Intelligent epidemic prevention system and method

Abstract

The present disclosure provides an intelligent epidemic prevention system, comprising: a calibration device; an access control system; a visible light camera, configured to capture a first calibration device image of the calibration device and a first user image of a user; a thermal imaging camera, configured to capture a second calibration device image of the calibration device and a second user image of the user; and a computing device, configured to calculate a calibration value based on the first calibration device image and the second calibration device image; the computing device determines whether the user is correctly wearing a mask based on the first user image, generating a first judgment result, and determines whether the user's body temperature is greater than or equal to a threshold temperature based on the calibration value and the second user image, generating a second judgment result; the computing device controls the access control system to open or close based on the first judgment result and the second judgment result. The present disclosure also relates to an intelligent epidemic prevention method.

Description

Technical Field

[0001] The present disclosure relates to an intelligent epidemic prevention system, and more particularly to an intelligent epidemic prevention system that monitors a user's body temperature and mask wearing status. Background Art

[0002] With the prevalence of infectious diseases, people entering and leaving public places are required to have their temperatures taken and wear masks to reduce the possibility of disease transmission. Therefore, checkpoints are often set up at the entrances and exits of public places (such as gates in public transportation systems) to measure the body temperature of people entering and leaving and check whether they are wearing masks.

[0003] Therefore, we urgently need an intelligent epidemic prevention system that can accurately and effectively automatically check the body temperature and mask wearing status of people entering and leaving, and combine it with the entrance and exit access control system and data analysis system to enhance the effectiveness of disease prevention and control and protect the health of the general public. Summary of the Invention

[0004] The term "embodiment" and similar words, such as "implementation," "configuration," "aspect," "example," and "option," are intended to refer broadly to all subject matter of the present disclosure and the claims that follow. Statements containing such words should be understood as not limiting the subject matter described in this specification or as limiting the meaning or scope of the claims that follow. The embodiments covered by this disclosure are defined by the following claims, not by this section. This section provides a general overview of the various aspects of the disclosure and introduces some concepts that will be further described in the "Implementation Methods" section. This section is not intended to identify key or essential features of the claimed subject matter. This section is also not intended to be used solely to determine the scope of the claimed subject matter. The subject matter should be understood by reference to appropriate portions of the entire disclosure, any or all of the drawings, and each claim.

[0005] One embodiment of the present disclosure provides an intelligent epidemic prevention system, comprising: a calibration device having a metal plate, and the metal plate is heated to a predetermined sensing temperature; an access control system; a visible light camera, which records a first calibration device image of the calibration device and a first user image of a user; a thermal imaging camera, which records a second calibration device image of the calibration device and a second user image of the user; and a computing device, which calculates a first position of the calibration device on the first calibration device image and a second position of the calibration device on the second calibration device image, calculates a base corresponding position of the calibration device on the second user image based on the first position, and calculates a calibration value based on the second position and the base corresponding position; wherein the computing device determines whether the user is wearing a mask correctly based on the first user image, and generates a first judgment result, and determines whether the user's body temperature is greater than or equal to a threshold temperature based on the calibration value and the second user image, and generates a second judgment result; wherein the computing device controls the access control system to be opened or closed based on the first judgment result and the second judgment result.

[0006] Another embodiment of the present disclosure provides an intelligent epidemic prevention method, comprising: recording a first calibration device image of a calibration device with a visible light camera, wherein the calibration device includes a metal plate, and the metal plate is heated to a predetermined sensing temperature; recording a second calibration device image of the calibration device with a thermal imaging camera; calculating a first position of the calibration device on the first calibration device image and a second position of the calibration device on the second calibration device image; calculating a base corresponding position of the calibration device on the second calibration device image based on the first position, and calculating a correction value based on the second position and the base corresponding position; recording a first user image of a user with a visible light camera; recording a second user image of the user with a thermal imaging camera; judging whether the user is wearing a mask correctly based on the first user image, generating a first judgment result, and judging whether the user's body temperature is greater than or equal to a threshold temperature based on the correction value and the second user image, generating a second judgment result; and controlling the opening or closing of an access control system based on the first judgment result and the second judgment result.

[0007] The above content in this section is not intended to represent every embodiment or every aspect of the present disclosure. On the contrary, the above content in this section only provides examples for some of the novel aspects and features described in this specification. The above features and advantages, as well as other features and advantages of the present disclosure, will be understood after reading the detailed description of the representative embodiments and modes for implementing the present invention below, and reading them together with the accompanying drawings and appendix claims. The additional aspects of the present disclosure will be understood by those skilled in the art of the present disclosure after reading the embodiments in the "Implementation Methods" below and reading them together with the drawings. A brief description of these drawings is given in the "Brief Description of Drawings" section below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The present disclosure and its advantages and figures are best understood after reading the following description of representative embodiments in conjunction with the accompanying drawings. These drawings are merely representative embodiments and should not be construed as limiting the scope of the embodiments or claims.

[0009] Figure 1 is a block diagram showing an intelligent epidemic prevention system;

[0010] Figure 2A is a schematic diagram showing an example of the imaging range of an RGB camera and a thermal imaging camera before calibration;

[0011] Figure 2B is a schematic diagram showing an example of the imaging range of a calibrated RGB camera and a thermal imaging camera;

[0012] Figure 3A A flow chart showing a camera calibration method suitable for Figure 2A RGB camera and thermal imaging camera shown;

[0013] Figure 3B A schematic diagram showing Figure 3A The calculation process of the camera calibration method in ;

[0014] Figure 3C A schematic diagram showing the use of Figure 3A and Figure 3B The process of calculating the correction value obtained by the camera calibration method in the image of the user to correct the facial area; and

[0015] Figure 4 A flow chart showing an automated personnel monitoring method suitable for Figure 1 The intelligent epidemic prevention system shown.

[0016] The description of the accompanying drawings is as follows:

[0017] 100: Intelligent epidemic prevention system

[0018] 101: Epidemic Prevention Device

[0019] 102: Computing device

[0020] 103: Calibration device

[0021] 104: RGB Camera

[0022] 106: Thermal Imaging Camera

[0023] 108: Access control system

[0024] 110: Display

[0025] 112: Data Analysis Device

[0026] 202A, 202B: First imaging range

[0027] 204A, 204B: Second imaging range

[0028] 206: Metal Plate

[0029] C1, C2: center points

[0030] 300: Method

[0031] 302-312: Steps

[0032] C: Center point

[0033] L1: First position

[0034] L2: Second position

[0035] L3: Basic corresponding position

[0036] B: Correction value

[0037] 202: First imaging range

[0038] 204: Second imaging range

[0039] R1, R2, R3: Area

[0040] P1~P4, P11~P14, P21~P24: vertex position

[0041] 400: Method

[0042] 402-424: Steps DETAILED DESCRIPTION

[0043] The present disclosure is susceptible to numerous modifications and alternative forms. Some representative embodiments are illustrated in the drawings and described in detail herein. However, it should be noted that the present disclosure is not intended to be limited to the particular forms disclosed herein. On the contrary, the present disclosure encompasses all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure, as defined by the appended claims.

[0044] The present disclosure provides a variety of different embodiments or examples for implementing different features of the subject matter described herein. The specific examples of components and configurations described below are presented to simplify the present disclosure. Of course, they are merely examples and are not intended to be limiting. For example, a configuration described below in which "a first feature is located above a second feature" may include embodiments in which the first and second features are in direct contact, and may also include embodiments in which additional features are located between the first and second features, preventing the first and second features from being in direct contact.

[0045] Furthermore, spatially relative terms such as "above," "below," "in front of," and "behind" may be used throughout this disclosure for simplicity to describe one element or feature in its relationship to another (or other) element or feature as depicted in the figures. These spatially relative terms are intended to encompass both the orientation depicted in the figures and different orientations of the device in use or operation. The device may be oriented in other orientations (rotated 90 degrees or at other orientations), in which case the spatially relative terms used throughout this disclosure should be interpreted accordingly.

[0046] Figure 1 Figure 1 is a block diagram showing an intelligent epidemic prevention system. Figure 1 In the present invention, the smart epidemic prevention system 100 includes at least one epidemic prevention device 101, wherein each epidemic prevention device 101 includes a computing device 102. The computing device 102 may be, for example, a central processing unit (CPU), a microcontroller (MCU), a system-on-a-chip (SoC), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a field-programmable logic device (FPLD), a field-programmable gate array (FPGA), a discrete logic device, or any other suitable computing device. The computing device 102 may also be a personal computer, a server, a workstation, or any other suitable computing device incorporating any of the above components. Each epidemic prevention device 101 also includes a visible light camera, such as a red-green-blue (RGB) camera 104. The following description uses the RGB camera 104 as an example, but the present disclosure is not limited thereto. Each epidemic prevention device 101 also includes a thermal imaging camera 106 (such as, but not limited to, an infrared thermal imager), an access control system 108 (such as, but not limited to, a mass transit entry gate system), and a display 110 (such as, but not limited to, a liquid crystal display (LCD)). The RGB camera 104, thermal imaging camera 106, access control system 108, and display 110 in each epidemic prevention device 101 are coupled to the computing device 102 in that epidemic prevention device 101. Each epidemic prevention device 101 is coupled to a data analysis device 112. The data analysis device 112 may be located remotely from the epidemic prevention device 101 (but not limited to this), such as (but not limited to) a public transportation system's traffic control center. The data analysis device 112 may be, for example, a personal computer, server, workstation, or any other suitable computing device.

[0047] Each epidemic prevention device 101 in the intelligent epidemic prevention system 100 can detect whether a user (e.g., a passenger entering a public transportation system) has a normal body temperature and whether the user is wearing a mask correctly. Based on the user's body temperature and mask wearing condition, the device 101 determines whether to allow the user to pass through the access control system 108. First, the display 110 displays information instructing the user to wear a mask correctly to allow measurements by the RGB camera 104 and the thermal imaging camera 106. The RGB camera 104 then captures a first user image (e.g., a visible light image) of the user, while the thermal imaging camera 106 captures a second user image (e.g., a temperature distribution map). The computing device 102 analyzes the first user image using image recognition technology (e.g., deep learning) to detect the user's facial position and determine whether the user is wearing a mask correctly. Based on the facial position, the computing device 102 then analyzes whether the temperature of the facial position in the second user image (i.e., the user's body temperature) is greater than or equal to a threshold temperature (e.g., 37.5°C). At this point, the display 110 displays the user's body temperature and mask wearing status as determined by the computing device 102. If the user is wearing a mask correctly and their body temperature is below the threshold temperature, the access control system 108 is activated, allowing the user to pass through the access control system 108. The display 110 then displays a message instructing the user to pass through the access control system 108. If the user is not wearing a mask correctly or their body temperature is greater than or equal to the threshold temperature, the access control system 108 is deactivated, prohibiting the user from passing through the access control system 108. The display 110 then displays a message instructing the user to put on a mask again or that their body temperature is abnormal.

[0048] In some embodiments, the epidemic prevention device 101 includes a calibration device 103. The epidemic prevention device 101 includes an RGB camera 104 and a thermal imaging camera 106. These two cameras may have errors in their imaging ranges during hardware configuration, resulting in inconsistencies between the facial position indicated by the image captured by the RGB camera 104 and the facial position in the image captured by the thermal imaging camera 106, thereby affecting the monitoring accuracy of the epidemic prevention device 101. To improve this situation, it is necessary to calibrate the imaging ranges of the RGB camera 104 and the thermal imaging camera 106. Figure 2A , the center point C1 of the first imaging range 202A of the RGB camera 104 and the center point C2 of the second imaging range 204A of the thermal imaging camera 106 are not aligned. To correct this error, the calibration device 103 can be placed at an appropriate distance (e.g., 30 to 50 cm) from the RGB camera 104 and the thermal imaging camera 106. The calibration device 103 can be used to calibrate the first imaging range 202A and the second imaging range 204A using the imaging position of the calibration device 103 on the RGB camera 104 and the thermal imaging camera 106 to align the center points C1 and C2. Figure 2BThe state after calibration is completed is shown. At this time, the center point C1 of the first imaging range 202B of the RGB camera 104 and the center point C2 of the second imaging range 204B of the thermal imaging camera 106 are aligned.

[0049] In this embodiment, the calibration device 103 includes a metal plate 206, which can be made of aluminum, iron, stainless steel or other suitable materials. The advantage of using a metal plate is that it is easy to heat, which facilitates imaging by the thermal imaging camera 106. The metal plate 206 has an appropriate size (for example, a square with a side length of 30 cm) and is placed within the intersection area of ​​the first imaging range 202A of the RGB camera 104 and the second imaging range 204A of the thermal imaging camera 106, so that both the RGB camera 104 and the thermal imaging camera 106 can record images of the metal plate 206. In this embodiment, the metal plate 206 has a plurality of hollow geometric figures (for example, but not limited to, Figure 2A and Figure 2B ), to facilitate the computing device 102 to identify the position of the metal plate 206 in the first imaging range of the RGB camera 104. For example, in some embodiments, the calibration device 103 further includes a heater (not shown) for heating the metal plate 206 to a predetermined sensing temperature to facilitate the computing device 102 to identify the position of the metal plate 206 in the second imaging range of the thermal imaging camera 106. In some embodiments, the predetermined sensing temperature is a sensing temperature greater than room temperature (e.g., 30-40 degrees Celsius).

[0050] Figure 3AThe flowchart illustrates a method 300 for calibrating the imaging ranges of the RGB camera 104 and the thermal imaging camera 106 so that their center points C1 and C2 are aligned. In step 302, a metal plate 206 (e.g., an aluminum plate) is heated to a predetermined sensing temperature to facilitate imaging and recognition by the thermal imaging camera 106. In step 304, the metal plate 206 (e.g., an aluminum plate) is placed within the intersection (or common imaging range) of the first imaging range 202A of the RGB camera 104 and the second imaging range 204A of the thermal imaging camera 106, allowing both the RGB camera 104 and the thermal imaging camera 106 to capture images of the metal plate 206. At this point, the RGB camera 104 captures a first calibration image of the metal plate 206, while the thermal imaging camera 106 captures a second calibration image of the metal plate 206. In step 306, the computing device 102 calculates a first position L1(x1, y1) of a certain point on the metal plate 206 (e.g., the center point C of the metal plate) on the first calibration device image (RGB image) based on the first calibration device image. In some embodiments, because the metal plate 206 is rectangular, the computing device 102 calculates the position of a corner of the metal plate 206 on the first calibration device image as the first position L1(x1, y1), or the position of a specific hollow geometric figure in the metal plate 206 on the first calibration device image as the first position L1(x1, y1). In step 308, the computing device 102 uses an algorithm to calculate (convert) a second position L2(x2, y2) of a certain point on the metal plate 206 (e.g., the center point C of the metal plate) on the second calibration device image (thermographic image) based on the second calibration device image. In certain embodiments, since the metal plate 206 is a rectangle, the computing device 102 calculates the position of the vertex of the metal plate 206 on the second calibration device image based on the first calibration device image as the second position L2(x2, y2), or the position of the specific hollow geometric figure in the metal plate 206 on the second calibration device image as the second position L2(x2, y2). Since the imaging ranges of the RGB camera 104 and the thermal imaging camera 106 are not necessarily the same (for example, the RGB camera 104 may have an imaging range of 1080 pixels (pixels) * 1920 pixels, while the thermal imaging camera 106 may have an imaging range of 960 pixels * 1280 pixels), the imaging ranges of the RGB camera 104 and the thermal imaging camera 106 correspond to each other in a mapping relationship. See. Figure 2A and Figure 3BWhen the center points C1 and C2 of the first imaging range 202A of the RGB camera 104 and the second imaging range 204A of the thermal imaging camera 106 are not aligned, the relative positions of the center point C of the metal plate 206 within the first imaging range 202A and the second imaging range 204A will have a mapping relationship (or algorithm) F1; for example, when the metal plate 206 is placed in the center of the first imaging range 202A, the first position L1 (x1, y1) will fall in the center of the first imaging range 202A, but the second position L2 (x2, y2) will not fall in the center of the second imaging range 204A.

[0051] At this time, in step 310, the computing device 102 calculates (converts) the basic corresponding position L3 (x3, y3) of the center point C of the metal plate 206 on the second imaging range 204A of the thermal imaging camera 106 based on the first position L1 (x1, y1) of the center point C of the metal plate 206 on the image of the first calibration device using the mapping relationship F1.

[0052] In step 312, the computing device 102 calculates the offset between the base corresponding position L3 (x3, y3) of the metal plate 206 on the second imaging range 204A and the current position (i.e., the second position L2 (x2, y2)), i.e., (x3-x2, y3-y2). The computing device 102 uses this offset as the correction value B of the second imaging range 204A of the thermal imaging camera 106. In other words, in the future, the computing device 102 will convert each pixel point in the first imaging range 202A into each pixel point in the second imaging range 204A of the thermal imaging camera 106, and will calibrate each pixel point in the second imaging range 204A with the correction value B to obtain a calibrated pixel point in the second imaging range 204B, thereby aligning the center points C1 and C2 of the imaging ranges of the RGB camera 104 and the thermal imaging camera 106, as shown in FIG. Figure 2B As shown. At this point, the relative positions of the metal plate 206 within the first imaging range 202B and the second imaging range 204B have a corrected mapping relationship. For example, when the metal plate 206 is placed in the exact center of the first imaging range 202B, the center point of the metal plate 206 will fall at the exact center C1 of the first imaging range 202B and the exact center C2 of the second imaging range 204B. It should be noted that the correction value B is a vector. Figure 3B The magnitude and direction of the correction value B shown in FIG is an example for the convenience of description only, and the correction value B may have any suitable magnitude and direction. Figure 3B The correction value B shown in FIG. 1 has a horizontal displacement, but the correction value B may have any suitable horizontal and vertical displacements.

[0053] Method 300 is performed once during hardware installation or troubleshooting, and does not need to be performed before each user measurement. After completing method 300, each time the computing device 102 converts any pixel in the first imaging range 202A of the RGB camera 104 (of the user image) into a pixel in the second imaging range 204A of the thermal imaging camera 106, it calibrates the corresponding result using the stored calibration value B. If there are other heat sources near the user's face (such as hot drinks, heat packs, etc.), the thermal imaging camera 106 may capture these external heat sources and mistakenly determine that the user's body temperature is too high. To address this issue, key facial features (such as the eyes and forehead) in the first user image can be detected to further determine the area in the calibrated second user image that actually belongs to the user's face.

[0054] Specifically, the computing device 102 detects facial landmarks in the first user image (eg, RGB image) using image recognition technology (eg, deep learning) and extracts a facial region from the first user image, such as Figure 3C The region R1 has four vertex positions P1 to P4. In the embodiment of the present invention, Figure 3C The first imaging range 202 in FIG. 1 is the imaging range of a user image, and the second imaging range 204 is the thermal imaging range of the user image. The computing device 102 uses a mapping relationship F1 to convert the four corner positions P1-P4 into corresponding positions in the second imaging range 204 of the thermal imaging camera 106 to obtain a base corresponding region, such as region R2, in the second imaging range 204. Corner positions P11-P14 of region R2 correspond to base corresponding positions of corner positions P1-P4 in the second imaging range 204. Subsequently, the computing device 102 corrects the positions of base corresponding region R2 (e.g., corner positions P11-P14) using a correction value (offset) B to obtain a corrected corresponding region R3 in the second imaging range 204. Corner positions P21-P24 of region R3 correspond to corrected corresponding positions of corner positions P11-P14 in the second imaging range 204. When the temperature within the calibrated corresponding region R3 is greater than or equal to the threshold temperature, the computing device 102 determines that the user's body temperature is greater than or equal to the threshold temperature. In certain embodiments, the computing device 102 may determine that the user's body temperature is greater than or equal to the threshold temperature based on the average or highest temperature value corresponding to the calibrated corresponding region R3. In certain embodiments, when the user wears a mask, facial key points may include characteristic features such as the eyes and forehead. When characteristic features such as the eyes and forehead are not detected, the computing device 102 may control the display 110 to display information instructing the user to expose the facial key points to prevent them from being obscured by external heat sources, thereby affecting the computing device 102's body temperature determination result.

[0055] Figure 4 A flow chart showing an automated personnel monitoring method suitable for Figure 1 The intelligent epidemic prevention system shown in the figure is in progress. Figure 4 Before the method 400 shown, a Figure 3A The method 300 shown is used to obtain the correction value B. Figure 4 In the method 400, the display 110 displays information instructing the user to wear the mask correctly and expose key points of the face to be recorded by the RGB camera 104 and the thermal imaging camera 106. Then, in step 404, the RGB camera 104 records a first user image (not shown) of the user and transmits it to the computing device 102. In step 406, the computing device 102 captures the user's facial features in the first user image and captures the facial area (e.g., Figure 3C At the same time, in step 416, the thermal imaging camera 106 records a second user image (not shown) of the user and transmits it to the computing device 102. In step 418, the computing device 102 analyzes the corrected corresponding area corresponding to the second user image (i.e., within the thermal imaging range of the thermal imaging camera 106) of the facial area obtained in step 406. For example, the computing device 102 calculates the facial area obtained in step 406 (e.g., Figure 3C A base corresponding area (eg, area R1 in the second user image) in the second user image (ie, in the thermal imaging range of the thermal imaging camera 106) Figure 3C The area R2 in the figure is corrected by using the correction value B to correct the base corresponding area (e.g. Figure 3C The computing device 102 determines the user's body temperature as being greater than or equal to the threshold temperature by determining the position of the region R2 in the image of the second user (i.e., the thermal imaging range of the thermal imaging camera 106). The computing device 102 determines that the user's body temperature is greater than or equal to the threshold temperature when the temperature (average temperature or maximum temperature) within the calibrated corresponding region R3 is greater than or equal to the threshold temperature.

[0056] In step 408, the computing device 102 determines whether the user is wearing the mask correctly based on the analysis results in step 406. If so, in step 420, the computing device 102 determines whether the user's body temperature is abnormal (greater than or equal to the threshold temperature) based on the analysis results in step 418. If not, in step 422, the computing device 102 controls the access control system 108 to open, allowing the user to pass, and in step 424, the display 110 displays a message that the user can enter the station. In other words, when the user wears the mask correctly and the body temperature is less than the threshold temperature, the access control system 108 is turned on. Subsequently, in step 414, the computing device 102 transmits the measurement results to the data analysis device 112 for subsequent data analysis.

[0057] If the user is not wearing a mask correctly in step 408, or if the user's body temperature is abnormal in step 420, then in step 410, computing device 102 controls access control system 108 to shut down, prohibiting the user from passing. In step 412, display 110 displays an error message indicating that the user is denied entry. In other words, when the user is not wearing a mask correctly, or their body temperature is greater than or equal to the threshold temperature, access control system 108 shuts down. Subsequently, in step 414, computing device 102 transmits the measurement results to data analysis device 112 for subsequent data analysis.

[0058] The measurement results transmitted to the data analysis device 112 represent user information for a single user. The data analysis device 112 can analyze at least one piece of user information from different epidemic prevention systems 101 (e.g., big data analysis) to allow relevant personnel to monitor the mask wearing status and the distribution of fevers among users in various locations (e.g., passengers at various stations on the mass rapid transit system) so that timely measures can be taken to prevent the spread of the disease. Specifically, the user information may include a first signal and a second signal, where the first signal indicates whether the user's body temperature is greater than or equal to a threshold temperature, and the second signal indicates whether the user is wearing a mask correctly. The data analysis device 112 generates an analysis result based on the at least one piece of user information. This analysis result may include the distribution of the first and second signals at different stations, namely, the distribution of fevers and mask wearing status at each station. Each epidemic prevention device 101 can record the number of people denied entry to the station due to abnormal body temperature each day and provide this data to the data analysis device 112. The data analysis device 112 can display the distribution of fevers and mask wearing status at each station, for example, through a platform graphical interface. Relevant personnel can use this platform's graphical interface to identify abnormally high fever rates and their proportions at each station, or to determine if there are any regional correlations. The platform's graphical interface can also be combined with other epidemic prevention information (such as confirmed case locations) to display hotspot alerts and issue clearance and tiered notifications, allowing relevant personnel to take timely measures to prevent the spread of the disease.

[0059] The foregoing description of embodiments, including the illustrated embodiments, is presented for ease of illustration and description only and is not intended to be exhaustive or to limit the disclosure to the precise form disclosed in the foregoing drawings and text. A person skilled in the art will readily recognize various modifications, adaptations, and uses of the disclosed embodiments. Based on the disclosure herein, various modifications may be made to the disclosed embodiments without departing from the spirit and scope of the disclosure. Therefore, the breadth and scope of the disclosure should not be limited to any of the foregoing embodiments.

[0060] Although certain aspects and features of the present disclosure have been depicted or described with respect to one or more implementations, others skilled in the art will be able to conceive of or understand equivalent substitutions or modifications after reading and understanding this specification and the accompanying drawings. In addition, although a particular feature of the present disclosure may be disclosed with respect to a single implementation among several implementations, that feature may be combined with one or more other features in other implementations if that is desirable or advantageous for any given or particular application.

[0061] The terms used herein are used only to describe specific embodiments and are not intended to limit the present disclosure. Unless otherwise indicated, the singular forms "a," "an," and "the" as used herein are intended to include the plural forms. In addition, the words "including," "comprising," "having," or variations thereof as used in "embodiments" and / or claims are intended to be inclusive, similar to the word "comprising."

[0062] Unless otherwise defined, all terms (including technical and scientific terms) used in this specification have the same meanings as those commonly understood by persons skilled in the art to which this invention belongs. Furthermore, unless otherwise explicitly defined in this specification, terms that are defined in commonly used dictionaries, for example, should be interpreted as having the same meaning as in the context of the relevant art and should not be interpreted in an idealized or overly formal manner.

Claims

1. An intelligent epidemic prevention system, comprising: A calibration device having a metal plate, wherein the metal plate is heated to a predetermined sensing temperature; an access control system; a visible light camera for capturing a first calibration device image of the calibration device and a first user image of a user; a thermal imaging camera for recording a second calibration device image of the calibration device and a second user image of the user; and a computing device for calculating a first position of the calibration device on the first calibration device image and a second position of the calibration device on the second calibration device image, calculating a base corresponding position of the calibration device on the second user image based on the first position, and calculating a calibration value based on the second position and the base corresponding position; The computing device determines whether the user is wearing the mask correctly based on the first user image, generating a first determination result, and determines whether the user's body temperature is greater than or equal to a threshold temperature based on the calibration value and the second user image, generating a second determination result; The computing device controls the access control system to be turned on or off according to the first judgment result and the second judgment result.

2. The intelligent epidemic prevention system as claimed in claim 1, wherein the predetermined sensing temperature is a temperature greater than room temperature, and the metal plate has a plurality of hollow geometric shapes.

3. The intelligent epidemic prevention system according to claim 1, wherein: In response to the user correctly wearing the mask and having a body temperature lower than the threshold temperature, the computing device controls the access control system to be turned on; and In response to the user not wearing the mask correctly or having a body temperature greater than or equal to the threshold temperature, the computing device controls the access control system to shut down.

4. The intelligent epidemic prevention system according to claim 3, wherein: The computing device captures a facial region in the first user image by detecting multiple facial landmarks in the first user image. The computing device calculates a base corresponding region for the facial region in the second user image and corrects the position of the base corresponding region using the correction value to obtain a corrected corresponding region in the second user image. When the temperature within the corrected corresponding region is greater than or equal to the threshold temperature, the computing device determines that the user's body temperature is greater than or equal to the threshold temperature.

5. The intelligent epidemic prevention system as claimed in claim 1, further comprising a data analysis device coupled to the computing device; In response to the opening or closing of the access control system, the computing device transmits at least one user information to the data analysis device; and The data analysis device generates an analysis result according to the at least one user information; in, Each of the user information includes: a first signal indicating whether the user's body temperature is greater than or equal to the threshold temperature; and A second signal indicating whether the user is wearing the mask correctly; and The analysis result includes the distribution of the first signal and the second signal at different sites.

6. An intelligent epidemic prevention method, comprising: Using a visible light camera, record a first calibration device image of a calibration device, wherein the calibration device comprises a metal plate heated to a predetermined sensing temperature; Using a thermal imaging camera to record a second calibration device image of the calibration device; Calculating a first position of the calibration device on the first calibration device image and a second position of the calibration device on the second calibration device image; Calculating a base corresponding position of the calibration device on the image of the second calibration device based on the first position, and calculating a calibration value based on the second position and the base corresponding position; Recording a first user image of a user with the visible light camera; recording a second user image of the user with the thermal imaging camera; Determining whether the user is wearing the mask correctly based on the first user image, generating a first determination result; and determining whether the user's body temperature is greater than or equal to a threshold temperature based on the calibration value and the second user image, generating a second determination result; as well as An access control system is controlled to be opened or closed according to the first judgment result and the second judgment result.

7. The intelligent epidemic prevention method according to claim 6, wherein the predetermined sensing temperature is a temperature greater than room temperature, and the metal plate has a plurality of hollow geometric shapes.

8. The intelligent epidemic prevention method according to claim 6, wherein: When the user wears the mask correctly and the body temperature is lower than the threshold temperature, the access control system is activated; and When the user does not wear a mask correctly or the body temperature is greater than or equal to the threshold temperature, the access control system is closed.

9. The intelligent epidemic prevention method according to claim 8, wherein the operations of determining whether the user is wearing the mask correctly and determining whether the user's body temperature is greater than or equal to the threshold temperature include: capturing a facial region in the first user image by detecting a plurality of facial landmarks in the first user image; Calculating a base corresponding region of the facial region in the second user image, and correcting a position of the base corresponding region using the correction value to obtain a corrected corresponding region in the second user image; as well as When the temperature within the calibrated corresponding area is greater than or equal to the threshold temperature, a computing device determines that the user's body temperature is greater than or equal to the threshold temperature.

10. The intelligent epidemic prevention method according to claim 6, further comprising: generating at least one user information in response to the opening or closing of the access control system; as well as generating an analysis result based on the at least one user information; Wherein, each user information includes: a first signal indicating whether the user's body temperature is greater than or equal to the threshold temperature; and A second signal indicating whether the user is wearing the mask correctly; and The analysis result includes the distribution of the first signal and the second signal at different sites.

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