Infrared spectrum-based blood sugar and blood fat noninvasive detection method and system

Abstract

The invention relates to the technical field of biomedical engineering, in particular to a blood sugar and blood fat noninvasive detection method and system based on infrared spectroscopy, and the method comprises the following steps: obtaining an infrared band light source, setting an incident phase, monitoring the position of a light wave interference peak, recording the phase offset of a transmission signal, calculating the spectral attenuation of a tissue level, and adjusting the intensity of the light source. And obtaining a light wave interference enhanced signal. According to the invention, by accurately setting the incident phase of the infrared light source and monitoring the position of the light wave interference peak, the collection efficiency of spectral data and the precision of signal analysis are remarkably improved, the light source intensity and spectral energy distribution are optimized, the sensitivity to blood sugar and blood fat is improved, the data accuracy is ensured, and the accuracy of data analysis is improved. By combining the pulse wave signal and the blood vessel diameter change data, the dynamic adjustment of the spectral data is realized, the influence of the blood vessel change on the measurement is effectively reduced, the stability and accuracy of the result are enhanced, and the reliability of non-invasive detection is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of biomedical engineering, and particularly relates to a non-invasive blood glucose and blood lipid detection method and system based on infrared spectroscopy. Background Art

[0002] The technical field of biomedical engineering encompasses a multidisciplinary field that utilizes engineering principles and methods to solve medical and biological problems. This technical field covers multiple directions such as medical imaging, biosensing, medical devices, biomaterials, tissue engineering, etc. Medical imaging involves means such as X-rays, magnetic resonance imaging, and ultrasound to obtain internal structural information of the human body. Biosensing is used to detect physiological and biochemical parameters in real time, including key indicators such as blood glucose, blood oxygen, and blood lipids. Medical devices include diagnostic, therapeutic, and rehabilitation equipment, such as electrocardiogram monitors, artificial hearts, etc. Biomaterials involve the design and application of materials such as artificial organs and drug carriers. Tissue engineering focuses on culturing cells and tissues in vitro to repair or replace human tissues and organs. This field relies on the development of multiple disciplines such as physics, chemistry, and electronic information to provide technical support for medical diagnosis, treatment, and health monitoring.

[0003] Among them, the non-invasive blood glucose and blood lipid detection method based on infrared spectroscopy refers to using infrared light within a specific wavelength range to perform spectral analysis on biological tissues to detect glucose and lipid components in the blood. This method includes key steps such as the selection and regulation of infrared light sources, the acquisition and analysis of spectral signals, the preprocessing of spectral data, the extraction of characteristic spectra, and the construction of models. The infrared light source irradiates the skin with near-infrared or mid-infrared light of a specific wavelength, and the molecular vibrations in the tissue absorb the light energy of the specific wavelength to generate spectral signals. The acquisition of spectral signals is completed through optical fibers or detectors, and numerical transformation methods are used to normalize, denoise, and correct the background of the original spectral data. The extraction of characteristic spectra selects characteristic bands related to blood glucose and blood lipid concentrations based on chemometric methods, and uses multiple regression or machine learning algorithms to construct a concentration prediction model.

[0004] Existing technologies are usually limited by individual biological characteristics in non-invasive blood component monitoring, such as the thickness and water content of the skin, etc. These factors have not been effectively considered in traditional technologies, affecting the accuracy and repeatability of measurement results. In addition, traditional technologies lack an effective compensation mechanism for dealing with dynamic changes in blood vessels, resulting in spectral data being easily interfered by blood flow pulsation and unable to stably reflect the true levels of blood glucose and blood lipids. This technical limitation may lead to an increase in the error of monitoring data in clinical applications, affecting the timely diagnosis and management of diseases. By combining optical wave interference technology and dynamic monitoring of blood vessel diameters, this problem has been effectively solved, improving the application value and clinical effect of the technology. Summary of the Invention

[0005] The object of the present invention is to solve the deficiencies existing in the prior art, and a non-invasive detection method and system for blood glucose and blood lipids based on infrared spectroscopy are proposed.

[0006] To achieve the above object, the present invention adopts the following technical solution: A non-invasive detection method for blood glucose and blood lipids based on infrared spectroscopy, comprising the following steps:

[0007] S1: Obtain an infrared band light source, set the incident phase, monitor the position of the light wave interference peak, record the phase shift amount of the transmitted signal, calculate the spectral attenuation amount of the tissue layer, adjust the light source intensity, and obtain an enhanced light wave interference signal;

[0008] S2: Based on the enhanced light wave interference signal, obtain the blood vessel diameter change data, calculate the pulse wave propagation velocity, analyze the spectral absorption peak shift trend, calculate the blood vessel constriction compensation factor, adjust the spectral signal characteristic peak value, and obtain a blood vessel matching spectral correction value;

[0009] S3: Based on the blood vessel matching spectral correction value, obtain the pulse wave signal to calculate the blood flow pulsation period, match the spectral signal time window, analyze the average spectral absorption within the pulsation period, calculate the influence range of blood flow pulsation on the spectral signal, adjust the spectral normalization parameter, compensate for the spectral absorption error caused by pulsation, calculate the blood flow state matching offset, and obtain a blood flow state spectral correction parameter;

[0010] S4: Based on the blood flow state spectral correction parameter, obtain the tissue optical scattering and absorption coefficients, calculate the transmission path, judge the light field distribution uniformity, optimize the light source incident angle and light wave coherence, and obtain a tissue layer spectral optimization factor.

[0011] As a further solution of the present invention, the enhanced light wave interference signal includes phase shift amount, attenuation amount calculation, and light source intensity adjustment, the blood vessel matching spectral correction value includes systolic peak value, pulse wave propagation velocity, and absorption intensity compensation, the blood flow state spectral correction parameter includes pulsation period matching, mean and deviation analysis, and normalization parameter, and the tissue layer spectral optimization factor includes scattering coefficient, transmission path, and light field distribution uniformity.

[0012] As a further solution of the present invention, the specific steps of obtaining an infrared band light source, setting the incident phase, monitoring the position of the light wave interference peak, recording the phase shift amount of the transmitted signal, calculating the spectral attenuation amount of the tissue layer, and adjusting the light source intensity to obtain an enhanced light wave interference signal are as follows:

[0013] S101: Obtain an infrared band light source, set the incident phase of the light source according to the characteristic absorption peaks of blood glucose and blood lipids, call the light source with the set phase to be incident on the skin tissue, monitor the position of the light wave interference peak, record the phase offset of the transmitted signal, call the recorded phase offset of the transmitted signal, calculate the propagation characteristic index of the light wave in the skin tissue, and obtain the transmitted signal phase data;

[0014] S102: Based on the transmitted signal phase data, monitor the changes in skin thickness and water content, call the skin thickness and water content parameters, analyze the influence on the spectral signal absorption ratio, obtain the transmitted energy distribution of the light wave at different tissue levels, calculate the attenuation of the band light energy at different tissue levels, and obtain the light energy attenuation ratio;

[0015] S103: Call the light energy attenuation ratio, adjust the intensity of the light source, balance the spectral energy distribution, monitor the change in the light wave interference peak value after adjustment, record the light wave coherent interference peak value, call the recorded light wave coherent interference peak value data, calculate the signal amplitude index of the interference enhancement, and obtain the light wave interference enhancement signal.

[0016] As a further solution of the present invention, based on the light wave interference enhancement signal, the specific steps to obtain the blood vessel diameter change data, calculate the pulse wave propagation velocity, analyze the spectral absorption peak offset trend, calculate the blood vessel constriction compensation factor, and adjust the spectral signal characteristic peak value to obtain the blood vessel matching spectral correction value are as follows:

[0017] S201: Based on the light wave interference enhancement signal, call the blood vessel diameter change data recorded by the optical volume plethysmography technology, calculate the systolic peak value, diastolic value and pulse wave propagation velocity, call the systolic peak value and diastolic data, calculate the change amount of the blood vessel systolic state, call the change amount of the blood vessel systolic state, and analyze the spectral absorption offset trend through the spectral absorption peak under different blood vessel differential systolic states;

[0018] S202: Based on the spectral absorption offset trend, analyze the change of the spectral absorption peak under different blood vessel differential systolic states, call the change data of the spectral absorption peak, calculate the blood vessel constriction compensation factor, analyze the adjustment amount of the light wave interference enhancement signal, and obtain the adjusted spectral signal characteristic peak value and absorption intensity, and obtain the spectral characteristic adjustment value;

[0019] S203: Call the spectral characteristic adjustment value, calculate the influence degree of the blood vessel dynamic change on the spectral signal, call the blood vessel constriction compensation factor for compensation and correction, and adjust the dynamic matching of the spectral signal to obtain the blood vessel matching spectral correction value.

[0020] As a further solution of the present invention, based on the vascular matching spectral correction value, the specific steps to obtain the pulse wave signal, calculate the blood flow pulsation period, match the spectral signal time window, analyze the average spectral absorption within the pulsation period, calculate the influence range of blood flow pulsation on the spectral signal, adjust the spectral normalization parameter, compensate for the spectral absorption error caused by pulsation, and calculate the blood flow state matching offset to obtain the blood flow state spectral correction parameter are as follows:

[0021] S301: Based on the vascular matching spectral correction value, obtain the synchronous acquisition data of the pulse wave signal, call the time series of the pulse wave signal, calculate the blood flow pulsation period index, call the blood flow pulsation period data, match the time window of the spectral signal, call the matched time window, and extract the average value and deviation of the spectral signal within the window to obtain the spectral signal time window eigenvalue;

[0022] S302: Based on the spectral signal time window eigenvalue, call the blood flow pulsation period data, calculate the fluctuation range of blood flow pulsation on the spectral signal, call the fluctuation range data, judge the change trend of the spectral signal under the blood flow pulsation state, adjust the normalization parameter of the spectral data according to the blood flow pulsation state, call the normalized adjusted spectral signal, and calculate the spectral absorption rate correction amount to obtain the spectral absorption rate adjustment value;

[0023] S303: Call the spectral absorption rate adjustment value, calculate the dynamic correction parameter of the spectral signal under the blood flow pulsation state, call the vascular matching spectral correction value, adjust the time matching of the spectral data, obtain the correction parameter of the spectral signal under the blood flow state, and obtain the blood flow state spectral correction parameter.

[0024] As a further solution of the present invention, the specific formula for the mean square deviation value of the spectral signal within the window is as follows:

[0025]

[0026] where V sig represents the mean square deviation value of the spectral signal within the window, N represents the number of spectral signal sample points participating in the calculation within this time window, S j represents the spectral signal intensity at the j-th time point, M represents the total number of all acquisition points within the time window, and S k represents the spectral signal intensity at the k-th time point within the time window.

[0027] As a further solution of the present invention, based on the blood flow state spectral correction parameter, the specific steps to obtain the tissue optical scattering and absorption coefficients, calculate the transmission path, judge the light field distribution uniformity, and optimize the light source incident angle and light wave coherence to obtain the tissue layer spectral optimization factor are as follows:

[0028] S401: Based on the optical scattering coefficient and local absorption coefficient of the skin tissue obtained from the blood flow state spectral correction parameter, call the data of the optical scattering coefficient and local absorption coefficient, calculate the transmission path index of light at different tissue depths, call the transmission path data, analyze the distribution characteristics of the light field at different tissue levels, judge the light field distribution balance, and obtain the light field balance parameter;

[0029] S402: Based on the light field balance parameter, call the spectral absorption rate data, analyze the absorption rate change range of different tissue levels, calculate the area index with a large absorption rate deviation, call the absorption rate deviation data, adjust the spectral signal intensity, call the adjusted spectral signal data, and obtain the spectral signal intensity adjustment value;

[0030] S403: Call the spectral signal intensity adjustment value, analyze the balanced distribution state of the spectral signal, call the blood flow state spectral correction parameter, calculate the optimization amount of the light source incident angle, adjust the light wave coherence, compensate for the influence of the tissue structure on the spectral signal, obtain the spectral optimization parameter at the tissue level, and obtain the spectral optimization factor at the tissue level.

[0031] As a further solution of the present invention, the calculation formula of the light wave coherence adjustment parameter is specifically:

[0032]

[0033] Among them, Ψ adj represents the light wave coherence adjustment parameter, W represents the total number of sampling points of the measured spectral signal, F v represents the spectral intensity of the v-th measurement point, D v represents the optical density of the tissue layer at the v-th measurement point, C ref represents the optical density compensation value of the reference tissue layer, F med represents the median spectral intensity of all measurement points, D avg represents the average optical density of all measurement points.

[0034] As a further solution of the present invention, the method further includes obtaining the change of the characteristic absorption peak based on the spectral optimization factor at the tissue level, analyzing the peak shift trend, adjusting the spectral reference value, screening multiple absorption peaks for joint calculation, and obtaining the spectral detection scheme;

[0035] The spectral detection scheme includes the characteristic peak position shift trend, the characteristic peak stability reference, and the characteristic peak joint calculation;

[0036] S501: Based on the tissue-level spectral optimization factor, obtain the change in the characteristic absorption peak of spectral data, call the characteristic absorption peak position data, analyze the offset trend of the biomolecular characteristic peak under different environmental factors, call the offset trend data, calculate the offset of the characteristic peak under the change of environmental factors, call the offset data, and obtain the stable reference value of the characteristic peak;

[0037] S502: Based on the stable reference value of the characteristic peak, call the characteristic peak offset data, calculate the offset adjustment amount of the spectral signal, correct the spectral signal reference value, screen multiple characteristic absorption peaks, call the tissue-level spectral optimization factor, and calculate the spectral characteristic parameters of the screened characteristic absorption peaks to obtain the combined calculation value of the characteristic peaks;

[0038] S503: Call the combined calculation value of the characteristic peaks, analyze the stability of the spectral signal to the blood glucose and blood lipid concentrations, call the corrected spectral signal data, calculate the optimization index of the spectral signal, identify the matching of the spectral data in blood glucose and blood lipid detection, obtain the optimized spectral signal parameters, and obtain the spectral detection scheme.

[0039] A non-invasive blood glucose and blood lipid detection system based on infrared spectroscopy, comprising:

[0040] The light source adjustment module obtains the light source in the infrared band, adjusts the incident phase of the light source to match the characteristic absorption peak of blood glucose and blood lipid, monitors the phase offset of the transmitted signal, calculates the attenuation of the band light energy in different tissue levels, and identifies the peak value of light wave coherent interference to obtain the light wave interference enhancement signal;

[0041] The blood vessel dynamic analysis module, based on the light wave interference enhancement signal, collects the blood vessel diameter change data, calculates the systolic peak value and the diastolic lowest point, analyzes the offset trend of the spectral absorption peak in the blood vessel contraction state, and obtains the blood vessel matching spectral correction value;

[0042] The pulse wave synchronous acquisition module, based on the blood vessel matching spectral correction value, synchronously acquires the pulse wave signal, calculates the blood flow pulsation period, analyzes the mean value and deviation of the spectral signal within the time window, and adjusts the spectral data normalization parameter to obtain the blood flow state spectral correction parameter;

[0043] The tissue-level spectral optimization module, based on the blood flow state spectral correction parameter, obtains the optical scattering coefficient and local absorption coefficient of the skin tissue, calculates the transmission path of light at different tissue depths, judges the light field distribution uniformity, adjusts the spectral signal intensity of the region with a large absorption rate deviation, and identifies the light source incident angle and light wave coherence to obtain the tissue-level spectral optimization factor;

[0044] Based on the tissue-level spectral optimization factor, the spectral signal feature analysis module obtains the change of the characteristic absorption peak of spectral data, analyzes the environmental factor offset trend of the position of the biomolecular characteristic peak, calculates the offset amount and adjusts the spectral signal reference value, screens multiple characteristic absorption peaks for joint calculation, and obtains a spectral detection scheme.

[0045] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0046] In the present invention, by precisely setting the incident phase of the infrared light source and monitoring the position of the light wave interference peak, the acquisition efficiency of spectral data and the accuracy of signal analysis are significantly improved, the light source intensity and spectral energy distribution are optimized, the sensitivity to blood glucose and blood lipids is enhanced, the data accuracy is ensured, and the spectral data is dynamically adjusted by combining the pulse wave signal and the blood vessel diameter change data, effectively reducing the influence of blood vessel changes on the measurement, enhancing the stability and accuracy of the results, and improving the reliability of non-invasive detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0048] Figure 1 It is a schematic diagram of the step flow of the present invention;

[0049] Figure 2 It is a flowchart of step S1 of the present invention;

[0050] Figure 3 It is a flowchart of step S2 of the present invention;

[0051] Figure 4 It is a flowchart of step S3 of the present invention;

[0052] Figure 5 It is a flowchart of step S4 of the present invention;

[0053] Figure 6 It is a flowchart of step S5 of the present invention;

[0054] Figure 7 It is a system module diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] The following will describe the technical solutions in the present invention with reference to the drawings.

[0056] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to give examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of the word "example" is intended to present concepts in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0057] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0058] In the embodiments of the present invention, sometimes subscripts such as W 1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0059] To make the technical problems to be solved, technical solutions and advantages of the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0060] Please refer to Figure 1 , a non-invasive blood glucose and blood lipid detection method based on infrared spectroscopy, comprising the following steps:

[0061] S1: Obtain an infrared band light source, set the incident phase of the light source according to the characteristic absorption peaks of blood glucose and blood lipids, monitor the position of the light wave interference peak and record the phase shift amount of the transmitted signal, analyze the influence of skin thickness and water content on the spectral signal absorption ratio, calculate the attenuation amount of the band light energy at different tissue levels, adjust the light source intensity to balance the spectral energy distribution, optimize the light wave coherent interference peak value, and obtain an enhanced light wave interference signal;

[0062] S2: Based on the obtained enhanced light wave interference signal and the blood vessel diameter change data recorded by optical plethysmography technology, calculate the systolic peak value, diastolic lowest point and pulse wave propagation velocity, compare the spectral absorption peak shift trend under different blood vessel systolic states, calculate the blood vessel contractility compensation factor, combine the enhanced light wave interference signal to adjust the spectral signal characteristic peak value and absorption intensity, compensate for the influence of blood vessel dynamic changes on spectral data, and obtain a blood vessel matching spectral correction value;

[0063] S3: Based on the vascular matching spectral correction value, acquire the synchronous acquisition data of the pulse wave signal, calculate the blood flow pulsation period and match the spectral signal time window, analyze the mean value and deviation of the spectral signal within the time window, judge the fluctuation range of the spectral data caused by blood flow pulsation, adjust the spectral data normalization parameter according to the blood flow pulsation state, and calculate the spectral absorption rate correction amount in combination with the vascular matching spectral correction value to obtain the spectral correction parameter for blood flow state;

[0064] S4: Based on the spectral correction parameter for blood flow state, acquire the optical scattering coefficient and local absorption coefficient of the skin tissue, calculate the transmission path of light at different tissue depths, judge the uniformity of the light field distribution, adjust the spectral signal intensity for the area with a large absorption rate deviation, and optimize the light source incident angle and light wave coherence in combination with the spectral correction parameter for blood flow state to compensate for the influence of the tissue structure on the spectral signal, obtaining the spectral optimization factor for tissue layers;

[0065] S5: Based on the spectral optimization factor for tissue layers, acquire the change of the characteristic absorption peak of the spectral data, analyze the offset trend of the position of the biomolecular characteristic peak with environmental factors, establish a reference for the stability of the characteristic peak, calculate the offset amount and adjust the spectral signal reference value, and screen multiple characteristic absorption peaks for joint calculation in combination with the spectral optimization factor for tissue layers to enhance the stability of the spectral signal to the blood glucose and blood lipid concentrations, obtaining the spectral detection scheme.

[0066] The light wave interference enhanced signal includes the calculation of the phase offset amount, attenuation amount, and adjustment of the light source intensity. The vascular matching spectral correction value includes the systolic peak value, pulse wave propagation velocity, and absorption intensity compensation. The spectral correction parameter for blood flow state includes pulsation period matching, mean value and deviation analysis, and normalization parameter. The spectral optimization factor for tissue layers includes the scattering coefficient, transmission path, and light field distribution uniformity. The spectral detection scheme includes the offset trend of the characteristic peak position, the reference for the stability of the characteristic peak, and the joint calculation of the characteristic peaks.

[0067] Please refer to Figure 2 , the specific steps of S1 are as follows:

[0068] S101: Acquire an infrared band light source, set the incident phase of the light source according to the characteristic absorption peaks of blood glucose and blood lipids, call the light source with the set phase to be incident on the skin tissue, monitor the position of the light wave interference peak, record the phase offset amount of the transmitted signal, call the recorded phase offset amount of the transmitted signal, and calculate the propagation characteristic index of light in the skin tissue to obtain the transmitted signal phase data;

[0069] Obtain an infrared-band light source, determine the characteristic absorption peaks of blood glucose and blood lipids, call the database to obtain the spectral absorption data of different concentrations of blood glucose and blood lipids, compare their absorption rates in different bands, and select the bands with obvious changes in absorption rate as the incident wavelengths of the detection light source. Set the incident phase of the light source, and through the adjustable light source control system, make the light source with the set phase incident on the skin tissue. Call the light wave coherence detection module to obtain the peak position formed by light wave interference, and combine the optical characteristic parameters of the skin tissue to calculate the transmission path length of the light wave in the tissue. Call the light wave transmission model to record the initial phase offset of the transmitted signal. Further, through the signal acquisition module, continuously monitor the change in the phase offset of the transmitted signal, compare the data at different time points, and calculate the dynamic offset value of the light wave phase with the change of tissue state. Finally, obtain the phase data of the transmitted signal.

[0070] S102: Based on the phase data of the transmitted signal, monitor the changes in skin thickness and water content, call the skin thickness and water content parameters, analyze the influence on the spectral signal absorption ratio, obtain the transmission energy distribution of light waves at different tissue levels, calculate the attenuation amount of band light energy at different tissue levels, and obtain the light energy attenuation ratio.

[0071] Call the skin thickness measurement device, use the ultrasonic probe to detect the skin thickness at different parts, obtain the skin thickness data at multiple positions, and calculate the average value of the skin thickness at each position. At the same time, call the skin water content detection module, use the dielectric constant measurement technology to monitor the change in skin water content under different environmental humidities, obtain the corresponding water content data, call the skin thickness and water content parameters, use the multi-point measurement data to calculate their influence on the spectral signal absorption ratio, through the normalization processing method, calculate the relationship curve between the skin thickness change and the light wave absorption ratio, and further analyze the absorption rate change under different water content conditions. Combine the optical attenuation formula to calculate the light energy transmission distribution at different tissue levels, obtain the transmission ratio of light wave energy at different levels, call the skin tissue model, calculate the light energy attenuation amount, and perform normalization processing on the light energy attenuation of different bands to obtain the final light energy attenuation ratio.

[0072] S103: Call the light energy attenuation ratio, adjust the intensity of the light source, balance the spectral energy distribution, monitor the change in the light wave interference peak after adjustment, record the light wave coherence interference peak, call the recorded light wave coherence interference peak data, calculate the signal amplitude index of interference enhancement, and obtain the light wave interference enhancement signal.

[0073] Adjust the emission power of the light source using the light source control unit. While ensuring the stable output power of the light source in the target wavelength band, adjust the relative intensities of the light sources in different wavelength bands to make the overall spectral energy distribution tend to be balanced. Use the optical wave interference monitoring module to collect the optical wave interference peak data after adjustment in real time and store the data. Call the stored interference peak data, calculate the change in the interference amplitude of the optical wave using Fourier transform, compare the interference signal intensities before and after adjustment, calculate the signal amplitude of the enhanced interference, screen out the adjustment plan with the most balanced light energy distribution through peak comparison, and record the final optical wave interference enhanced signal.

[0074] Please refer to Figure 3 , and the specific steps of S2 are as follows:

[0075] S201: Based on the optical wave interference enhanced signal, call the data of the blood vessel diameter change recorded by the photoplethysmography technique, calculate the systolic peak, diastolic, and pulse wave propagation velocity, call the systolic peak and diastolic data, calculate the change amount of the blood vessel contraction state, call the change amount of the blood vessel contraction state, and analyze the spectral absorption shift trend through the spectral absorption peaks under different differential blood vessel contraction states.

[0076] Call the photoplethysmography technique to obtain the data of the blood vessel diameter change, use a photoelectric sensor to detect the expansion and contraction of the blood vessel caused by blood flow, record the cross-sectional area of the blood vessel at different time points, and calculate the systolic peak, diastolic value, and pulse wave propagation velocity of the blood vessel in combination with the time series data. Call the systolic peak and diastolic data of the blood vessel, calculate the diameter change rate of the blood vessel in different states, obtain the change amount of the blood vessel contraction state through differential operation on the continuously measured data, call the change amount of the blood vessel contraction state, combine the spectral detection data of the tissue layer, compare the spectral absorption characteristics under different contraction states, obtain the spectral absorption peak shift situation, and quantify the spectral absorption shift trend by calculating the spectral absorption peak displacement between adjacent measurement points.

[0077] S202: Based on the spectral absorption shift trend, analyze the changes in the spectral absorption peaks under different differential blood vessel contraction states, call the change data of the spectral absorption peaks, calculate the blood vessel contraction compensation factor, analyze the adjustment amount of the optical wave interference enhanced signal, obtain the peak value and absorption intensity of the adjusted spectral signal characteristics, and obtain the spectral characteristic adjustment value.

[0078] Call the spectral data of continuous measurement, calculate the displacement of the spectral absorption peak under different vasoconstriction states, call the spectral absorption peak change data of multiple measurement points, compare the absorption peak offset amounts in different time periods, screen out the stable offset intervals, and calculate the spectral offset mean value of each time point. Further calculate the vasoconstriction compensation factor, use the relationship between the vasoconstriction amount and the spectral offset to calculate the compensation factor value, and perform normalization processing according to the vascular elasticity parameters of different individuals. Call the compensation factor to adjust the optical wave interference enhancement signal. By dynamically adjusting the spectral intensity, the compensation factor acts on the optical wave phase correction, corrects the wavelength matching degree of the optical wave signal, and records the characteristic peak value of the spectral signal after adjustment in real time. Call the adjusted characteristic peak value data, calculate the spectral absorption intensity, and summarize the characteristic values after spectral adjustment to obtain the spectral characteristic adjustment value.

[0079] S203: Call the spectral characteristic adjustment value, calculate the influence degree of vascular dynamic changes on the spectral signal, call the vasoconstriction compensation factor for compensation and correction, and adjust the dynamic matching of the spectral signal to obtain the vascular matching spectral correction value;

[0080] Calculate the influence degree of vascular dynamic changes on the spectral signal, compare the offset amounts of the spectral characteristic peak values before and after adjustment, calculate the spectral offset correction ratio, call the vasoconstriction compensation factor for matching compensation, use the spectral data under different vascular states to adjust the dynamic matching of the spectral signal, call multiple groups of spectral characteristic values, screen the optimal matching parameters, adjust the incident angle and phase of the optical wave, optimize the interference conditions of the spectral signal, combine the change trend of the interference enhancement signal, calculate the final corrected spectral signal correction amount, record the finally corrected spectral matching data, and obtain the vascular matching spectral correction value.

[0081] Please refer to Figure 4 , the specific steps of S3 are as follows:

[0082] S301: Based on the vascular matching spectral correction value, obtain the synchronous acquisition data of the pulse wave signal, call the time series of the pulse wave signal, calculate the blood flow pulsation cycle index, call the blood flow pulsation cycle data, match the time window of the spectral signal, call the matched time window, and extract the mean value and deviation amount of the spectral signal within the window to obtain the spectral signal time window characteristic value;

[0083] The specific calculation formula for the mean square deviation value of the spectral signal within the window is:

[0084]

[0085] Among them, V sig represents the mean square deviation value of the spectral signal within the window, N represents the number of spectral signal sample points participating in the calculation within this time window, S jrepresents the spectral signal intensity at the j-th time point, M represents the total number of all acquisition points within the time window, S k represents the spectral signal intensity at the k-th time point within the time window:

[0086] This formula is used to calculate the mean square deviation of the spectral signal within a specific time window, which is a key step in extracting the eigenvalue of the spectral signal time window from the actual measurement data. The deviation reflects how the spectral intensity of each sampling point fluctuates relative to the average intensity of the entire time window.

[0087] Parameter settings:

[0088] N: The number of spectral signal sample points participating in the calculation within the time window, assumed to be 5, which means that within a specific time window, the spectral instrument has completed 5 measurements of the spectral intensity.

[0089] M: The same as N, also 5 in this example, indicating that the total number of acquisition points is the same as the number of sampling points participating in the calculation.

[0090] S j : The spectral signal intensity at the j-th time point, and the data is from the real-time measurement of the spectral instrument.

[0091] S k : The spectral signal intensity at the k-th time point within the time window, which is used to calculate the average spectral intensity of all points within the window.

[0092] Actual data example:

[0093] Suppose the spectral signal intensities collected at 5 consecutive time points are:

[0094] S 1 = 100, S 2 = 102, S 3 = 98, S 4 = 101, S 5 = 99;

[0095] Calculation process:

[0096] Calculate the average intensity of the spectral signal:

[0097]

[0098] Calculate the deviation of each point:

[0099] |S 1 - Average| = |100 - 100| = 0;

[0100] |S 2 - Average| = |102 - 100| = 2;

[0101] |S 3 -Average| = |98 - 100| = 2;

[0102] |S 4 -Average| = |101 - 100| = 1;

[0103] |S 5 -Average| = |99 - 100| = 1;

[0104] Calculate the mean square deviation:

[0105]

[0106] Result interpretation: V sig The value of is 1.2, indicating that within this time window, the average deviation of the spectral signal relative to the average value is 1.2, which is an indicator for measuring the stability of the spectral signal. A smaller V sig value indicates that the spectral signal is relatively stable within this time window. This result is directly related to the eigenvalue of the spectral signal time window obtained at the end of the step and provides important data for subsequent spectral analysis.

[0107] Detailed parameter explanation: V sig represents the mean square deviation value of the spectral signal within the window, N represents the number of spectral signal sample points participating in the calculation within this time window, S j represents the spectral signal intensity at the j-th time point, M represents the total number of all acquisition points within the time window, S k represents the spectral signal intensity at the k-th time point within the time window.

[0108] S302: Based on the eigenvalue of the spectral signal time window, call the blood flow pulsation cycle data, calculate the fluctuation range of the blood flow pulsation on the spectral signal, call the fluctuation range data, judge the change trend of the spectral signal under the blood flow pulsation state, adjust the normalization parameter of the spectral data according to the blood flow pulsation state, call the normalized adjusted spectral signal, calculate the spectral absorption rate correction amount, and obtain the spectral absorption rate adjustment value;

[0109] Call the blood flow pulsation cycle data, extract the spectral signal change data within consecutive cardiac cycles, calculate the fluctuation range of the spectral signal at different pulsation stages, call the fluctuation range data, calculate the spectral absorption rate difference of the spectral signal during systole and diastole, judge the change trend of the spectral signal under the blood flow pulsation state, compare the spectral signal characteristic values within multiple pulse wave cycles, calculate the displacement amount of the spectral peak position, call the spectral peak offset data, determine the change direction and amplitude of the spectral signal, adjust the normalization parameter of the spectral data according to the blood flow pulsation state, call the normalized spectral signal, compare the absorption intensity changes before and after adjustment, calculate the spectral absorption rate correction amount, and finally obtain the spectral absorption rate adjustment value.

[0110] S303: Call the spectral absorption rate adjustment value, calculate the dynamic correction parameter of the spectral signal under the blood flow pulsation state, call the spectral correction value for vascular matching, adjust the time matching of the spectral data, obtain the correction parameter of the spectral signal under the blood flow state, and get the spectral correction parameter for the blood flow state;

[0111] Calculate the dynamic correction parameter of the spectral signal under the blood flow pulsation state, call the pulse wave signal data, calculate the instantaneous offset amount of the spectral signal within the pulse wave cycle, compare the change trends of the spectral signal within different pulse wave cycles, extract the offset trend parameter of the spectral signal, call the spectral correction value for vascular matching, adjust the time matching of the spectral data, compare the spectral fluctuation amplitudes before and after adjustment, extract the time period with the smallest fluctuation as the optimal matching time window, obtain the correction parameter of the spectral signal under the blood flow state, calculate the change amplitude of the spectral signal within the pulse wave cycle, and adjust the final matching parameter of the spectral data according to the blood flow pulsation state to get the spectral correction parameter for the blood flow state.

[0112] Please refer to Figure 5 , the specific steps of S4 are as follows:

[0113] S401: Based on the spectral correction parameter for the blood flow state, obtain the optical scattering coefficient and local absorption coefficient of the skin tissue, call the optical scattering coefficient and local absorption coefficient data, calculate the transmission path index of light at different tissue depths, call the transmission path data, analyze the distribution characteristics of the light field at the tissue level, judge the light field distribution uniformity, and obtain the light field uniformity parameter;

[0114] Call an optical measurement device to obtain the optical scattering coefficient and local absorption coefficient of skin tissue, record the optical scattering data of multiple measurement points, and calculate the average scattering coefficient of different tissue layers. Call the absorption coefficient data, calculate the transmission path index of light at different tissue depths in combination with the scattering coefficient. Call the transmission path data, compare the transmission lengths of different tissue layers, extract the change trend of the transmission path. Call the light field distribution data, calculate the scattering attenuation amount of light waves in different tissue layers, compare the light energy attenuation in different regions, screen the tissue regions with large changes in light field energy density, judge the uniformity of the light field in tissue layers, calculate the average value and standard deviation of the light field density distribution, screen the regions where the deviation of the light field density distribution exceeds the set threshold, and calculate the light field uniformity parameters of different regions, and finally obtain the light field uniformity parameters.

[0115] S402: Based on the light field uniformity parameters, call the spectral absorption rate data, analyze the change range of the absorption rate of different tissue layers, calculate the regional index with a large absorption rate deviation. Call the absorption rate deviation data, adjust the spectral signal intensity, and call the adjusted spectral signal data to obtain the adjusted value of the spectral signal intensity;

[0116] Call the spectral absorption rate data, extract the spectral absorption rates of different tissue layers, and calculate the change range of the absorption rates of different tissue layers. Compare the spectral absorption rate data, calculate the absorption differences of tissues at different depths, screen the tissue regions with large absorption rate deviations. Call the absorption rate deviation data, calculate the energy loss amount of the spectral signal in the regions with large absorption deviations, record the loss amount data, compare the spectral energy changes before and after adjustment, calculate the spectral energy compensation value. Call the spectral signal data after adjustment and compensation, adjust the light source power output, match the light field uniformity parameters of different tissue layers, calculate the corrected values of the spectral intensity of different tissue layers, obtain the adjusted spectral signal data, and finally obtain the adjusted value of the spectral signal intensity.

[0117] S403: Call the adjusted value of the spectral signal intensity, analyze the uniform distribution state of the spectral signal, call the spectral correction parameters for blood flow state, calculate the optimization amount of the light source incident angle, adjust the light wave coherence, compensate for the influence of the tissue structure on the spectral signal, obtain the spectral optimization parameters at the tissue layer level, and obtain the spectral optimization factor at the tissue layer level;

[0118] The specific calculation formula for the light wave coherence adjustment parameter is:

[0119]

[0120] Among them, Ψ adj represents the light wave coherence adjustment parameter, W represents the total number of sampling points of the measured spectral signal, F v represents the spectral intensity of the v-th measurement point, D v represents the optical density of the tissue layer at the v-th measurement point, C refRepresents the optical density compensation value of the reference tissue layer, F med Represents the median spectral intensity of all measurement points, D avg Represents the mean optical density of all measurement points:

[0121] This formula is used to calculate the optical wave coherence adjustment parameter, which quantifies the influence of tissue structure on the spectral signal. This calculation involves spectral intensity, tissue layer optical density, and reference optical density compensation value. By calculating the degree of change of the spectral signal relative to the tissue layer, the optical wave coherence is adjusted, and finally the matching of the spectral signal under the tissue level is optimized.

[0122] Parameter acquisition method:

[0123] W represents the total number of spectral signal sampling points measured, obtained by the spectral detection device. Setting W = 5 means that the data of five measurement points are recorded within this analysis window.

[0124] F v Represents the spectral intensity of the v-th measurement point, collected by the spectrometer, with the unit of mW. Set the measurement data as F 1 = 3.2, F 2 = 3.5, F 3 = 3.1, F 4 = 3.3, F 5 = 3.4.

[0125] D v Represents the optical density of the tissue layer at the v-th measurement point, obtained by the optical scattering measurement system, with the unit of cm -1 , set the measurement data as D 1 = 0.65, D 2 = 0.72, D 3 = 0.68, D 4 = 0.70, D 5 = 0.66.

[0126] C ref Represents the optical density compensation value of the reference tissue layer, obtained through tissue comparison experiments. Set C ref = 0.10 cm -1 .

[0127] F med Represents the median spectral intensity of all measurement points. Calculate the median:

[0128] F med = 3.3 mW;

[0129] D avg Represents the mean optical density of all measurement points:

[0130]

[0131] Calculation process:

[0132] Calculate the spectral normalization value of each measurement point:

[0133]

[0134] Calculate the normalized value of the median measurement point:

[0135]

[0136] Calculate the square of each deviation:

[0137] (4.267 - 4.839) 2 = (-0.572) 2 = 0.327;

[0138] (4.268 - 4.839) 2 = (-0.571) 2 = 0.326;

[0139] (3.974 - 4.839) 2 = (-0.865) 2 = 0.748;

[0140] (4.125 - 4.839) 2 = (-0.714) 2 = 0.510;

[0141] (4.474 - 4.839) 2 = (-0.365) 2 = 0.133;

[0142] Calculate the optical wave coherence adjustment parameter:

[0143]

[0144] Analysis of calculation results: The calculated optical wave coherence adjustment parameter Ψ adj = 1.43 reflects the adjustment requirement of the spectral signal in the tissue layer structure. The magnitude of this value determines the optimization amplitude of the optical wave coherence matching. If this value is large, it indicates that the distribution of the spectral signal in the tissue layer is uneven, and a larger adjustment of the incident angle is required to optimize the spectral signal matching. This result directly affects the calculation of the subsequent tissue layer spectral optimization factor and is used to further optimize the spectral detection scheme.

[0145] Please refer to Figure 6 , the specific steps of S5 are:

[0146] S501: Based on the tissue-level spectral optimization factor, obtain the change in the characteristic absorption peak of spectral data, call the position data of the characteristic absorption peak, analyze the offset trend of the biomolecular characteristic peak under different environmental factors, call the offset trend data, calculate the offset of the characteristic peak under the change of environmental factors, call the offset data, and obtain the stable reference value of the characteristic peak;

[0147] When calculating the stable reference value of the characteristic peak, we need to determine the offset of the characteristic absorption peak under different environmental factors (such as temperature, humidity, blood flow state) and calculate the stable reference benchmark value. Here, the mean calculation method of the characteristic peak offset is adopted, and the standard deviation is combined to evaluate the stability of the characteristic peak. Let the actual measured wavelength of a certain characteristic absorption peak be λ i , and the wavelength set obtained after n measurements under different environmental factors is {λ 1 , λ 2 , …, λ n}}, then the stable reference value λ stable of the characteristic peak is calculated as follows:

[0148]

[0149] To evaluate the stability of the characteristic peak, the standard deviation σ λ of the characteristic peak offset needs to be calculated:

[0150]

[0151] If the standard deviation σ λ is lower than the set threshold δ λ , then the wavelength of this characteristic peak can be used as the stable reference value, otherwise the measurement data needs to be further screened.

[0152] Calculation steps

[0153] Data collection

[0154] Select a certain characteristic absorption peak, such as 1540 nm (the infrared absorption peak of a certain biomolecule).

[0155] Perform n = 5 measurements under different environmental factors (temperature, humidity, blood flow state), and the obtained wavelength data are as follows:

[0156] λ 1 = 1539.8, λ 2 = 1540.2, λ 3 = 1539.9, λ 4 = 1540.1, λ 5 = 1540.0 nm;

[0158] Calculate the stable reference value of the characteristic peak

[0159] Substitute into the mean formula:

[0160]

[0161] Calculate the standard deviation of the characteristic peak

[0162] Calculate the deviation of each wavelength from the mean value:

[0163] (λ 1 -λ stable ) 2 =(1539.8 - 1540.0) 2 = 0.04;

[0164] (λ 2 -λ stable ) 2 =(1540.2 - 1540.0) 2 = 0.04;

[0165] (λ 3 -λ stable ) 2 =(1539.9 - 1540.0) 2 = 0.01;

[0166] (λ 4 -λ stable ) 2 =(1540.1 - 1540.0) 2 = 0.01;

[0167] (λ 5 -λ stable ) 2 =(1540.0 - 1540.0) 2 = 0;

[0168] Calculate the standard deviation:

[0169]

[0170] Judge the stability of the characteristic peak

[0171] Set the threshold δ λ = 0.15 nm.

[0172] Since σ λ = 0.141 < δ λ = 0.15, the stability condition is satisfied, so 1540.0 nm is used as the stable reference value of the characteristic peak.

[0173] Finally, obtain the stable reference value λ of the characteristic peak stable = 1540.0 nm.

[0174] S502: Based on the stable reference value of the characteristic peak, call the characteristic peak offset data, calculate the offset adjustment amount of the spectral signal, correct the spectral signal reference value, screen multiple characteristic absorption peaks, call the spectral optimization factor at the tissue level, calculate the spectral characteristic parameters of the screened characteristic absorption peaks, and obtain the combined calculation value of the characteristic peaks;

[0175] Call the characteristic peak offset data, compare the difference between the actual offset value and the stable reference value of the characteristic peak in the measurement environment, calculate the offset adjustment amount of the spectral signal, call the adjustment amount data, correct the spectral signal reference value, compare the change of the spectral signal before and after correction, screen multiple characteristic absorption peaks, call the spectral optimization factor at the tissue level, extract the spectral characteristic data of the characteristic absorption peaks at different tissue levels, compare the absorption intensities of the characteristic peaks at different tissue levels, calculate the normalization parameters of the characteristic absorption peaks, match the spectral energy distribution of the characteristic peaks at different levels, calculate the change trend of the characteristic absorption peaks in different spectral measurement time periods, obtain the absorption mean value of the spectral signal at different tissue levels, and compare the change of the characteristic peaks at different time points, and finally obtain the combined calculation value of the characteristic peaks.

[0176] S503: Call the combined calculation value of the characteristic peaks, analyze the stability of the spectral signal to blood glucose and blood lipid concentrations, call the corrected spectral signal data, calculate the optimization index of the spectral signal, identify the matching of the spectral data in blood glucose and blood lipid detection, obtain the optimized spectral signal parameters, and obtain the spectral detection scheme;

[0177] Analyze the stability of the spectral signal to blood glucose and blood lipid concentrations, call the corrected spectral signal data, extract the spectral absorption characteristics at different blood glucose and blood lipid concentrations, compare the influence of different individual blood glucose and blood lipid concentrations on the spectral absorption peaks, calculate the optimization index of the spectral signal, extract the spectral optimization values at different time points, compare the offset degree of the spectral signal at different blood glucose and blood lipid concentrations, screen the stable interval of the spectral signal under the change of blood glucose and blood lipid concentrations, identify the matching of the spectral data in blood glucose and blood lipid detection, calculate the spectral signal correction parameters under different concentration conditions, extract the spectral absorption data at different blood glucose and blood lipid levels, compare the change of the spectral signal of different individuals, screen the optimal spectral parameters suitable for blood glucose and blood lipid detection, calculate the error range of the spectral signal among different individuals, and finally obtain the spectral detection scheme after obtaining the optimal spectral parameters.

[0178] Please refer to Figure 7 , a non-invasive blood glucose and blood lipid detection system based on infrared spectroscopy, including:

[0179] The light source adjustment module obtains the light source in the infrared band, adjusts the incident phase of the light source to match the characteristic absorption peak of blood glucose and blood lipid, monitors the phase offset amount of the transmitted signal, calculates the attenuation amount of the band light energy in different tissue levels, and identifies the peak value of light wave coherent interference to obtain the light wave interference enhancement signal;

[0180] The vascular dynamic analysis module enhances signals based on light wave interference, collects data on blood vessel diameter changes, calculates the systolic peak and diastolic lowest point, analyzes the offset trend of the spectral absorption peak in the blood vessel contraction state, and obtains the spectral correction value for blood vessel matching;

[0181] The pulse wave synchronous acquisition module synchronously acquires pulse wave signals based on the spectral correction value for blood vessel matching, calculates the blood flow pulsation period, analyzes the mean value and deviation of the spectral signals within the time window, adjusts the spectral data normalization parameters, and obtains the spectral correction parameters for blood flow state;

[0182] The tissue layer spectral optimization module obtains the optical scattering coefficient and local absorption coefficient of the skin tissue based on the spectral correction parameters for blood flow state, calculates the transmission path of light at different tissue depths, judges the uniformity of the light field distribution, adjusts the spectral signal intensity in the area with a large absorption rate deviation, identifies the light source incident angle and light wave coherence, and obtains the tissue layer spectral optimization factor;

[0183] The spectral signal feature analysis module obtains the change of the characteristic absorption peak of the spectral data based on the tissue layer spectral optimization factor, analyzes the offset trend of the environmental factors at the position of the biomolecular characteristic peak, calculates the offset amount and adjusts the spectral signal reference value, screens multiple characteristic absorption peaks for joint calculation, and obtains the spectral detection scheme.

[0184] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claimed rights.

Claims

1. A non-invasive detection method for blood glucose and blood lipids based on infrared spectroscopy, characterized in that: The following steps are involved: S1: Obtain an infrared light source, set the incident phase, monitor the position of the light wave interference peak, record the phase offset of the transmission signal, calculate the spectral attenuation of the tissue level, adjust the light source intensity, and obtain the light wave interference enhancement signal; S2: Based on the light wave interference enhancement signal, obtain the blood vessel diameter change data, calculate the pulse wave propagation velocity, analyze the spectral absorption peak shift trend, calculate the vascular contraction compensation factor, adjust the spectral signal characteristic peak value, and obtain the blood vessel matching spectral correction value; S3: Based on the blood vessel matching spectrum correction value, the pulse wave signal is obtained to calculate the blood flow pulsation period, the spectrum signal time window is matched, the spectrum absorption mean within the pulsation period is analyzed, the influence range of the blood flow pulsation on the spectrum signal is calculated, the spectrum normalization parameter is adjusted, the spectrum absorption error caused by the pulsation is compensated, the blood flow state matching offset is calculated, and the blood flow state spectrum correction parameter is obtained; S4: Based on the blood flow state spectrum correction parameters, the tissue optical scattering and absorption coefficients are obtained, the transmission path is calculated, the light field distribution balance is determined, the light source incident angle and light wave coherence are optimized, and the tissue level spectrum optimization factor is obtained.

2. The non-invasive detection method of blood glucose and blood lipids based on infrared spectroscopy according to claim 1, characterized in that: The light wave interference enhancement signal includes phase offset, attenuation calculation, and light source intensity adjustment; the blood vessel matching spectrum correction value includes contraction peak, pulse wave propagation velocity, and absorption intensity compensation; the blood flow state spectrum correction parameters include pulsation period matching, mean and deviation analysis, and normalization parameters; the tissue level spectrum optimization factors include scattering coefficient, transmission path, and light field distribution balance.

3. The non-invasive detection method of blood glucose and blood lipids based on infrared spectroscopy according to claim 1, characterized in that: The specific steps of obtaining an infrared light source, setting the incident phase, monitoring the position of the light wave interference peak, recording the phase offset of the transmission signal, calculating the attenuation of the tissue level spectrum, adjusting the light source intensity, and obtaining the light wave interference enhancement signal are as follows: S101: Obtain an infrared light source, set the incident phase of the light source according to the characteristic absorption peaks of blood sugar and blood lipids, call the light source with the set phase to be incident on the skin tissue, monitor the position of the light wave interference peak, record the phase offset of the transmission signal, call the recorded phase offset of the transmission signal, calculate the propagation characteristic index of the light wave in the skin tissue, and obtain the transmission signal phase data; S102: Based on the transmission signal phase data, monitor the changes in skin thickness and water content, call the skin thickness and water content parameters, analyze the impact on the spectral signal absorption ratio, obtain the transmission energy distribution of the light wave at the differentiated tissue level, calculate the attenuation of the band light energy at the differentiated tissue level, and obtain the light energy attenuation ratio; S103: calling the light energy attenuation ratio, adjusting the intensity of the light source, balancing the spectral energy distribution, monitoring the change of the adjusted light wave interference peak value, recording the light wave coherent interference peak value, calling the recorded light wave coherent interference peak value data, calculating the interference enhanced signal amplitude index, and obtaining the light wave interference enhanced signal.

4. The non-invasive detection method of blood glucose and blood lipids based on infrared spectroscopy according to claim 1, characterized in that: Based on the light wave interference enhancement signal, the specific steps of obtaining blood vessel diameter change data, calculating the pulse wave propagation velocity, analyzing the spectral absorption peak shift trend, calculating the vascular contraction compensation factor, adjusting the spectral signal characteristic peak value, and obtaining the blood vessel matching spectral correction value are as follows: S201: Based on the light wave interference enhancement signal, the blood vessel diameter change data recorded by the optical volumetric recording technology is called to calculate the contraction peak value, relaxation and pulse wave propagation velocity, the contraction peak value and relaxation data are called to calculate the contraction state change of the blood vessel, the contraction state change of the blood vessel is called, and the spectral absorption shift trend is obtained by analyzing the spectral absorption peak under the differential contraction state of the blood vessel; S202: Based on the spectral absorption shift trend, analyzing the change of the spectral absorption peak under the differentiated vascular contraction state, calling the change data of the spectral absorption peak, calculating the vascular contraction compensation factor, analyzing the adjustment amount of the light wave interference enhancement signal, obtaining the adjusted spectral signal characteristic peak value and absorption intensity, and obtaining the spectral characteristic adjustment value; S203: calling the spectral feature adjustment value, calculating the influence of the dynamic change of blood vessels on the spectral signal, calling the vascular contraction compensation factor to perform compensation correction, adjusting the dynamic matching of the spectral signal, and obtaining the vascular matching spectral correction value.

5. The non-invasive detection method of blood glucose and blood lipids based on infrared spectroscopy according to claim 1, characterized in that: Based on the blood vessel matching spectrum correction value, the pulse wave signal is obtained to calculate the blood flow pulsation period, the spectrum signal time window is matched, the spectrum absorption mean within the pulsation period is analyzed, the influence range of the blood flow pulsation on the spectrum signal is calculated, the spectrum normalization parameter is adjusted, the spectrum absorption error caused by the pulsation is compensated, the blood flow state matching offset is calculated, and the specific steps of obtaining the blood flow state spectrum correction parameter are as follows: S301: based on the blood vessel matching spectrum correction value, obtaining pulse wave signal synchronous acquisition data, calling the time series of the pulse wave signal, calculating the blood flow pulsation cycle index, calling the blood flow pulsation cycle data, matching the time window of the spectrum signal, calling the matched time window, extracting the mean and deviation of the spectrum signal in the window, and obtaining the characteristic value of the spectrum signal time window; S302: based on the time window characteristic value of the spectral signal, calling the blood flow pulsation cycle data, calculating the fluctuation range of the blood flow pulsation on the spectral signal, calling the fluctuation range data, judging the change trend of the spectral signal under the blood flow pulsation state, adjusting the normalization parameter of the spectral data according to the blood flow pulsation state, calling the normalized and adjusted spectral signal, calculating the spectral absorbance correction amount, and obtaining the spectral absorbance adjustment value; S303: Call the spectral absorbance adjustment value, calculate the dynamic correction parameters of the spectral signal under the blood flow pulsation state, call the blood vessel matching spectral correction value, adjust the time matching of the spectral data, obtain the correction parameters of the spectral signal under the blood flow state, and obtain the blood flow state spectral correction parameters.

6. The non-invasive detection method of blood glucose and blood lipids based on infrared spectroscopy according to claim 5, characterized in that: The calculation formula of the mean square deviation value of the spectral signal in the window is specifically: Among them, V sig represents the mean square deviation of the spectral signal in the window, N represents the number of spectral signal sample points involved in the calculation in the time window, S j represents the spectral signal intensity at the jth time point, M represents the total number of all acquisition points in the time window, and S k Represents the spectral signal intensity at the kth time point in the time window.

7. The non-invasive detection method of blood glucose and blood lipids based on infrared spectroscopy according to claim 1, characterized in that: Based on the blood flow state spectrum correction parameters, the specific steps of obtaining tissue optical scattering and absorption coefficients, calculating the transmission path, judging the light field distribution balance, optimizing the light source incident angle and light wave coherence, and obtaining the tissue level spectrum optimization factor are as follows: S401: Based on the blood flow state spectrum correction parameter, the optical scattering coefficient and the local absorption coefficient of the skin tissue are obtained, the optical scattering coefficient and the local absorption coefficient data are called, the transmission path index of the light at the differentiated tissue depth is calculated, the transmission path data is called, the distribution characteristics of the light field at the tissue level are analyzed, the light field distribution balance is determined, and the light field balance parameter is obtained; S402: Based on the light field balance parameter, calling the spectral absorption rate data, analyzing the absorption rate variation range of the differentiated tissue levels, calculating the regional index with large absorption rate deviation, calling the absorption rate deviation data, adjusting the spectral signal intensity, calling the adjusted spectral signal data, and obtaining the spectral signal intensity adjustment value; S403: Call the spectral signal intensity adjustment value, analyze the balanced distribution state of the spectral signal, call the blood flow state spectral correction parameter, calculate the optimization amount of the light source incident angle, adjust the light wave coherence, compensate for the influence of tissue structure on the spectral signal, obtain the spectral optimization parameters at the tissue level, and obtain the spectral optimization factor at the tissue level.

8. The non-invasive detection method of blood glucose and blood lipids based on infrared spectroscopy according to claim 1, characterized in that: The optical wave coherence adjustment parameter calculation formula is specifically: Among them, adj represents the light wave coherence adjustment parameter, W represents the total number of spectral signal sampling points measured, and F v represents the spectral intensity of the vth measurement point, D v represents the optical density of the tissue layer at the vth measurement point, C ref Represents the optical density compensation value of the reference tissue layer, F med represents the median spectral intensity of all measurement points, D avg Represents the mean optical density of all measurement points.

9. The non-invasive detection method of blood glucose and blood lipids based on infrared spectroscopy according to claim 1, characterized in that: The method further includes, based on the tissue-level spectrum optimization factor, obtaining characteristic absorption peak changes, analyzing peak shift trends, adjusting spectrum reference values, screening multiple absorption peaks for joint calculation, and obtaining a spectrum detection scheme; The spectrum detection scheme includes characteristic peak position shift trend, characteristic peak stability benchmark, characteristic peak joint calculation S501: based on the tissue-level spectrum optimization factor, obtaining characteristic absorption peak changes of spectrum data, calling characteristic absorption peak position data, analyzing the shift trend of biological molecule characteristic peaks under differentiated environmental factors, calling the shift trend data, calculating the shift amount of characteristic peaks under changes in environmental factors, calling the shift amount data, and obtaining a stable reference value of the characteristic peak; S502: Based on the characteristic peak stable reference value, calling the characteristic peak offset data, calculating the offset adjustment of the spectral signal, correcting the spectral signal reference value, screening multiple characteristic absorption peaks, calling the tissue level spectrum optimization factor, calculating the spectral characteristic parameters of the screened characteristic absorption peaks, and obtaining the characteristic peak joint calculation value; S503: Call the characteristic peak joint calculation value, analyze the stability of the spectral signal to the blood glucose and blood lipid concentration, call the corrected spectral signal data, calculate the optimization index of the spectral signal, identify the matching of the spectral data in the blood glucose and blood lipid detection, obtain the optimized spectral signal parameters, and obtain the spectral detection plan.

10. A non-invasive detection system for blood sugar and blood lipids based on infrared spectroscopy, characterized in that: According to a non-invasive method for detecting blood glucose and blood lipids based on infrared spectroscopy according to any one of claims 1 to 9, the system comprises: The light source adjustment module obtains the infrared light source, adjusts the incident phase of the light source to match the characteristic absorption peak of blood glucose and blood lipids, monitors the phase offset of the transmission signal, calculates the attenuation of the band light energy at the differentiated tissue level, identifies the peak of light wave coherence interference, and obtains the light wave interference enhancement signal; The vascular dynamic analysis module collects the data of vascular diameter change based on the light wave interference enhancement signal, calculates the contraction peak and the diastolic minimum, analyzes the shift trend of the spectral absorption peak under the vascular contraction state, and obtains the vascular matching spectral correction value; The pulse wave synchronous acquisition module synchronously acquires the pulse wave signal based on the blood vessel matching spectrum correction value, calculates the blood flow pulsation period, analyzes the mean and deviation of the spectrum signal in the time window, adjusts the spectrum data normalization parameters, and obtains the blood flow state spectrum correction parameters; The tissue-level spectrum optimization module obtains the optical scattering coefficient and local absorption coefficient of the skin tissue based on the blood flow state spectrum correction parameters, calculates the transmission path of light at the differentiated tissue depth, determines the balance of the light field distribution, adjusts the spectral signal intensity of the area with large absorption rate deviation, identifies the incident angle of the light source and the coherence of the light wave, and obtains the tissue-level spectrum optimization factor; The spectral signal characteristic analysis module obtains the characteristic absorption peak changes of the spectral data based on the tissue-level spectral optimization factor, analyzes the environmental factor offset trend of the characteristic peak position of the biological molecule, calculates the offset and adjusts the spectral signal baseline value, screens multiple characteristic absorption peaks for joint calculation, and obtains a spectral detection solution.

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