Detection model-based analyte detection method, system, medium, and device

Abstract

To provide a method, a system, a medium, and a device for detecting an analyte based on a detection model in the field of optical analysis.SOLUTION: An analyte detection method based on a detection model includes a model input step of obtaining an analyte detection model and providing an input amount to the analyte detection model, and a model output step of collecting an output amount of the analyte detection model, wherein the input amount includes spectral data reflecting non-uniform distribution of a reflection signal or an excitation signal generated from an analyte when irradiated with light, the output amount includes an analyte detection result, and the analyte detection result includes information of the analyte correlated with the spectral data. In the present disclosure, the interfered input spectral data is processed through the detection model to obtain an accurate output result, thereby improving the accuracy of the model output.SELECTED DRAWING: Figure 9

Description

[Technical Field]

[0001] The present invention relates to the field of optical analysis, and in particular to methods, systems, media and devices for detecting analytes based on a detection model. [Background technology]

[0002] Diabetes mellitus is a disorder of carbohydrate, protein and fat metabolism caused by absolute or relative deficiency of insulin secretion and / or impaired insulin utilization, with hyperglycemia being the main symptom.

[0003] Currently, blood glucose detection methods, whether in hospitals or at home, are all invasive, i.e., blood is directly drawn from the patient and the blood glucose level is detected by electrochemical methods, which causes certain physiological pain to the patient.

[0004] In light of this, existing technologies are becoming capable of non-invasive blood glucose detection, which obtains blood glucose concentrations without damaging human tissues. Generally, spectral analysis technology involves performing an infrared scan on a test site on the human body, and then analyzing the blood glucose concentration in the human body based on the spectrum.

[0005] The invention patent with publication number CN118078277A discloses a non-invasive blood glucose detection method based on hyperspectral data analysis, which includes the steps of: acquiring a hyperspectral image dataset of a blood vessel region of the skin of a human body, the hyperspectral image dataset including two-dimensional spatial data and one-dimensional spectral data; identifying a target pixel of the blood vessel location based on the two-dimensional spatial data and extracting target spectral data corresponding to the target pixel; and model training an initial regression model based on the target spectral data to obtain a predictive regression model, which is used to non-invasively detect the blood glucose concentration of a subject.

[0006] The invention patent with publication number CN107505268A discloses a blood glucose detection method and system, which includes a step S1 of acquiring hyperspectral image data of a detection target area, a step S2 of extracting vein region spectral image data from the hyperspectral image data, and a step S3 of calculating and obtaining a blood glucose concentration based on the vein region spectral image data.

[0007] The invention patent with publication number CN107192690B discloses a non-invasive blood glucose detection method using near-infrared spectroscopy and a method for training the detection network model thereof, which first uses near-infrared spectroscopy data and corresponding invasive blood glucose concentration data to train multiple artificial neural networks, and then selects two of the artificial neural networks as basic structures. Based on this, a particle swarm algorithm is used to optimize the weight coefficients of the two artificial neural networks to obtain a detection network model, and the weight coefficients are used to adjust the contribution rates of the two artificial neural networks in the detection network model, thereby overcoming the differences between individual and individual differences in daily physiological laws.

[0008] As can be seen from the above, conventional intravascular analyte detection models using spectral data only collect and analyze spectral data from the area where the blood vessels are located. However, the spectral data also contains objects such as skin and subcutaneous tissue, which also generate spectral data, which may affect the accuracy of the model's output results. Summary of the Invention [Problem to be solved by the invention]

[0009] 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 detecting an analyte based on a detection model. [Means for solving the problem]

[0010] The method for detecting an analyte based on the detection model provided by the present invention comprises: a model input step of obtaining an analyte detection model and providing input quantities to the analyte detection model; and collecting an output of the analyte detection model; wherein the input quantity includes spectral data reflecting a non-uniform distribution of a reflected signal or an excited signal generated from the analyte when irradiated with light; The output quantity includes a detection result of the analyte, and the detection result of the analyte includes information about the analyte that correlates with the spectral data.

[0011] Preferably, the input amount is: 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 a first image of the imaging area collected from an area where an infrared irradiation target is located, selecting detection points from the detection point candidate areas, selecting reference points from the reference point candidate areas, and respectively obtaining spectral data of the detection points and spectral data of the reference points.

[0012] Preferably, the input amount is: The method includes selecting, from the detection point candidate area, as a detection point, one image point having a grayscale value that satisfies a predetermined requirement, or selecting a combination of the image point and adjacent image points as a detection point, based on the grayscale value of the image point in a second image of the imaging area collected at the same position under ultraviolet irradiation; selecting, from the reference point candidate area, one image point whose grayscale value of the selected detection point is within a predetermined deviation range, or selecting a combination of the image point and multiple adjacent image points as reference points; and calculating and obtaining fluorescence spectrum data of the detection point and the reference point.

[0013] Preferably, the detection model employs 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, wherein the convolutional layers and the activation function layers are distributed at intervals, and the activation function employed in the activation function layer is a Relu function.

[0014] Preferably, the convolutional kernel size of each layer in the convolutional neural network model is 1, the number of convolutional kernels in the first convolutional layer is 32, and the number of convolutional kernels in the second convolutional layer is 64, all of which are used to extract features of the analyte, and the output of the convolutional layer is nonlinearly transformed through an activation function, and a flattening layer flattens the output of the convolutional layer into a one-dimensional vector, which is convenient for connection to the subsequent fully connected layer, and the final output dimension is 1; During the detection model training process, the Adam optimizer is used to train the model, and the mean square error is used as the loss function, while the mean absolute error is calculated as the performance index for evaluating the detection model.

[0015] Preferably, by using deep learning with convolutional operations, after feature combination, one neuron outputs one intermediate result value, and the intermediate result value and the two infrared IR feature intensity values ​​are used for model training again to further correct the prediction error of the analyte, and finally output the predicted analyte concentration value.

[0016] The method for detecting an analyte provided by the present invention comprises steps of a method for detecting an analyte based on the detection model.

[0017] An analyte detection system based on the detection model provided by the present invention comprises: a model input module that obtains an analyte detection model and provides input quantities to the analyte detection model; a model output module that collects an output of the analyte detection model; wherein the input quantity includes spectral data reflecting a non-uniform distribution of a reflected signal or an excited signal generated from the analyte when irradiated with light; The output quantity includes a detection result of the analyte, and the detection result of the analyte includes information about the analyte that correlates with the spectral data.

[0018] Preferably, the input amount is: 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 a first image of the imaging area collected from an area where an infrared irradiation target is located, selecting detection points from the detection point candidate areas, selecting reference points from the reference point candidate areas, and respectively obtaining spectral data of the detection points and spectral data of the reference points.

[0019] Preferably, the input amount is: The method includes selecting, from the detection point candidate area, as a detection point, one image point having a grayscale value that satisfies a predetermined requirement, or selecting a combination of the image point and adjacent image points as a detection point, based on the grayscale value of the image point in a second image of the imaging area collected at the same position under ultraviolet irradiation; selecting, from the reference point candidate area, one image point whose grayscale value of the selected detection point is within a predetermined deviation range, or selecting a combination of the image point and multiple adjacent image points as reference points; and calculating and obtaining fluorescence spectrum data of the detection point and the reference point.

[0020] Preferably, the detection model employs 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, wherein the convolutional layers and the activation function layers are distributed at intervals, and the activation function employed in the activation function layer is a Relu function.

[0021] Preferably, the convolutional kernel size of each layer in the convolutional neural network model is 1, the number of convolutional kernels in the first convolutional layer is 32, and the number of convolutional kernels in the second convolutional layer is 64, all of which are used to extract features of the analyte, and the output of the convolutional layer is nonlinearly transformed through an activation function, and a flattening layer flattens the output of the convolutional layer into a one-dimensional vector, which is convenient for connection to the subsequent fully connected layer, and the final output dimension is 1; During the detection model training process, the Adam optimizer is used to train the model, and the mean square error is used as the loss function, while the mean absolute error is calculated as the performance index for evaluating the detection model.

[0022] Preferably, by using deep learning with convolutional operations, after feature combination, one neuron outputs one intermediate result value, and the intermediate result value and the two infrared IR feature intensity values ​​are used for model training again to further correct the prediction error of the analyte, and finally output the predicted analyte concentration value.

[0023] The analyte detection system provided by the present invention includes a module of the analyte detection system based on the detection model.

[0024] The present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements steps of a method for detecting an analyte based on the detection model.

[0025] 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 steps of a method for detecting an analyte based on the detection model. [Effects of the Invention]

[0026] Compared with the prior art, the present invention has the following beneficial effects: 1. By constructing and training a detection model, the present application can still obtain accurate output results even when spectral data with interference factors is input, thereby improving the output accuracy of the model. 2. The technical solution of the present application does not require electrochemical reaction with the analyte, the detection method is simpler, and the goal of non-invasive detection can be achieved, especially when detecting analytes in vivo. 3. The present application utilizes the uneven distribution of the analyte in the imaging area to obtain spectral data in different areas, and since the distribution of components other than the analyte in the imaging area is relatively uniform, the difference in spectral data in different areas 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. 4. This application avoids the traditional method of using Raman spectroscopy 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.

[0027] 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]

[0028] [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] 10 is a flowchart of a seventh embodiment. [Figure 10] 1 is a structural schematic diagram of an analyte detection watch provided by Example 5. FIG. [Figure 11] FIG. 1 is a schematic diagram of the back of an analyte detection watch. [Figure 12] FIG. 1 is an exploded view of an analyte detection watch. [Figure 13] FIG. 1 is a schematic diagram of an analyte detection wristwatch in use. DETAILED DESCRIPTION OF THE INVENTION

[0029] 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.

[0030] Example 1 FIG. 1 is a flow chart of this embodiment, and the method for detecting an analyte based on the detection model of this embodiment includes the following steps:

[0031] 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.

[0032] 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 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.

[0033] 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.

[0034] 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.

[0035] Analysis step: Acquiring information about the analyte in the imaging region based on the acquired spectral data, where the analyte information includes information about the analyte correlated with the spectral data. Because the distribution of the analyte in different regions varies, the reflected or excited signals generated by the analyte when irradiated with light also vary. 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 regions with blood vessels and skin regions without blood vessels to obtain corresponding spectral data, or to irradiate skin regions with thick blood vessels and skin regions 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.

[0036] Example 2 This embodiment is based on the first embodiment and takes glucose detection in human blood vessels as an example to provide a non-invasive glucose detection method, which includes the following steps:

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] Analysis stage: 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 subject's spectral data 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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 intermediate result, Output1, and the two infrared IR feature intensity values ​​are then used for further model training 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 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] Example 3 This example is based on Example 2, and replaces infrared light with visible light to provide a non-invasive glucose detection method, which includes the following steps:

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] Analysis stage: 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 subject's spectral data and accurate test results such as blood test results, use the spectral data as the input of the detection model, and use the blood test results as the output of the detection model to train the detection model.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] Example 4 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:

[0065] 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.

[0066] 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, peroxide, 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.

[0067] 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.

[0068] Analysis module: Based on the acquired spectral data, the analysis module acquires information about the analyte in the imaging area, 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 by the analyte when irradiated with light are also different. By utilizing this characteristic, the difference between the spectral data can be obtained, and the information about the analyte correlated with the spectral data, such as the concentration of the analyte, can be accurately reflected.

[0069] 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.

[0070] Example 5 Shown in Figure 7 is an electronic device of this embodiment, specifically an analyte detection device 200. The detection device 200 is a portable, non-invasive detection device for the human body, which can be used as a standalone detection device or integrated into a wristwatch or mobile phone, realizing convenient and rapid detection of analytes on the body surface.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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 filter coefficients, and the pixel-level optical filter structures with different spectral wave filter 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] According to the above description, FIG. 10 shows a wristwatch for detecting an analyte provided by this embodiment. The front of the wristwatch is a display, and as shown in FIG. 11, the back of the wristwatch is a light-transmitting window with a built-in detection device 200. As shown in FIG. 12, 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 13, the watch can also be worn on the inside of the wrist to more accurately identify the location of the venous blood vessels.

[0081] Example 6 FIG. 8 is a structural schematic diagram of an electronic device provided by an embodiment of the present application, which includes at least one processor 501, as shown in FIG. 8, 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 above-mentioned analyte detection method.

[0082] 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.

[0083] 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.

[0084] 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-described method for detecting an analyte.

[0085] 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.

[0086] Example 7 FIG. 9 is a flowchart of this embodiment, and the method for detecting an analyte based on the detection model of this embodiment includes the following steps:

[0087] a model input step of obtaining an analyte detection model and providing input quantities to the analyte detection model; Based on the grayscale distribution of image points in a first image of an imaging area collected in an area where an infrared irradiation target is located (i.e., the skin where a vein is located), the imaging area is divided into a detection point candidate area and a reference point candidate area, a detection point is selected from the detection point candidate area, and a reference point is selected from the reference point candidate area, and spectral data of the detection point and the reference point are respectively obtained.

[0088] Based on the grayscale value of the image point in a second image of the imaging area collected at the same position under ultraviolet irradiation, one image point having a grayscale value that meets predetermined requirements is selected as the detection point from the detection point candidate area, or a combination of the image point and adjacent image points is selected as the detection point; one image point whose grayscale value of the selected detection point is within a predetermined deviation range is selected as the reference point from the reference point candidate area, or a combination of the image point and multiple adjacent image points is selected as the reference point; and fluorescence spectrum data of the detection point and the reference point are calculated and obtained.

[0089] A model output step: collecting the output of the analyte detection model; wherein the input quantity includes spectral data reflecting a non-uniform distribution of a reflected signal or an excited signal generated from the analyte when irradiated with light; The output quantity includes a detection result of the analyte, and the detection result of the analyte includes information about the analyte that correlates with the spectral data.

[0090] The detection model employs 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 employed in the activation function layer is a Relu function.

[0091] The convolution kernel size 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. All of these are used to extract the features of the analyte (i.e., blood glucose level features), and the output of the convolutional layer is nonlinearly transformed through an activation function. A flattening layer flattens the output of the convolutional layer into a one-dimensional vector, which is convenient for connection to the subsequent fully connected layer. The final output dimension is 1. During the detection model training process, the Adam optimizer is used to train the model, and the mean square error is used as the loss function, while the mean absolute error is calculated as the performance index for evaluating the detection model.

[0092] Through deep learning using convolutional operations, after combining the features, one neuron outputs one intermediate result value, and the intermediate result value and the two infrared IR feature brightness values ​​are used for model training again, and the prediction error of the analyte (i.e., blood glucose) is further corrected, and the final predicted blood glucose concentration value is output.

[0093] Example 8 The analyte detection system based on the detection model of this embodiment includes the following modules: a model input module for obtaining an analyte detection model and providing input quantities to the analyte detection model; Based on the grayscale distribution of image points in a first image of an imaging area collected in an area where an infrared irradiation target is located (i.e., the skin where a vein is located), the imaging area is divided into a detection point candidate area and a reference point candidate area, a detection point is selected from the detection point candidate area, and a reference point is selected from the reference point candidate area, and spectral data of the detection point and the reference point are respectively obtained.

[0094] Based on the grayscale value of the image point in a second image of the imaging area collected at the same position under ultraviolet irradiation, one image point having a grayscale value that meets predetermined requirements is selected as the detection point from the detection point candidate area, or a combination of the image point and adjacent image points is selected as the detection point; one image point whose grayscale value of the selected detection point is within a predetermined deviation range is selected as the reference point from the reference point candidate area, or a combination of the image point and multiple adjacent image points is selected as the reference point; and fluorescence spectrum data of the detection point and the reference point are calculated and obtained.

[0095] a model output module that collects the output of the analyte detection model; wherein the input quantity includes spectral data reflecting a non-uniform distribution of a reflected signal or an excited signal generated from the analyte when irradiated with light; The output quantity includes a detection result of the analyte, and the detection result of the analyte includes information about the analyte that correlates with the spectral data.

[0096] The detection model employs 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 employed in the activation function layer is a Relu function.

[0097] The convolution kernel size 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. All of these are used to extract the features of the analyte (i.e., blood glucose level features), and the output of the convolutional layer is nonlinearly transformed through an activation function. A flattening layer flattens the output of the convolutional layer into a one-dimensional vector, which is convenient for connection to the subsequent fully connected layer. The final output dimension is 1. During the detection model training process, the Adam optimizer is used to train the model, and the mean square error is used as the loss function, while the mean absolute error is calculated as the performance index for evaluating the detection model.

[0098] Through deep learning using convolutional operations, after combining the features, one neuron outputs one intermediate result value, and the intermediate result value and the two infrared IR feature brightness values ​​are used for model training again, and the prediction error of the analyte (i.e., blood glucose) is further corrected, and the final predicted blood glucose concentration value is output.

[0099] 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.

[0100] 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]

[0101] 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. 1. A method for detecting an analyte based on a detection model, comprising: a model input step of obtaining an analyte detection model and providing input quantities to the analyte detection model; and collecting an output of the analyte detection model; wherein the input quantity includes spectral data reflecting a non-uniform distribution of a reflected signal or an excited signal generated from the analyte when irradiated with light; The method for detecting an analyte based on the detection model, characterized in that the output quantity includes a detection result of the analyte, and the detection result of the analyte includes information about the analyte that correlates with the spectral data.

2. The input amount is The method includes dividing an imaging area into a detection point candidate area and a reference point candidate area based on a grayscale distribution of image points in a first image of the imaging area collected from an area where an infrared irradiation target is located, selecting detection points from the detection point candidate area, selecting reference points from the reference point candidate area, and respectively obtaining spectral data of the detection points and spectral data of the reference points. A method for detecting an analyte based on the detection model of claim 1.

3. The input amount is The method includes: selecting, as a detection point, one image point having a grayscale value that satisfies a predetermined requirement from the position of a detection point candidate region in the second image, based on the grayscale value of the image point in a second image of the imaging region collected at the same position under ultraviolet irradiation, or selecting a combination of the image point and an adjacent image point as the detection point; selecting, as a reference point, one image point having a grayscale value that falls within a predetermined deviation range from the position of a reference point candidate region in the second image, or selecting a combination of the image point and a plurality of adjacent image points as reference points; and calculating and acquiring fluorescence spectrum data of the detection point and the reference point. A method for detecting an analyte based on the detection model of claim 2.

4. The detection model employs 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, wherein the convolutional layers and the activation function layers are distributed at intervals, and the activation function employed in the activation function layer is a Relu function. A method for detecting an analyte based on the detection model of claim 1.

5. The convolutional kernel size of each layer in the convolutional neural network model is 1, the number of convolutional kernels in the first convolutional layer is 32, and the number of convolutional kernels in the second layer is 64, all of which are used to extract features of the analyte, and the output of the convolutional layer is nonlinearly transformed through an activation function, and a flattening layer flattens the output of the convolutional layer into a one-dimensional vector for convenient connection to the subsequent fully connected layer, and the final output dimension is 1; During the detection model training process, the Adam optimizer is used to train the model, and the mean squared error is used as the loss function. At the same time, the mean absolute error is calculated as the performance index for evaluating the detection model. A method for detecting an analyte based on the detection model of claim 4.

6. By using deep learning with convolutional operations, one neuron is output as one intermediate result value after feature combination, and the intermediate result value and the two infrared IR feature brightness values ​​are again subjected to model training, and the prediction error of the analyte is further corrected, and the final predicted analyte concentration value is output. A method for detecting an analyte based on the detection model of claim 5.

7. 1. A method for detecting an analyte, comprising: A method for detecting an analyte, characterized in that it comprises the steps of a method for detecting an analyte based on a detection model according to any one of claims 1 to 6.

8. 1. An analyte detection system based on a detection model, comprising: a model input module that obtains an analyte detection model and provides input quantities to the analyte detection model; a model output module that collects an output of the analyte detection model; wherein the input quantity includes spectral data reflecting a non-uniform distribution of a reflected signal or an excited signal generated from the analyte when irradiated with light; The analyte detection system based on the detection model, wherein the output quantity includes a detection result of the analyte, and the detection result of the analyte includes information of the analyte that correlates with the spectral data.

9. The input amount is The method includes dividing an imaging area into a detection point candidate area and a reference point candidate area based on a grayscale distribution of image points in a first image of the imaging area collected from an area where an infrared irradiation target is located, selecting detection points from the detection point candidate area, selecting reference points from the reference point candidate area, and acquiring spectral data of the detection points and spectral data of the reference points, respectively. An analyte detection system based on the detection model of claim 8.

10. The input amount is Based on the grayscale value of an image point in a second image of the imaging area collected at the same position under ultraviolet irradiation, one image point having a grayscale value that satisfies a predetermined requirement is selected as a detection point from the position of a detection point candidate area in the second image, or a combination of the image point and an adjacent image point is selected as a detection point; one image point having a grayscale value that falls within a predetermined deviation range is selected as a reference point from the position of a reference point candidate area in the second image, or a combination of the image point and a plurality of adjacent image points is selected as reference points; and fluorescence spectrum data of the detection point and the reference point are calculated and obtained. An analyte detection system based on the detection model of claim 9.

11. The detection model employs 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, wherein the convolutional layers and the activation function layers are distributed at intervals, and the activation function employed in the activation function layer is a Relu function. An analyte detection system based on the detection model of claim 8.

12. The convolutional kernel size of each layer in the convolutional neural network model is 1, the number of convolutional kernels in the first convolutional layer is 32, and the number of convolutional kernels in the second layer is 64, all of which are used to extract features of the analyte, and the output of the convolutional layer is nonlinearly transformed through an activation function, and a flattening layer flattens the output of the convolutional layer into a one-dimensional vector for convenient connection to the subsequent fully connected layer, and the final output dimension is 1; During the detection model training process, the Adam optimizer is used to train the model, and the mean squared error is used as the loss function. At the same time, the mean absolute error is calculated as the performance index for evaluating the detection model. An analyte detection system based on the detection model of claim 11.

13. By using deep learning with convolutional operations, one neuron is output as one intermediate result value after feature combination, and the intermediate result value and the two infrared IR feature brightness values ​​are again subjected to model training, and the prediction error of the analyte is further corrected, and the final predicted analyte concentration value is output. An analyte detection system based on the detection model of claim 12.

14. 1. A system for detecting an analyte, comprising: An analyte detection system, characterized in that it comprises a module of the analyte detection system based on the detection model according to any one of claims 8 to 13.

15. 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, steps of a method for detecting an analyte based on the detection model according to any one of claims 1 to 6 are realized.

16. 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, steps of a method for detecting an analyte based on the detection model according to any one of claims 1 to 6 are realized.

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