Use of spectral data of analytes and analyte detection methods, systems, media, and devices

Abstract

To provide a method, a system, a medium, and a device for utilizing spectral data of an analyte and detecting the analyte in the field of optical analysis.SOLUTION: A method for using spectral data of an object to be analyzed includes an analysis phase of inputting obtained spectral data into a local detection model to obtain information of the object to be analyzed and providing a correction option, an information uploading phase of obtaining correction information input by a user after the correction option is triggered and uploading the correction information and the obtained spectral data to a cloud platform, a model training phase of training the cloud detection model by the cloud platform using the obtained spectral data as input and using the obtained correction information as output, and a model updating phase of updating the local detection model based on the trained cloud detection model.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to the field of optical analysis, and in particular to the use of analyte spectral data and analyte detection methods, systems, media and devices. [Background technology]

[0002] Although the prior art can realize real-time detection of blood glucose levels based on blood glucose spectral data, there are still limitations in the method of identifying all people's spectral data by training a unified detection model, because different people's spectral data have different characteristics due to individual differences, and it is difficult to fully acquire such a large number of characteristics. Therefore, the analysis of spectral data by the detection model still has the problem of producing inaccurate detection results for certain users.

[0003] Patent document US20160287147A1 discloses a non-invasive in vivo measurement device using Raman spectroscopy to measure blood glucose concentration in a living body. In addition to the above drawbacks, such a solution currently has the drawback of relying on a laboratory-level Raman spectroscopy system for implementation, which is large and expensive.

[0004] Furthermore, Patent Document CN118078277A discloses a non-invasive blood glucose detection method based on hyperspectral data analysis, which achieves non-invasive detection. In addition to the above drawbacks, this document uses absorption spectroscopy, and the collected and analyzed spectral signals include not only the spectral signal of blood glucose but also the spectral signals of components such as skin tissue, resulting in a mixture of spectral signals of different wavelengths, making it difficult to finely separate and extract the spectral signals related to blood glucose. At the same time, factors such as differences in the excitation light source, human skin color, and epidermal layer thickness also affect the intensity of the spectral signals, resulting in differences in the intensity of the spectral signals. The final collected spectral signals are easily affected and cannot be strongly correlated with blood glucose concentration, thereby affecting the accurate measurement of blood glucose concentration. Patent Document CN108542402A also has the same problem.

[0005] Patent document CN117503123A discloses a multi-wavelength near-infrared non-invasive blood glucose detection system and method that uses multiple sensors and modules to simultaneously acquire multiple types of biological signals, such as fingertip infrared information, facial infrared information, and forehead temperature information, and then integrates the multiple types of information for analysis and judgment.The disadvantages of this method are that too many biological signals need to be collected from different locations, the blood glucose concentrations in different parts of the body are different, the cost is high, and it is impossible to achieve portable real-time detection effects.At the same time, the excessive amount of information makes the detection algorithm complicated. Summary of the Invention [Problem to be solved by the invention]

[0006] In view of the shortcomings of the prior art, it is an object of the present invention to provide a method, system, medium and device for utilizing analyte spectral data and detecting the analyte. [Means for solving the problem]

[0007] The method for utilizing spectral data of an analyte provided by the present invention includes: an analysis step of inputting the acquired spectral data into a local detection model to obtain analyte information and provide correction options; an information upload step of acquiring correction information input by a user after the correction option is triggered, and uploading the correction information and the acquired spectral data to a cloud platform; a model training stage in which the cloud platform uses the acquired spectral data as input and the acquired correction information as output to train a cloud detection model; and a model update stage for updating a local detection model based on the trained cloud detection model.

[0008] Further, the analyzing step includes collecting a first image corresponding to the first region under illumination with light within a first wavelength range, and collecting a second image corresponding to the first region under illumination with light within a second wavelength range; the first image includes data reflecting a distribution state in an imaging area of ​​a reflected signal or an excited signal generated by an object to be analyzed when irradiated with a light beam; the second image includes spectral data in the imaging region that reflects a reflected signal or an excited signal generated by the analyte when irradiated with light; Based on the distribution state data, spectral data at a required position is obtained from the second image.

[0009] Further, the light within the first wavelength range includes infrared light, and the analyzing step further comprises: The method includes dividing the imaging area into detection point candidate areas and reference point candidate areas based on the grayscale distribution of image points in the first image, selecting detection points from the detection point candidate areas, selecting reference points from the reference point candidate areas, and respectively acquiring spectral data of the detection points and spectral data of the reference points.

[0010] Furthermore, the light within the second wavelength range includes ultraviolet light, and the analyzing step further comprises: The method includes selecting, based on the grayscale value of the image point in the second image, one image point having a grayscale value that satisfies a predetermined requirement from the detection point candidate area as the detection point, or selecting a combination of the image point and an adjacent image point as the detection point; selecting, from the reference point candidate area, one image point whose grayscale value of the selected detection point is within a predetermined deviation range as the reference point, or selecting a combination of the image point and a plurality of adjacent image points as reference points; and calculating and obtaining fluorescence spectrum data of the detection point and the reference point.

[0011] Furthermore, the second wavelength range is 300 to 390 nanometers, and the second wavelength range is outside the effective response range of the imaging spectrum detection device; Light in the second wavelength range can excite the analyte with a fluorescent emission signal, the main peak of the fluorescent spectrum of which is located within the effective response range of the imaging spectral detection device.

[0012] Analyte detection methods provided by the present invention include methods that utilize the above-described analyte spectral data.

[0013] The system for utilizing spectral data of an analyte provided by the present invention comprises: an analysis module that inputs the acquired spectral data into a local detection model to obtain analyte information and provide correction options; an information upload module for acquiring correction information input by a user after the correction option is triggered, and uploading the correction information and the acquired spectral data to a cloud platform; The cloud platform includes a model training module that uses the acquired spectral data as input and the acquired correction information as output to train a cloud detection model; and a model updating module that updates a local detection model based on the trained cloud detection model. The analysis module further includes collecting a first image corresponding to the first region under illumination with light within a first wavelength range, and collecting a second image corresponding to the first region under illumination with light within a second wavelength range; the first image includes data reflecting a distribution state in an imaging area of ​​a reflected signal or an excited signal generated by an object to be analyzed when irradiated with a light beam; the second image includes spectral data in the imaging region that reflects a reflected signal or an excited signal generated by the analyte when irradiated with light; Based on the distribution state data, spectral data at a required position is obtained from the second image.

[0014] Furthermore, the light within the first wavelength range includes infrared light, and the analysis module The method includes dividing the imaging area into detection point candidate areas and reference point candidate areas based on the grayscale distribution of image points in the first image, selecting detection points from the detection point candidate areas, selecting reference points from the reference point candidate areas, and respectively acquiring spectral data of the detection points and spectral data of the reference points.

[0015] Furthermore, the light within the second wavelength range includes ultraviolet light, and the analysis module The method includes selecting, based on the grayscale value of the image point in the second image, one image point having a grayscale value that satisfies a predetermined requirement from the detection point candidate area as the detection point, or selecting a combination of the image point and an adjacent image point as the detection point; selecting, from the reference point candidate area, one image point whose grayscale value of the selected detection point is within a predetermined deviation range as the reference point, or selecting a combination of the image point and a plurality of adjacent image points as reference points; and calculating and obtaining fluorescence spectrum data of the detection point and the reference point.

[0016] Furthermore, the second wavelength range is 300 to 390 nanometers, and the second wavelength range is outside the effective response range of the imaging spectrum detection device; Light in the second wavelength range can excite the analyte with a fluorescent emission signal, the main peak of the fluorescent spectrum of which is located within the effective response range of the imaging spectral detection device.

[0017] The analyte detection system provided by the present invention includes the above-described system for utilizing analyte spectral data.

[0018] The present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for utilizing spectral data of an analyte described above.

[0019] The present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, which, when executed by the processor, realizes the steps of the method for utilizing spectral data of an analyte described above. [Effects of the Invention]

[0020] Compared with the prior art, the present invention has the following beneficial effects: 1. The present application can update the local detection model for a specific user, so that the local detection model can achieve more accurate detection for the user. 2. The present application utilizes the uneven distribution of the analyte in the imaging region to obtain spectral data in different regions, and since the distribution of components other than the analyte in the imaging region is relatively uniform, the difference in spectral data in different regions can directly reflect information about the analyte, such as the concentration of the analyte, which is correlated with the spectral data after substantially excluding the influence of non-analytes. 3. The spectral data obtained using the method for utilizing spectral data of analytes provided by the present application is more accurate, and therefore the accuracy of the trained model is also improved. 4. The technical solution of the present application does not require electrochemical reaction with the analyte, and the detection method is simpler, so that the purpose of non-invasive detection can be achieved. 5. The present application avoids the traditional method of using Raman to measure analytes, and instead uses fluorescence spectroscopy for detection, thereby achieving low-cost and compact detection systems and achieving the purpose of real-time detection.

[0021] Other features, objects and advantages of the present invention will become more apparent through the detailed description given below, given by way of non-limiting example with reference to the drawings in which: [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a flowchart of the first embodiment. [Figure 2] FIG. 1 is a schematic diagram of a first image collected in Example 2. [Figure 3] FIG. 10 is a schematic diagram of a second image collected in Example 2. [Figure 4] FIG. 10 is a diagram illustrating the principle of a detection model according to a second embodiment. [Figure 5] FIG. 1 is a schematic diagram of detection point-reference point spectrum data obtained in Example 2. [Figure 6] These are experimental results of the accuracy of the analytical results of the analytical model. [Figure 7] 1 is a structural schematic diagram of an analyte detection device provided by Example 5. FIG. [Figure 8] FIG. 1 is a structural schematic diagram of an electronic device provided by Example 6. [Figure 9] 1 is a structural schematic diagram of a wristwatch for detecting an analyte provided by Example 5. FIG. [Figure 10] FIG. 1 is a schematic diagram of the back of an analyte detection wristwatch. [Figure 11]FIG. 1 is an exploded view of a wristwatch for detecting an analyte. [Figure 12] FIG. 1 is a schematic diagram of a wristwatch for detecting an analyte in use. DETAILED DESCRIPTION OF THE INVENTION

[0023] The present invention will be described in detail below with reference to specific examples. The following examples will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any way. It should be noted that those skilled in the art may make some modifications and improvements without departing from the concept of the present invention, all of which fall within the protection scope of the present invention.

[0024] Example 1 FIG. 1 is a flow chart of this embodiment, and the method for utilizing the spectral data of the analyte of this embodiment includes the following steps.

[0025] Data collection stage: Obtaining historical spectral data of multiple targets; Specifically, the data collection step includes the following steps:

[0026] Imaging step: Irradiating a first region with light within a predetermined wavelength range through a light source, and imaging the first region through an imaging spectrum detector to obtain an image of the imaging region. By irradiating the first region with light within the predetermined wavelength range, distribution data and spectral data of the reflected or excited signals generated by the analyte when the light is irradiated in the imaging region can be reflected in the image. The first region may be a specific region on the surface of human skin. To prevent external light such as ambient light from affecting the detection, the collection window of the imaging spectrum detector must be tightly attached to the surface of the human skin in the first region. The imaging region refers to the region within the lens range of the imaging spectrum detector. Generally, the imaging region may be a part of the first region or the same region as the first region.

[0027] Since light beams with different wavelength ranges are required to obtain analyte distribution data and spectral data, two methods can be used: light beams with a wider wavelength range provided by one light source, or light beams with narrower wavelength ranges provided by two light sources. When one light source is used, the wavelength range of the light beam provided by the light source must simultaneously cover the wavelength range for obtaining analyte distribution data and the wavelength range for obtaining analyte spectral data. When two light sources are used, the two light sources provide different light beams, with one light beam having a wavelength covering the wavelength range for obtaining analyte distribution data and the other light beam having a wavelength covering the wavelength range for obtaining analyte spectral data. At the same time, when one light source is used, only one image is captured, and when two light sources are used, two images are captured. For ease of processing, the capturing areas of the two images must be the same, i.e., the collection window of the imaging spectrum detection device does not move on the surface of human skin.

[0028] In the present application, the analyte may be vascular glucose, ketones, alcohol, lactate, oxygen, hemoglobin A1C, acetylcholine, amylase, bilirubin, cholesterol, chorionic gonadotropin, creatine kinase (e.g., CK-MB), creatine, creatinine, DNA, fructosamine, glutamine, growth hormone, hormones, peroxides, prostate-specific antigen, prothrombin, RNA, thyroid-stimulating hormone, or troponin, or may be a drug such as an antibiotic (e.g., gentamicin, vancomycin, etc.), digitoxin, digoxin, a drug of abuse, theophylline, or warfarin. In embodiments where more than one analyte is detected, the analytes may be monitored at the same or different times. In other embodiments, the analyte may be any other substance within a body surface that can be noninvasively detected using the present invention.

[0029] Spectral acquisition step: Acquire spectral data from the image, which reflects the non-uniform distribution of reflected or excited signals generated by the object to be analyzed when the light beam is irradiated in the imaging region via the imaging spectral detection device. Specifically, the imaging region can be divided based on different distribution status data, and positions for acquiring spectral data from different sections can be selected.

[0030] data reduction step: screening the spectral data of the first time slot and the spectral data of the second time slot for each day based on the data collection time, and obtaining corresponding analyte information based on the spectral data of the first time slot and the spectral data of the second time slot, respectively, wherein the analyte information includes analyte information correlated with the spectral data; Specifically, the data reduction step includes the following steps:

[0031] Based on the acquired spectral data, information about the analyte in the imaging area is acquired, and the analyte information includes information about the analyte correlated with the spectral data. Because the distribution of the analyte in different sections is different, the reflected or excited signals generated by the analyte when irradiated with light also differ. Taking human skin as an example, it is divided into three parts: the epidermis, the dermis, and the subcutaneous tissue, and blood vessels such as veins are located in the subcutaneous tissue. UV light can be used to irradiate skin areas with blood vessels and skin areas without blood vessels to obtain corresponding spectral data, or to irradiate skin areas with thick blood vessels and skin areas with thin blood vessels to obtain corresponding spectral data. The difference between the two spectral data can reflect information about the analyte correlated with the spectral data in the blood vessels. Intermediate information, such as data about the degree of impact of the analyte on the spectral data, can be obtained for further analysis, or information such as the concentration of the analyte can be directly obtained through an analytical model.

[0032] An information uploading step: providing a correction option, and after the correction option is triggered, obtaining the correction information input by the user, and uploading the correction information and the obtained spectral data to a cloud platform; Model training stage: The cloud platform uses the acquired spectral data as input and the acquired correction information as output to train a cloud detection model; Model update stage: Update the local detection model based on the trained cloud detection model.

[0033] Example 2 This embodiment is based on the first embodiment and takes glucose detection in human blood vessels as an example to provide a method for utilizing glucose spectrum data, which includes the following steps:

[0034] Data collection stage: Obtaining historical spectral data of multiple targets; Specifically, the data collection step includes the following steps:

[0035] Imaging step: Irradiate the skin where veins are located on the wrist, back of the hand, etc. with infrared light in a first wavelength range of 800 to 1000 nanometers, preferably in the near-infrared band, to capture a first image of the imaging area, and then irradiate the same location with ultraviolet light in a second wavelength range of 300 to 390 nanometers to capture a second image of the imaging area.

[0036] 2, where the horizontal axis is the horizontal coordinate of the first image, the vertical axis is the vertical coordinate of the first image, the white box represents the pixel point block of the detection point selected on the venous blood vessel, and the black box represents the pixel point block of the reference point selected on the surrounding skin. In the first image, some of the infrared light penetrates the human skin, and some of it is absorbed by the human skin. At the same time, the area where the venous blood vessel is located is absorbed in large amounts by the venous blood vessel, so the grayscale value of the pixel in the area where the venous blood vessel is located is smaller, and the grayscale value of the pixel in the area where the non-venous blood vessel is located is larger, which makes it easy to divide the imaging area into the area where the venous blood vessel is located and the area where the non-venous blood vessel is located.

[0037] As shown in Figure 3, the horizontal axis represents the horizontal coordinate of the second image, the vertical axis represents the vertical coordinate of the second image, the white box represents the pixel point block of the detection point selected on the venous blood vessel, and the black box represents the pixel point block of the reference point selected on the surrounding skin. Because it is difficult to distinguish between venous and non-venous blood vessels in the second image, it is necessary to distinguish between them in the first image. The purpose of using excitation light in the second wavelength range of 300-390 nanometers is to obtain a high-quality effective fluorescence spectral signal. This is because the main response band of the imaging spectral detection device is between 400 and 800 nm. If the wavelength of the excitation light used is less than 300 nm, the main peak of the fluorescence spectrum of the excited fluorescence emission signal is located in the <400 nm band, making it difficult for the imaging spectral detection device to obtain a high-quality effective fluorescence spectral signal. If the wavelength of the excitation light used exceeds 390 nm, the excitation light itself will be visible light, and the spectral signal of the excitation light will be superimposed on the fluorescence spectral signal, making it difficult to extract an effective fluorescence spectral signal without interference from the excitation light. Glucose in venous blood vessels absorbs ultraviolet light in the wavelength range of 300-390 nanometers and then emits a fluorescent emission signal in the visible light band of 400-800 nm, which is within the effective response range of the imaging spectral detector. The characteristic spectral intensity of this fluorescent emission signal is positively correlated with the glucose concentration, resulting in higher fluorescence excitation efficiency.

[0038] Spectrum acquisition step: based on the grayscale distribution of the pixel points in the first image, divide the imaging area into an area where venous blood vessels are located and an area where non-venous blood vessels are located, select a detection point from the position of the area where venous blood vessels are located corresponding to the second image, select a reference point from the position of the area where non-venous blood vessels are located corresponding to the second image, and respectively obtain the spectral data of the detection point and the spectral data of the reference point in the second image. Specifically, based on the grayscale values ​​of the pixel points in the second image, select one pixel point having a grayscale value that meets a predetermined requirement as the detection point from the area where venous blood vessels are located, or select a combination of this pixel point and adjacent pixel points as the detection point, and select one pixel point having a grayscale value within a predetermined deviation range from the area where non-venous blood vessels are located as the reference point, or select a combination of this pixel point and multiple adjacent pixel points as the reference point, and calculate and obtain the fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point in the second image. The spectral data can be selected from a single pixel point of the detection point, the reference point, or an average of a combination of multiple pixel points, and can be appropriately selected based on the width of the blood vessel. Averaging a combination of multiple pixel points improves the signal-to-noise ratio but is limited by the width of the blood vessel and avoids acquiring data from areas outside the blood vessel. Selecting a single pixel point provides high spatial resolution and is suitable for situations with thin blood vessels, but has a lower signal-to-noise ratio. As a preset requirement for grayscale values, it is possible to use the point with the smallest grayscale value as the detection point, but this application is not limited to this. The calculation results are shown in Figure 5, where the horizontal axis is wavelength (unit: nm) and the vertical axis is relative radiance (unit: W / nm). The solid line represents the spectral data of the detection point, and the dotted line represents the spectral data of the reference point.Here, the reason why the grayscale value of the reference point and the grayscale value of the selected detection point are within the predetermined deviation range is that the skin in the imaging area has influencing factors such as skin color, blemishes, and cosmetics, which may directly affect the spectral data of the reference point. However, the first image does not distinguish the areas of these influencing factors, so by setting a predetermined deviation range of the grayscale value, these influencing factors can be effectively excluded. Furthermore, because the grayscale value and the grayscale value of the selected detection point are within the predetermined deviation range, it is guaranteed that a reference point close to the detection point, such as the edge of a venous blood vessel, will be selected. This ensures that, excluding the blood vessels, the parameters of the remaining epidermis, dermis, and subcutaneous tissue, such as color and thickness, are closest. This makes it possible to exclude as much as possible the influence of non-analyte objects due to the deviation between the spectral data of the detection point and the spectral data of the reference point.

[0039] In addition to the spectral reconstruction algorithm, the method of obtaining the spectral data is to form the radiation calibration coefficient through the radiation calibration, and calculate the grayscale value * radiation calibration coefficient to obtain the spectral line.

[0040] When a combination of multiple pixel points is selected from the detection point, the fluorescence spectrum data of the detection point can be the average value of the fluorescence spectrum data of these pixel points. At the same time, the number of detection points and reference points can be one or more. When the number of detection points and reference points is multiple, the average value of the fluorescence spectrum data of all the detection points and the average value of the fluorescence spectrum data of all the reference points can be calculated respectively.

[0041] data reduction step: screening the spectral data of the first time slot and the spectral data of the second time slot for each day based on the data collection time, and obtaining corresponding analyte information based on the spectral data of the first time slot and the spectral data of the second time slot, respectively, wherein the analyte information includes analyte information correlated with the spectral data; Specifically, the data reduction step includes the following steps:

[0042] The acquired spectral data of the detection points and reference points is preprocessed and then input into a trained detection model, which outputs the glucose concentration or an intermediate result showing the correlation between glucose and the spectral data. When training the detection model, it is necessary to simultaneously acquire the spectral data of the subject and accurate test results such as blood test results, and use the spectral data as the input of the detection model and the blood test results as the output of the detection model to train the detection model.

[0043] The detection model may employ a convolutional neural network model, which sequentially includes an input layer, at least two convolutional layers, at least two activation function layers, a flattened layer, a fully connected layer, and an output layer, where the convolutional layers and the activation function layers are spaced apart, and the activation function used in the activation function layer is a Relu function.

[0044] Here, the size of the convolution kernel of each layer in the convolutional neural network model is 1, the number of convolution kernels in the first convolutional layer is 32, and the number of convolution kernels in the second layer is 64. Both are used to extract blood glucose features and nonlinearly transform the output of the convolutional layer through an activation function. The Flatten layer flattens the output of the convolutional layer into a one-dimensional vector to facilitate connection to the subsequent fully connected layer, and the final output dimension is 1. In the model training process, the Adam optimizer is used to train the model, and the mean squared error is used as the loss function, and the mean absolute error is simultaneously calculated as the performance indicator for model evaluation.

[0045] If the output result of the detection model is a glucose concentration, the training for obtaining the detection model is stopped when the error between the output result and the measured standard glucose concentration value satisfies a preset condition. If the output result of the detection model is an intermediate result correlating glucose with spectral data, such as an intermediate neuron result, the training for obtaining the detection model is stopped when the error between the output result and the intermediate neuron result satisfies a preset condition. The intermediate neuron result is further processed by model correction to obtain the glucose concentration.

[0046] As shown in Figure 4, the input layer is a spectral data input layer obtained by preprocessing the original spectral data. The hidden layer is an intermediate hidden layer that uses deep learning convolutional operations to combine features and output the final predicted blood glucose concentration value as the output layer. Deep learning convolutional operations can also output one neuron, Output1, as an intermediate result after combining features. The model is then trained again on the intermediate result, Output1, and the two infrared IR feature intensity values ​​to further correct the blood glucose prediction error and output the final predicted blood glucose concentration value, Output2. The degree of training of the detection model can be determined by setting different parameters as needed. The extracted multiple glucose feature values ​​are continuously trained according to different parameter settings. When the error between the output result and the standard glucose value of the above label value meets the requirements, the training for obtaining the detection model is stopped.

[0047] Through multiple repeated training, the neurons can learn the corresponding change rules between different glucose concentrations and glucose spectrum features of different samplers, thereby improving the generality of the detection model and achieving the goal of predicting the glucose concentrations of different users.

[0048] The entire glucose detection process does not require blood sampling or skin puncture or skin implantation, but rather obtains the subject's spectral information based on the fluorescence spectrum, and then obtains the subject's glucose detection result based on the spectral information, thereby avoiding pain and discomfort and improving the discomfort and convenience of detection. This method allows for detailed distinction between the spectral signals at the blood vessel site and the skin site, allowing for accurate extraction of the subsequent glucose signal, and at the same time, the intensity of the spectral signal and the glucose concentration are closely correlated, achieving accurate measurement of glucose concentration, more accurate detection results, and more convenient processing.

[0049] Figure 6 shows a schematic diagram of the experimental results of the trained detection model. The horizontal axis represents the reference blood glucose concentration (unit: mmol / L) collected by the blood glucose meter, and the vertical axis represents the blood glucose concentration (unit: mmol / L) predicted by the patented method. The total number of samples collected by the subjects was 2,037, of which 1,537 samples were in the training set and 500 samples were in the prediction set. The figure shows the distribution of the detection results of the detection model. The MARD value of the predicted samples was 11.32%, and they were classified into region A and region B. The samples in region A accounted for 87.03%, and the samples in region B accounted for 12.77%, indicating that the detection accuracy of the detection model is relatively high.

[0050] An information uploading step: providing a correction option, and after the correction option is triggered, obtaining the correction information input by the user, and uploading the correction information and the obtained spectral data to a cloud platform; Model training stage: The cloud platform uses the acquired spectral data as input and the acquired correction information as output to train a cloud detection model; Model update stage: Update the local detection model based on the trained cloud detection model.

[0051] Example 3 This embodiment is based on the second embodiment, but replaces infrared light with visible light, and provides another method for utilizing glucose spectrum data, which includes the following steps:

[0052] Data collection stage: Obtaining historical spectral data of multiple targets; Specifically, the data collection step includes the following steps:

[0053] Imaging step: Visible light is irradiated onto the skin where veins are located, such as on the wrist or back of the hand, to capture a first image of the imaging area, and ultraviolet light within a second wavelength range of 300 to 390 nanometers is irradiated onto the same location to capture a second image of the imaging area.

[0054] In the first image, the color of the area where venous blood vessels are located is different from the color of the area where non-venous blood vessels are located, so the imaged area can be easily divided into areas where venous blood vessels are located and areas where non-venous blood vessels are located.

[0055] Because it is difficult to distinguish between venous and non-venous blood vessels in the second image, it is necessary to distinguish between the first and second images. The purpose of using excitation light in the second wavelength range of 300-390 nanometers is to obtain a high-quality effective fluorescence spectral signal. This is because the main response band of the imaging spectral detection device is 400-800 nm. If the wavelength of the excitation light used is less than 300 nm, the main peak of the fluorescence spectrum of the excited fluorescence emission signal is located in the <400 nm band, making it difficult for the imaging spectral detection device to obtain a high-quality effective fluorescence spectral signal. If the wavelength of the excitation light used is greater than 390 nm, the excitation light itself is visible light, and the spectral signal of the excitation light is superimposed on the fluorescence spectral signal, making it difficult to extract the effective fluorescence spectral signal without interference from the spectral signal of the excitation light. Glucose in the venous blood vessels absorbs ultraviolet light in the wavelength range of 300 to 390 nanometers, and then emits fluorescent radiation signals in the visible light band of 400 to 800 nm, which is located within the effective response range of the imaging spectrum detection device. The characteristic spectral intensity of the fluorescent radiation signals is positively correlated with the glucose concentration and has higher fluorescence excitation efficiency.

[0056] Spectrum acquisition step: based on the grayscale distribution of the pixel points in the first image, divide the imaging area into an area where venous blood vessels are located and an area where non-venous blood vessels are located, select a detection point from the area where venous blood vessels are located and a reference point from the area where non-venous blood vessels are located, and obtain the spectral data of the detection point and the spectral data of the reference point, respectively. Specifically, based on the grayscale values ​​of the pixel points in the second image, select one pixel point having a grayscale value that meets a predetermined requirement from the area where venous blood vessels are located, or select a combination of this pixel point and its adjacent pixel points as the detection point, and select one pixel point from the area where non-venous blood vessels is located, whose grayscale value of the selected detection point is within a predetermined deviation range, or select a combination of this pixel point and multiple adjacent pixel points as reference points, and calculate and obtain the fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point. Here, the reason why the grayscale value of the reference point and the grayscale value of the selected detection point are within a preset deviation range is that the skin in the imaging area has influencing factors such as skin color, blemishes, cosmetics, etc., which may directly affect the spectral data of the reference point. However, in the first image, the areas of all influencing factors are not distinguished at the same time, and by setting a preset deviation range of the grayscale values, these influencing factors can be effectively excluded.

[0057] When a combination of multiple pixel points is selected from the detection point, the fluorescence spectrum data of the detection point can be the average value of the fluorescence spectrum data of these pixel points. At the same time, the number of detection points and reference points can be one or more. When the number of detection points and reference points is multiple, the average value of the fluorescence spectrum data of all the detection points and the average value of the fluorescence spectrum data of all the reference points can be calculated respectively.

[0058] data reduction step: screening the spectral data of the first time slot and the spectral data of the second time slot for each day based on the data collection time, and obtaining corresponding analyte information based on the spectral data of the first time slot and the spectral data of the second time slot, respectively, wherein the analyte information includes analyte information correlated with the spectral data; Specifically, the data reduction step includes the following steps:

[0059] The acquired spectral data of the detection point and reference point are preprocessed and then input into a trained detection model to output the glucose concentration. When training the detection model, it is necessary to simultaneously acquire the spectral data of the subject and accurate test results such as blood test results, and use the spectral data as the input of the detection model and the blood test results as the output of the detection model to train the detection model.

[0060] The detection model may employ a convolutional neural network model, which sequentially includes an input layer, at least two convolutional layers, at least two activation function layers, a flattened layer, a fully connected layer, and an output layer, where the convolutional layers and the activation function layers are spaced apart, and the activation function used in the activation function layer is a Relu function.

[0061] Here, the size of the convolution kernel of each layer in the convolutional neural network model is 1, the number of convolution kernels in the first convolutional layer is 32, and the number of convolution kernels in the second layer is 64. Both are used to extract blood glucose features and nonlinearly transform the output of the convolutional layer through an activation function. The Flatten layer flattens the output of the convolutional layer into a one-dimensional vector to facilitate connection to the subsequent fully connected layer, and the final output dimension is 1. In the model training process, the Adam optimizer is used to train the model, and the mean squared error is used as the loss function, and the mean absolute error is simultaneously calculated as the performance indicator for model evaluation.

[0062] When the error between the output result of the detection model and the standard glucose value satisfies a preset condition, the training for obtaining the detection model is stopped.

[0063] The degree of training of the detection model needs to set different parameters as needed, and the extracted multiple glucose feature values ​​are continuously learned according to different parameter settings. When the error between the output result and the standard glucose value of the above label value meets the requirement, the training to obtain the detection model is stopped.

[0064] Through multiple repeated training, the neurons can learn the corresponding change rules between different glucose concentrations and glucose spectrum features of different samplers, thereby improving the generality of the detection model and achieving the goal of predicting the glucose concentrations of different users.

[0065] The entire glucose detection process does not require blood sampling or skin puncture, but rather obtains the subject's spectral information based on the fluorescence spectrum, and then obtains the subject's glucose detection result based on the spectral information, thereby avoiding pain and discomfort and improving the discomfort and convenience of detection. This method can finely distinguish the spectral signals from the blood vessel site and the skin site, allowing the subsequent glucose signal to be accurately extracted, and at the same time, the intensity of the spectral signal and the glucose concentration are closely correlated, achieving accurate measurement of the glucose concentration, more accurate detection results, and more convenient processing.

[0066] An information uploading step: providing a correction option, and after the correction option is triggered, obtaining the correction information input by the user, and uploading the correction information and the obtained spectral data to a cloud platform; Model training stage: The cloud platform uses the acquired spectral data as input and the acquired correction information as output to train a cloud detection model; Model update stage: Update the local detection model based on the trained cloud detection model.

[0067] Example 4 This example provides a method for detecting an analyte, which includes the following steps:

[0068] Data collection step: A light source provides light within a predetermined wavelength range to illuminate a first region, and an imaging spectrum detector captures an image of the first region to obtain an image of the region. By irradiating the first region with light within the predetermined wavelength range, the image can reflect distribution data and spectral data of the reflected or excited signals generated by the object to be analyzed when the light is irradiated in the imaging region. The first region may be a specific region on the surface of human skin. The lens of the imaging spectrum detector must be tightly attached to the surface of the human skin in the first region to prevent external light, such as ambient light, from affecting the detection. The imaging region refers to the region within the lens range of the imaging spectrum detector. Generally, the imaging region may be a part of the first region or the same region as the first region.

[0069] Since light rays with different wavelength ranges are required to obtain analyte distribution data and spectral data, two methods can be used: light rays with a wider wavelength range provided by one light source, or light rays with narrower wavelength ranges provided by two light sources. When one light source is used, the wavelength range of the light rays provided by the light source must simultaneously cover the wavelength range for obtaining analyte distribution data and the wavelength range for obtaining analyte spectral data. When two light sources are used, the two light sources provide different light rays, with the wavelength of one light ray covering the wavelength range for obtaining analyte distribution data and the wavelength of the other light ray covering the wavelength range for obtaining analyte spectral data. At the same time, when one light source is used, only one image is captured, and when two light sources are used, two images are captured. For ease of processing, the imaging area of ​​the two images must be the same, i.e., the lens of the imaging spectrum detection device does not move on the surface of human skin.

[0070] In the present application, the analyte may be glucose, ketones, alcohol, lactate, oxygen, hemoglobin A1C, acetylcholine, amylase, bilirubin, cholesterol, chorionic gonadotropin, creatine kinase (e.g., CK-MB), creatine, creatinine, DNA, fructosamine, glutamine, growth hormone, hormones, peroxides, prostate-specific antigen, prothrombin, RNA, thyroid-stimulating hormone, or troponin in blood, or may be a drug such as an antibiotic (e.g., gentamicin, vancomycin, etc.), digitoxin, digoxin, a drug of abuse, theophylline, or warfarin. In embodiments where more than one analyte is detected, the analytes may be monitored at the same or different times. In other embodiments, the analyte may be any other substance within a body surface that can be noninvasively detected using the present invention.

[0071] The imaging spectral detector acquires spectral data from the image, which reflects the non-uniform distribution of the reflected or excited signals generated by the object to be analyzed when the imaging region is irradiated with light via the imaging spectral detector. Specifically, the imaging region can be divided based on different distribution data, and positions for acquiring spectral data from different sections can be selected.

[0072] Data reduction step: Based on the acquired spectral data, information on the analyte in the imaging area is acquired, including information on the analyte correlated with the spectral data. Because the distribution of the analyte in different sections is different, the reflected or excited signals generated by the analyte when irradiated with light also differ. Taking human skin as an example, it is divided into three parts: the epidermis, the dermis, and the subcutaneous tissue, and blood vessels such as veins are located in the subcutaneous tissue. UV light can be used to irradiate skin areas with blood vessels and skin areas without blood vessels to acquire corresponding spectral data, or to irradiate skin areas with thick blood vessels and skin areas with thin blood vessels to acquire corresponding spectral data. The difference between the two spectral data can reflect information on the analyte correlated with the spectral data in the blood vessels. Intermediate information, such as data on the degree of impact of the analyte on the spectral data, can be acquired for further analysis, or information such as the concentration of the analyte can be directly acquired through an analytical model.

[0073] Example 5 This embodiment provides a system for utilizing spectral data of an analyte, which can be realized by performing the process steps of the method for utilizing spectral data of an analyte, i.e., those skilled in the art can understand the method for utilizing spectral data of an analyte as a preferred embodiment of the system for utilizing spectral data of an analyte. The system for utilizing spectral data of an analyte includes the following modules:

[0074] Data collection module: acquires historical spectral data of multiple targets; Specifically, the data collection module includes the following modules:

[0075] The imaging module includes a light source that provides light within a predetermined wavelength range to illuminate a first region, and an imaging spectrum detector that captures the first region to obtain an image of the imaging region. By irradiating the imaging region with light within the predetermined wavelength range, the image can reflect distribution data and spectral data of the reflected or excited signals generated by the analyte when the light is irradiated in the imaging region. Because different wavelength ranges are required to obtain distribution data and spectral data of the analyte, the light may be light of two corresponding wavelength ranges, or one wavelength range may be wider and cover the two required wavelength ranges. When two types of light are used, two images are obtained, and for ease of processing, the imaging regions of the two images typically need to be the same.

[0076] Spectral acquisition module: Acquires spectral data from the image, which reflects the non-uniform distribution of reflected or excited signals generated by the object to be analyzed when the imaging area is irradiated with light via the imaging spectrum detection device. Specifically, the imaging area can be divided based on different distribution status data, and positions for acquiring spectral data from different sections can be selected.

[0077] a data reduction module: screening the spectral data of the first time slot and the spectral data of the second time slot of each day based on the data collection time, and obtaining corresponding analyte information based on the spectral data of the first time slot and the spectral data of the second time slot, respectively, wherein the analyte information includes analyte information correlated with the spectral data; Specifically, the data reduction module includes the following modules:

[0078] Based on the acquired spectral data, information about the analyte in the imaging region is acquired, including information about the analyte correlated with the spectral data. Because the distribution of the analyte in different sections is different, the reflected or excited signals generated from the analyte when irradiated with light also differ. By utilizing this characteristic, the difference between the two spectral data can be acquired, thereby accurately reflecting information about the analyte correlated with the spectral data, such as the concentration of the analyte.

[0079] According to the above description, FIG. 9 shows a wristwatch for detecting an analyte provided by this embodiment. The front of the wristwatch is a display, and as shown in FIG. 10, the back of the wristwatch is a light-transmitting window with a built-in detection device 200. As shown in FIG. 11, the light source 201 and the first band-pass filter 204 are all annular structures. The light-emitting modules of the light source 201 are distributed in a ring shape, and the emitted light is optically filtered by the first band-pass filter 204 to output light of a required wavelength, which is then irradiated onto the human body through the light-transmitting window on the back of the wristwatch. The reflected signal or excitation signal from the human body enters the light-transmitting window, passes through the central hollow part of the light source 201 and the first band-pass filter 204, and enters the second band-pass filter 206 through the lens 205. After being optically filtered by the second band-pass filter 206, it enters the imaging spectrum detection device 202. The imaging spectrum detector 202 is mounted on a circuit board 207, and at the same time, a controller 203 (not shown) of the detector device 200 is also mounted on the circuit board 207. As shown in Figure 12, the watch can also be worn on the inside of the wrist to more accurately identify the location of the venous blood vessels.

[0080] Example 6 This embodiment provides an analyte detection system, which can be realized by performing the process steps of the analyte detection method, i.e., those skilled in the art can understand the analyte detection method as a preferred embodiment of the analyte detection system. The analyte detection system includes the following modules:

[0081] The data collection module includes a light source that provides light within a predetermined wavelength range to illuminate a first region, and an imaging spectrum detector that captures an image of the first region to obtain an image of the region. By irradiating the light within the predetermined wavelength range, the image can reflect distribution data and spectral data of the reflected or excited signals generated by the analyte when the light is irradiated in the region. Because different wavelength ranges are required to obtain distribution data and spectral data of the analyte, the light may be light of two corresponding wavelength ranges, or one wavelength range may be wider and cover the two required wavelength ranges. When two types of light are used, two images are obtained, and for ease of processing, the two images typically need to have the same imaging area.

[0082] In the present application, the analyte may be glucose, ketones, alcohol, lactate, oxygen, hemoglobin A1C, acetylcholine, amylase, bilirubin, cholesterol, chorionic gonadotropin, creatine kinase (e.g., CK-MB), creatine, creatinine, DNA, fructosamine, glutamine, growth hormone, hormones, peroxides, prostate-specific antigen, prothrombin, RNA, thyroid-stimulating hormone, troponin, or drugs such as antibiotics (e.g., gentamicin, vancomycin, etc.), digitoxin, digoxin, drugs of abuse, theophylline, and warfarin in an animal's blood vessels. In embodiments where more than one analyte is detected, the analytes can be monitored at the same or different times. In other embodiments, the analyte may be other substances in the fluid.

[0083] The imaging spectral detector acquires spectral data from the image, which reflects the non-uniform distribution of the reflected or excited signals generated by the object to be analyzed when the imaging region is irradiated with light via the imaging spectral detector. Specifically, the imaging region can be divided based on different distribution data, and positions for acquiring spectral data from different sections can be selected.

[0084] Data reduction module: Based on the acquired spectral data, information on the analyte in the imaging area is acquired, including information on the analyte correlated with the spectral data. Because the distribution of the analyte in different areas is different, the reflected or excited signals generated by the analyte when irradiated with light also differ. By utilizing this characteristic, the difference between the two spectral data can be acquired, thereby accurately reflecting the information on the analyte correlated with the spectral data, such as the concentration of the analyte.

[0085] Those skilled in the art will recognize that in addition to implementing the system provided by the present invention and its respective devices, modules, and units purely in the form of computer-readable program codes, the system provided by the present invention and its respective devices, modules, and units can also realize similar functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. by performing logic programming in method steps. Therefore, the system provided by the present invention and its various devices, modules, and units can be considered as hardware components, and the devices, modules, and units included therein that realize various functions can also be considered as structures within the hardware components, and the devices, modules, and units for implementing various functions can be considered both as software modules for implementing methods or as structures within the hardware components.

[0086] Example 7 FIG. 7 shows an electronic device of this embodiment, specifically, a device 200 for detecting spectral data of an analyte. The device 200 for detecting spectral data of an analyte is a portable, non-invasive detection device for the human body, which can be a standalone detection device or can be incorporated into a wristwatch or a mobile phone, thereby realizing simple and rapid detection of an analyte on the body surface.

[0087] The detection device 200 includes a light source 201, an imaging spectrum detector 202, a controller 203, a first bandpass filter 204, a second bandpass filter 206, and a lens 205. The controller 203 establishes an electrical or communication connection with the light source 201 and the imaging spectrum detector 202, respectively.

[0088] The light source 201 can provide light within a predetermined wavelength range. Since light of different wavelength ranges is required to obtain distribution data and spectral data of an analyte, two methods can be used: one light source that can provide light of a wider wavelength range, or two light sources that provide light of narrower wavelength ranges. When a single light source is used, the wavelength range of the light provided by the light source must simultaneously cover the wavelength range for obtaining distribution data of the analyte, such as a halogen lamp, and the wavelength range for obtaining spectral data of the analyte. When two light sources are used, the two light sources provide different light beams, with one light beam having a wavelength covering the wavelength range for obtaining distribution data of the analyte, and the other light beam having a wavelength covering the wavelength range for obtaining spectral data of the analyte, such as a combination of infrared and ultraviolet light, or a combination of visible light and ultraviolet light.

[0089] To uniformly illuminate the imaging area 100, a ring-shaped light source can be used, which has multiple light-emitting modules uniformly distributed on the same circumference. When there are two types of light sources, the light-emitting modules of the two types of light sources are arranged relative to each other.

[0090] The imaging spectral detector 202 can image the imaging region 100 to obtain a corresponding image upon command, and can also obtain corresponding spectral data upon command. The imaging spectral detector 202 includes a sensor and a periodic pixel-level optical filter structure disposed on a surface of the sensor. The periodic pixel-level optical filter structure is used to perform spectral modulation on an incident optical signal, thereby enabling the sensor to generate an image containing the spectral information of interest.

[0091] The periodic pixel-level optical filter structure includes a plurality of optical filter pixel channels having pixel-level structures of different shapes, each having the same specification and size, uniformly arranged, and each having a length and width that is an integer multiple of the pixel point size of the pixel sensor. The optical filter pixel channels of the pixel-level optical filter structures of different shapes correspond to different spectral wave filtering coefficients, and the pixel-level optical filter structures with different spectral wave filtering coefficients are periodically arranged after being combined in a fixed order. The sensor modulates the received first detected light through the periodic pixel-level optical filter structures arranged on its surface to form a mosaic image containing spectral information, and then uses an algorithm to reconstruct a grayscale image containing the test spectral information.

[0092] The controller 203 is configured to control the light source to provide light within a predetermined wavelength range to illuminate a first region, control the imaging spectrum detector to image the first region, and acquire an image of the imaging region. The controller 203 is configured to control the imaging spectrum detector to acquire spectral data from the image, the spectral data reflecting the non-uniform distribution of reflected or excited signals generated by the analyte when the light is irradiated in the imaging region. Based on the acquired spectral data, information about the analyte in the imaging region is acquired, where the information about the analyte correlates with the spectral data. When the light source 201 is one type, one image is acquired; when the light source 201 is two types, two images are acquired. When the first light source is turned on, the second light source is turned off; similarly, when the second light source is turned on, the first light source is turned off, so that the two light sources do not interfere with each other.

[0093] The first bandpass filter 204 is located between the light source 201 and the imaging area 100, and its function is to pass light within a predetermined wavelength range and block light outside the predetermined wavelength range, thereby reducing the influence of other external light on the detection results.

[0094] The second bandpass filter 206 is located between the imaging spectrum detector 202 and the lens 205, and its function is to pass light in the wavelength range in which the reflection signal or excitation signal generated by the analyte when irradiated with light is located, and to block light in other wavelength ranges, thereby reducing the influence of reflection signals or excitation signals other than those of the analyte on the detection results.

[0095] The lens 205 can be used for focusing to obtain a clear image. In another embodiment, the second bandpass filter 206 can be located on one side of the lens 205, away from the imaging spectrum detector 202, but the present invention is not limited thereto.

[0096] Example 8 FIG. 8 is a structural schematic diagram of an electronic device provided by an embodiment of the present application, which, as shown in FIG. 8, includes at least one processor 501 and a memory 502 communicatively connected to the at least one processor 501, wherein the memory 502 stores instructions executable by the at least one processor 501, the instructions are executed by the at least one processor 501, and the at least one processor 501 can perform the method for utilizing the spectral data of the analyte.

[0097] Here, the memory 502 and the processor 501 are connected in a bus manner, which may include any number of interconnected buses and bridges, connecting various circuits of one or more processors 501 and memories 502. The bus may also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be further described in the present invention. A bus interface provides an interface between the bus and a transceiver. The transceiver may be a single component or multiple components, for example, multiple receivers and transmitters, providing a unit for communicating with various other devices via a transmission medium. Data processed by the processor 501 is transmitted over a wireless medium via an antenna, which receives data and transmits data to the processor 501.

[0098] The processor 501 is responsible for bus management and general processing, and may also provide a variety of functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. The memory 502 may be used to store data used by the processor 501 when performing operations.

[0099] The present invention further provides a computer-readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the above-mentioned method for utilizing spectral data of an analyte.

[0100] That is, those skilled in the art will understand that all or some of the steps of implementing the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a storage medium containing some instructions that enable a device (which may be a single-chip microcomputer, chip, etc.) or processor to execute all or some of the steps of the methods described in each embodiment of the present application. The storage medium includes various media that can store program code, such as a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0101] Those skilled in the art will understand that the above embodiments are specific examples for realizing the present invention, and that in actual applications, various changes in form and details are possible without departing from the spirit and scope of the present invention.

[0102] The above describes specific examples of the present invention. The present invention is not limited to the above specific embodiments, and it should be understood that those skilled in the art can make various changes or modifications within the scope of the claims without affecting the essential content of the present invention. Unless inconsistent, the examples and features of the examples in this application can be combined with each other in any way. [Explanation of symbols]

[0103] 100: Imaging area 200:Detection device 201: Light source 202: Imaging spectrum detector 203: Controller 204: 1st bandpass filter 205: Lens 206: Second bandpass filter 207: Circuit board 501: Processor 502: Memory

Claims

1. A method for utilizing spectral data of an analyte, comprising: an analysis step of inputting the acquired spectral data into a local detection model to obtain analyte information and provide correction options; an information upload step of acquiring correction information input by a user after the correction option is triggered, and uploading the correction information and the acquired spectral data to a cloud platform; a model training stage in which the cloud platform uses the acquired spectral data as input and the acquired correction information as output to train a cloud detection model; and a model updating step of updating a local detection model based on the trained cloud detection model.

2. The analyzing step includes collecting a first image corresponding to the first region under illumination with light within a first wavelength range and collecting a second image corresponding to the first region under illumination with light within a second wavelength range; the first image includes data reflecting a distribution state in an imaging area of ​​a reflected signal or an excited signal generated by an object to be analyzed when irradiated with a light beam; the second image includes spectral data in the imaging region reflecting a reflected signal or an excited signal generated by the analyte when irradiated with light; The spectral data of a required position is acquired from the second image based on the distribution status data. A method for utilizing the spectral data of an object to be analyzed according to claim 1.

3. The light within the first wavelength range includes infrared light, and the analyzing step comprises: the method further comprising: dividing the imaging area into detection point candidate areas and reference point candidate areas based on a grayscale distribution of image points in the first image; selecting detection points from positions of the detection point candidate areas in the second image; selecting reference points from positions of the reference point candidate areas corresponding to the second image; and acquiring spectral data of the detection points and spectral data of the reference points, respectively. A method for utilizing the spectral data of an object to be analyzed according to claim 2.

4. The light within the second wavelength range includes ultraviolet light, and the analyzing step comprises: and calculating and acquiring fluorescence spectrum data of the detection point and the reference point. A method for utilizing the spectral data of an object to be analyzed according to claim 3.

5. the second wavelength range is 300 to 390 nanometers, and the second wavelength range is outside the effective response range of an imaging spectral detection device; The light in the second wavelength range can excite a fluorescent emission signal from the analyte, and a main peak of the fluorescent spectrum of the fluorescent emission signal is located within an effective response range of the imaging spectral detection device. A method for utilizing the spectral data of an object to be analyzed according to claim 4.

6. 1. A method for detecting an analyte, comprising:

6. A method for detecting an analyte, comprising the method for utilizing the spectral data of the analyte according to claim 1.

7. A system for utilizing spectral data of an analyte, comprising: an analysis module that inputs the acquired spectral data into a local detection model to obtain analyte information and provide correction options; an information upload module for acquiring correction information input by a user after the correction option is triggered, and uploading the correction information and the acquired spectral data to a cloud platform; The cloud platform includes a model training module that uses the acquired spectral data as input and the acquired correction information as output to train a cloud detection model; and a model updating module that updates a local detection model based on the trained cloud detection model.

8. The analysis module includes collecting a first image corresponding to the first region under illumination with light within a first wavelength range, and collecting a second image corresponding to the first region under illumination with light within a second wavelength range; the first image includes data reflecting a distribution state in an imaging area of ​​a reflected signal or an excited signal generated by an object to be analyzed when irradiated with a light beam; the second image includes spectral data in the imaging region reflecting a reflected signal or an excited signal generated by the analyte when irradiated with light; The spectral data of a required position is acquired from the second image based on the distribution status data. The system for utilizing spectral data of an object to be analyzed according to claim 7.

9. The light within the first wavelength range includes infrared light, and the analysis module the method further comprising: dividing the imaging area into detection point candidate areas and reference point candidate areas based on a grayscale distribution of image points in the first image; selecting detection points from positions of the detection point candidate areas in the second image; selecting reference points from positions of the reference point candidate areas corresponding to the second image; and acquiring spectral data of the detection points and spectral data of the reference points, respectively. The system for utilizing spectral data of an object to be analyzed according to claim 8.

10. The light within the second wavelength range includes ultraviolet light, and the analysis module and calculating and acquiring fluorescence spectrum data of the detection point and the reference point. The system for utilizing spectral data of an object to be analyzed according to claim 9.

11. the second wavelength range is 300 to 390 nanometers, and the second wavelength range is outside the effective response range of an imaging spectral detection device; The light in the second wavelength range can excite a fluorescent emission signal from the analyte, and a main peak of the fluorescent spectrum of the fluorescent emission signal is located within an effective response range of the imaging spectral detection device. The system for utilizing spectral data of an object to be analyzed according to claim 10.

12. 1. An analyte detection system comprising: An analyte detection system, comprising the system for utilizing spectral data of an analyte according to any one of claims 7 to 11.

13. A computer-readable storage medium having a computer program stored thereon, A computer-readable storage medium having stored thereon the computer program, characterized in that when the computer program is executed by a processor, the steps of the method for utilizing spectral data of an analyte according to any one of claims 1 to 5 are realized.

14. 1. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, An electronic device comprising the memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that when the computer program is executed by the processor, the steps of the method for utilizing spectral data of an analyte according to any one of claims 1 to 5 are realized.

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