Cell repair data monitoring method and system

Abstract

The invention belongs to the technical field of cell repair monitoring, and discloses a cell repair data monitoring method and system, and the method comprises the steps: synchronously collecting a short-distance diffuse reflection spectrum and a long-distance diffuse reflection spectrum of skin in a tattooing region, carrying out the spectrum smoothing processing and baseline correction, calculating the difference between the two spectrums, and obtaining a difference spectrum; characteristic spectrum parameters are extracted from the characteristic spectrum parameters to determine the skin cell repair starting state grade, the problem of evaluation interference caused by factors such as tattoo pigment residues, tissue fluid exudation and individual skin color differences is solved, and objective evaluation of the skin cell repair starting state of the tattoo area can be achieved.

Description

Technical Field

[0001] The present application relates to the technical field of cell repair monitoring, and in particular to a method and system for monitoring cell repair data. Background Art

[0002] With the growing popularity of tattoo art and advancements in technology, consumers' expectations for tattoo results have expanded beyond the artistic quality of the design to include a heightened focus on post-tape skin health recovery. After a tattoo is completed, tattoo artists urgently need an effective method to instantly and accurately assess the status of skin cell repair initiation, enabling them to provide personalized post-operative care recommendations. This directly impacts both the final quality of the tattoo and the patient's skin health. However, the current tattoo industry faces significant bottlenecks in immediate post-tape assessment technology.

[0003] Currently, the commonly used assessment method in the industry still relies heavily on the tattoo artist's personal experience, subjectively judging the state of repair through visual observation of factors such as the degree of redness and swelling of the tattooed skin and the presence of tissue fluid exudation. This traditional method not only lacks objective quantitative criteria but also relies heavily on the operator's accumulated experience. This makes it difficult to effectively guarantee the scientific and accurate evaluation results, which in turn limits the relevance and effectiveness of post-operative repair recommendations. Furthermore, due to significant individual skin variations, even experienced tattoo artists struggle to accurately distinguish normal physiological reactions caused by the tattoo procedure from potential delayed repair or abnormal initiation of repair based solely on visual observation, which undoubtedly poses a challenge to the accuracy and effectiveness of post-operative care.

[0004] As an emerging non-invasive testing method, spectral analysis technology offers new possibilities for objectively assessing skin condition. Theoretically, by analyzing the diffuse reflectance spectral signals generated by the interaction of skin tissue with light of specific wavelengths, it is possible to obtain in-depth physiological and biochemical information about the skin's deep layers, providing a scientific basis for objectively assessing the state of cellular repair. However, applying spectral analysis technology to tattoos presents a series of unprecedented technical challenges.

[0005] First, the tattooing process inevitably introduces pigment particles into the skin tissue. These pigment particles strongly absorb and scatter light of specific wavelengths, creating a complex spectral background that easily obscures the weak spectral signature generated by the skin's own cell repair. Immediately after the tattoo, pigment particles are highly concentrated in the skin's surface and shallow dermis, where their spectral interference is even more pronounced. This poses a significant challenge to accurately extracting the characteristic spectral information of cell repair.

[0006] Secondly, the tattooing procedure itself causes micro-invasive damage to skin tissue, which in turn triggers interstitial fluid exudation and an inflammatory response. Interstitial fluid has a complex composition, containing proteins, lipids, cellular debris, and other substances. These components also alter the optical properties of the skin, causing additional interference with the spectral signal. Further complicating matters, the amount and composition of interstitial fluid exudation vary with individual differences and the specific circumstances of the tattooing procedure, making spectral interference even more difficult to predict and effectively eliminate, seriously affecting the accuracy of spectral analysis results.

[0007] Furthermore, individual skin color variations are a long-standing challenge for spectral analysis technology in skin testing. People with different skin tones have vastly different melanin content, leading to significant variations in the skin's absorption and reflection properties for different wavelengths of light. This variation can cause spectral baseline drift and signal intensity variations, severely impacting the accuracy and reliability of spectral data analysis. In tattoo scenarios, skin color variations, combined with multiple interfering factors such as tattoo pigment and tissue fluid exudation, complicate and difficult the interpretation and analysis of spectral signals.

[0008] In the immediate post-tatto assessment of skin cell repair status, effectively overcoming multiple complex interference factors such as residual tattoo pigment, tissue fluid leakage, and individual skin color differences is a key technical challenge that needs to be addressed. Designing a portable, easy-to-use monitoring system and supporting data processing methods to quickly and non-invasively identify the characteristic signals reflecting the true repair activation state of skin cells from the interfered spectral signals is crucial. This allows for reliable assessment and personalized guidance of the post-tatto skin cell repair activation state. Effectively addressing this challenge will greatly enhance the professionalism and scientific nature of tattoo services, providing strong support for ensuring tattoo effectiveness and the skin health of customers.

[0009] In view of the above problems, the existing technology is in urgent need of improvement. Summary of the Invention

[0010] The purpose of this application is to provide a cell repair data monitoring method and system, which can achieve an objective assessment of the activation status of skin cell repair in the tattoo area.

[0011] In a first aspect, the present application provides a cell repair data monitoring method for instantly evaluating the skin cell repair initiation status of the tattoo area after the tattoo operation is completed. The method comprises the following steps: A1. Illuminate the tattooed area with light of a specific wavelength range and simultaneously receive the short-range and long-range diffuse reflectance spectra of the skin tissue at receiving locations at different radial distances from the illumination source. The receiving location for the long-range diffuse reflectance spectrum is located at a greater radial distance from the illumination source than the receiving location for the short-range diffuse reflectance spectrum. A2. Perform spectral smoothing and baseline correction on the short-range diffuse reflectance spectra and the long-range diffuse reflectance spectra in sequence; A3. Adjust the intensity of the corrected short-range diffuse reflectance spectrum so that the intensity of the adjusted short-range diffuse reflectance spectrum matches the intensity of the surface interference component in the corrected long-range diffuse reflectance spectrum; A4. Calculate the difference between the corrected long-distance diffuse reflectance spectrum and the adjusted short-distance diffuse reflectance spectrum to obtain a difference spectrum; A5. Extract characteristic spectral parameters reflecting the initiation state of cell repair from the difference spectrum; A6. Determine the skin cell repair activation status level in the tattoo area based on the extracted characteristic spectral parameters.

[0012] Preferably, step A1 includes: The tattoo area skin is irradiated with light of a wavelength range of 400nm-1000nm at an incident angle of 28 degrees-32 degrees, and the short-distance diffuse reflection spectrum and long-distance diffuse reflection spectrum of the skin tissue are synchronously received at receiving positions with different radial distances from the irradiation light source.

[0013] Preferably, step A2 includes: A201. For short-range diffuse reflectance spectra and long-range diffuse reflectance spectra, a Savitzky-Golay filter is used for spectral smoothing. A202. For the short-range diffuse reflectance spectra and long-range diffuse reflectance spectra after spectral smoothing, the asymmetric least squares method is used to perform baseline correction.

[0014] Preferably, step A3 includes: A301. Using multiple proportional coefficient values ​​within a preset proportional coefficient range, respectively, the intensity of the corrected short-distance diffuse reflectance spectrum is adjusted to obtain multiple first short-distance diffuse reflectance spectra; A302. Calculate the root mean square error between each first short-distance diffuse reflectance spectrum and the corrected long-distance diffuse reflectance spectrum input spectrum; A303. Fit a curve showing how the root mean square error (RMS) changes with the scale factor, and extract the scale factor corresponding to the minimum RMS error as the optimal scale factor. A304. Use the optimal proportional coefficient to adjust the intensity of the corrected short-distance diffuse reflectance spectrum to obtain an adjusted short-distance diffuse reflectance spectrum.

[0015] Preferably, step A302 includes: For each first short-distance diffuse reflectance spectrum and the corrected long-distance diffuse reflectance spectrum, the average reflectance of each spectrum within a plurality of preset wavelength ranges is calculated; wherein the preset wavelength ranges are selected based on the absorption band of the tattoo pigment, the absorption band of the tissue fluid, and the characteristic absorption band reflecting the state of cell repair; Obtaining weight coefficients for each preset wavelength range; wherein the wavelength range that contributes more to the evaluation of cell repair status has a higher weight; According to the average reflectivity of each preset wavelength range and the corresponding weight coefficient, the weighted root mean square error value between each first short-distance diffuse reflection spectrum and the corrected long-distance diffuse reflection spectrum is calculated as the effective root mean square error value.

[0016] Preferably, step A4 includes: A401. Divide the tattoo area into a plurality of sub-areas, and for each sub-area, calculate the difference between the corrected long-distance diffuse reflectance spectrum and the adjusted short-distance diffuse reflectance spectrum to obtain a sub-difference spectrum for each sub-area; A402. Calculate the root mean square value of the second derivative of the short-range diffuse reflectance spectrum of each sub-region as a representation of the degree of uneven tissue fluid exudation in each sub-region; A403. Based on the characterization value of the uneven degree of tissue fluid exudation in each sub-region, a weighted average of the sub-difference spectra of each sub-region is performed to obtain a difference spectrum of the tattoo region; wherein, the sub-region with a higher characterization value of the uneven degree of tissue fluid exudation has a lower weight of the corresponding sub-difference spectrum.

[0017] Preferably, step A402 includes: Calculate the average reflectance of the original short-range diffuse reflectance spectrum of each sub-region in the tattoo pigment absorption band and the water absorption band; The tattoo pigment influence factor is calculated based on the average reflectivity of each sub-area in the tattoo pigment absorption band. The tattoo pigment influence factor is negatively correlated with the average reflectivity. The tissue fluid impact factor is calculated based on the average reflectivity of each sub-region in the water absorption band. The tissue fluid impact factor is negatively correlated with the average reflectivity. Calculate the correction coefficient for each sub-region based on the tattoo pigment influence factor and the tissue fluid influence factor; the correction coefficient is used to reduce the influence of the tattoo pigment and tissue fluid on the root mean square value of the second-order derivative; Correcting the original short-distance diffuse reflectance spectrum of each sub-region using the correction coefficient to obtain a second short-distance diffuse reflectance spectrum; The root mean square value of the second derivative of the second short-range diffuse reflectance spectrum of each sub-region is calculated as a characterization value of the uneven degree of tissue fluid exudation in each sub-region.

[0018] Preferably, step A5 includes: The integral values ​​of the difference spectrum within multiple preset characteristic wavelength ranges are calculated to obtain multiple characteristic spectral parameters; wherein the preset characteristic wavelength ranges are selected based on the characteristic absorption peak positions of key biomolecules in the cell repair process, including hemoglobin, collagen and elastin.

[0019] Preferably, step A6 includes: The extracted characteristic spectral parameters are input into a pre-trained skin cell repair activation status evaluation model to obtain the skin cell repair activation status level of the tattoo area.

[0020] In a second aspect, the present application provides a cell repair data monitoring system for instantly evaluating the skin cell repair initiation status in the tattoo area after the tattoo operation is completed, the device comprising a spectrum acquisition device and a spectrum analyzer; The spectrum collection device includes an illumination light source, a first spectrum receiver, and a second spectrum receiver, wherein the radial distance between the second spectrum receiver and the illumination light source is greater than the radial distance between the first spectrum receiver and the illumination light source; the illumination light source is used to emit light in a specific wavelength range to illuminate the skin of the tattoo area, the first spectrum receiver is used to receive the diffuse reflectance spectrum of the skin tissue to obtain a short-distance diffuse reflectance spectrum, and the second spectrum receiver is used to synchronously receive the diffuse reflectance spectrum of the skin tissue to obtain a long-distance diffuse reflectance spectrum; The spectrum analyzer includes: A preprocessing module is used to perform spectral smoothing and baseline correction on the short-distance diffuse reflectance spectrum and the long-distance diffuse reflectance spectrum in sequence; An intensity adjustment module is used to adjust the intensity of the corrected short-distance diffuse reflectance spectrum so that the intensity of the adjusted short-distance diffuse reflectance spectrum matches the intensity of the surface interference component in the corrected long-distance diffuse reflectance spectrum; a difference calculation module, used to calculate the difference between the corrected long-distance diffuse reflectance spectrum and the adjusted short-distance diffuse reflectance spectrum to obtain a difference spectrum; A feature extraction module is used to extract characteristic spectrum parameters reflecting the cell repair initiation state from the difference spectrum; The level determination module is used to determine the skin cell repair activation state level of the tattoo area based on the extracted characteristic spectral parameters.

[0021] Beneficial Effects: The present application provides a cell repair data monitoring method and system, which synchronously collects the short-range diffuse reflectance spectrum and the long-range diffuse reflectance spectrum of the skin in the tattoo area. After spectral smoothing and baseline correction, the difference between the two is calculated to obtain a difference spectrum, from which characteristic spectral parameters are extracted to determine the level of skin cell repair activation status. This solves the evaluation interference problem caused by factors such as tattoo pigment residue, tissue fluid exudation, and individual skin color differences, and can achieve an objective evaluation of the skin cell repair activation status in the tattoo area. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Flowchart of the cell repair data monitoring method provided in an embodiment of the present application.

[0023] Figure 2 This is a schematic diagram of the structure of the cell repair data monitoring system provided in an embodiment of the present application.

[0024] Figure 3 This is a structural diagram of a spectrum acquisition device.

[0025] Explanation of reference numerals: 1. Spectral acquisition device; 101. Illumination light source; 102. First spectral receiver; 103. Second spectral receiver; 104. Pen-type main body; 105. Switch button; 2. Spectral analyzer; 201. Preprocessing module; 202. Intensity adjustment module; 203. Difference calculation module; 204. Feature extraction module; 205. Level determination module. DETAILED DESCRIPTION

[0026] The technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. The components of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for which protection is claimed, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0027] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0028] refer to Figure 1 This application proposes a cell repair data monitoring method for instantly evaluating the skin cell repair initiation status of the tattoo area after the tattoo operation is completed. The method comprises the following steps: A1. Illuminate the tattooed area with light of a specific wavelength range and simultaneously receive the short-range and long-range diffuse reflectance spectra of the skin tissue at receiving locations at different radial distances from the illumination source. The receiving location for the long-range diffuse reflectance spectrum is located at a greater radial distance from the illumination source than the receiving location for the short-range diffuse reflectance spectrum. A2. Perform spectral smoothing and baseline correction on the short-range diffuse reflectance spectra and the long-range diffuse reflectance spectra in sequence; A3. Adjust the intensity of the corrected short-range diffuse reflectance spectrum so that the intensity of the adjusted short-range diffuse reflectance spectrum matches the intensity of the surface interference component in the corrected long-range diffuse reflectance spectrum; A4. Calculate the difference between the corrected long-distance diffuse reflectance spectrum and the adjusted short-distance diffuse reflectance spectrum to obtain a difference spectrum; A5. Extract characteristic spectral parameters reflecting the initiation state of cell repair from the difference spectrum; A6. Determine the skin cell repair activation status level in the tattoo area based on the extracted characteristic spectral parameters.

[0029] In step A1, the illumination light source may be a laser, an LED light source, or a halogen lamp. Synchronously receiving the short-range diffuse reflectance spectrum and the long-range diffuse reflectance spectrum of the skin tissue at receiving positions at different radial distances from the illumination light source means using two or more spectral receivers to synchronously collect spectral signals reflected from the skin tissue. The spectral receiver may be a photodiode array, a CCD detector, or a CMOS detector. The radial distance between the receiving position of the short-range diffuse reflectance spectrum and the illumination light source is relatively small, for example, 0.5 mm to 2 mm. The radial distance between the receiving position of the long-range diffuse reflectance spectrum and the illumination light source is relatively large, for example, 3 mm to 10 mm. The difference in radial distance causes short-range diffuse reflectance spectra to primarily reflect information about the skin's surface, while long-range diffuse reflectance spectra reflect information about the skin's deeper layers. Specifically, when the receiver is closer to the illumination source, it primarily receives photons that have undergone less scattering, which primarily propagate through the skin's surface. Therefore, short-range diffuse reflectance spectra primarily reflect information about the skin's surface, such as tattoo pigment and tissue fluid. When the receiver is farther from the illumination source, it primarily receives photons that have undergone more scattering, which travel a longer path through the skin and can reach deeper tissue. Therefore, long-range diffuse reflectance spectra can reflect information about the skin's deeper layers, such as the state of cell repair. Synchronous reception ensures that short-range and long-range diffuse reflectance spectra are collected under the same physiological conditions, reducing errors introduced by temporal variations.

[0030] In step A2, spectral smoothing is performed to reduce spectral noise and improve the signal-to-noise ratio. Spectral smoothing can be performed using methods such as moving average filtering, Savitzky-Golay filtering, or wavelet transform. Baseline correction is performed to eliminate spectral baseline drift and reduce the impact of factors such as individual skin color differences on spectral data. Baseline correction can be performed using methods such as polynomial fitting, asymmetric least squares, or derivative spectroscopy. Spectral smoothing is performed before baseline correction to ensure accurate baseline correction while removing noise.

[0031] In step A3, intensity adjustment refers to scaling the intensity of the short-range diffuse reflectance spectrum using a proportional coefficient. The proportional coefficient can be obtained using an optimization algorithm, such as a grid search method, a genetic algorithm, or a particle swarm optimization algorithm. Intensity matching is performed to ensure that the intensity of the adjusted short-range diffuse reflectance spectrum is as close as possible to the surface interference component in the long-range diffuse reflectance spectrum, thereby effectively removing the surface interference in subsequent steps.

[0032] In step A4, the spectral difference can be obtained by directly subtracting the adjusted short-range diffuse reflectance spectrum from the corrected long-range diffuse reflectance spectrum. This difference spectrum largely eliminates the influence of surface interference components and can more clearly reflect the spectral information related to deep skin tissue and cell repair.

[0033] In step A5, characteristic spectral parameters refer to pre-defined key indicators that reflect the activation status of cell repair. These parameters may include reflectance at a specific wavelength, integral values ​​within a specific wavelength range, derivative values ​​of the spectrum, or peak positions of the spectrum. The selection of these parameters is based on changes in key biomolecules during the cell repair process, such as hemoglobin, collagen, and elastin.

[0034] In step A6, the skin cell repair activation status level refers to a quantitative classification of the skin repair status, such as "not activated," "mildly activated," "moderately activated," and "heavily activated." The status level can be determined using methods such as threshold judgment, cluster analysis, or machine learning classification models. Machine learning classification models can use algorithms such as support vector machines, random forests, or neural networks.

[0035] Specifically, the cell repair data monitoring method proposed in this application utilizes light within a specific wavelength range to illuminate the skin in the tattooed area and simultaneously collect short- and long-range diffuse reflectance spectra at different radial distances. Short-range diffuse reflectance spectra primarily reflect surface interference information, such as residual tattoo pigment, tissue fluid exudation, and individual skin color variations. Long-range diffuse reflectance spectra, on the other hand, contain information on the cell repair activation status in deeper layers of the skin tissue. Spectral smoothing and baseline correction improve spectral data quality and effectively mitigate individual skin color variations. An intensity adjustment step ensures that the intensity of surface interference components in the short- and long-range diffuse reflectance spectra matches, paving the way for subsequent interference elimination. Calculation of the difference spectrum effectively removes surface interference information and highlights spectral features related to the cell repair activation status. Extraction of characteristic spectral parameters quantifies key information about the cell repair activation status. Ultimately, the determination of the skin cell repair activation status level enables an objective and accurate assessment of the skin's condition after tattooing. This method overcomes the shortcomings of traditional methods, which rely on the tattoo artist's experience, are highly subjective, and have a low degree of quantification, achieving an objective, quantitative, and immediate assessment of the skin cell repair activation status after tattooing.

[0036] Through the above technical solution, the present application can effectively overcome the interference of various complex interference factors such as tattoo pigment residue, tissue fluid exudation and individual skin color differences in the evaluation of the initiation status of skin cell repair after tattooing, and realize objective and accurate evaluation of the initiation status of skin cell repair after tattooing, providing tattoo artists with real-time and quantitative skin repair status information, assisting them in providing customers with more accurate post-operative care recommendations.

[0037] In some preferred embodiments, step A1 includes: The tattoo area skin is irradiated with light of a wavelength range of 400nm-1000nm at an incident angle of 28 degrees-32 degrees, and the short-distance diffuse reflection spectrum and long-distance diffuse reflection spectrum of the skin tissue are synchronously received at receiving positions with different radial distances from the irradiation light source.

[0038] Among them, for light with a wavelength range of 400nm-1000nm, a specific light source configured to emit light in this wavelength range can be used. For example, a wide-spectrum light source including visible light and near-infrared bands can be used, and the wavelength can be selected by using filters or other optical elements to ensure that the wavelength of light irradiated to the skin falls within the range of 400nm-1000nm. The incident angle is set to 28 degrees to 32 degrees, and can be controlled and adjusted by the operator holding a handheld spectrum acquisition device, or automatically adjusted by a spectrum acquisition device that can automatically adjust the direction of the light. By limiting the wavelength range and incident angle, the spectrum acquisition process is standardized and precise, thereby ensuring the quality of the acquired spectral signal.

[0039] Specifically, in step A1, the wavelength range is limited to 400nm-1000nm, and light covering the visible light and near-infrared spectrum regions is used to irradiate the skin of the tattoo area. Light in this wavelength range can penetrate the surface of the skin, reach the deep layer of the skin tissue, interact with the cells and biomolecules in the skin tissue, and then generate a spectral signal containing information about the cell repair status. At the same time, this wavelength range can avoid the strong absorption band in water, thereby reducing the interference of water on the spectral signal and improving the spectral signal-to-noise ratio. The incident angle is limited to 28 degrees to 32 degrees. By controlling the incident angle, the angle at which the light irradiates the skin is standardized, thereby reducing the fluctuation of the spectral signal caused by the angle difference and improving the stability and repeatability of data acquisition. The incident angle is within the range of 28 degrees to 32 degrees, ensuring that the light can effectively penetrate the surface of the skin, while reducing the interference of surface reflection and improving the signal-to-noise ratio of spectral acquisition. Therefore, limiting the wavelength range to 400nm-1000nm and the incident angle to 28 degrees-32 degrees ensures the quality of the spectral signal, laying the foundation for the subsequent accurate evaluation of the skin cell repair activation status level in the tattoo area, and overcoming the problem that improper selection of light wavelength range and irradiation angle will affect the quality of the spectral signal, and thus affect the accuracy of the cell repair activation status evaluation results.

[0040] In some embodiments, step A2 comprises: A201. For short-range diffuse reflectance spectra and long-range diffuse reflectance spectra, a Savitzky-Golay filter is used for spectral smoothing. A202. For the short-range diffuse reflectance spectra and long-range diffuse reflectance spectra after spectral smoothing, the asymmetric least squares method is used to perform baseline correction.

[0041] Step A201 uses a Savitzky-Golay filter to smooth the short-range and long-range diffuse reflectance spectra. The Savitzky-Golay filter, a digital filter, slides across the spectral data and performs a polynomial least-squares fit on the data points within the sliding window, thereby smoothing the spectra. By using the Savitzky-Golay filter, noise in the spectra is effectively filtered out while preserving the spectral characteristics as much as possible, avoiding spectral distortion caused by oversmoothing.

[0042] Among them, step A202 uses the asymmetric least squares method to perform baseline correction on the spectrum after spectral smoothing. The asymmetric least squares method fits the spectral baseline in an iterative manner. During the fitting process, asymmetric weights are applied to the spectral data points, so that the fitted baseline can closely follow the baseline drift of the spectrum, while preventing the characteristic peaks of the spectrum from being incorrectly fitted as the baseline. As a result, the baseline drift of the spectrum is effectively corrected, eliminating the interference of baseline drift on subsequent spectral analysis and improving the quality of spectral data. The spectral smoothing process in step A201 is performed before the baseline correction in step A202, ensuring that the baseline correction is performed on the noise-reduced spectral data, further improving the accuracy and stability of the baseline correction.

[0043] Specifically, in order to address the problem of unstable spectral smoothing and baseline correction effects, the effect and stability of spectral preprocessing can be guaranteed by limiting the spectral smoothing to use Savitzky-Golay filter and the baseline correction to use asymmetric least squares method. In the spectral smoothing process, the Savitzky-Golay filter can effectively filter out noise and well retain spectral feature information, thereby ensuring the effect of spectral smoothing. In the baseline correction, the asymmetric least squares method can effectively correct the spectral baseline drift, improve the quality of spectral data, and ensure the effect of baseline correction. Therefore, after the spectral preprocessing of step A2, the quality of spectral data is improved, providing high-quality spectral data for the subsequent calculation of difference spectra and extraction of characteristic spectral parameters, thereby ensuring the accuracy and reliability of the determination of the level of skin cell repair startup status.

[0044] In some embodiments, the Savitzky-Golay filter's filter window width is set to 5 data points, and the polynomial order is set to 2nd order, thereby achieving a balance between noise filtering and preservation of spectral detail information during the spectral smoothing process. In the asymmetric least squares method, the asymmetric weight parameter is set to 0.01, and the smoothing parameter is set to 10 to the power of 5, thereby effectively correcting baseline drift while avoiding spectral information distortion caused by overcorrection. Spectral smoothing and baseline correction are automatically performed by spectral analysis software or program code, ensuring the efficiency and accuracy of the spectral preprocessing process.

[0045] In some embodiments, step A3 comprises: A301. Using multiple proportional coefficient values ​​within a preset proportional coefficient range, respectively, the intensity of the corrected short-distance diffuse reflectance spectrum is adjusted to obtain multiple first short-distance diffuse reflectance spectra; A302. Calculate the root mean square error between each first short-distance diffuse reflectance spectrum and the corrected long-distance diffuse reflectance spectrum input spectrum; A303. Fit a curve showing how the root mean square error (RMS) changes with the scale factor, and extract the scale factor corresponding to the minimum RMS error as the optimal scale factor. A304. Use the optimal proportional coefficient to adjust the intensity of the corrected short-distance diffuse reflectance spectrum to obtain an adjusted short-distance diffuse reflectance spectrum.

[0046] Among them, in step A301, first, it is necessary to determine a preset proportional coefficient range, which can be set according to actual application scenarios and empirical data, for example, it can be set to 0.5-1.5. Then, within the preset proportional coefficient range, multiple proportional coefficient values ​​are selected, and the number of selections can be adjusted according to the accuracy requirements and computing resources. For example, 10, 20 or more can be selected. The selected proportional coefficient values ​​can be uniformly distributed or non-uniformly distributed. Using each selected proportional coefficient value, the intensity of the corrected short-distance diffuse reflectance spectrum is adjusted respectively. The intensity adjustment method can be a simple multiplication, that is, the intensity value of the corrected short-distance diffuse reflectance spectrum is multiplied by the proportional coefficient value to obtain the adjusted short-distance diffuse reflectance spectrum, that is, the first short-distance diffuse reflectance spectrum. Through step A301, multiple first short-distance diffuse reflectance spectra with different intensities can be obtained, laying the foundation for finding the optimal proportional coefficient in the subsequent steps.

[0047] In step A302, the root mean square error (RMS) value can effectively reflect the degree of difference between the two spectra. The purpose of calculating the RMS error value is to quantify the degree of match between the short-range diffuse reflectance spectrum and the long-range diffuse reflectance spectrum under different intensity adjustment schemes. The smaller the RMS error value, the smaller the difference between the two spectra and the higher the degree of match. Conversely, the larger the RMS error value, the greater the difference between the two spectra and the lower the degree of match. By calculating the RMS error value, a quantitative indicator can be provided for finding the optimal proportional coefficient in subsequent steps.

[0048] Among them, in step A303, multiple root mean square error values ​​are calculated by step A302, and each root mean square error value corresponds to a proportional coefficient value. The proportional coefficient value is used as the independent variable and the root mean square error value is used as the dependent variable, and a curve of the root mean square error value changing with the proportional coefficient value can be fitted. There are many ways to fit the curve, for example, polynomial fitting, spline fitting, Gaussian fitting, etc. From the fitted curve, the minimum root mean square error value can be extracted, and the proportional coefficient value corresponding to the minimum root mean square error value is the optimal proportional coefficient. The optimal proportional coefficient refers to the proportional coefficient that minimizes the difference between the short-distance diffuse reflection spectrum and the long-distance diffuse reflection spectrum and has the highest matching degree. By fitting the curve and extracting the optimal proportional coefficient, the proportional coefficient that makes the intensity of the short-distance diffuse reflection spectrum and the intensity of the surface interference component in the long-distance diffuse reflection spectrum reach the best matching state can be accurately found.

[0049] In step A304, the optimal scaling factor obtained in step A303 is used to adjust the intensity of the corrected short-range diffuse reflectance spectrum. This intensity adjustment can be performed by simple multiplication, where the intensity value of the corrected short-range diffuse reflectance spectrum is multiplied by the optimal scaling factor to obtain the final adjusted short-range diffuse reflectance spectrum. Step A304 ensures that the intensity of the final adjusted short-range diffuse reflectance spectrum closely matches the intensity of the surface interference component in the long-range diffuse reflectance spectrum, thereby laying the foundation for more accurate elimination of surface interference and obtaining a more accurate difference spectrum.

[0050] Specifically, in this solution, first, step A301 proposes using multiple proportional coefficient values ​​within a preset proportional coefficient range to adjust the intensity of the corrected short-range diffuse reflectance spectrum, thereby obtaining multiple first short-range diffuse reflectance spectra. By using multiple proportional coefficient values ​​for adjustment, short-range diffuse reflectance spectra with different intensity levels can be obtained, providing a basis for subsequently finding the optimal intensity match. Then, step A302 proposes calculating the root mean square error (RMS) between each first short-range diffuse reflectance spectrum and the corrected long-range diffuse reflectance spectrum. The RMS error (RMS) value can effectively reflect the degree of difference between the two spectra. By calculating the RMS error (RMS) value, the degree of match between the short-range diffuse reflectance spectrum and the long-range diffuse reflectance spectrum under different intensity adjustment schemes can be quantified. Next, step A303 proposes fitting a curve of the RMS error value as it changes with the proportional coefficient value, and extracting the proportional coefficient value corresponding to the minimum RMS error value as the optimal proportional coefficient. By fitting the curve and extracting the proportional coefficient corresponding to the minimum RMS error value, the proportional coefficient that minimizes the difference between the short-range diffuse reflectance spectrum and the long-range diffuse reflectance spectrum, i.e., the highest degree of match, can be accurately found, and this proportional coefficient is used as the optimal proportional coefficient. Finally, step A304 proposes using the optimal scaling factor to adjust the intensity of the corrected short-range diffuse reflectance spectrum, obtaining the final adjusted short-range diffuse reflectance spectrum. Using the optimal scaling factor for intensity adjustment ensures that the intensity of the final adjusted short-range diffuse reflectance spectrum closely matches the intensity of the surface interference component in the long-range diffuse reflectance spectrum, thereby laying the foundation for more accurate elimination of surface interference and obtaining a more accurate difference spectrum.

[0051] Through the above technical solution, the present application makes the intensity adjustment of the short-distance diffuse reflectance spectrum more precise, thereby being able to more effectively eliminate surface interference, improve the accuracy of the difference spectrum, and ultimately enhance the accuracy and reliability of the cell repair initiation status assessment.

[0052] In some preferred embodiments, step A302 includes: For each first short-distance diffuse reflectance spectrum and the corrected long-distance diffuse reflectance spectrum, the average reflectance of each spectrum within a plurality of preset wavelength ranges is calculated; wherein the preset wavelength ranges are selected based on the absorption band of the tattoo pigment, the absorption band of the tissue fluid, and the characteristic absorption band reflecting the state of cell repair; Obtaining weight coefficients for each preset wavelength range; wherein the wavelength range that contributes more to the evaluation of cell repair status has a higher weight; According to the average reflectivity of each preset wavelength range and the corresponding weight coefficient, the weighted root mean square error value between each first short-distance diffuse reflection spectrum and the corrected long-distance diffuse reflection spectrum is calculated as the effective root mean square error value.

[0053] Among them, for each first short-distance diffuse reflectance spectrum and the corrected long-distance diffuse reflectance spectrum, the average reflectance of each spectrum within multiple preset wavelength ranges is calculated respectively. This means that before the root mean square error value is calculated, the spectrum is first divided into multiple preset wavelength ranges. For each wavelength range, the average reflectance of the short-distance diffuse reflectance spectrum and the long-distance diffuse reflectance spectrum within the wavelength range is calculated. Specifically, this can be achieved in the following manner: for example, the preset wavelength ranges can be set to three, namely 400nm-600nm, 600nm-800nm, and 800nm-1000nm. For each wavelength range, the average reflectance of all wavelength points of the short-distance diffuse reflectance spectrum and the long-distance diffuse reflectance spectrum within the wavelength range is calculated respectively, so as to obtain the average reflectance of each spectrum within each preset wavelength range. By calculating the average reflectance, the influence of spectral noise on the subsequent root mean square error value calculation result can be reduced, thereby ensuring the accuracy of the calculated root mean square error value. The selection of the preset wavelength range is based on the absorption bands of the tattoo pigment, the tissue fluid, and the characteristic absorption band reflecting the state of cellular repair. This means that the division of the preset wavelength range takes into account multiple factors, including the influence of the tattoo pigment and tissue fluid on the spectrum, as well as spectral information reflecting the state of cellular repair. Specifically, the tattoo pigment absorption band is typically in the visible light band, and the tissue fluid absorption band is typically in the near-infrared band. The characteristic absorption band reflecting the state of cellular repair can be determined based on the locations of characteristic absorption peaks of key biomolecules in the cellular repair process, such as hemoglobin, collagen, and elastin. By selecting the preset wavelength range, the subsequent calculation of the root mean square error (RMS) value prioritizes spectral information relevant to assessing the state of cellular repair, while minimizing the influence of spectral information not relevant to assessing the state of cellular repair, such as the tattoo pigment absorption band and the tissue fluid absorption band, on the RMS error calculation. This improves the reliability of the RMS error calculation and lays the foundation for the subsequent accurate determination of the optimal scaling factor.

[0054] Among them, obtaining the weight coefficients of each preset wavelength range means that for each preset wavelength range divided, a corresponding weight coefficient needs to be set. The size of the weight coefficient reflects the contribution of the spectral information of the wavelength range to the evaluation of the cell repair status. Specifically, the greater the contribution of the wavelength range to the evaluation of the cell repair status, the higher the weight, and vice versa. The acquisition of the weight coefficient can be achieved in a variety of ways. For example, the weight coefficients of each wavelength range can be determined in advance through expert experience, or the weight coefficients of each wavelength range can be obtained through training based on a large amount of experimental data through a machine learning algorithm. By setting the weight coefficients, when the root mean square error value is subsequently calculated, the spectral information that contributes greatly to the evaluation of the cell repair status can be paid more attention, further improving the reliability of the root mean square error value calculation results, and laying the foundation for obtaining an accurate optimal proportional coefficient in the subsequent calculation.

[0055] Among them, according to the average reflectivity of each preset wavelength range and the corresponding weight coefficient, the weighted root mean square error value between each first short-distance diffuse reflectance spectrum and the corrected long-distance diffuse reflectance spectrum is calculated. As an effective root mean square error value, it means that after obtaining the average reflectivity and weight coefficient of each preset wavelength range, the weighted root mean square error value needs to be calculated based on these parameters. The calculation formula of the weighted root mean square error value can be expressed as: The weighted root mean square error value = sqrt[Σ(weight coefficient i * (short-distance average reflectivity i - long-distance average reflectivity i)^2)], where i represents the i-th preset wavelength range, short-distance average reflectivity i and long-distance average reflectivity i represent the average reflectivity of the short-distance diffuse reflectance spectrum and the long-distance diffuse reflectance spectrum within the i-th preset wavelength range, respectively, weight coefficient i represents the weight coefficient of the i-th preset wavelength range, and Σ represents the summation of all preset wavelength ranges. By calculating the weighted root mean square error value, the final root mean square error value can more accurately reflect the difference in the spectrum, laying the foundation for subsequently obtaining an accurate optimal proportional coefficient.

[0056] Specifically, in the cell repair data monitoring method, step A302 is proposed to optimize the RMS error (RMS) calculation method to accurately obtain the optimal scaling factor. First, the average reflectance within multiple preset wavelength ranges is calculated for each first short-range diffuse reflectance spectrum and the corrected long-range diffuse reflectance spectrum. These preset wavelength ranges are selected based on the absorption bands of tattoo pigment, tissue fluid, and characteristic absorption bands reflecting the cell repair state. By calculating the average reflectance, the impact of spectral noise is reduced, ensuring the accuracy of the RMS error calculation. By selecting the preset wavelength ranges, the RMS error calculation prioritizes spectral information relevant to cell repair assessment and reduces the influence of non-relevant spectral information such as tattoo pigment and tissue fluid, thereby improving the reliability of the RMS error calculation results. Next, weighting coefficients are determined for each preset wavelength range. The wavelength range with the greatest contribution to cell repair assessment is assigned a higher weight. By setting the weighting coefficients, the RMS error calculation prioritizes spectral information that contributes significantly to cell repair assessment, further improving the reliability of the RMS error calculation results. Finally, a weighted RMS error (RMS) value is calculated based on the average reflectance of each preset wavelength range and the corresponding weight coefficient. This is used as the effective RMS error value. By calculating the weighted RMS error value, the final RMS error value can more accurately reflect the spectral differences, laying the foundation for the subsequent accurate optimal scaling coefficient, thereby improving the accuracy of the cell repair status assessment results.

[0057] Through the above-mentioned technical solution, the present application can ensure that the calculated root mean square error value can more accurately reflect the differences in the spectrum, thus laying the foundation for the subsequent accurate optimal proportional coefficient, thereby improving the accuracy of the cell repair status assessment results. At the same time, by selecting a preset wavelength range and setting a weighting coefficient, the calculation of the root mean square error value can focus more on spectral information related to the cell repair status assessment, reducing the influence of spectral information related to the cell repair status assessment, such as tattoo pigment and tissue fluid, on the root mean square error calculation results, thereby improving the reliability of the root mean square error calculation results.

[0058] In some embodiments, step A4 comprises: A401. Divide the tattoo area into a plurality of sub-areas, and for each sub-area, calculate the difference between the corrected long-distance diffuse reflectance spectrum and the adjusted short-distance diffuse reflectance spectrum to obtain a sub-difference spectrum for each sub-area; A402. Calculate the root mean square value of the second derivative of the short-range diffuse reflectance spectrum of each sub-region as a representation of the degree of uneven tissue fluid exudation in each sub-region; A403. Based on the characterization value of the uneven degree of tissue fluid exudation in each sub-region, a weighted average of the sub-difference spectra of each sub-region is performed to obtain a difference spectrum of the tattoo region; wherein, the sub-region with a higher characterization value of the uneven degree of tissue fluid exudation has a lower weight of the corresponding sub-difference spectrum.

[0059] In step A401, the tattoo area is divided into multiple smaller sub-areas. For example, the tattoo area can be divided into a uniformly distributed rectangular grid, or irregularly divided according to the shape of the tattoo pattern. In step A1, the corresponding short-range diffuse reflectance spectrum and long-range diffuse reflectance spectrum are collected for each sub-area. These diffuse reflectance spectra are processed in steps A2 and A3. Thus, for each sub-area, the difference between the corrected long-range diffuse reflectance spectrum and the adjusted short-range diffuse reflectance spectrum is calculated, thereby obtaining a sub-difference spectrum corresponding to each sub-area.

[0060] In step A402, the second-order derivative RMS value of the short-range diffuse reflectance spectrum of each subregion is calculated. This value can be used as an indicator to quantify the uneven degree of tissue fluid exudation in each subregion. The second-order derivative RMS value is sensitive to subtle changes in the spectrum. Uneven tissue fluid exudation often causes subtle local fluctuations in the spectrum, which can be effectively captured by the second-order derivative RMS value.

[0061] In step A403, the sub-difference spectra of each sub-region are weighted averaged based on the characterization value of the degree of tissue fluid exudation unevenness calculated in step A402. Specifically, sub-regions with higher characterization values ​​of the degree of tissue fluid exudation unevenness indicate that the spectral signal of the sub-region may be significantly disturbed by tissue fluid exudation. Therefore, during the weighted averaging process, the weights of the sub-difference spectra corresponding to these sub-regions are reduced. Conversely, the weights of the sub-difference spectra corresponding to sub-regions with lower characterization values ​​of the degree of tissue fluid exudation unevenness are increased. Thus, through weighted averaging, the impact of tissue fluid exudation unevenness on the final tattoo area difference spectrum can be reduced, improving the accuracy and representativeness of the difference spectrum. For example, the weight coefficient can be set to 1 / (1+second-order derivative root mean square value). Thus, the higher the degree of tissue fluid exudation unevenness of the sub-region, the smaller the weight of the corresponding sub-difference spectrum in the weighted averaging.

[0062] Specifically, to address the potential uneven extent of tissue fluid exudation in the tattoo area, step A4 improves the accuracy of difference spectrum calculation through sub-region division and weighted averaging. First, the tattoo area is divided into multiple sub-regions, and a difference spectrum is independently calculated for each sub-region. This reduces the impact of regional spectral differences caused by uneven tissue fluid exudation on the overall assessment result. Then, by calculating the second-order derivative root mean square value of the short-range diffuse reflectance spectrum of each sub-region, a quantitative characterization of the uneven extent of tissue fluid exudation is achieved. The second-order derivative root mean square value can effectively reflect subtle spectral changes and is used to characterize the uneven extent of tissue fluid exudation. Finally, based on the characterization value of the uneven extent of tissue fluid exudation, the sub-difference spectra of each sub-region are weighted averaged to suppress interference from uneven tissue fluid exudation. Sub-regions with higher characterization values ​​for uneven tissue fluid exudation have lower weights for their corresponding sub-difference spectra. This weighted averaging method reduces the impact of spectra from regions with higher levels of uneven tissue fluid exudation on the final difference spectrum, improving the accuracy and representativeness of the difference spectrum, thereby enhancing the reliability of the assessment of cell repair initiation status.

[0063] Preferably, step A402 may include: Calculate the average reflectance of the original short-range diffuse reflectance spectrum of each sub-region in the tattoo pigment absorption band and the water absorption band; The tattoo pigment influence factor is calculated based on the average reflectivity of each sub-area in the tattoo pigment absorption band. The tattoo pigment influence factor is negatively correlated with the average reflectivity. The tissue fluid impact factor is calculated based on the average reflectivity of each sub-region in the water absorption band. The tissue fluid impact factor is negatively correlated with the average reflectivity. Calculate the correction coefficient for each sub-region based on the tattoo pigment influence factor and the tissue fluid influence factor; the correction coefficient is used to reduce the influence of the tattoo pigment and tissue fluid on the root mean square value of the second-order derivative; Correcting the original short-distance diffuse reflectance spectrum of each sub-region using the correction coefficient to obtain a second short-distance diffuse reflectance spectrum; The root mean square value of the second derivative of the second short-range diffuse reflectance spectrum of each sub-region is calculated as a characterization value of the uneven degree of tissue fluid exudation in each sub-region.

[0064] Calculating the average reflectance of each subregion's original short-range diffuse reflectance spectrum within the tattoo pigment absorption band and the water absorption band is intended to quantify the influence of tattoo pigment and tissue fluid on the spectrum. This requires calculating the average reflectance of the original short-range diffuse reflectance spectrum within a specific wavelength range. The tattoo pigment absorption band can be selected from the primary absorption wavelength range of the tattoo pigment. For example, if the tattoo pigment is red, the 500nm-550nm band can be selected as the tattoo pigment absorption band. The water absorption band can be selected from the typical absorption wavelength range of water, for example, the 950nm-980nm band can be selected as the water absorption band. The average reflectance can be calculated as either an arithmetic mean or a weighted average, with weights set based on the importance of each wavelength within the wavelength range.

[0065] The tattoo pigment influence factor is calculated based on the average reflectance of each subregion within the tattoo pigment absorption band. The tattoo pigment influence factor is negatively correlated with the average reflectance and represents the degree of spectral interference caused by the tattoo pigment. This negative correlation means that if a subregion has a higher average reflectance within the tattoo pigment absorption band, it indicates a lower concentration of the tattoo pigment in that region, resulting in less spectral interference and a smaller tattoo pigment influence factor. Conversely, if the average reflectance is lower, it indicates a higher concentration of the tattoo pigment, resulting in greater interference and a larger tattoo pigment influence factor. The specific calculation formula for the tattoo pigment influence factor can be customized based on the actual situation. For example, the tattoo pigment influence factor can be set equal to a constant divided by the average reflectance, or a more complex nonlinear negative correlation function can be used.

[0066] The tissue fluid impact factor is calculated based on the average reflectivity of each subregion in the water absorption band. The tissue fluid impact factor is negatively correlated with the average reflectivity and is used to characterize the degree of interference of tissue fluid on the spectrum. Similar to the tattoo pigment impact factor, the tissue fluid impact factor is also negatively correlated with the average reflectivity. If the average reflectivity of a subregion in the water absorption band is high, it indicates less tissue fluid exudation and less interference, and the tissue fluid impact factor should be small. Conversely, if the average reflectivity is low, it indicates more tissue fluid exudation and greater interference, and the tissue fluid impact factor should be large. The specific calculation formula for the tissue fluid impact factor can also be set according to actual conditions, using a calculation method similar to that of the tattoo pigment impact factor or a different method.

[0067] A correction factor is calculated for each subregion based on the tattoo pigment and tissue fluid factors. This factor is used to reduce the influence of both the tattoo pigment and tissue fluid on the RMS value of the second-order derivative. This means the correction factor comprehensively accounts for the effects of both the tattoo pigment and tissue fluid. It is then used to correct the original short-range diffuse reflectance spectrum to reduce the impact of these two interfering factors on the subsequent RMS value calculation of the second-order derivative. The correction factor can be calculated as a linear combination of the tattoo pigment and tissue fluid factors, such as a weighted average, or as a nonlinear combination. The correction factor is designed to provide a stronger correction to the original spectrum when either the tattoo pigment or tissue fluid factor is large, and a weaker correction when it is low.

[0068] Correcting the original short-range diffuse reflectance spectra of each subregion using the correction coefficients to obtain the second short-range diffuse reflectance spectrum involves mathematically transforming the original short-range diffuse reflectance spectra using the calculated correction coefficients to obtain corrected spectral data. This correction can be performed using additive or multiplicative spectral correction, or even more complex functional transformations. This correction reduces the spectral interference components of the tattoo pigment and tissue fluid in the second short-range diffuse reflectance spectrum, thereby more prominently reflecting the spectral information of the skin tissue itself.

[0069] Calculating the root mean square (RMS) value of the second derivative of the second short-range diffuse reflectance spectrum for each subregion as a measure of the unevenness of tissue fluid exudation in each subregion involves calculating the second derivative of the corrected second short-range diffuse reflectance spectrum and further calculating the RMS value of the second derivative. The RMS value of the second derivative can effectively amplify subtle spectral variations, suppress spectral noise, and more sensitively reflect spectral differences caused by tissue fluid exudation. This value, used as a measure of the unevenness of tissue fluid exudation, can be used in subsequent steps to perform weighted averaging of the sub-difference spectra.

[0070] Specifically, to assess the immediate activation of skin cell repair after tattooing, and to more accurately characterize the uneven degree of tissue fluid exudation in each subregion while minimizing interference from the spectral properties of the tattoo pigment and tissue fluid, the present invention, in step A402, first calculates the average reflectance of the original short-range diffuse reflectance spectrum of each subregion in the tattoo pigment absorption band and the water absorption band, quantitatively assessing the degree of influence of the tattoo pigment and tissue fluid on each subregion. Next, the tattoo pigment influence factor and the tissue fluid influence factor are calculated based on the average reflectance. The influence factors are set to be negatively correlated with the average reflectance, effectively reflecting the degree of interference. Furthermore, a correction coefficient is calculated based on these two influence factors. This coefficient can be adjusted based on the degree of interference in each subregion, achieving adaptive correction. The correction coefficient is then used to correct the original spectrum to obtain a second short-range diffuse reflectance spectrum, reducing spectral interference. Finally, the root mean square value of the second derivative of the second short-range diffuse reflectance spectrum is calculated as a measure of the uneven degree of tissue fluid exudation. The above steps provide a more accurate representation of the degree of uneven tissue fluid exudation, laying the foundation for the subsequent accurate calculation of the difference spectrum. Compared to directly calculating the second-order derivative root mean square value from the original spectrum, this application scheme can effectively reduce the spectral interference between tattoo pigment and tissue fluid, improve the accuracy of the representation of uneven tissue fluid exudation, and thus enhance the reliability of the assessment of the initiation status of cell repair.

[0071] As a preferred embodiment, the solution of this application is specifically implemented as follows: Assuming the tattoo pigment is red, the tattoo pigment absorption band is selected to be 520nm-540nm, and the water absorption band is selected to be 960nm-970nm. For a sub-region of the tattoo area, first calculate the average reflectance R_dye in the 520nm-540nm band and the average reflectance R_water in the 960nm-970nm band of the original short-range diffuse reflectance spectrum. The tattoo pigment influence factor Dye_factor is calculated as 1 / R_dye, and the tissue fluid influence factor Water_factor is calculated as 1 / R_water. The correction factor Correction_factor is calculated as (Dye_factor + Water_factor) / 2. The original short-range diffuse reflectance spectrum is multiplied and corrected using the Correction_factor to obtain a second short-range diffuse reflectance spectrum. Finally, the second-order derivative of the second short-range diffuse reflectance spectrum is calculated using the central difference method, and the root mean square value of the second-order derivative is calculated as a representative value of the uneven degree of tissue fluid exudation in this sub-region. The above steps are repeated for each sub-region of the tattoo area to obtain a characterization value of the uneven degree of tissue fluid exudation in each sub-region.

[0072] Through the above technical solution, the present application can more accurately characterize the uneven degree of tissue fluid exudation in each sub-area, reduce the interference of tattoo pigment and tissue fluid on spectral analysis, improve the accuracy of subsequent difference spectrum calculation, and provide strong guarantees for the accurate assessment of the initiation status of skin cell repair in the tattoo area.

[0073] In some embodiments, step A5 comprises: The integral values ​​of the difference spectrum within multiple preset characteristic wavelength ranges are calculated to obtain multiple characteristic spectral parameters; wherein the preset characteristic wavelength ranges are selected based on the characteristic absorption peak positions of key biomolecules in the cell repair process, including hemoglobin, collagen and elastin.

[0074] The establishment of a preset characteristic wavelength range is fundamental to the precise extraction of characteristic parameters achieved in this technical solution. During the cell repair process, various biomolecules play a key role. For example, hemoglobin is involved in oxygen transport and inflammatory responses, while collagen and elastin are important components of the extracellular matrix and are closely related to tissue reconstruction. These biomolecules each have characteristic spectral absorption peaks. Therefore, the preset characteristic wavelength range is selected to cover the locations of the characteristic absorption peaks of these key biomolecules. For example, hemoglobin has multiple absorption peaks in the visible light region, while collagen and elastin have characteristic absorption peaks in the near-infrared region. In specific implementations, the preset characteristic wavelength range can be set to a wavelength range that includes the hemoglobin absorption peak, such as 500nm-600nm, and a wavelength range that includes the collagen and elastin absorption peaks, such as 900nm-1000nm. The integral values ​​of the difference spectra within these preset wavelength ranges are calculated, and the resulting integral values ​​reflect the intensity of the spectral signal within the corresponding wavelength ranges. Because the preset wavelength ranges are associated with the locations of the characteristic absorption peaks of key biomolecules, these integral values ​​can effectively characterize the changes in key biomolecules during the activation of cell repair. By calculating the integral values ​​of multiple preset characteristic wavelength ranges, multiple characteristic spectral parameters can be obtained. These parameters together constitute a multi-dimensional characteristic vector for subsequent cell repair initiation status level evaluation.

[0075] Specifically, step A5 aims to extract key information from the difference spectrum that effectively reflects the activation of cell repair. Factors such as tattoo pigment, tissue fluid exudation, and individual skin color variations can interfere with the spectral signal, making it difficult to directly extract cell repair information from the original spectrum. Obtaining a difference spectrum can, to a certain extent, eliminate these interfering factors. On this basis, step A5 further focuses on key biomolecules involved in the cell repair process. By presetting characteristic wavelength ranges and calculating integral values, characteristic spectral parameters associated with these key biomolecules are extracted. The operating principle is that the activation of cell repair is inevitably accompanied by changes in the concentration or morphology of specific biomolecules, and these changes leave traces on the spectrum. By selecting wavelength ranges corresponding to the characteristic absorption peaks of key biomolecules and calculating the integral value of the difference spectrum within these ranges, the spectral signal related to cell repair can be amplified while suppressing noise interference. The integral operation itself also has the effect of smoothing noise and enhancing signal stability. As a result, the extracted characteristic spectral parameters can more accurately and reliably reflect the true activation of skin cell repair, providing high-quality data support for subsequent grade assessment. Compared with directly using the original spectrum or broadly extracting spectral features, the technical solution of step A5 is more targeted and effective, and can improve the accuracy and reliability of the assessment of cell repair initiation status.

[0076] In some specific embodiments, the preset characteristic wavelength ranges are set to three, namely: a first preset characteristic wavelength range, covering the Soret band absorption peak of hemoglobin, for example, 400nm-450nm; a second preset characteristic wavelength range, covering the Q band absorption peak of hemoglobin, for example, 530nm-570nm; and a third preset characteristic wavelength range, covering the characteristic absorption peaks of collagen and elastin, for example, 950nm-980nm. For the difference spectrum, the integral values ​​within the wavelength ranges of 400nm-450nm, 530nm-570nm, and 950nm-980nm are calculated respectively to obtain three characteristic spectrum parameters, which are recorded as parameter 1, parameter 2, and parameter 3 respectively. Parameters 1 and 2 reflect the changes in hemoglobin, and parameter 3 reflects the changes in collagen and elastin.

[0077] In some real-time approaches, step A6 includes: The extracted characteristic spectral parameters are input into a pre-trained skin cell repair activation status evaluation model to obtain the skin cell repair activation status level of the tattoo area.

[0078] The training process of the skin cell repair activation status assessment model can include: collecting a large amount of tattoo area skin data (short-range diffuse reflectance spectra and long-range diffuse reflectance spectra) with known skin cell repair activation status levels, extracting characteristic spectral parameters for each data set, and using these characteristic spectral parameters and the corresponding skin cell repair activation status levels as training data to train the skin cell repair activation status assessment model. After model training is completed, in actual application, when characteristic spectral parameters of new tattoo area skin are obtained, these characteristic spectral parameters are input into the pre-trained skin cell repair activation status assessment model. Through internal calculations within the model, the skin cell repair activation status level of the tattoo area is predicted and output. The skin cell repair activation status assessment model can adopt a variety of machine learning algorithms, such as support vector machines, neural networks, random forests, etc. The input of the model is the characteristic spectral parameters extracted in step A5, and the output of the model is the skin cell repair activation status level of the tattoo area.

[0079] Specifically, after obtaining the difference spectrum and extracting characteristic spectral parameters reflecting the cell repair activation state, a pre-trained skin cell repair activation state assessment model is utilized to accurately determine the skin cell repair activation state level in the tattooed area. This model learns from a large amount of known skin cell repair activation state level data and the corresponding characteristic spectral parameters, establishing a mapping relationship between the characteristic spectral parameters and the skin cell repair activation state level. When the characteristic spectral parameters of the tattooed area are input into the model, it comprehensively considers various complex factors and outputs an objective and accurate assessment result of the skin cell repair activation state level. This improves the accuracy and reliability of the assessment results, providing a scientific and effective tool for immediate assessment after tattooing.

[0080] refer to Figure 2 , the present application provides a cell repair data monitoring system for instantly evaluating the skin cell repair initiation status of the tattoo area after the tattoo operation is completed, the device includes a spectrum acquisition device 1 and a spectrum analyzer 2; The spectrum collection device 1 includes an irradiation light source 101, a first spectrum receiver 102, and a second spectrum receiver 103. The radial distance between the second spectrum receiver 103 and the irradiation light source 101 is greater than the radial distance between the first spectrum receiver 102 and the irradiation light source 101. The irradiation light source 101 is used to emit light of a specific wavelength range to irradiate the skin of the tattoo area (such as Figure 2 A in FIG), the first spectrum receiver 102 is used to receive the diffuse reflectance spectrum of the skin tissue (such as Figure 2 B) to obtain a short-distance diffuse reflection spectrum, and the second spectrum receiver 103 is used to synchronously receive the diffuse reflection spectrum of the skin tissue (such as Figure 2 C) to obtain the long-distance diffuse reflectance spectrum; Spectrum Analyzer 2 includes: A pre-processing module 201 is used to perform spectrum smoothing and baseline correction on the short-distance diffuse reflectance spectrum and the long-distance diffuse reflectance spectrum in sequence (for the specific process, refer to step A2 above); Intensity adjustment module 202 is used to adjust the intensity of the corrected short-distance diffuse reflectance spectrum so that the intensity of the adjusted short-distance diffuse reflectance spectrum matches the intensity of the surface interference component in the corrected long-distance diffuse reflectance spectrum (for details, refer to step A3 above); A difference calculation module 203 is used to calculate the difference between the corrected long-distance diffuse reflectance spectrum and the adjusted short-distance diffuse reflectance spectrum to obtain a difference spectrum (for the specific process, refer to step A4 above); Feature extraction module 204, used to extract characteristic spectrum parameters reflecting the cell repair activation state from the difference spectrum (for the specific process, refer to step A5 above); The level determination module 205 is used to determine the skin cell repair activation state level of the tattoo area based on the extracted characteristic spectral parameters (for details, refer to step A6 above).

[0081] The spectrum acquisition device 1 and spectrum analyzer 2 work together to monitor cell repair data. Spectral acquisition device 1 is used to collect spectral information from skin tissue. An illumination light source 101 emits light within a specific wavelength range, for example, 400nm-1000nm, which is irradiated onto the skin in the tattooed area. A first spectrum receiver 102 and a second spectrum receiver 103 simultaneously receive diffuse reflectance spectra from the skin tissue at different distances from illumination light source 101. First spectrum receiver 102 is positioned radially close to illumination light source 101 to receive short-range diffuse reflectance spectra, which primarily reflect information from the skin's surface and contain significant surface interference components, such as tattoo pigment and tissue fluid. Second spectrum receiver 103 is positioned radially farther from illumination light source 101 to receive long-range diffuse reflectance spectra, which can penetrate deeper into the skin tissue, reflecting information from deeper layers while minimizing the influence of surface interference components. Spectral analyzer 2 is used to process and analyze the collected spectral data. The preprocessing module 201 first preprocesses the short-range diffuse reflectance spectrum and the long-range diffuse reflectance spectrum. The preprocessing operation includes spectral smoothing and baseline correction, which are used to reduce spectral noise and correct spectral baseline drift. The intensity adjustment module 202 then adjusts the intensity of the corrected short-range diffuse reflectance spectrum. The purpose of the adjustment is to make the intensity of the adjusted short-range diffuse reflectance spectrum match the intensity of the surface interference components in the corrected long-range diffuse reflectance spectrum as much as possible. The difference calculation module 203 calculates the difference between the corrected long-range diffuse reflectance spectrum and the intensity-adjusted short-range diffuse reflectance spectrum. By subtracting, the surface interference components in the spectrum can be effectively eliminated to obtain a difference spectrum. The difference spectrum can more accurately reflect the cell repair activation status information of the deep skin tissue. The feature extraction module 204 extracts characteristic spectral parameters from the difference spectrum. These characteristic spectral parameters are closely related to the cell repair activation status, such as the integral value of the characteristic absorption peaks of key biomolecules such as hemoglobin, collagen and elastin. Finally, the level determination module 205 determines the skin cell repair activation state level of the tattoo area based on the extracted characteristic spectral parameters using a pre-trained skin cell repair activation state evaluation model, which can be divided into levels such as "normal activation", "delayed activation" and "abnormal activation".

[0082] Specifically, the cell repair data monitoring system operates as follows: First, a spectrum acquisition device 1 synchronously collects short-range and long-range diffuse reflectance spectra of the tattooed skin. The short-range diffuse reflectance spectrum primarily reflects surface information, while the long-range diffuse reflectance spectrum reflects deeper layers. After the spectrum analyzer 2 receives the spectral data, the preprocessing module 201 smoothes and baseline-corrects both spectra to eliminate noise and baseline drift. The intensity adjustment module 202 adjusts the intensity of the short-range diffuse reflectance spectrum to align the intensity of its surface interference components with those in the long-range diffuse reflectance spectrum. Next, the difference calculation module 203 calculates the difference between the long-range diffuse reflectance spectrum and the adjusted short-range diffuse reflectance spectrum to generate a difference spectrum. This difference spectrum effectively suppresses surface interference and highlights spectral features related to deeper tissue and cell repair. The feature extraction module 201 extracts characteristic spectral parameters from the difference spectrum, such as the spectral integral within a specific wavelength range, that reflect the activation status of cell repair. The level determination module 205 inputs these characteristic spectral parameters into a pre-established evaluation model, which analyzes them and outputs a level of cell repair activation status for the tattooed skin. As a result, the system realizes the instant, objective and quantitative evaluation of the activation status of skin cell repair after tattoo operation, overcoming the shortcomings of traditional methods that rely on experience, are highly subjective and difficult to accurately evaluate. It also solves the influence of interfering factors such as tattoo pigments, tissue fluid exudation and individual skin color differences on the evaluation results, and improves the accuracy and reliability of the evaluation.

[0083] In order to facilitate operation, the spectrum acquisition device 1 can be used as follows Figure 3 The pen-type data acquisition device shown includes a pen-type main body 104 suitable for holding in the hand, an illumination light source 101, a first spectrum receiver 102, and a second spectrum receiver 103 are arranged at the front end of the pen-type main body 104, and a switch button 105 is also provided on the side of the pen-type main body 104. The spectrum acquisition device 1 is connected to the spectrum analyzer 2 via a wired or wireless method.

[0084] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A cell repair data monitoring method for instantly evaluating the skin cell repair activation status of the tattoo area after the tattoo operation is completed, characterized in that: The steps of the method include: A1. Illuminate the tattooed area with light of a specific wavelength range and simultaneously receive the short-range and long-range diffuse reflectance spectra of the skin tissue at receiving locations at different radial distances from the illumination source. The receiving location for the long-range diffuse reflectance spectrum is located at a greater radial distance from the illumination source than the receiving location for the short-range diffuse reflectance spectrum. A2. Perform spectral smoothing and baseline correction on the short-range diffuse reflectance spectra and the long-range diffuse reflectance spectra in sequence; A3. Adjust the intensity of the corrected short-range diffuse reflectance spectrum so that the intensity of the adjusted short-range diffuse reflectance spectrum matches the intensity of the surface interference component in the corrected long-range diffuse reflectance spectrum; A4. Calculate the difference between the corrected long-distance diffuse reflectance spectrum and the adjusted short-distance diffuse reflectance spectrum to obtain a difference spectrum; A5. Extract characteristic spectral parameters reflecting the initiation state of cell repair from the difference spectrum; A6. Determine the skin cell repair activation status level in the tattoo area based on the extracted characteristic spectral parameters.

2. A cell repair data monitoring method according to claim 1, characterized in that: Step A1 includes: The tattoo area skin is irradiated with light of a wavelength range of 400nm-1000nm at an incident angle of 28 degrees-32 degrees, and the short-distance diffuse reflection spectrum and long-distance diffuse reflection spectrum of the skin tissue are synchronously received at receiving positions with different radial distances from the irradiation light source.

3. A cell repair data monitoring method according to claim 1, characterized in that: Step A2 includes: A201. For short-range diffuse reflectance spectra and long-range diffuse reflectance spectra, a Savitzky-Golay filter is used for spectral smoothing. A202. For the short-range diffuse reflectance spectra and long-range diffuse reflectance spectra after spectral smoothing, the asymmetric least squares method is used to perform baseline correction.

4. A cell repair data monitoring method according to claim 1, characterized in that: Step A3 includes: A301. Using multiple proportional coefficient values ​​within a preset proportional coefficient range, respectively, the intensity of the corrected short-distance diffuse reflectance spectrum is adjusted to obtain multiple first short-distance diffuse reflectance spectra; A302. Calculate the root mean square error between each first short-distance diffuse reflectance spectrum and the corrected long-distance diffuse reflectance spectrum input spectrum; A303. Fit a curve showing how the root mean square error (RMS) changes with the scale factor, and extract the scale factor corresponding to the minimum RMS error as the optimal scale factor. A304. Use the optimal proportional coefficient to adjust the intensity of the corrected short-distance diffuse reflectance spectrum to obtain an adjusted short-distance diffuse reflectance spectrum.

5. A cell repair data monitoring method according to claim 4, characterized in that: Step A302 includes: For each of the first short-range diffuse reflectance spectra and the corrected long-range diffuse reflectance spectra, the average reflectance of each spectrum within a plurality of preset wavelength ranges is calculated; wherein the preset wavelength ranges are selected based on the absorption band of the tattoo pigment, the absorption band of the tissue fluid, and the characteristic absorption band reflecting the state of cell repair; Obtaining weight coefficients for each preset wavelength range; wherein the wavelength range that contributes more to the evaluation of cell repair status has a higher weight; According to the average reflectivity of each preset wavelength range and the corresponding weight coefficient, the weighted root mean square error value between each first short-distance diffuse reflection spectrum and the corrected long-distance diffuse reflection spectrum is calculated as the effective root mean square error value.

6. A cell repair data monitoring method according to claim 1, characterized in that: Step A4 includes: A401. Divide the tattoo area into a plurality of sub-areas, and for each sub-area, calculate the difference between the corrected long-distance diffuse reflectance spectrum and the adjusted short-distance diffuse reflectance spectrum to obtain a sub-difference spectrum for each sub-area; A402. Calculate the root mean square value of the second derivative of the short-range diffuse reflectance spectrum of each sub-region as a representation of the degree of uneven tissue fluid exudation in each sub-region; A403. Based on the characterization value of the uneven degree of tissue fluid exudation in each sub-region, a weighted average of the sub-difference spectra of each sub-region is performed to obtain a difference spectrum of the tattoo region; wherein, the sub-region with a higher characterization value of the uneven degree of tissue fluid exudation has a lower weight of the corresponding sub-difference spectrum.

7. A cell repair data monitoring method according to claim 6, characterized in that: Step A402 includes: Calculate the average reflectance of the original short-range diffuse reflectance spectrum of each sub-region in the tattoo pigment absorption band and the water absorption band; The tattoo pigment influence factor is calculated based on the average reflectivity of each sub-area in the tattoo pigment absorption band. The tattoo pigment influence factor is negatively correlated with the average reflectivity. The tissue fluid impact factor is calculated based on the average reflectivity of each sub-region in the water absorption band. The tissue fluid impact factor is negatively correlated with the average reflectivity. Calculate the correction coefficient for each sub-region based on the tattoo pigment influence factor and the tissue fluid influence factor; the correction coefficient is used to reduce the influence of the tattoo pigment and tissue fluid on the root mean square value of the second-order derivative; Correcting the original short-distance diffuse reflectance spectrum of each sub-region using the correction coefficient to obtain a second short-distance diffuse reflectance spectrum; The root mean square value of the second derivative of the second short-range diffuse reflectance spectrum of each sub-region is calculated as a characterization value of the uneven degree of tissue fluid exudation in each sub-region.

8. The cell repair data monitoring method according to claim 1, characterized in that: Step A5 includes: The integral values ​​of the difference spectrum within multiple preset characteristic wavelength ranges are calculated to obtain multiple characteristic spectral parameters; wherein the preset characteristic wavelength ranges are selected based on the characteristic absorption peak positions of key biomolecules in the cell repair process, including hemoglobin, collagen and elastin.

9. A cell repair data monitoring method according to claim 1, characterized in that: Step A6 includes: The extracted characteristic spectral parameters are input into a pre-trained skin cell repair activation status evaluation model to obtain the skin cell repair activation status level of the tattoo area.

10. A cell repair data monitoring system for instantly evaluating the skin cell repair activation status of the tattooed area after the tattoo operation is completed, characterized in that: The device includes a spectrum acquisition device and a spectrum analyzer; The spectrum collection device includes an illumination light source, a first spectrum receiver, and a second spectrum receiver, wherein the radial distance between the second spectrum receiver and the illumination light source is greater than the radial distance between the first spectrum receiver and the illumination light source; the illumination light source is used to emit light in a specific wavelength range to illuminate the skin of the tattoo area, the first spectrum receiver is used to receive the diffuse reflectance spectrum of the skin tissue to obtain a short-distance diffuse reflectance spectrum, and the second spectrum receiver is used to synchronously receive the diffuse reflectance spectrum of the skin tissue to obtain a long-distance diffuse reflectance spectrum; The spectrum analyzer includes: A preprocessing module is used to perform spectral smoothing and baseline correction on the short-distance diffuse reflectance spectrum and the long-distance diffuse reflectance spectrum in sequence; An intensity adjustment module is used to adjust the intensity of the corrected short-distance diffuse reflectance spectrum so that the intensity of the adjusted short-distance diffuse reflectance spectrum matches the intensity of the surface interference component in the corrected long-distance diffuse reflectance spectrum; a difference calculation module, used to calculate the difference between the corrected long-distance diffuse reflectance spectrum and the adjusted short-distance diffuse reflectance spectrum to obtain a difference spectrum; A feature extraction module is used to extract characteristic spectrum parameters reflecting the cell repair initiation state from the difference spectrum; The level determination module is used to determine the skin cell repair activation state level of the tattoo area based on the extracted characteristic spectral parameters.

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