Sampling method, device, electronic device and storage medium for antigen detection

Abstract

The application belongs to the field of detection, and particularly relates to a sampling method and device for antigen detection, an electronic device and a storage medium. The method comprises the following steps: obtaining a face image in a recognition area; determining a nasal cavity area according to the face image; when a nasal swab is monitored in the recognition area, identifying whether a current sampling meets a preset effective sampling condition; if yes, determining that the current sampling is effective; wherein the effective sampling condition comprises that a swab head of the nasal swab moves in a first motion track in a target depth of the nasal cavity area for a preset time length. That is, according to the face image, the nasal cavity area can be determined, and when the nasal swab is monitored in the recognition area, whether the current sampling meets the preset effective sampling condition is identified, so that the user is reminded to sample with a standard action, thereby improving the accuracy of the sampling result.

Description

Technical Field

[0001] This application belongs to the field of testing, and in particular relates to a sampling method, apparatus, electronic device and storage medium for antigen detection. Background Technology

[0002] There are two methods for nucleic acid testing: one is polymerase chain reaction (PCR), which is known as the gold standard for COVID-19 testing, and the other is COVID-19 antigen testing based on immunology. Compared with PCR, antigen testing does not require more professional techniques and equipment. Users can complete the antigen self-test using only the components provided in the test kit.

[0003] However, when performing antigen self-testing, inaccurate sampling results often occur due to improper sampling procedures, making it difficult to guarantee the accuracy of the test results. Summary of the Invention

[0004] This application provides a sampling method, apparatus, electronic device, and storage medium for antigen detection, which can improve the accuracy of sampling results.

[0005] In a first aspect, embodiments of this application provide a sampling method for antigen detection, comprising:

[0006] Acquire the face image in the recognition area;

[0007] Based on the facial image, the nasal cavity region is determined;

[0008] When a nasal swab is detected in the identification area, it is determined whether the current sampling meets the preset valid sampling conditions;

[0009] If the conditions are met, then the current round of sampling is considered valid.

[0010] The effective sampling conditions include: the swab head of the nasal swab moves along a first trajectory for a preset duration within the target depth of the nasal cavity region.

[0011] The step of determining the nasal cavity region based on the facial image includes:

[0012] Based on the facial image, determine the nasal cavity point cloud data;

[0013] The nasal cavity region is determined based on the nasal cavity point cloud data.

[0014] The step of determining the nasal cavity region based on the nasal cavity point cloud data includes:

[0015] Based on the nasal cavity point cloud data, a nasal cavity model is constructed;

[0016] The nasal cavity region is determined based on the nasal cavity model.

[0017] The step of identifying whether the current sampling meets the preset valid sampling conditions includes:

[0018] Identify whether the swab tip of the nasal swab has moved into the nasal cavity area;

[0019] If the swab moves into the nasal cavity area, it is determined whether the swab tip of the nasal swab is within the target depth of the nasal cavity area;

[0020] If within the target depth, then identify whether the movement trajectory of the nasal swab head within the target depth is the first movement trajectory;

[0021] If it is the first motion trajectory, then identify whether the duration of the nasal swab moving along the first motion trajectory is not less than the preset duration;

[0022] If the sampling duration is not less than the preset duration, then the current sampling round is determined to meet the preset valid sampling conditions.

[0023] The step of identifying whether the swab head of the nasal swab has moved into the nasal cavity area includes:

[0024] Determine the second motion trajectory of the nasal swab within the recognition area;

[0025] If the second motion trajectory matches the preset sampling trajectory, it is determined that the swab head of the nasal swab has moved into the nasal cavity area.

[0026] Wherein, identifying whether the swab head of the nasal swab is within the target depth of the nasal cavity region includes:

[0027] Determine the length of the nasal swab within the nasal cavity region;

[0028] If the length of the nasal swab within the nasal cavity area is within a preset range, then the swab tip is determined to be within the target depth within the nasal cavity area.

[0029] Wherein, identifying whether the movement trajectory of the swab head within the target depth is the first movement trajectory includes:

[0030] Determine the number of times the swab head of the nasal swab rotates or reciprocates within the target depth;

[0031] If the number of rotations is greater than the preset number of rotations, or the number of reciprocating movements is greater than the preset number of movements, then the movement trajectory of the nasal swab head within the target depth is determined to be the first movement trajectory.

[0032] Secondly, embodiments of this application provide a sampling device for antigen detection, comprising:

[0033] The acquisition module is used to acquire face images within the recognition area;

[0034] The determination module is used to determine the nasal cavity region based on the facial image;

[0035] The identification module is used to identify whether the current sampling meets the preset valid sampling conditions when a nasal swab is detected in the identification area.

[0036] The sampling module is used to determine the validity of the current sampling if the conditions are met.

[0037] The effective sampling conditions include: the swab head of the nasal swab moves along a first trajectory for a preset duration within the target depth of the nasal cavity region.

[0038] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a sampling method for antigen detection as described in any of the first aspects.

[0039] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, characterized in that, when executed by a processor, the computer program implements the sampling method for antigen detection as described in any of the first aspects.

[0040] The beneficial effects of this application's embodiments compared to existing technologies are as follows: The technical solution of this application acquires a facial image in the recognition area; determines the nasal cavity region based on the facial image; when a nasal swab is detected in the recognition area, it identifies whether the current sampling meets preset valid sampling conditions; if so, it determines that the current sampling is valid; wherein, the valid sampling conditions include: the swab head of the nasal swab moves within the target depth of the nasal cavity region along a first motion trajectory for a preset duration. That is, this application's embodiments can determine the nasal cavity region based on a facial image; when a nasal swab is detected in the recognition area, it identifies whether the current sampling meets preset valid sampling conditions, reminding the user to perform sampling with standard actions, thereby improving the accuracy of the sampling results. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a schematic diagram illustrating an application scenario of a sampling method for antigen detection provided in an embodiment of this application;

[0043] Figure 2 This is a schematic flowchart of a sampling method for antigen detection provided in an embodiment of this application;

[0044] Figure 3a This is a schematic flowchart illustrating a method for determining the nasal cavity region provided in an embodiment of this application;

[0045] Figure 3b This is an example diagram of a face midline and a nose tip transverse outline provided in an embodiment of this application;

[0046] Figure 4 This is a schematic flowchart illustrating a specific method of S302 provided in an embodiment of this application;

[0047] Figure 5a This is a schematic flowchart illustrating a method for identifying whether the current round of sampling meets preset valid sampling conditions, provided in an embodiment of this application.

[0048] Figure 5b This is an example diagram illustrating how to determine the length of a nasal swab within the nasal cavity region, as provided in an embodiment of this application.

[0049] Figure 5c This is an example diagram illustrating another method for determining the length of a nasal swab within the nasal cavity region, as provided in an embodiment of this application.

[0050] Figure 6 This is a schematic flowchart illustrating a specific method of S501 provided in an embodiment of this application;

[0051] Figure 7a This is a schematic flowchart illustrating a specific method of S503 provided in an embodiment of this application;

[0052] Figure 7b This is an example diagram illustrating how to determine the number of reciprocating movements of a nasal swab head within a target depth, as provided in an embodiment of this application.

[0053] Figure 8 This is a schematic diagram of the structure of a sampling device for antigen detection provided in an embodiment of this application;

[0054] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0055] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail. In other instances, specific technical details in various embodiments can be referred to mutually, and specific systems not described in one embodiment can be referred to in other embodiments.

[0056] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0057] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0058] References to "embodiments of this application" or "some embodiments" in this specification mean that one or more embodiments of this application include specific features, structures, or characteristics described in connection with that embodiment. Therefore, phrases such as "in other embodiments," "an embodiment of this application," and "other embodiments of this application" appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0059] Furthermore, in the description of this application and the appended claims, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0060] There are two methods for nucleic acid testing: one is polymerase chain reaction (PCR), which is known as the gold standard for COVID-19 testing, and the other is COVID-19 antigen testing based on immunology. Compared with PCR, antigen testing does not require more professional techniques and equipment. Users can complete the antigen self-test using only the components provided in the test kit.

[0061] However, when performing antigen self-testing, inaccurate sampling results often occur due to improper sampling procedures, making it difficult to guarantee the accuracy of the test results.

[0062] To address the aforementioned deficiencies, the inventive concept of this application is as follows:

[0063] This application embodiment can determine the nasal cavity region based on a facial image; when a nasal swab is detected in the recognition region, it identifies whether the current sampling meets the preset valid sampling conditions and reminds the user to perform sampling with standard actions, thereby improving the accuracy of the sampling results.

[0064] To illustrate the technical solution of this application, specific embodiments are described below.

[0065] Please refer to Figure 1 , Figure 1 This is a schematic diagram illustrating an application scenario of an antigen detection sampling method according to an embodiment of this application. For ease of explanation, only the parts relevant to this application are shown. The application scenario includes an electronic device 10. The electronic device 10 includes a camera assembly 101, a display 102, and a processor 103.

[0066] The camera assembly 101 is an input / output device of the electronic device 10. The camera assembly 101 is equipped with an image sensor, which converts user facial image information into electrical signals. The image sensor can be a pixel array composed of a charge-coupled device (CCD), a complementary metal-oxide-semiconductor transistor (CMOS), an avalanche diode (AD), a single-photon avalanche diode (SPAD), etc. The array size represents the resolution of the depth camera. The pixels of the image sensor 121 can also be in the form of single dots, linear arrays, etc. This application embodiment does not limit the type of image sensor.

[0067] Display 102 is another input / output device of electronic device 10. A display is a tool that displays facial image information on a screen via a transmission device and then reflects it to the human eye. The display can be a cathode ray tube display, a plasma display, a liquid crystal display, etc. The embodiments of this application do not limit the type of display.

[0068] The display 102 and the camera assembly 101 are connected via an A / D converter, which converts analog signals such as images and sounds into electrical signals.

[0069] The processor 103 is electrically connected to both the imaging component 101 and the display 102. The processor 103 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. When the processor 103 executes a computer program, it can acquire a face image in the recognition area; determine the nasal cavity region based on the face image; when a nasal swab is detected in the recognition area, it identifies whether the current sampling meets preset valid sampling conditions; if so, the current sampling is deemed valid.

[0070] This application does not limit the specific structure of the electronic device 10; the electronic device 10 may include components such as... Figure 1 The examples shown have more or fewer parts, or combine certain parts, or have different parts. Figure 1 This is merely an illustrative description and should not be construed as a specific limitation of this application. For example, it may also include network access devices and RGB (Red, Green, Blue) sensors, whereby the RGB sensor can be used to acquire RGB images from the user, and the processor fuses the RGB image with the low-resolution image obtained by the imaging component to obtain a higher-resolution image.

[0071] Please refer to Figure 2 , Figure 2 This is a schematic flowchart of a sampling method for antigen detection provided in an embodiment of this application. Figure 2 The method in the example can be executed by an electronic device. For example... Figure 2 As shown, the method includes: S201 to S204.

[0072] S201. The electronic device acquires a face image in the recognition area.

[0073] Specifically, the recognition area can be the entire screen of the display or a portion of the screen, such as configuring a facial outline area on the screen.

[0074] When a face is aligned with the recognition area, the camera component captures a facial image, and the electronic device can then obtain the facial image within the recognition area.

[0075] S202. The electronic device determines the nasal cavity region based on the facial image.

[0076] The embodiments of this application are intended to detect whether the sampling action of the nasal swab in the nasal cavity is standard when the user performs antigen testing, and therefore it is necessary to determine the nasal cavity area.

[0077] Please refer to Figure 3a , Figure 3a This is a schematic flowchart illustrating a method for determining the nasal cavity region provided in an embodiment of this application. Figure 3a The method in the example can be executed by an electronic device. For example... Figure 3a As shown, the method includes: S301 to S302.

[0078] S301. The electronic device determines the nasal cavity point cloud data based on the face image.

[0079] Specifically, when acquiring a face image in the recognition area, the electronic device can extract face point cloud data using the FaceMesh 3D surface extraction model. In other embodiments, face point cloud data can also be acquired using binocular stereo vision technology. This application does not limit the method for acquiring face point cloud data.

[0080] In this embodiment of the application, when determining the nasal cavity point cloud data, the midline of the face and the transverse outline of the nose tip can be determined based on the face point cloud data.

[0081] Specifically, the midline outline can represent the contours of the most prominent facial features, including feature point cloud data of the forehead, nasal cavity, mouth, and chin, and is the region where facial features converge. The transverse outline of the nasal tip is used to represent the curvature information of important feature points such as the alar of the nose in the nasal cavity.

[0082] In this embodiment of the application, when determining the midline of the face and the horizontal outline of the tip of the nose, it is first necessary to establish a three-dimensional coordinate system. The three-dimensional coordinate system is the face width direction (positive direction horizontal to the right), the Y-axis is the face length direction (positive direction vertical upward), and the Z-axis is the face depth direction (positive direction perpendicular to the XY plane outward).

[0083] Secondly, the nose tip point is determined based on the established three-dimensional coordinate system and facial point cloud data.

[0084] Specifically, based on the established three-dimensional coordinate system and prior knowledge of the mesh-controlled vertices, it is known that among all face point cloud coordinates, the point with the largest local depth value can only be the most prominent point on the forehead, the tip of the nose, the most prominent point on the mouth, and the most prominent point on the chin. Since the tip of the nose is located near the center of the face, the point with the largest depth value near the center of the face can be used as the tip of the nose.

[0085] Finally, after determining the tip of the nose, the midline of the face can be determined based on the intersection of the YOZ plane passing through the tip of the nose and the curved surface of the face, and the transverse contour of the tip of the nose can be determined based on the intersection of the XOZ plane passing through the tip of the nose and the curved surface of the face. For an example, please refer to... Figure 3b , Figure 3b This is an example diagram of a face midline and a nose tip transverse outline provided in an embodiment of this application. Figure 3b The left curve in the figure represents the midline of the face, and the right curve represents the transverse contour of the nose tip.

[0086] In this embodiment of the application, after determining the midline and the nasal tip transverse contour, the curvature of the points on the contour is calculated to obtain the point with the largest local curvature, and the point with the largest local curvature is taken as the target feature point on the contour.

[0087] Please refer to Figure 3b In this embodiment, the target feature points on the contour line include forehead point 1, glabella point 2, nasal root point 3, nasal tip point 4, nasal tip point 5, upper lip point 6, mouth point 7, lower lip point 8, chin point 9, and chin point 10.

[0088] The target feature points on the transverse contour line of the nasal tip include left nasal alar point 1, nasal tip point 2, and right nasal alar point 3.

[0089] Specifically, when determining target feature points on the midline of the face, the coordinates of the tip of the nose can be used to identify these points. For example, the method for determining the root of the nose is as follows: search for the first Z-value inflection point above the tip of the nose among all feature points on the midline; this inflection point is the root of the nose. The subnasal point is the first feature point below the tip of the nose, and the glabella point is the first potential feature point above the root of the nose. This process continues. This allows the location of target feature points on the midline of the face.

[0090] In this embodiment, the target feature points on the midline of the face are arranged in descending order of Y value, as follows: forehead point, glabella point, root of nose point, tip of nose point, subnasal point, upper lip point, mouth point, lower lip point, chin point, and subchinal point.

[0091] In this embodiment, the method for determining the target feature points on the transverse contour line of the nasal tip is the same as the method for determining the target feature points on the midline contour line, and will not be repeated here. The target feature points on the transverse contour line of the nasal tip, from left to right, are the left alar point, the nasal tip point, and the right alar point.

[0092] In this embodiment of the application, the nasal root point, nasal tip point, nasal inframammary point, upper lip point, left nasal wing point, nasal tip point, and right nasal wing point among the target feature points are determined as nasal cavity point cloud data.

[0093] S302, Electronic devices determine the nasal cavity region based on nasal cavity point cloud data.

[0094] The method for determining the nasal cavity region provided in this application embodiment is as follows: the position of the nasal cavity point cloud data is taken as the position of interest, and the region of interest is determined based on the position of interest.

[0095] For example, the coordinates of the nasal root point, left nasal ala point, and right nasal ala point in the nasal cavity point cloud data are used as the locations of interest. A region of interest extraction algorithm is then used to extract the region enclosed by these three points, thereby determining the region of interest. This embodiment of the application does not limit the region of interest extraction algorithm.

[0096] For example, the left and right nasal alar points can be used as the two vertices of the lower boundary of a rectangle, and the nasal root point can be used as the midpoint of the upper boundary of the rectangle to extract the region of interest. The area within this rectangle is the region of interest. As another example, the left and right nasal alar points can be used as the two vertices of the lower boundary of a triangle, and the nasal root point can be used as the intersection of the two sides of the triangle to extract the triangle. The area within this triangle is the region of interest. Of course, this embodiment can also use regions within bounding boxes of other shapes to represent the region of interest; this embodiment does not limit this.

[0097] Please refer to the method for determining the nasal cavity region provided in this application embodiment. Figure 4 . Figure 4 This is a schematic flowchart illustrating a specific method of S302 provided in an embodiment of this application. Figure 4 The method in the example can be executed by an electronic device. For example... Figure 4 As shown, the method includes: S401 to S402.

[0098] S401: The electronic device constructs a nasal cavity model based on the nasal cavity point cloud data.

[0099] Specifically, the electronic device is equipped with software for building 3D models. By inputting the nasal cavity point cloud data into the 3D modeling software, a nasal cavity model can be constructed. This application embodiment does not limit the type of 3D modeling software.

[0100] S402. Electronic equipment determines the nasal cavity region based on the nasal cavity model.

[0101] Specifically, in this embodiment, the region formed by the point cloud data in the nasal cavity model can be defined as the nasal cavity region. The nasal cavity region in this embodiment is a three-dimensional region.

[0102] In other embodiments, the region formed by a portion of the point cloud data in the nasal cavity model can be defined as the nasal cavity region. This nasal cavity region is a planar region. For example, a rectangular bounding box can be used to mark this planar region, with the left and right nasal alar points as the two vertices of the lower boundary of the rectangle, and the nasal root point as the midpoint of the upper boundary of the rectangle. Another example is to use a triangular bounding box to mark the planar region, with the left and right nasal alar points as the two vertices of the lower boundary of the triangle, and the nasal root point as the intersection of the two sides of the triangle. Of course, other shapes of bounding boxes can also be used to mark the planar region in this embodiment, and this embodiment is not limited to this.

[0103] S203. When the electronic device detects a nasal swab in the identification area, it identifies whether the current sampling meets the preset valid sampling conditions.

[0104] Specifically, the effective sampling conditions include: the swab tip of the nasal swab moves along a first motion trajectory for a preset duration within the target depth of the nasal cavity region.

[0105] After acquiring a face image of the recognition area, the electronic device uses a target detection algorithm to monitor whether the target appearing in the recognition area is a nasal swab. After detecting the nasal swab, it identifies whether the current sampling meets the preset valid sampling conditions based on the movement trajectory of the nasal swab and the nasal cavity area.

[0106] Please refer to Figure 5a , Figure 5a This is a schematic flowchart illustrating a method for identifying whether the current round of sampling meets preset valid sampling conditions, provided in an embodiment of this application. Figure 5a The method in the example can be executed by an electronic device. For example... Figure 5a As shown, the method includes: S501 to S505.

[0107] S501, The electronic device identifies whether the swab head of the nasal swab has moved into the nasal cavity area.

[0108] Specifically, the electronic device can determine whether the nasal swab tip has moved into the nasal cavity area based on the movement trajectory of the swab tip within the recognition area. Please refer to [link / reference needed] for details. Figure 6 .

[0109] Figure 6 This is a schematic flowchart illustrating a specific method of S501 provided in an embodiment of this application. Figure 6 The method in the example can be executed by an electronic device. For example... Figure 6 As shown, the method includes: S601 to S602.

[0110] S601, The electronic device determines the second motion trajectory of the nasal swab within the recognition area.

[0111] Specifically, after detecting that the target is a nasal swab, the electronic device monitors whether the nasal swab is a moving target. In this embodiment, methods such as frame difference and Gaussian mixture model can be used to monitor whether the nasal swab is a moving target.

[0112] Secondly, after determining the moving target, the center point of the nasal swab can be obtained. In this embodiment, the center point of the nasal swab can be obtained by taking the center point of the circumscribed rectangle of the nasal swab, or by calculating the centroid of the nasal swab.

[0113] Finally, the center points of the same nasal swab are connected to obtain the second motion trajectory of the nasal swab within the recognition area. In this embodiment, the same nasal swab can be detected by a target tracking method, and the center points of the same nasal swab can be connected to form a trajectory. The target tracking method can be any method such as Kalman filtering, matching search, or deep learning; this embodiment does not limit the target tracking method.

[0114] S602. If the second motion trajectory of the electronic device matches the preset sampling trajectory, it is determined that the swab head of the nasal swab has moved into the nasal cavity area.

[0115] In this embodiment, the movement of the nasal swab tip into the nasal cavity area can be determined by establishing a trajectory recognition model. That is, a second motion trajectory is input into the trajectory recognition model, and the model's output result can be used to determine whether the second motion trajectory conforms to a preset sampling trajectory. If it is a sampling trajectory, it is determined that the nasal swab tip has moved into the nasal cavity area.

[0116] The training method for the trajectory recognition model is as follows:

[0117] Multiple sets of sample data are acquired. The sample data includes sampling trajectory data and other trajectory data. The sampling trajectory refers to the trajectory of the nasal swab moving into the nasal cavity area in the recognition area. Other trajectories refer to the trajectories in the recognition area other than the sampling trajectory, such as the trajectory of a finger moving into the nasal cavity area and the trajectory of the nasal swab moving into a non-nasal cavity area.

[0118] Determine the standard type corresponding to each sample data, that is, mark the sampling trajectory and other trajectories as different types.

[0119] Input each sample data into the initial trajectory recognition model to obtain the prediction type output by the initial trajectory recognition model;

[0120] Based on the standard type and prediction type corresponding to each sample data, determine the prediction accuracy of the initial trajectory recognition model;

[0121] When the prediction accuracy does not meet the preset conditions (for example, the prediction accuracy is less than 80% to 90%, such as the prediction accuracy is less than 85%), the model parameters of the initial trajectory recognition model are adjusted, and the trajectory recognition model with the adjusted model parameters is determined as the initial trajectory recognition model. Then, the process returns to the steps of inputting each sample data into the initial trajectory recognition model to obtain the prediction type output by the initial trajectory recognition model, as well as subsequent steps.

[0122] When the prediction accuracy meets the preset conditions, the training is considered complete, and the initial trajectory recognition model is identified as the completed trajectory recognition model.

[0123] In other embodiments, another method for identifying the movement of the swab tip into the nasal cavity area is as follows:

[0124] First, determine the position of the swab tip and the nasal cavity area.

[0125] Specifically, the location of the swab tip region and the nasal cavity region can be determined using an extraction of interest algorithm. For example, the swab tip region can be represented by a rectangle, and the nasal cavity region can be represented by a rectangle.

[0126] Determine the position of the swab head based on the swab head area, and determine the position of the nasal cavity area based on the nasal cavity area.

[0127] Specifically, the coordinates of the rectangle representing the swab tip area indicate the position of the swab tip. Similarly, the coordinates of the rectangle representing the nasal cavity area indicate the position of the swab tip.

[0128] Then, determine whether the location of the nasal cavity area includes the location of the swab tip.

[0129] Specifically, determine whether the coordinate set of the region containing the rectangle representing the nasal cavity region contains the coordinate set of the region containing the rectangle representing the swab head region.

[0130] Finally, if the location of the nasal cavity region includes the location of the swab tip, then it is determined that the swab tip has moved into the nasal cavity region.

[0131] S502. If the electronic device moves into the nasal cavity area, it identifies whether the swab head of the nasal swab is within the target depth of the nasal cavity area.

[0132] Specifically, in this application embodiment, a method for identifying whether the swab tip of a nasal swab is within a target depth in the nasal cavity region is as follows: if the length of the nasal swab within the nasal cavity region is within a preset numerical range, then it is determined that the swab tip of the nasal swab is within the target depth in the nasal cavity region.

[0133] Specifically, first, determine the length of the nasal swab within the nasal cavity area.

[0134] In this embodiment of the application, the method for determining the length of the nasal swab within the nasal cavity region includes, but is not limited to:

[0135] 1. Determine the length of the nasal swab within the nasal cavity region based on the length of the rectangular frame representing the nasal swab entering the rectangular frame representing the nasal cavity region.

[0136] 2. Determine the length of the nasal swab within the nasal cavity region based on the length of the rectangle representing the nasal swab within the rectangle representing the nasal cavity region but not within the rectangle representing the nasal cavity region.

[0137] Please refer to Figure 5b , Figure 5b This is an example diagram illustrating how to determine the length of a nasal swab within the nasal cavity region, as provided in an embodiment of this application. Figure 5b In this method, the length of the nasal swab within the nasal cavity can be determined directly by calculating the length of C, or the length of the nasal swab within the nasal cavity can be determined by calculating the length of D and then calculating the length of C based on the length of D and the total length of the nasal swab.

[0138] 3. Determine the length of the nasal swab within the nasal cavity region based on the length and tilt angle of the rectangular frame representing the nasal swab entering the rectangular frame representing the nasal cavity region, or determine the length of the nasal swab within the nasal cavity region based on the length and tilt angle of the rectangular frame representing the nasal swab entering the rectangular frame representing the nasal cavity region but not entering the rectangular frame representing the nasal cavity region.

[0139] Specifically, the tilt angle refers to the angle between the line connecting the root of the nose and the tip of the nose and the Y-axis.

[0140] Please refer to Figure 5c , Figure 5c This is an example diagram illustrating another method for determining the length of a nasal swab within the nasal cavity region, as provided in this application embodiment. In this application embodiment, the length determined using methods 1 and 2 is the length of line segment b, without considering the tilt angle. To make the determined length of the nasal swab within the nasal cavity region more accurate, this application embodiment determines the length a of the nasal swab within the nasal cavity region based on the length of b calculated using methods 1 and 2 and the tilt angle.

[0141] In this embodiment, the tilt angle θ can be determined based on the position coordinates of the nasal root point and the nasal tip point.

[0142] For example, if the coordinates of the root of the nose are 3(X1, Y1) and the coordinates of the tip of the nose are 4(X2, Y2), then the tilt angle can be calculated using the following formula:

[0143] θ=arctan[(Y1-Y2) / (X1-X2)].

[0144] In this embodiment, the length 'a' of the nasal swab within the nasal cavity region can be calculated using the following formula:

[0145]

[0146] Secondly, if the length of the nasal swab within the nasal cavity area is within a preset value range, then the swab tip is determined to be within the target depth within the nasal cavity area.

[0147] Specifically, when calculating the length of the nasal swab within the nasal cavity using Method 1 or Method 2, the following formula can be used to determine whether the length of the nasal swab within the nasal cavity falls within a preset range:

[0148] The side length of the rectangle representing half of the nasal cavity region is ≤ b ≤ the side length of the rectangle representing the nasal cavity region.

[0149] When the length of b is within the above-mentioned preset value range, it is determined that the swab tip of the nasal swab is within the target depth in the nasal cavity area.

[0150] When calculating the length of the nasal swab within the nasal cavity using Method 3, the following formula can be used to determine whether the length of the nasal swab within the nasal cavity falls within a preset range:

[0151] The distance between 1 / 2 the tip of the nose and the root of the nose is less than or equal to a.

[0152] The distance between the tip of the nose and the root of the nose can be calculated using the following formula:

[0153]

[0154] When the length of 'a' is within the aforementioned preset value range, it is determined that the swab tip of the nasal swab is within the target depth of the nasal cavity area.

[0155] S503. If the electronic device is within the target depth, it identifies whether the movement trajectory of the nasal swab head within the target depth is the first movement trajectory.

[0156] Specifically, the first motion trajectory is the standard motion trajectory of the nasal swab when the sampling action meets the standard.

[0157] Figure 7a This is a schematic flowchart illustrating a specific method of S503 provided in an embodiment of this application. Figure 7a The method in the example can be executed by an electronic device. For example... Figure 7a As shown, the method includes: S701 to S702.

[0158] S701, Electronic equipment determines the number of rotations or reciprocating movements of the nasal swab head within the target depth.

[0159] In this embodiment of the application, it is first necessary to determine the motion trajectory of the nasal swab head within the target depth. The method for determining this trajectory is the same as the method for determining the second motion trajectory of the nasal swab within the recognition area, and will not be described again here.

[0160] In other embodiments, another method for determining the motion trajectory of the nasal swab head within the target depth is to capture the user's hand motion trajectory using dynamic capture technology, which can then characterize the motion trajectory of the nasal swab head within the target depth.

[0161] In this embodiment of the application, after determining the motion trajectory of the nasal swab head within the target depth, the number of rotations of the nasal swab head within the target depth can be determined based on the rotation target detection algorithm.

[0162] In other embodiments, after determining the motion trajectory of the nasal swab head within the target depth, the three-dimensional coordinates of each position in the motion trajectory of the nasal swab head within the target depth can be obtained, and the number of times the maximum and minimum values ​​of the X-axis occur in the three-dimensional coordinates of each position can be counted. Based on the number of times the maximum and minimum values ​​of the X-axis occur, the number of reciprocating movements of the nasal swab head within the target depth can be determined.

[0163] Please refer to Figure 7b , Figure 7b This is an example diagram illustrating how to determine the number of reciprocating movements of a nasal swab head within a target depth, as provided in an embodiment of this application. Figure 7b In the diagram, A1 represents the left nasal cavity region, and A2 represents the right nasal cavity region. Figure 7b Nasal swab B moves within the right nasal cavity region. a, b, c, and d represent four moments during the movement of nasal swab B within the right nasal cavity region. a and d represent the start and end moments of the movement, while c and d represent intermediate moments. Of course, in this embodiment, the intermediate moments of the nasal swab movement include, but are not limited to, moments c and d; this embodiment only uses moments c and d as examples. In determining the number of reciprocating movements of the nasal swab head within the target depth, this embodiment obtains the three-dimensional coordinates of the positions corresponding to moments a, b, c, and d in the movement trajectory of the nasal swab head within the target depth. The number of times the maximum and minimum values ​​of the X-axis occur in the three-dimensional coordinates of the positions corresponding to moments a, b, c, and d are counted. Based on the number of occurrences of the maximum and minimum values ​​of the X-axis, the number of reciprocating movements of the nasal swab head within the target depth is determined.

[0164] S702. If the number of rotations of the electronic device is greater than the preset number of rotations, or the number of reciprocating movements is greater than the preset number of movements, then the movement trajectory of the nasal swab head within the target depth is determined to be the first movement trajectory.

[0165] Specifically, the preset number of rotations is 4 to 6, for example, 5 times, and the preset number of movements is 4 to 6, for example, 5 times.

[0166] In this embodiment of the application, if the electronic device determines that the number of rotations is greater than 5 times or the number of reciprocating movements is greater than 5 times, then the movement trajectory of the nasal swab head within the target depth is determined to be the first movement trajectory.

[0167] S504. If the electronic device is on the first motion trajectory, then identify whether the duration of the nasal swab moving on the first motion trajectory is not less than the preset duration.

[0168] Specifically, the preset duration is 14 to 16 seconds, for example, 15 seconds. The electronic device determines the target motion trajectory of the swab head in the nasal cavity area as the first motion trajectory using the method of S503, and at the same time identifies whether the duration of the nasal swab moving along the first motion trajectory is not less than 15 seconds.

[0169] In this embodiment of the application, if the movement trajectory of the nasal swab head within the target depth is determined to be the first movement trajectory, then identifying whether the duration of the nasal swab moving along the first movement trajectory is not less than a preset duration is to identify whether the user is sampling with standard actions, so that the sample on the swab head is sufficient, thereby ensuring the accuracy of the antigen detection results.

[0170] S505. If the sampling duration is not less than the preset duration, then the current sampling round is determined to meet the preset valid sampling conditions.

[0171] Specifically, if the sampling time is not less than 15 seconds, then the current sampling round is determined to meet the preset valid sampling conditions.

[0172] S204. If the conditions are met, then the sampling in this round is considered valid.

[0173] Specifically, if the current round of sampling is determined to meet the preset valid sampling conditions, then the current round of sampling is considered valid.

[0174] In summary, the technical solution of this application involves acquiring a facial image within the recognition area; determining the nasal cavity region based on the facial image; and identifying whether the current sampling meets preset valid sampling conditions when a nasal swab is detected in the recognition area. If the conditions are met, the current sampling is deemed valid. The valid sampling conditions include: the swab head moves along a first trajectory within a target depth in the nasal cavity region, and the nasal swab moves along the first trajectory for a target duration, with the target depth exceeding a preset depth and the target duration exceeding a preset duration. In other words, this embodiment can determine the nasal cavity region based on a facial image; when a nasal swab is detected in the recognition area, it identifies whether the current sampling meets preset valid sampling conditions, reminding the user to perform sampling with standard movements, thereby improving the accuracy of the sampling results.

[0175] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0176] Please refer to Figure 8 , Figure 8 This is a schematic diagram of the structure of a sampling device for antigen detection provided in an embodiment of this application. The device includes:

[0177] The acquisition module 81 is used to acquire the face image in the recognition area.

[0178] The determination module 82 is used to determine the nasal cavity region based on the face image.

[0179] The identification module 83 is used to identify whether the current sampling meets the preset valid sampling conditions when a nasal swab is detected in the identification area.

[0180] The sampling module 84 is used to determine that the current round of sampling is valid if certain conditions are met. Valid sampling conditions include: the swab tip of the nasal swab moves along a first trajectory within the target depth of the nasal cavity for a preset duration.

[0181] Among them, the determination module 82 is also used to determine the nasal cavity point cloud data based on the face image;

[0182] The nasal cavity region is determined based on nasal cavity point cloud data.

[0183] Among them, the determination module 82 is also used to construct a nasal cavity model based on the nasal cavity point cloud data;

[0184] The nasal cavity region is determined based on a nasal cavity model.

[0185] The identification module 83 is also used to identify whether the swab head of the nasal swab has moved into the nasal cavity area;

[0186] If the movement reaches the nasal cavity area, it is determined whether the swab tip of the nasal swab is within the target depth within the nasal cavity area;

[0187] If within the target depth, then identify whether the movement trajectory of the nasal swab head within the target depth is the first movement trajectory;

[0188] If it is the first motion trajectory, then identify whether the duration of the nasal swab moving along the first motion trajectory is not less than the preset duration;

[0189] If the sampling duration is not less than the preset duration, then the current sampling round is determined to meet the preset valid sampling conditions.

[0190] The recognition module 83 is also used to determine the second motion trajectory of the nasal swab within the recognition area;

[0191] If the second motion trajectory matches the preset sampling trajectory, it is determined that the swab head of the nasal swab has moved into the nasal cavity area.

[0192] The identification module 83 is also used to determine the length of the nasal swab within the nasal cavity area;

[0193] If the length of the nasal swab within the nasal cavity area is within a preset range, then the swab tip is determined to be within the target depth within the nasal cavity area.

[0194] The identification module 83 is also used to determine the number of times the swab head of the nasal swab rotates or moves back and forth within the target depth;

[0195] If the number of rotations is greater than the preset number of rotations, or the number of reciprocating movements is greater than the preset number of movements, then the movement trajectory of the nasal swab head within the target depth is determined to be the first movement trajectory.

[0196] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0197] like Figure 9 As shown, this application embodiment also provides an electronic device 200, including a memory 21, a processor 22, and a computer program 23 stored in the memory 21 and executable on the processor 22. When the processor 22 executes the computer program 23, it implements the sampling method for antigen detection in the above embodiments.

[0198] The processor 22 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0199] The memory 21 can be an internal storage unit of the electronic device 200. The memory 21 can also be an external storage device of the electronic device 200, such as a plug-in hard drive, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the electronic device 200. Furthermore, the memory 21 can include both internal and external storage units of the electronic device 200. The memory 21 is used to store computer programs and other programs and data required by the electronic device 200. The memory 21 can also be used to temporarily store data that has been output or will be output.

[0200] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the sampling method for antigen detection in the above embodiments.

[0201] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the antigen detection sampling method of the above embodiments.

[0202] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable storage medium can include at least: any entity or device capable of carrying computer program code to a photographic device / electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable storage media cannot be electrical carrier signals or telecommunication signals.

[0203] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0204] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0205] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0206] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A sampling method for antigen detection, characterized in that, include: Acquire the face image in the recognition area; Based on the face point cloud data in the face image, determine the midline of the face and the cross-sectional outline of the nose tip. Calculate the curvature of the points on the midline contour, obtain the point with the largest local curvature among the points on the midline contour, and take the point with the largest local curvature among the points on the midline contour as the target feature point on the midline contour. The target feature points on the midline contour include the forehead point, the glabella point, the root of the nose point, the tip of the nose point, the infranasal point, the upper lip point, the mouth point, the lower lip point, the suprachinal point, and the subchinal point. Calculate the curvature of the points on the cross-sectional contour line of the nose tip, obtain the point with the largest local curvature among the points on the cross-sectional contour line of the nose tip, and take the point with the largest local curvature among the points on the cross-sectional contour line of the nose tip as the target feature point on the cross-sectional contour line of the nose tip. The target feature point on the cross-sectional contour line of the nose tip includes the left ala point, the nose tip point, and the right ala point. The nasal root point, nasal tip point, nasal inframammary point, and lip point among the target feature points on the midline contour and the left nasal wing point, nasal tip point, and right nasal wing point among the target feature points on the nasal tip transverse contour are determined as nasal cavity point cloud data. Based on the nasal cavity point cloud data, a nasal cavity model is constructed; The nasal cavity region is determined based on the nasal cavity model; When a nasal swab is detected in the recognition area, a second motion trajectory of the nasal swab within the recognition area is determined; If the second motion trajectory matches the preset sampling trajectory, it is determined that the swab head of the nasal swab has moved into the nasal cavity area; If the swab moves into the nasal cavity area, the length of the nasal swab in the nasal cavity area is determined; wherein, when determining the length of the nasal swab in the nasal cavity area, it is calculated based on the projected length of the nasal swab in the nasal cavity area and the tilt angle of the nasal swab, the tilt angle being determined based on the angle between the line connecting the root of the nose and the tip of the nose and the vertical direction; If the length of the nasal swab within the nasal cavity area is within a preset value range, then it is determined that the swab tip of the nasal swab is within the target depth within the nasal cavity area; If within the target depth, then identify whether the movement trajectory of the nasal swab head within the target depth is the first movement trajectory; If it is the first motion trajectory, then identify whether the duration of the nasal swab moving along the first motion trajectory is not less than a preset duration; If the sampling duration is not less than the preset duration, then the current sampling round is determined to meet the preset valid sampling conditions.

2. The sampling method according to claim 1, characterized in that, The step of identifying whether the movement trajectory of the swab head within the target depth is the first movement trajectory includes: Determine the number of times the swab head of the nasal swab rotates or reciprocates within the target depth; If the number of rotations is greater than the preset number of rotations, or the number of reciprocating movements is greater than the preset number of movements, then the movement trajectory of the nasal swab head within the target depth is determined to be the first movement trajectory.

3. A sampling device for antigen detection, characterized in that, include: The acquisition module is used to acquire face images within the recognition area; The determination module is used to determine the midline of the face and the transverse contour of the nose tip based on the face point cloud data in the face image; calculate the curvature of the points on the midline, obtain the point with the largest local curvature among the points on the midline, and take the point with the largest local curvature among the points on the midline as the target feature point on the midline. The target feature points on the midline include the forehead point, glabella point, root of the nose point, tip of the nose point, infranasal point, upper lip point, mouth point, lower lip point, suprachinal point, and subchinal point; and calculate the curvature of the points on the transverse contour of the nose tip. The point with the largest local curvature among the points on the cross-sectional contour line of the nasal tip is obtained and used as the target feature point on the cross-sectional contour line of the nasal tip. The target feature points on the cross-sectional contour line of the nasal tip include the left ala point, the nasal tip point, and the right ala point. The nasal root point, the nasal tip point, the infranasal point, and the upper lip point among the target feature points on the midline contour line, and the left ala point, the nasal tip point, and the right ala point among the target feature points on the cross-sectional contour line of the nasal tip are determined as nasal cavity point cloud data. A nasal cavity model is constructed based on the nasal cavity point cloud data. The nasal cavity region is determined based on the nasal cavity model; The identification module is used to determine a second motion trajectory of the nasal swab within the identification area when a nasal swab is detected in the identification area; if the second motion trajectory conforms to a preset sampling trajectory, it is determined that the swab head of the nasal swab has moved into the nasal cavity area; if it has moved into the nasal cavity area, the length of the nasal swab within the nasal cavity area is determined; wherein, when determining the length of the nasal swab within the nasal cavity area, it is calculated based on the projected length of the nasal swab within the nasal cavity area and the tilt angle of the nasal swab, the tilt angle being determined based on the angle between the line connecting the root of the nose and the tip of the nose and the vertical direction; if the length of the nasal swab within the nasal cavity area is within a preset numerical range, it is determined that the swab head of the nasal swab is within a target depth within the nasal cavity area; if it is within the target depth, it is identified whether the motion trajectory of the swab head within the target depth is a first motion trajectory; if it is the first motion trajectory, it is identified whether the duration of the nasal swab moving along the first motion trajectory is not less than a preset duration; The sampling module is used to determine that the current round of sampling meets the preset valid sampling conditions if the sampling duration is not less than the preset duration.

4. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the sampling method for antigen detection as described in any one of claims 1 or 2.

5. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the sampling method for antigen detection as described in any one of claims 1 or 2.

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