Method and system for testing analyte, medium, and device

Abstract

The invention provides an analyte detection method, system, medium and equipment, and the method comprises the following steps: an imaging step: irradiating a first region through single-wavelength infrared light in a first wavelength range, and imaging the first region to obtain a first image of an imaging region; irradiating the first area through ultraviolet light in a second wavelength range and imaging the first area to obtain a second image of the imaging area; a spectrum acquisition step: acquiring gray level distribution condition data reflecting the analyte from the first image; according to the gray level distribution condition data, acquiring an infrared light intensity value and fluorescence spectrum data reflecting an analyte at a required position from the first image and the second image respectively; and an analysis step: obtaining the information of the analyte in the imaging area according to the infrared light intensity value and the fluorescence spectrum data. According to the method, the area where the blood vessel is located and the area where the non-blood vessel is located are distinguished through the single-wavelength infrared light, accurate selection of the detection point and the reference point is further achieved, and the detection result is more accurate.

Description

(19) State Intellectual Property Office (12) Invention Patent Application (10) Application Publication Number (43) Application Publication Date (21) Application Number 202410951452.1 (22) Application Date 2024.07.16 (71) Applicant Guangzhou Ruixin Microelectronics Co., Ltd. Address Room 1703, No. 188, Kaitai Avenue, Huangpu District, Guangzhou City, Guangdong Province 510535 (72) Inventor Zheng Hongzhi (74) Patent Agency Shanghai Duan & Duan Law Firm 31334 Patent Attorney Han Chengling (51) Int.Cl. A61B 5 / 1455 (2006.01) A61B 5 / 145 (2006.01) A61B 5 / 00 (2006.01) G06V 40 / 145 (2022.01) G06V 10 / 143 (2022.01) G06V 10 / 25 (2022.01) G06V 10 / 60 (2022.01) G06T 7 / 00 (2017.01) (54) Invention Title: Method, System, Medium and Device for Detection of Analytes (57) Abstract: This invention provides a method, system, medium and device for detecting analytes. The method includes: an imaging step: irradiating a first region with single-wavelength infrared light within a first wavelength range and imaging the first region to obtain a first image of the imaging region; irradiating the first region with ultraviolet light within a second wavelength range and imaging the first region to obtain a second image of the imaging region; a spectral acquisition step: acquiring grayscale distribution data reflecting the analyte from the first image; acquiring infrared light intensity values ​​and fluorescence spectral data reflecting the analyte at the required positions from the first image and the second image, respectively, based on the grayscale distribution data; an analysis step: obtaining information about the analyte in the imaging region based on the infrared light intensity values ​​and fluorescence spectral data. This application uses single-wavelength infrared light to distinguish between regions where blood vessels are located and regions where blood vessels are not located, further enabling accurate selection of detection points and reference points, making the detection results more accurate. Claims (2 pages), Description (13 pages), Drawings (6 pages), CN 121337332 A, 2026.01.16, CN 1 21 33 73 32 A. 1. A method for detecting an analyte, characterized in that it comprises: an imaging step: irradiating a first region with single-wavelength infrared light within a first wavelength range and imaging the first region to obtain a first image of the imaging region; and irradiating the first region with ultraviolet light within a second wavelength range and imaging the first region to obtain a second image of the imaging region; a spectral acquisition step: acquiring grayscale distribution data reflecting the non-uniform distribution of the analyte in the imaging region from the first image; and acquiring, based on the grayscale distribution data, the position of the analyte at a desired location from the first image.The infrared light intensity values ​​are unevenly distributed in the imaging region; based on the grayscale distribution data, fluorescence spectral data reflecting the uneven distribution of the analyte in the imaging region at the desired location is obtained from the second image; Analysis steps: information about the analyte in the imaging region is obtained based on the obtained infrared light intensity values ​​and fluorescence spectral data, wherein the information about the analyte includes information relating the analyte to the infrared light intensity values ​​and fluorescence spectral data. 2. The method for detecting the analyte according to claim 1, wherein the spectral acquisition step includes: dividing the imaging region into a candidate region for detection points and a candidate region for reference points based on the grayscale distribution of pixels in the first image; selecting a detection point from the corresponding position in the second image from the candidate region for detection points; selecting a reference point from the corresponding position in the second image from the candidate region for reference points; obtaining the infrared light intensity values ​​of the detection point and the reference point in the first image respectively; and obtaining the spectral data of the detection point and the reference point in the second image respectively. 3. The method for detecting an analyte according to claim 2, characterized in that the spectral acquisition step includes: selecting a pixel whose gray value meets a preset requirement from the candidate region of detection points as a detection point, or a combination of the pixel and its adjacent pixels as a detection point, based on the gray value of the pixels in the second image; selecting a pixel whose gray value is within a preset deviation range from the selected detection point from the candidate region of reference points as a reference point, or a combination of the pixel and multiple adjacent pixels as a reference point; and calculating the fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point. 4. The method for detecting an analyte according to claim 3, characterized in that the second wavelength range is 300-390 nanometers, and the second wavelength range is outside the effective response range of the imaging spectral detection device; the light in the second wavelength range can excite a fluorescence radiation signal from the analyte, and the main peak of the fluorescence spectrum of the fluorescence radiation signal is within the effective response range of the imaging spectral detection device. 5. The method for detecting an analyte according to claim 3, characterized in that the analysis step includes inputting the acquired infrared light intensity value and the infrared light intensity value of the reference point, as well as the acquired fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point, into a trained analyte detection model, and outputting analyte information. 6. An analyte detection system, characterized in that it comprises: an imaging module: irradiating a first region with single-wavelength infrared light within a first wavelength range and imaging the first region to obtain a first image of the imaging region; and irradiating the first region with ultraviolet light within a second wavelength range and imaging the first region to obtain a second image of the imaging region; a spectral acquisition module: acquiring grayscale distribution data reflecting the uneven distribution of the analyte in the imaging region from the first image; and acquiring, based on the grayscale distribution data, the position of the analyte reflecting the analyte at a desired location from the first image.The infrared light intensity values ​​are unevenly distributed in the imaging region; based on the grayscale distribution data, fluorescence spectral data reflecting the uneven distribution of the analyte in the imaging region at the required position is obtained from the second image; Analysis module: information about the analyte in the imaging region is obtained based on the obtained infrared light intensity values ​​and fluorescence spectral data, wherein the information about the analyte includes information related to the analyte and the spectral data. 7. The analyte detection system according to claim 6, wherein the spectral acquisition module includes: (Claims 1 / 2 page 2 CN 121337332 A) The imaging region is divided into a candidate region for detection points and a candidate region for reference points based on the grayscale distribution of pixels in the first image; a detection point is selected from the corresponding position in the second image from the candidate region for detection points; a reference point is selected from the corresponding position in the second image from the candidate region for reference points; the infrared light intensity values ​​of the detection point and the reference point in the first image are obtained respectively; and the spectral data of the detection point and the reference point in the second image are obtained respectively. 8. The analyte detection system according to claim 7, wherein the spectral acquisition module comprises: selecting a pixel whose gray value meets a preset requirement as a detection point or a combination of the pixel and its adjacent pixels as a detection point from a candidate region of detection points based on the gray value of the pixels in the second image; selecting a pixel whose gray value is within a preset deviation range from the selected detection point as a reference point or a combination of the pixel and multiple adjacent pixels as a reference point from a candidate region of reference points; and calculating the fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point. 9. The analyte detection system according to claim 8, wherein the second wavelength range is 300-390 nanometers, and the second wavelength range is outside the effective response range of the imaging spectral detection device; the light in the second wavelength range can excite a fluorescence radiation signal from the analyte, and the main peak of the fluorescence spectrum of the fluorescence radiation signal is within the effective response range of the imaging spectral detection device. 10. The analyte detection system according to claim 8, characterized in that the analysis module includes inputting the acquired infrared light intensity value and the infrared light intensity value of the reference point, as well as the acquired fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point, into a trained analyte detection model, and outputting analyte information. 11. A computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements the steps of the analyte detection method according to any one of claims 1 to 5. 12. An electronic device, comprising a 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 a processor, it implements the steps of the analyte detection method according to any one of claims 1 to 5.Claims 2 / 2 pages 3 CN 121337332 A Method, System, Medium and Equipment for Detection of Analytes Technical Field

[0001] This invention relates to the field of optical analysis, specifically to a method, system, medium and equipment for detecting analytes. Background Art

[0002] In the detection technology for analytes, commonly used detection technologies include electrochemical methods and optical methods. Taking the detection of glucose in the human body as an example: Patent document US20100065441A1 discloses an analyte monitoring system, equipment and method, in which a sensor is implanted under the skin of the human body to undergo an electrochemical reaction with the glucose under the skin, thereby obtaining the glucose level. The advantage of this approach is that glucose data can be collected in real time as needed throughout the day without the need for multiple punctures to collect blood, which greatly facilitates the user. However, its disadvantage is that it uses an invasive method to implant the sensor under the skin of the human body. Patent document US20160287147A1 discloses a device for non-invasive in vivo measurement by Raman spectroscopy, which uses Raman spectroscopy to measure in vivo blood glucose concentration. The advantage of this approach is that it has higher accuracy compared to the electrochemical method in US20100065441A1. However, its disadvantage is that it currently requires a laboratory-grade Raman spectroscopy system, which is bulky and expensive.

[0003] In addition, patent document CN118078277A discloses a non-invasive blood glucose detection method based on hyperspectral data analysis, achieving non-invasive detection. This document uses absorption spectroscopy, and the spectral signals collected and analyzed include not only the spectral signals of blood glucose but also the spectral signals of components such as skin tissue. The spectral signals of different wavelengths are superimposed, making it difficult to finely separate and extract the spectral signals related to blood glucose. At the same time, factors such as the excitation light source, human skin color, and differences in epidermal 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, affecting the accurate measurement of blood glucose concentration. Similarly, patent document CN108542402A also has the same problem.

[0004] Products using electrochemical methods for detection have been developed for over 10 years, while Raman spectroscopy remains difficult to industrialize due to its lack of portability for real-time detection, unlike patent document US20100065441A1.

[0005] Patent document CN117503123A discloses a non-invasive blood glucose detection system and method based on multi-wavelength near-infrared spectroscopy. It employs multiple sensors and modules to simultaneously acquire various biological signals such as fingertip infrared information, facial infrared information, and forehead temperature information, fusing these signals for analysis and judgment. Its drawback lies in the need to collect too many biological signals, and the location...Blood glucose concentrations vary across different body parts, making it costly and unable to achieve portable, real-time detection. Furthermore, the excessive information leads to complex detection algorithms.

[0006] To address the shortcomings of the prior art, the purpose of this invention is to provide a method, system, medium, and device for detecting analytes.

[0007] A method for detecting an analyte according to the present invention includes:

[0008] An imaging step: irradiating a first region with single-wavelength infrared light within a first wavelength range and imaging the first region to obtain a first image of the imaging region; and irradiating the first region with ultraviolet light within a second wavelength range and imaging the first region to obtain a second image of the imaging region; Specification 1 / 13 pages 4 CN 121337332 A

[0009] A spectral acquisition step: acquiring grayscale distribution data reflecting the non-uniform distribution of the analyte in the imaging region from the first image; acquiring infrared light intensity values ​​reflecting the non-uniform distribution of the analyte in the imaging region at a desired location from the first image based on the grayscale distribution data; acquiring fluorescence spectral data reflecting the non-uniform distribution of the analyte in the imaging region at a desired location from the second image based on the grayscale distribution data;

[0010] An analysis step: obtaining information about the analyte in the imaging region based on the acquired infrared light intensity values ​​and fluorescence spectral data, wherein the information about the analyte includes information relating the analyte to the infrared light intensity values ​​and fluorescence spectral data.

[0011] Further, the spectral acquisition step includes:

[0012] Dividing the imaging area into a candidate detection point area and a candidate reference point area according to the grayscale distribution of pixels in the first image, selecting a detection point from the candidate detection point area, selecting a reference point from the candidate reference point area, acquiring the infrared light intensity values ​​of the detection point and the reference point in the first image respectively, and acquiring the spectral data of the detection point and the reference point in the second image respectively.

[0013] Further, the spectral acquisition step includes:

[0014] According to the grayscale values ​​of pixels in the second image, selecting a pixel whose grayscale value meets a preset requirement from the candidate detection point area as a detection point or a combination of the pixel and its neighboring pixels as a detection point, selecting a pixel whose grayscale value is within a preset deviation range from the selected detection point from the candidate reference point area as a reference point or a combination of the pixel and its multiple neighboring pixels as a reference point, and calculating the fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point.

[0015] Further, the second wavelength range is 300-390 nanometers, which is outside the effective response range of the imaging spectral detection device;

[0016] Light in the second wavelength range can excite the analyte to emit a fluorescence radiation signal, wherein the fluorescence radiation signal...The main peak of the fluorescence spectrum is located within the effective response range of the imaging spectral detection device.

[0017] Further, the analysis step includes inputting the acquired infrared light intensity value and the infrared light intensity value of the reference point, as well as the acquired fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point, into the trained analyte detection model, and outputting the analyte information.

[0018] A detection system for an analyte according to the present invention includes:

[0019] an imaging module: irradiating a first region with single-wavelength infrared light within a first wavelength range and imaging the first region to obtain a first image of the imaging region; and irradiating the first region with ultraviolet light within a second wavelength range and imaging the first region to obtain a second image of the imaging region;

[0020] a spectral acquisition module: acquiring grayscale distribution data reflecting the non-uniform distribution of the analyte in the imaging region from the first image; acquiring infrared light intensity values ​​reflecting the non-uniform distribution of the analyte in the imaging region at a desired location from the first image based on the grayscale distribution data; and acquiring fluorescence spectral data reflecting the non-uniform distribution of the analyte in the imaging region at a desired location from the second image based on the grayscale distribution data;

[0021] an analysis module: obtaining information about the analyte in the imaging region based on the acquired infrared light intensity values ​​and fluorescence spectral data, wherein the information about the analyte includes information relating the analyte to the spectral data.

[0022] Further, the spectrum acquisition module includes:

[0023] Dividing the imaging area into a detection point candidate area and a reference point candidate area according to the grayscale distribution of pixels in the first image, selecting a detection point from the detection point candidate area, selecting a reference point from the reference point candidate area, acquiring the infrared light intensity values ​​of the detection point and the reference point in the first image respectively, and acquiring the spectral data of the detection point and the reference point in the second image respectively. Specification 2 / 13 Page 5 CN 121337332 A

[0024] Further, the spectrum acquisition module includes:

[0025] According to the grayscale values ​​of pixels in the second image, selecting a pixel whose grayscale value meets a preset requirement from the detection point candidate area as a detection point or a combination of the pixel and adjacent pixels as a detection point, selecting a pixel whose grayscale value is within a preset deviation range from the selected detection point from the reference point candidate area as a reference point or a combination of the pixel and multiple adjacent pixels as a reference point, and calculating the fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point.

[0026] Further, the second wavelength range is 300-390 nanometers, which is outside the effective response range of the imaging spectral detection device;

[0027] Light in the second wavelength range can excite fluorescence radiation signals in the analyte, wherein the fluorescence radiation signal...The main peak of the fluorescence spectrum is located within the effective response range of the imaging spectral detection device.

[0028] Further, the analysis module includes inputting the acquired infrared light intensity value and the infrared light intensity value of the reference point, as well as the acquired fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point, into the trained analyte detection model, and outputting the analyte information.

[0029] According to the present invention, a computer-readable storage medium storing a computer program is provided, wherein when the computer program is executed by a processor, the steps of the analyte detection method are implemented.

[0030] According to the present invention, an electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of the analyte detection method are implemented.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. The present application uses single-wavelength infrared light to distinguish between the area where blood vessels are located and the area where blood vessels are located, and further enables accurate selection of detection points and reference points, making the detection results more accurate.

[0033] 2. The technical solution of this application does not require electrochemical reaction with the analyte, making the detection method more convenient, especially when detecting analytes in living organisms, achieving the purpose of non-invasive detection.

[0034] 3. This application utilizes the non-uniform distribution of the analyte in the imaging region to obtain spectral data from different regions. Since the distribution of other components besides the analyte in the imaging region is relatively uniform, the differences in spectral data from different regions can directly reflect the information related to the analyte and spectral data after basically excluding the influence of non-analytes, such as the concentration of the analyte.

[0035] 4. This application uses fluorescence spectroscopy for detection, avoiding the traditional method of measuring analytes using Raman spectroscopy, thereby achieving low cost and miniaturization of the detection system, and achieving the purpose of real-time detection.

[0036] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0037] FIG1 is a flowchart of Embodiment 1;

[0038] FIG2 is a schematic diagram of the first image acquired in Embodiment 3;

[0039] FIG3 is a schematic diagram of the second image acquired in Embodiment 3;

[0040] FIG4 is a schematic diagram of the detection model of Embodiment 3;

[0041] FIG5 is a schematic diagram of the detection point-reference point spectral data obtained in Embodiment 3;

[0042] FIG6 is the experimental results of the accuracy of the analysis results of the analysis model; Specification 3 / 13 pages 6 CN 121337332 A

[0043] FIG7 is a structural schematic diagram of an analyte detection device provided in Embodiment 6;

[0044] FIG8 is a structural schematic diagram of an electronic device provided in Embodiment 7;

[0045] Figure 9 is a structural schematic diagram of an analytical substance detection watch provided in Embodiment 5;

[0046] Figure 10 is a schematic diagram of the back of the analytical substance detection watch;

[0047] Figure 11 is an exploded view of the analytical substance detection watch;

[0048] Figure 12 is a schematic diagram of the analytical substance detection watch in use.

[0049] In the figures:

[0050] 100: Imaging area; 200: Detection device;

[0051] 201: Light source; 202: Imaging spectral detection device;

[0052] 203: Controller; 204: First bandpass filter;

[0053] 205: Lens; 206: Second bandpass filter;

[0054] 207: Circuit board; 501: Processor;

[0055] 502: Memory. Detailed Description

[0056] The present invention will be described in detail below with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but do not limit the present invention in any way. It should be noted that, for those skilled in the art, several changes and improvements can be made without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0057] Example 1

[0058] Figure 1 is a flowchart of this embodiment. This embodiment is a method for detecting an analyte, including:

[0059] Imaging step: Light within a preset wavelength range is provided by a light source to illuminate a first region, and an imaging spectral detection device is used to image the first region to obtain an image of the imaging region. By illuminating the first region with light within a preset wavelength range, the image can reflect the distribution data and spectral data of the reflection signal or excitation signal generated by the analyte under light irradiation in the imaging region. The first region can be a certain area on the surface of human skin. In order to avoid the influence of external light such as ambient light on the detection, the acquisition window of the imaging spectral detection device needs to be tightly attached to the surface of human skin in the first region, and the imaging region refers to the area within the lens range of the imaging spectral detection device. In general, the imaging region can be a part of the first region, or it can be the same region as the first region.

[0060] Since obtaining analyte distribution data and spectral data requires illumination from light sources with different wavelength ranges, there are two possible implementation methods: the light source can provide light with a larger wavelength range, or two light sources can each provide light with a smaller wavelength range. When there is only one light source, the wavelength range of the light provided by that light source needs to simultaneously cover the wavelength range capable of obtaining analyte distribution data and the wavelength range capable of obtaining analyte spectral data. When there are two light sources, the two light sources provide different wavelengths; one light source covers the wavelength range capable of obtaining analyte distribution data, and the other light source covers the wavelength range capable of obtaining analyte spectral data. Meanwhile,When there is only one light source, one image is formed; when there are two light sources, two images are formed. For ease of processing, the imaging areas of the two images must be the same, that is, the acquisition window of the imaging spectral detection device must not move on the surface of the human skin.

[0061] In this application, the analytes can be glucose, ketones, alcohols, lactate, oxygen, hemoglobin A1C, acetylcholine, amylase, bilirubin, cholesterol, human 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 drugs such as antibiotics (e.g., gentamicin, vancomycin, etc.), digitalis, digoxin, abused drugs, theophylline, or warfarin. In embodiments that detect two or more analytes, the analytes can be monitored at the same or different times. In other embodiments, the analyte can also be other substances within the body surface, enabling non-invasive detection through this invention.

[0062] Spectral acquisition step: Spectral data reflecting the uneven distribution of reflection or excitation signals generated by the analyte under light irradiation in the imaging area is acquired from the image using an imaging spectral detection device. Specifically, the imaging area can be partitioned according to different distribution data to facilitate the selection of locations from different partitions for acquiring spectral data.

[0063] Analysis step: Information about the analyte in the imaging area is obtained based on the acquired spectral data. The information about the analyte includes information related to the analyte and the spectral data. Since the distribution of the analyte differs in different partitions, the reflection or excitation signals generated by the analyte under light irradiation will also differ. Taking human skin as an example, it is divided into three parts: epidermis, dermis, and subcutaneous tissue, with veins and other blood vessels located in the subcutaneous tissue. Ultraviolet light irradiation can yield corresponding spectral data for both vascular and avascular skin areas, or for skin areas with thicker and thinner blood vessels. The difference between the two spectral data can reflect the correlation between the analyte in the blood vessels and the spectral data, such as the degree of influence of the analyte on the spectral data, for further analysis, or directly obtain information such as the concentration of the analyte through an analytical model.

[0064] Example 2

[0065] This example is based on Example 1, using single-wavelength infrared light and ultraviolet light as light sources, to provide a method for detecting an analyte, including:

[0066] Imaging step: irradiating a first region with single-wavelength infrared light within a first wavelength range and imaging the first region to obtain a first image of the imaging region; and irradiating the first region with ultraviolet light within a second wavelength range and...Imaging the first region yields a second image of the imaging region;

[0067] Spectral acquisition step: acquiring grayscale distribution data reflecting the non-uniform distribution of the analyte in the imaging region from the first image; acquiring infrared light intensity values ​​reflecting the non-uniform distribution of the analyte in the imaging region at the desired location from the first image based on the grayscale distribution data; acquiring fluorescence spectral data reflecting the non-uniform distribution of the analyte in the imaging region at the desired location from the second image based on the grayscale distribution data;

[0068] Analysis step: obtaining information about the analyte in the imaging region based on the acquired infrared light intensity values ​​and fluorescence spectral data, wherein the information about the analyte includes information relating the analyte to the infrared light intensity values ​​and fluorescence spectral data.

[0069] The spectral acquisition step includes:

[0070] dividing the imaging region into a candidate region for detection points and a candidate region for reference points based on the grayscale distribution of pixels in the first image; selecting a detection point from the candidate region for detection points and a reference point from the candidate region for reference points; acquiring the infrared light intensity values ​​of the detection point and the reference point in the first image, and acquiring the spectral data of the detection point and the reference point in the second image, respectively.

[0071] The spectral acquisition step includes:

[0072] Based on the gray value of the pixels in the second image, selecting a pixel whose gray value meets the preset requirements from the candidate detection point region as a detection point or a combination of the pixel and its adjacent pixels as a detection point; selecting a pixel whose gray value is within the preset deviation range of the selected detection point from the candidate reference point region as a reference point or a combination of the pixel and its multiple adjacent pixels as a reference point; and calculating the fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point.

[0073] The second wavelength range is 300-390 nanometers, and the second wavelength range is outside the effective response range of the imaging spectral detection device;

[0074] The light in the second wavelength range can excite the analyte to emit a fluorescence radiation signal, and the main peak of the fluorescence spectrum of the fluorescence radiation signal is within the effective response range of the imaging spectral detection device.

[0075] The analysis step includes inputting the acquired infrared light intensity value and the infrared light intensity value of the reference point, as well as the acquired fluorescence spectrum data of the detection point and the fluorescence spectrum data of the reference point, into the trained analyte detection model, and outputting the analyte information.

[0076] The imaging step includes: acquiring a first image under single-wavelength infrared light irradiation, and acquiring a second image under ultraviolet light irradiation in the wavelength range of 300-390 nanometers;

[0077] The first image includes data reflecting the grayscale distribution of the reflected signal generated by the analyte under single-wavelength infrared light irradiation in the imaging area;

[0078] The second image includes fluorescence spectral data reflecting the fluorescence radiation signal excited by ultraviolet light irradiation of the analyte in the imaging area;

[0079] The spectral acquisition step includes: dividing the imaging area into a candidate region for detection points with smaller gray values ​​and a candidate region for reference points with larger gray values ​​according to the gray distribution data; selecting a pixel with a gray value that meets a preset requirement from the first image and the second image respectively as a detection point or a combination of the pixel and its adjacent pixels as a detection point according to the candidate region for the detection point; selecting a pixel with a gray value within a preset deviation range from the gray value of the detection point from the first image and the second image respectively as a reference point or a combination of the pixel and its multiple adjacent pixels as a reference point according to the candidate region for the reference point;

[0080] Substituting the gray value of the detection point in the first image into the spectral reconstruction algorithm to obtain the infrared light intensity value of the detection point; substituting the gray value of the reference point in the first image into the spectral reconstruction algorithm to obtain the infrared light intensity value of the reference point;

[0081] Substituting the gray values ​​of the detection points in the second image into the spectral reconstruction algorithm, the fluorescence spectral data of the detection points is obtained; substituting the gray values ​​of the reference points in the second image into the spectral reconstruction algorithm, the fluorescence spectral data of the reference points is obtained;

[0082] The analysis steps include: inputting the infrared light intensity value, fluorescence spectral data of the detection points and the infrared light intensity value, fluorescence spectral data of the reference points into the trained analyte detection model, and outputting information on the association between the analyte and the infrared light intensity value, fluorescence spectral data;

[0083] The method of training the analyte detection model includes: simultaneously acquiring the infrared light intensity value, fluorescence spectral data and accurate test results of the tested object, using the infrared light intensity value and fluorescence spectral data as the input of the detection model, and using the accurate test results as the output of the detection model to train the detection model.

[0084] Example 3

[0085] This example is based on Example 1, taking glucose detection in human blood vessels as an example, and provides a non-invasive glucose detection method, including:

[0086] Imaging step: Irradiating the skin at the location of the vein, such as the wrist or back of the hand, with infrared light in the first wavelength range of 800-1000 nm, and acquiring a first image of the imaging area. The first wavelength range is preferably near-infrared. And irradiating the same location with ultraviolet light in the second wavelength range of 300-390 nm, and acquiring a second image of the imaging area.

[0087] As shown in Figure 2, 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 selected detection point pixel block on the vein, and the black box represents the selected reference point pixel block on the surrounding skin. In the first image, part of the infrared light will penetrate the human skin and part will be absorbed by the human skin. At the same time, ... (Instruction manual 6 / 13 pages 9 CN 121337332 A)In areas where veins are located, the light is also absorbed by the veins, resulting in smaller pixel gray values ​​in vein areas and larger pixel gray values ​​in non-vein areas. This allows for easy division of the imaging area into vein and non-vein areas.

[0088] As shown in Figure 3, the horizontal axis represents the horizontal coordinate of the second image, and the vertical axis represents the vertical coordinate of the second image. The white box represents the selected detection point pixel block on the vein, and the black box represents the selected reference point pixel block on the surrounding skin. In the second image, it is difficult to distinguish between vein and non-vein areas, so the first image is needed for differentiation. The excitation light in the second wavelength range of 300-390 nm is used to obtain high-quality effective fluorescence spectral signals. This is because the main response band of the imaging spectral detection device is located in the 400-800 nm range. When the wavelength of the excitation light used is less than 300 nm, the main peak of the fluorescence spectrum of the excited fluorescence radiation signal is located in the <400 nm band, making it difficult for the imaging spectral detection device to obtain high-quality effective fluorescence spectral signals. When the wavelength of the excitation light used is > 390nm, the excitation light itself is also visible light. The spectral signal of the excitation light is superimposed with the fluorescence spectral signal, making it difficult to eliminate the interference of the spectral signal of the excitation light and extract the effective fluorescence spectral signal. After absorbing ultraviolet light in the wavelength range of 300-390 nm, glucose in the vein can emit fluorescence radiation signal in the visible light band of 400-800 nm. This band is within the effective response range of the imaging spectral detection device. The characteristic spectral intensity of this fluorescence radiation signal is positively correlated with the concentration of glucose and has high fluorescence excitation efficiency.

[0089] Spectral acquisition steps: According to the gray distribution of the pixels in the first image, the imaging area is divided into the area where the vein is located and the area where the non-vein is located. Detection points are selected from the positions in the second image corresponding to the areas where the vein is located, and reference points are selected from the positions in the second image corresponding to the areas where the non-vein is located. The spectral data of the detection points and the spectral data of the reference points in the second image are obtained respectively. Specifically, based on the grayscale values ​​of pixels in the second image, a pixel with a grayscale value meeting preset requirements is selected from the region where the vein is located as a detection point, or a combination of that pixel and its neighboring pixels is selected as a detection point. A pixel with a grayscale value within a preset deviation range from the selected detection point is selected from the region where the vein is not located as a reference point, or a combination of that pixel and several neighboring pixels is selected as a reference point. The fluorescence spectrum data of the detection point and the fluorescence spectrum data of the reference point are then calculated in the second image. The spectral data is taken from a single pixel of the detection point or reference point, or an average of multiple pixels, which can be appropriately selected based on the width of the blood vessel. Averaging multiple pixels can improve the signal-to-noise ratio, but is limited by the width of the blood vessel, avoiding the area outside the blood vessel. Selecting a single pixel is not recommended.It has high resolution and is suitable for cases with thin blood vessels, but the signal-to-noise ratio is low. The preset requirement for grayscale values ​​can be to use the point with the smallest grayscale value as the detection point, but this application does not impose this limitation. The calculation results are shown in Figure 5, where the horizontal axis represents wavelength (in nm), the vertical axis represents relative radiance (in W / nm), the solid line represents the spectral data of the detection point, and the dashed line represents the spectral data of the reference point. The reason why the grayscale value of the reference point and the grayscale value of the selected detection point are within the preset deviation range is that the skin in the imaging area may have influencing factors such as skin color, spots, and cosmetics, which will directly affect the spectral data of the reference point. The first image cannot distinguish the areas with these influencing factors. By setting the preset deviation range for grayscale values, these influencing factors can be effectively eliminated. In addition, the fact that the grayscale value and the grayscale value of the selected detection point are within the preset deviation range ensures that the selection of the reference point is close to the detection point. For example, selecting it at the edge of the vein ensures that, apart from the blood vessel, the color, thickness, and other parameters of the epidermis, dermis, and subcutaneous tissue are as close as possible, so that the deviation between the spectral data of the detection point and the spectral data of the reference point minimizes the influence of non-analytes.

[0090] Besides the spectral reconstruction algorithm, spectral data can also be obtained by forming radiometric calibration coefficients through prior radiometric calibration, and then calculating the spectral line by multiplying the gray value by the radiometric calibration coefficients.

[0091] When selecting a combination of multiple pixels at the detection point, the fluorescence spectral data of that detection point can be the average of the fluorescence spectral data of these pixels. Simultaneously, the number of detection points and reference points can be one or more. When there are multiple detection points and reference points, the average of the fluorescence spectral data of all detection points and the average of the fluorescence spectral data of all reference points can be calculated separately.

[0092] Analysis steps: After preprocessing, the spectral data of the acquired detection points and reference points are input into the trained detection model, which outputs the glucose concentration or intermediate results relating glucose to the spectral data. During training, the detection model needs to simultaneously acquire the spectral data of the tested object and accurate test results, such as blood test results. The spectral data is used as the input to the detection model, and the blood test results are used as the output to train the detection model.

[0093] The detection model can employ a convolutional neural network (CNN) model, which sequentially includes an input layer, at least two convolutional layers, at least two activation function layers, a Flatten layer, a fully connected layer, and an output layer. The convolutional layers and activation function layers are spaced apart. The activation function used in the activation function layers is the ReLU function.

[0094] In this CNN model, the kernel size of each convolutional layer is 1, and the number of kernels in the first convolutional layer is...32. The second layer has 64 convolutional kernels, all used to extract blood glucose features, and the output of the convolutional layer is nonlinearly transformed by the activation function. The flatten layer flattens the output of the convolutional layer into a one-dimensional vector, which is convenient for connecting the subsequent fully connected layers. The final output dimension is 1. The Adam optimizer is used to train the model during the model training process, 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 model evaluation.

[0095] When the output result of the detection model is glucose concentration, if the error between the output result and the measured standard glucose concentration value meets the preset condition, the training is stopped and the detection model is obtained. When the output result of the detection model is an intermediate result of glucose and spectral data association, such as the result of the intermediate neuron, if the error between the output result and the result of the intermediate neuron meets the preset condition, the training is stopped and the detection model is obtained. Further model correction processing is performed on the result of the intermediate neuron to obtain the glucose concentration.

[0096] As shown in Figure 4, the input layer is the spectral data input layer, which is obtained by preprocessing the two infrared IR feature brightness values ​​and the original spectral data. The hidden layer is an intermediate hidden layer. Through deep learning with convolution operations, it performs feature combination and outputs the final predicted blood glucose concentration value. Alternatively, the original spectral data can be preprocessed and used as the input layer. The hidden layer is an intermediate hidden layer. Through deep learning with convolution operations, it performs feature combination and outputs a neuron as Output1 as an intermediate result value. The intermediate result value Output1 and two infrared (IR) feature brightness values ​​are used to train the model again to further correct the blood glucose prediction error and output the final predicted blood glucose concentration value Output2. The training degree of the detection model needs to be set with different parameters as needed. The extracted glucose feature values ​​will continuously learn according to the different parameter settings until the error between the output result and the standard glucose value of the above label value meets the requirements. Then, the training stops and the detection model is obtained.

[0097] Through multiple iterations of training, the neurons learn the corresponding change rules between different glucose concentrations and glucose spectral features of different samplers, thereby improving the universality of the detection model and enabling it to predict the glucose concentration of different users.

[0098] The entire glucose detection process does not require puncturing the skin to collect blood or implant a needle. It acquires the spectral information of the subject based on fluorescence spectroscopy and obtains the glucose detection result based on this spectral information, avoiding pain and discomfort and improving the comfort and convenience of the test. This method can precisely distinguish the spectral signals of blood vessels and skin, making it possible to accurately extract the glucose signal in the future. At the same time, it also makes the spectral signal strongly correlated with the glucose concentration, realizing accurate measurement of glucose concentration, resulting in more accurate detection results and more convenient processing.

[0099] Figure 6 shows a schematic diagram of the experimental effect of the trained detection model. The horizontal axis is the reference blood glucose concentration (unit: mmol / L) collected by the blood glucose meter, and the vertical axis is the blood glucose concentration (unit: mmol / L) predicted by the method of this patent. The total sample size of the test subjects was 2037, of which 1537 were training set samples and 500 were prediction set samples. As can be seen from the figure, the distribution of detection results of the detection model of the specification 8 / 13 page 11 CN 121337332 A is shown. The MARD value of the predicted samples is 11.32%. The vast majority of samples fall in area A and area B. Among them, the proportion of samples falling in area A is 87.03% and the proportion of samples falling in area B is 12.77%, indicating that the detection accuracy of the detection model is high.

[0100] Example 4

[0101] This example is based on Example 3, replacing infrared light with visible light, and providing another non-invasive glucose detection method, including:

[0102] Imaging step: Irradiate the skin at the location of the vein, such as the wrist or back of the hand, with visible light, and acquire the first image of the imaging area. And a second image of the imaging area is acquired by irradiating the same location with ultraviolet light in the second wavelength range of 300-390 nm.

[0103] In the first image, since the color of the area where the vein is located differs from that of the area where the non-vein is located, the imaging area can be easily divided into the area where the vein is located and the area where the non-vein is located.

[0104] In the second image, since it is difficult to distinguish between the area where the vein is located and the area where the non-vein is located, the first image is needed for differentiation. The excitation light in the second wavelength range of 300-390 nm is used to obtain a high-quality effective fluorescence spectrum signal. This is because the main response band of the imaging spectral detection device is located in the 400-800 nm range. When the wavelength of the excitation light used is less than 300 nm, the main peak of the fluorescence spectrum of the excited fluorescence radiation signal is located in the <400 nm band, and the imaging spectral detection device has difficulty obtaining a high-quality effective fluorescence spectrum signal. When the wavelength of the excitation light used is > 390 nm, the excitation light itself is also visible light. The spectral signal of the excitation light is superimposed with the fluorescence spectral signal, making it difficult to eliminate the interference of the spectral signal of the excitation light and extract the effective fluorescence spectral signal. After absorbing ultraviolet light in the wavelength range of 300-390 nm, glucose in the vein can emit fluorescence radiation signals in the visible light band of 400-800 nm. This band is within the effective response range of the imaging spectral detection device. The characteristic spectral intensity of this fluorescence radiation signal is positively correlated with the concentration of glucose and has high fluorescence excitation efficiency.

[0105] Spectral acquisition steps: According to the gray distribution of the pixels in the first image, the imaging area is divided into the area where the vein is located and the area where the non-vein is located. Detection points are selected from the second image corresponding to the area where the vein is located, and from the non-vein area...Reference points are selected in the second image corresponding to the area where the vein is located, and the spectral data of the detection point and the spectral data of the reference point are obtained respectively. Specifically, based on the gray values ​​of the pixels in the second image, a pixel with a gray value that meets the preset requirements or a combination of the pixel and its neighboring pixels is selected from the area where the vein is located as a detection point. A pixel with a gray value within a preset deviation range from the selected detection point or a combination of the pixel and its multiple neighboring pixels is selected from the area where the vein is located as a reference point, and the fluorescence spectral data of the detection point and the fluorescence spectral data of the reference point are calculated. The reason why the gray value of the reference point is within the preset deviation range from the gray value of the selected detection point is that the skin in the imaging area may have influencing factors such as skin color, spots, and cosmetics, which will directly affect the spectral data of the reference point. The first image cannot distinguish the areas of all influencing factors at the same time. By setting the preset deviation range of the gray value, these influencing factors can be effectively eliminated.

[0106] When a combination of multiple pixels is selected as a detection point, the fluorescence spectral data of the detection point can be the average value of the fluorescence spectral data of these pixels. Meanwhile, the number of detection points and reference points can be one or more. When there are multiple detection points and reference points, the average value of the fluorescence spectral data of all detection points and the average value of the fluorescence spectral data of all reference points can be calculated separately.

[0107] Analysis steps: After preprocessing, the spectral data of the acquired detection points and reference points are input into the trained detection model, and the glucose concentration is output. When the detection model is trained, it is necessary to acquire the spectral data of the test object and the accurate test results, such as the blood test results, at the same time. The spectral data is used as the input of the detection model, and the blood test results are used as the output of the detection model. The detection model is trained.

[0108] The detection model can adopt a convolutional neural network model, which includes an input layer, at least two convolutional layers, at least two activation function layers, a Flatten layer, a fully connected layer and an output layer in sequence. The convolutional layers and the activation function layers are distributed alternately. The activation function used by the activation function layer is the ReLU function.

[0109] In the convolutional neural network model, each convolutional kernel has a size of 1. The first convolutional layer has 32 kernels, and the second convolutional layer has 64 kernels, both used to extract blood glucose features. The output of the convolutional layer is non-linearly transformed through an activation function. The Flatten layer flattens the output of the convolutional layer into a one-dimensional vector, facilitating connection to subsequent fully connected layers, resulting in a final output dimension of 1. During model training, the Adam optimizer is used for model training, and the mean squared error is used as the loss function. The mean absolute error is also calculated as a performance metric for model evaluation.

[0110] If the error between the output of the detection model and the standard glucose value meets the preset condition, training is stopped and the detection model is obtained.

[0111] The training degree of the detection model needs to be set with different parameters as needed. The extracted glucose feature values ​​will continuously learn according to the setting of different parameters until the error between the output result and the standard glucose value of the above label value meets the requirements, then training is stopped and the detection model is obtained.

[0112] Through multiple iterations of training, the neurons learn the corresponding change rules between different glucose concentrations and glucose spectral features of different samplers, thereby improving the universality of the detection model and enabling the prediction of glucose concentrations of different users.

[0113] The entire glucose detection process does not require blood collection or skin puncture. It obtains the spectral information of the test subject based on fluorescence spectroscopy and obtains the glucose detection result of the test subject based on the spectral information, avoiding pain and discomfort and improving the comfort and convenience of the detection. This method can finely distinguish the spectral signals of blood vessels and skin, providing the possibility for accurate extraction of subsequent glucose signals. At the same time, it also makes the spectral signal strongly correlated with the glucose concentration, realizing accurate measurement of glucose concentration, making the detection results more accurate and the processing more convenient.

[0114] Example 5

[0115] This example provides an analyte detection system. The analyte detection system can be implemented by executing the steps of the analyte detection method. That is, 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:

[0116] An imaging module: a light source provides light within a preset wavelength range to illuminate a first region, and an imaging spectral detection device images the first region to obtain an image of the imaging region. By illuminating the first region with light within a preset wavelength range, the image can reflect the distribution data and spectral data of the reflected signal or excitation signal generated by the analyte under light illumination in the imaging region. Since different wavelength ranges are required to obtain the distribution data and spectral data of the analyte, the light can be two corresponding wavelength ranges, or it can be a light with a larger wavelength range that covers both required wavelength ranges. When there are two types of light, two images are obtained. For ease of processing, it is usually required that the imaging areas of the two images are the same.

[0117] In this application, the analyte may be glucose, ketones, alcohols, lactate, oxygen, hemoglobin A1C, acetylcholine, amylase, bilirubin, cholesterol, human 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 antibiotics (e.g., gentamicin, vancomycin, etc.) and digitalis from animal blood vessels.Toxins, digoxin, abused drugs, theophylline, and warfarin. In embodiments that detect more than one analyte, the analytes may be monitored at the same or different times. In other embodiments, the analyte may also be other substances in the liquid.

[0118] Spectrum acquisition module: Acquires spectral data from the image through an imaging spectral detection device, reflecting the uneven distribution of the reflection or excitation signals generated by the analyte under light irradiation in the imaging area. Specifically, the imaging area can be partitioned according to different distribution data to facilitate the selection of the location for acquiring spectral data from different partitions.

[0119] Analysis module: Obtains information about the analyte in the imaging area based on the acquired spectral data. The information about the analyte includes information related to the analyte and the spectral data. Since the distribution of the analyte is different in different partitions, the reflection or excitation signals generated by the analyte under light irradiation will also be different. Utilizing this characteristic, the difference in spectral data between the two can be obtained, thereby accurately reflecting the information related to the analyte and the spectral data, such as the concentration of the analyte.

[0120] Those skilled in the art know that, in addition to implementing the system and its various devices, modules, and units provided by the present invention in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers, the same functions can be achieved by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; the devices, modules, and units for implementing various functions can also be considered as both software modules for implementing the method and structures within the hardware component.

[0121] Embodiment 6

[0122] Figure 7 shows 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. It can be a standalone detection device or integrated into a watch or mobile phone, thereby enabling convenient and quick detection of analytes on the body surface.

[0123] The detection device 200 includes: a light source 201, an imaging spectral detection device 202, a controller 203, a first bandpass filter 204, a second bandpass filter 206, and a lens 205. The controller 203 establishes an electrical connection or a communication connection with the light source 201 and the imaging spectral detection device 202, respectively.

[0124] The light source 201 is capable of providing light within a preset wavelength range. Because it is necessary to acquire data on the distribution of the analyte and...Spectral data requires illumination from light of different wavelength ranges, so there are two ways to achieve this: one light source that can provide light with a wide wavelength range; or two light sources that each provide light with a narrow wavelength range. When there is only one light source, the wavelength range of the light provided by the light source needs to cover both the wavelength range for obtaining analyte distribution data and the wavelength range for obtaining analyte spectral data, such as a halogen lamp. When there are two light sources, the two light sources provide different light, one light whose wavelength covers the wavelength range for obtaining analyte distribution data and the other light whose wavelength covers the wavelength range for obtaining analyte spectral data, such as an infrared lamp combined with an ultraviolet lamp, or a visible light lamp combined with an ultraviolet lamp.

[0125] To ensure uniform illumination of the imaging area 100, a ring light source can be used, which has multiple light-emitting modules evenly distributed on the same circumference. When there are two light sources, the light-emitting modules of the two light sources are arranged alternately.

[0126] The imaging spectral detection device 202 can image the imaging area 100 according to instructions to obtain the corresponding image, and can obtain the corresponding spectral data according to instructions. The imaging spectral detection device 202 includes a sensor and a periodic pixel-level filter structure disposed on the surface of the sensor. The periodic pixel-level filter structure is used to spectrally modulate the incoming light signal so that the sensor can generate an image containing the spectral information to be measured.

[0127] The periodic pixel-level filter structure includes multiple filter pixel channels with different shapes. The multiple filter pixel channels have the same size and are uniformly arranged, and their length and width are integer multiples of the pixel size in the image sensor. The filter pixel channels of different shapes correspond to different spectral filtering coefficients. The pixel-level filter structures with different spectral filtering coefficients are periodically arranged after being combined in a fixed order. The sensor modulates the received first detection light through the periodic pixel-level filter structure disposed on its surface to form a mosaic image containing spectral information. Then, an algorithm is used to reconstruct a grayscale image containing the spectral information to be measured.

[0128] The controller 203 is configured to control the light source to provide light within a preset wavelength range to illuminate the first region, and to control the imaging spectral detection device to image the first region to obtain an image of the imaging region. The controller also controls the imaging spectral detection device to acquire spectral data from the image reflecting the uneven distribution of reflection or excitation signals generated by the analyte under light illumination within the imaging region. Information about the analyte in the imaging region is obtained based on the acquired spectral data. This information includes information related to the analyte and the spectral data. When there is only one type of light source 201, one image is captured; when there are two types of light sources 201, two images are captured. When the first type of light source is on, the second type of light source is off; similarly, when the second type of light source is on, the first type of light source is off, and the two do not interfere with each other.

[0129] The first bandpass filter 204 is located between the light source 201 and the imaging area 100. Its function is to allow light within a preset wavelength range to pass through, while blocking light outside the preset wavelength range, thereby reducing the influence of other external light on the detection results.

[0130] The second bandpass filter 206 is located between the imaging spectral detection device 202 and the lens 205. Its function is to allow light within the wavelength range of the reflected signal or excitation signal generated by the analyte when it is irradiated to pass through, while blocking light in other wavelength ranges, thereby reducing the influence of the reflected signal or excitation signal of non-analytes on the detection results.

[0131] The lens 205 can be used for focusing in order to obtain a clear image. In other embodiments, the second bandpass filter 206 can also be located on the side of the lens 205 away from the imaging spectral detection device 202, and the present invention does not limit this.

[0132] According to the above description, FIG9 shows an analyte detection watch provided in this embodiment. The front of the watch is a display, as shown in FIG10, and the back of the watch has a light-transmitting window and a built-in detection device 200. As shown in Figure 11, both the light source 201 and the first bandpass filter 204 are ring-shaped structures. The light-emitting modules of the light source 201 are arranged in a ring. The emitted light is filtered by the first bandpass filter 204 and outputs light with the required wavelength, which shines onto the human body through the light-transmitting window on the back of the watch. The reflected or excitation signal from the human body enters the light-transmitting window, passes through the hollowed-out part in the middle of the light source 201 and the first bandpass filter 204, passes through the lens 205, and enters the second bandpass filter 206. After being filtered by the second bandpass filter 206, it enters the imaging spectral detection device 202. The imaging spectral detection device 202 is mounted on the circuit board 207. At the same time, the controller 203 (not shown in the figure) of the detection device 200 is also mounted on the circuit board 207. As shown in Figure 12, in order to more accurately identify the location of veins, the watch can be worn on the inside of the wrist.

[0133] Example 7

[0134] FIG8 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. As shown in FIG8, it includes at least one processor 501; and a memory 502 communicatively connected to at least one processor 501; wherein, the memory 502 stores instructions executable by at least one processor 501, and the instructions are executed by at least one processor 501 to enable at least one processor 501 to perform the above-described method for detecting analytes.

[0135] Wherein, the memory 502 and the processor 501 are connected by a bus. The bus may include any number of interconnected buses and bridges. The bus connects various circuits of one or more processors 501 and the memory 502 together. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits together.As is well known in the art, the present invention will not describe it further. A bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 501 is transmitted via an antenna on pages 12 / 13 of the wireless medium specification, 15 CN 121337332 A. Furthermore, the antenna also receives data and transmits it to processor 501.

[0136] Processor 501 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management, and other control functions. Memory 502 can be used to store data used by processor 501 during operation.

[0137] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for detecting analytes.

[0138] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. The program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes: USB flash drive, mobile hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and other media that can store program code.

[0139] Those skilled in the art will understand that the above embodiments are specific embodiments of the present invention. In practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of the present invention.

[0140] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above. Those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the substantive content of the present invention. In the absence of conflict, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Instruction manual, page 13 / 13, 16 CN 121337332 A, Figure 1, Figure 2; Instruction manual drawing, page 1 / 6, 17 CN 121337332 A, Figure 3, Figure 4; Instruction manual drawing, page 2 / 6, 18 CN 121337332 A, Figure 5, Figure 6; Instruction manual drawing, page 3 / 6, 19 CN 121337332 A, Figure 7, Figure 8; Instruction manual drawing, page 4 / 6, 20 CN 121337332 A, Figure 9.Figure 10, Sheet 5 / 6 of the Drawings, CN 121337332 A, Figure 11, Figure 12, Sheet 6 / 6 of the Drawings, CN 121337332 A Abstract Abdominal ultrasound examination method, system and device Abstract The present invention provides a method and a system for detecting an analyte, a medium, and a device. The method comprises: an imaging step of irradiating a first area with single- wavelength infrared light within a first wavelength range and imaging the first area to obtain a first image of an imaging area; and irradiating the first area with ultraviolet light within a second wavelength range and imaging the first area to obtain a second image of the imaging area; a spectral acquisition step of obtaining, from the first image, grayscale distribution data reflecting the analyte; and, based on the grayscale distribution data, respectively obtaining, from the first image and the second image, infrared light intensity values and fluorescence spectral data reflecting the analyte at required positions; an analysis step of obtaininginformation of the analyte in the imaging area based on the infrared light intensity values and the fluorescence spectral data. In the present application, differentiation between a region where blood vessels are located and a region where blood vessels are not located is achieved by single-wavelength infrared light, thereby further enabling accurate selection of a detection point and a reference point and making detection results more accurate.

Claims

1. A method of detecting an analyte, characterized by, The method comprises: an imaging step of irradiating a first region with monochromatic infrared light in a first wavelength range and imaging the first region to obtain a first image of the imaged region; and irradiating the first region with ultraviolet light in a second wavelength range and imaging the first region to obtain a second image of the imaged region; a spectrum acquisition step of acquiring, from the first image, data reflecting the uneven distribution of the analyte in the imaged region; acquiring, from the first image, an infrared intensity value reflecting the uneven distribution of the analyte in the imaged region at a desired position according to the data reflecting the uneven distribution of the analyte; and acquiring, from the second image, fluorescence spectrum data reflecting the uneven distribution of the analyte in the imaged region at the desired position according to the data reflecting the uneven distribution of the analyte; an analysis step of obtaining information of the analyte in the imaged region according to the acquired infrared intensity value and fluorescence spectrum data, wherein the information of the analyte comprises information associated with the infrared intensity value and the fluorescence spectrum data.

2. The method of claim 1, wherein The spectrum acquisition step comprises: dividing the imaged region into a detection point candidate region and a reference point candidate region according to the gray scale distribution of the pixel points in the first image, selecting a detection point from the corresponding position of the detection point candidate region in the second image, selecting a reference point from the corresponding position of the reference point candidate region in the second image, respectively acquiring the infrared intensity value of the detection point and the reference point in the first image, and respectively acquiring the spectrum data of the detection point and the reference point in the second image.

3. The method of claim 2, wherein the analyte is a protein. The spectrum acquisition step comprises: selecting a pixel point with a gray scale value meeting a preset requirement from the detection point candidate region as a detection point or a combination of the pixel point and adjacent pixel points as a detection point, selecting a pixel point with a gray scale value within a preset deviation range from the selected detection point as a reference point or a combination of the pixel point and multiple adjacent pixel points as a reference point according to the gray scale value of the pixel points in the second image, and calculating the fluorescence spectrum data of the detection point and the fluorescence spectrum data of the reference point.

4. The method of claim 3, wherein the analyte is a protein. The second wavelength range is 300-390 nm, and the second wavelength range is outside the effective response range of the imaging spectrum detection device. The light in the second wavelength range can excite the analyte to emit a fluorescence radiation signal, and the main peak of the fluorescence spectrum of the fluorescence radiation signal is within the effective response range of the imaging spectrum detection device.

5. The method for detecting the analyte according to claim 3, characterized in that, The analysis step comprises inputting the acquired infrared intensity value and the infrared intensity value of the reference point, and the acquired fluorescence spectrum data of the detection point and the fluorescence spectrum data of the reference point into a trained analyte detection model, and outputting the information of the analyte.

6. A system for detecting an analyte, characterized by The method comprises: an imaging module for irradiating a first region with monochromatic infrared light in a first wavelength range and imaging the first region to obtain a first image of the imaged region; and irradiating the first region with ultraviolet light in a second wavelength range and imaging the first region to obtain a second image of the imaged region; a spectrum acquisition module for acquiring, from the first image, data reflecting the uneven distribution of the analyte in the imaged region; acquiring, from the first image, an infrared intensity value reflecting the uneven distribution of the analyte in the imaged region at a desired position according to the data reflecting the uneven distribution of the analyte; and acquiring, from the second image, fluorescence spectrum data reflecting the uneven distribution of the analyte in the imaged region at the desired position according to the data reflecting the uneven distribution of the analyte; The spectrum acquisition module: acquires data reflecting non-uniform distribution of the analyte in the imaging area from the gray scale distribution of the first image; acquires infrared intensity values reflecting non-uniform distribution of the analyte in the imaging area from the first image at the required position according to the gray scale distribution data; acquires fluorescence spectrum data reflecting non-uniform distribution of the analyte in the imaging area from the second image at the required position according to the gray scale distribution data; The analysis module: obtains information of the analyte in the imaging area according to the acquired infrared intensity values and fluorescence spectrum data, and the information of the analyte includes information associated with the spectrum data.

7. The system for detecting an analyte according to claim 6, wherein The spectrum acquisition module comprises: According to the gray scale distribution of the pixel points in the first image, the imaging area is divided into a detection point candidate area and a reference point candidate area, a detection point is selected from the corresponding position of the second image in the detection point candidate area, a reference point is selected from the corresponding position of the second image in the reference point candidate area, infrared intensity values of the detection point and the reference point in the first image are acquired respectively, and spectrum data of the detection point and the reference point in the second image are acquired respectively.

8. The system for detecting an analyte according to claim 7, wherein The spectrum acquisition module comprises: According to the gray scale value of the pixel points in the second image, a pixel point with a gray scale value meeting a preset requirement is selected from the detection point candidate area as a detection point or a combination of the pixel point and adjacent pixel points as a detection point, a pixel point with a gray scale value within a preset deviation range from the gray scale value of the selected detection point is selected from the reference point candidate area as a reference point or a combination of the pixel point and multiple adjacent pixel points as a reference point, and fluorescence spectrum data of the detection point and fluorescence spectrum data of the reference point are calculated.

9. The system for detection of an analyte according to claim 8, wherein, The second wavelength range is 300-390 nanometers, and the second wavelength range is located outside the effective response range of the imaging spectrum detection device; The light in the second wavelength range can excite the analyte to emit a fluorescence radiation signal, and a main peak of a fluorescence spectrum of the fluorescence radiation signal is located within the effective response range of the imaging spectrum detection device.

10. The analyte detection system of claim 8, wherein, The analysis module comprises inputting the acquired infrared intensity values of the detection point and the reference point, and the acquired fluorescence spectrum data of the detection point and the fluorescence spectrum data of the reference point into a trained analyte detection model, and outputting information of the analyte.

11. A computer readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the analyte detection method in any one of claims 1 to 5.

12. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The computer program is executed by the processor to realize the steps of the analyte detection method in any one of claims 1 to 5.

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