to claim 4, characterized in that, the optical module also includes fixing the above-mentioned fiber bundle group, monochromatic light source group, photoelectric sensor In one mechanical structure. 6 . The in vivo real-time non-invasive physiological parameter acquisition device based on multi-fiber bundles according to claim 4 , wherein the monochromatic light source group is composed of LEDs of various monochromatic wavelengths. 7 . 7. A kind of in vivo real-time non-invasive physiological parameter acquisition device based on multi-fiber bundles according to claim 4, characterized in that, the electrical unit also includes a dual-channel preamplifier unit and a processor unit, and is provided with A dual-channel filter unit that filters the voltage signal converted from changes in transmitted light intensity and reflected light intensity, and a dual-channel digitally controlled amplifier unit that selects an appropriate multiple for amplification; located between the voltage-current conversion unit and the processor unit, the An analog selection unit controlled by the processor unit and capable of selecting the output of the corresponding channel, a voltage follower unit is arranged between the analog selection unit and the processor unit; the electrical unit also includes a display and input unit. 8. A kind of in vivo real-time non-invasive physiological parameter acquisition device based on multi-fiber bundles according to claim 7, characterized in that, said dual-channel preamplifier unit, dual-channel filter unit, dual-channel numerically controlled amplification unit, processing The controller unit, display and input unit, voltage follower unit, analog selection unit, and voltage-current conversion unit are powered by the DC-DC voltage conversion unit. 9. A kind of in vivo real-time non-invasive physiological parameter acquisition device based on multi-fiber bundles according to claim 7, characterized in that, the processor unit contains D/A conversion and A/D conversion; The unit is set with reference values and linear correction values at different magnifications.