A self-powered intelligent mask detection device and method

Abstract

The present invention discloses a self-powered intelligent mask detection device and method, comprising a mask body, and also comprising: an inner layer, detachably connected to the mask body; a carbon dioxide concentration sensor, embedded in the inner layer; a flexible pressure sensor, embedded in the inner layer; a gas sensor, embedded in the inner layer; a flexible thermoelectric module, detachably installed on the human chest; a flexible photoelectric module, connected to the mask body; and a main control module, connected to the carbon dioxide concentration sensor, the flexible pressure sensor, the gas sensor, the flexible thermoelectric module and the flexible photoelectric module; the present invention can detect relevant vital signs of the user and has the ability of self-power supply. Compared with the traditional lithium battery power supply method, the present invention does not require additional power supply and is more convenient to use.

Description

Technical Field

[0001] The present invention belongs to the technical field of micro-nano electromechanical system sensors, and in particular relates to a self-powered intelligent mask detection device and method. Background Art

[0002] The global spread of the novel coronavirus has negatively impacted people's lives and national economies worldwide. Due to epidemic prevention and control measures, masks have become an essential part of people's lives, and are now worn in public. However, current masks have limited functionality, and some people may be accustomed to wearing them while exercising outdoors. However, prolonged use can lead to oxygen deprivation and discomfort. As people's quality of life improves, they are becoming more concerned about their health. A wide variety of illnesses are emerging, and each can be prevented by monitoring specific health indicators. However, currently, testing some health indicators requires visits to hospitals or specialized health centers, which is time-consuming, labor-intensive, and potentially expensive. The development of wearable technology and flexible sensors has led to the emergence of numerous portable monitoring devices that can provide users with professional health monitoring anytime, anywhere. However, most wearable devices currently rely on lithium batteries, which require regular charging or replacement. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the present invention provides a self-powered intelligent mask detection device and method to solve the above problems.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: a self-powered intelligent mask detection device, including a mask body, and also including:

[0005] The inner layer is detachably connected to the main body of the mask and is used to carry components;

[0006] A carbon dioxide concentration sensor is embedded in the inner layer and is used to detect the carbon dioxide concentration;

[0007] A flexible pressure sensor, embedded in the inner layer, is used to obtain the user's breathing rate;

[0008] A gas sensor, embedded in the inner layer, is used to analyze the composition of the user's exhaled gas;

[0009] Flexible thermoelectric module, which is detachable and installed on the human chest, is used to convert thermal energy into electrical energy;

[0010] A flexible photoelectric module connected to the mask body for converting light energy into electrical energy; and

[0011] The main control module is connected to the carbon dioxide concentration sensor, flexible pressure sensor, gas sensor, flexible thermoelectric module and flexible photoelectric module for device control and data processing.

[0012] On the basis of the above technical solutions, the present invention also provides the following optional technical solutions:

[0013] Further technical solution: The main control module includes a main control board and a main control MCU unit, an energy collection and conversion circuit, a wireless communication module, and a voice broadcast module installed on the main control board. The energy collection and conversion circuit, the wireless communication module, and the voice broadcast module are all connected to the main control MCU unit. The wireless communication module and the voice broadcast module are connected to the energy collection and conversion module. The energy collection and conversion unit is connected to the flexible thermoelectric module and the flexible photoelectric module through wire B and wire C respectively. The main control MCU unit is connected to the carbon dioxide concentration sensor, the flexible pressure sensor, and the gas sensor through wire A. A BP neural network is provided in the main control MCU unit.

[0014] Further technical solution: The energy collection and conversion circuit includes a micro energy collection and storage circuit and a DC-DC current conversion circuit. The DC-DC current conversion circuit is connected to the energy collection and storage circuit. The DC-DC conversion circuit is connected to the wireless communication module, the voice broadcast module and the main control MCU unit. The energy collection and storage circuit is connected to the flexible thermoelectric module through wire B, and the energy collection and storage circuit is connected to the flexible photoelectric module through wire C.

[0015] Further technical solution: The carbon dioxide concentration sensor includes a carbonate auxiliary electrode, a reference electrode, a sensitive electrode A, a NASICON, an Al2O3 ceramic tube and a heating coil. The carbonate auxiliary electrode, the reference electrode, the sensitive electrode A, the NASICON, and the Al2O3 ceramic tube are each provided in two groups and are symmetrically arranged linearly on both sides of the heating coil. The carbonate auxiliary electrode is connected to the main control module via wire A.

[0016] Further technical solution: The gas sensor is a flexible special metal oxide semiconductor gas sensor, which includes a sensitive electrode B, a heating electrode, an insulating layer, a main body A, a micro-hot plate and a main body B. The sensitive electrode B, the heating electrode, the insulating layer, the main body A, the micro-hot plate and the main body B are arranged linearly in sequence, and the sensitive electrode B is connected to the main control MCU unit through a wire A.

[0017] Further technical solution: The flexible pressure sensor includes a PDMS film, interdigital electrodes, PZT nanofibers and a PET substrate, the PDMS film, interdigital electrodes, PZT nanofibers and PET substrate are arranged linearly in sequence, and the interdigital electrodes are connected to the main control module through wire A.

[0018] Further technical solution: A flexible nose clip is provided on the mask body, and the flexible nose clip is made of a composite flexible material.

[0019] Further technical solution: The BP neural network algorithm includes an input layer, a hidden layer and an output layer. The input layer takes carbon dioxide concentration data and respiratory rate data as input data and outputs rest-needing data and normal situation data in the output layer.

[0020] Further technical solution: The BP neural network adopts the gradient descent method to obtain the objective function during training. Specifically, during the forward propagation of data, the hidden layer output H needs to be calculated first, and then the weight w between the input data and the input layer and the hidden layer needs to be calculated. ij , hidden layer threshold a, we can get the output H of the hidden layer j for:

[0021]

[0022] Among them, j = 1, 2, 3...; and then the connection weight between the hidden layer and the output layer, w jk The output layer threshold b can be used to obtain the output layer output O k for:

[0023]

[0024] Where k = 1, 2, 3..., m; Since there is an error between the output value and the expected value, the error needs to be propagated backwards to update the weights and thresholds. k And the expected output y can get the prediction error e k :

[0025] e k =y k -O k

[0026] The two updated weights w ij 、w jk for:

[0027]

[0028] Where i = 1, 2, 3, ..., n, j = 1, 2, 3, ..., l, k = 1, 2, 3, ..., m; η represents the learning rate; then the threshold is updated:

[0029]

[0030] Finally, the threshold is updated to the neural network structure, and the above process is repeated until the error drops to the ideal range, and the neural network training is completed.

[0031] A self-powered intelligent mask detection method, based on the above-mentioned self-powered intelligent mask detection device, includes the following steps:

[0032] S1. Wear the mask body normally on the face, install the flexible photoelectric module on the outer surface of the mask, install the flexible thermoelectric module on one side of the user's chest, install the system main control module on the other side of the user's chest, and connect the flexible photoelectric module, flexible thermoelectric module and energy collection and conversion unit with wires, and use the flexible photoelectric module and flexible thermoelectric module to power the main control module;

[0033] S2. Use the main control module to detect whether the output voltage of the energy collection and conversion circuit reaches the normal operating voltage of 3.3V. If it reaches the normal operating voltage, use the carbon dioxide concentration sensor and the flexible pressure sensor to collect carbon dioxide concentration data and respiratory rate data in turn. Input the above two data into the BP neural network preset in the main control MCU unit for calculation and determine whether the user needs to take off the mask for proper rest. If rest is required, a voice prompt is given through the voice broadcast module;

[0034] S3. Use the gas sensor to collect the gas exhaled by the user and detect its composition data to determine whether the user's health condition is abnormal. If abnormal, a voice prompt is given through the voice broadcast module;

[0035] S4. All collected physiological indicator data are sent to the user's mobile phone APP via the wireless communication module. The APP will process the received data and generate a daily health report for the user to use as a reference for his or her health status;

[0036] S5: Wait until the output voltage of the energy collection and conversion circuit reaches the standard, and repeat the process of S1-4.

[0037] Beneficial effects

[0038] The present invention provides a self-powered intelligent mask detection device and method, which has the following beneficial effects compared with the prior art:

[0039] 1. The present invention uses a carbon dioxide concentration sensor and a flexible pressure sensor. Under the control of the main control board, it can obtain the carbon dioxide concentration in the mask and the user's breathing rate when the user wears the mask, so as to determine whether the user needs to take off the mask for a proper rest. Compared with traditional masks, it can remind users to avoid wearing the mask for a long time and causing discomfort such as hypoxia. In addition, the combination of voice prompts makes the present invention more humane.

[0040] 2. The flexible special metal oxide semiconductor gas sensor used in the present invention can measure the composition of the user's exhaled gas. By analyzing the gas composition data, the user's physical health status can be determined. Compared with traditional detection instruments, the present invention is more portable;

[0041] 3. The flexible photovoltaic and thermoelectric modules used in this invention, combined with the energy collection and conversion circuits in the system motherboard, enable this invention to be self-powered. Compared to traditional lithium battery power supply methods, this invention does not require regular charging or battery replacement, reducing resource waste and environmental pollution, and increasing the portability of this invention.

[0042] 4. The present invention adopts a flexible nose clip. With the help of flexible textile materials, the bridge of the nose will not feel oppressed when wearing it compared to traditional masks, so it is more comfortable;

[0043] 5. The present invention has a supporting mobile phone app that can process the collected physiological information data and generate a daily health report containing detailed physical health information for users to read and refer to. Compared with traditional detectors that require users to compare the measured parameters, the present invention detects physiological indicators more concisely and clearly. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is the overall structural diagram of the present invention.

[0045] Figure 2 This is a schematic diagram of the present invention being worn by a user.

[0046] Figure 3 This is a structural diagram of the internal components of the carbon dioxide concentration sensor in the present invention.

[0047] Figure 4 This is a diagram of the internal device structure of the flexible special metal oxide semiconductor gas sensor in the present invention.

[0048] Figure 5 This is a structural diagram of the internal components of the flexible pressure sensor in the present invention.

[0049] Figure 6 This is a structural diagram of the internal components of the thermoelectric module in the present invention.

[0050] Figure 7 This is a diagram of the surface material structure of the thermoelectric module in the present invention.

[0051] Figure 8 This is a structural diagram of the BP neural network deployed in the system main control MCU unit in the present invention.

[0052] Figure 9 It is the overall workflow diagram of the present invention.

[0053] Figure marking notes: 1. Mask body; 2. Flexible photoelectric module; 3. Wire A; 4. Main control module; 5. Wire B; 6. Flexible thermoelectric module; 7. Wire C; 8. Carbonate auxiliary electrode; 9. Reference electrode; 10. Al2O3 ceramic tube; 11. Heating coil; 12. Sensitive electrode A; 13. Sensitive electrode B; 14. Heating electrode; 15. Insulating layer; 16. Main body A; 17. Micro hot plate; 18. Main body B; 19. PDMS film; 20. Interdigital electrode; 21. PZT nanofiber; 22. PET substrate; 23. Heat collection layer; 24. Thermocouple; 25. Heat dissipation layer; 26. Thermoelectric device; 27. Flexible high thermal conductivity material; 28. Input layer; 29. ​​Hidden layer; 30. Output layer; 31. Carbon dioxide concentration data; 32. Respiration rate data; 33. Rest required data output; 34. Normal situation data output. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0056] See also Figures 1 to 9 , provided in one embodiment of the present invention, is a self-powered intelligent mask detection device, comprising a mask body 1, and further comprising:

[0057] The inner layer is detachably connected to the mask body 1 and is used to carry components;

[0058] A carbon dioxide concentration sensor is embedded in the inner layer and is used to detect the carbon dioxide concentration;

[0059] A flexible pressure sensor, embedded in the inner layer, is used to obtain the user's breathing rate;

[0060] A gas sensor, embedded in the inner layer, is used to analyze the composition of the user's exhaled gas;

[0061] Flexible thermoelectric module 6, which is detachable and installed on the human chest to convert thermal energy into electrical energy;

[0062] The flexible photoelectric module 2 is connected to the mask body 1 and is used to convert light energy into electrical energy;

[0063] The main control module 4 is connected to the carbon dioxide concentration sensor, flexible pressure sensor, gas sensor, flexible thermoelectric module 6 and flexible photoelectric module 2, and is used for device control and data processing. The main control module 4 includes a main control board and a main control MCU unit, an energy collection and conversion circuit, a wireless communication module, and a voice broadcast module installed on the main control board. The energy collection and conversion circuit, the wireless communication module and the voice broadcast module are all connected to the main control MCU unit, the wireless communication module and the voice broadcast module are connected to the energy collection and conversion module, the energy collection and conversion circuit is connected to the flexible thermoelectric module 6 through a wire B5, the energy collection and conversion circuit is connected to the flexible photoelectric module 2 through a wire C7, the main control MCU unit is connected to the carbon dioxide concentration sensor, the flexible pressure sensor and the gas sensor through a wire A3, and a BP neural network is provided in the main control MCU unit.

[0064] Specifically, the main control MCU unit uses an STM32F407ZGT6 chip as the main control chip, and its main function is to control other modules and coordinate the normal operation of the entire system.

[0065] Specifically, the energy collection and conversion circuit includes a micro-energy collection and storage circuit and a DC-DC current conversion circuit. The DC-DC current conversion circuit is connected to the energy collection and storage circuit, and the DC-DC conversion circuit is connected to the wireless communication module, the voice broadcast module, and the main control MCU unit. The energy collection and storage circuit is connected to the flexible thermoelectric module 6 via wire B5, and the energy collection and storage circuit is connected to the flexible photoelectric module 2 via wire C7. The micro-energy collection and storage circuit uses a micro-energy collection chip and a supercapacitor to collect the current output by the energy conversion part into the supercapacitor for storage. The micro-energy collection and storage circuit works intermittently, and the supercapacitor storage part is connected to the DC-DC current conversion circuit to ensure a stable output of current at a certain voltage, providing energy for the present invention.

[0066] Specifically, the carbon dioxide concentration sensor includes a carbonate auxiliary electrode 8, a reference electrode 9, a sensitive electrode A12, NASICON, an Al2O3 ceramic tube 10, and a heating coil 11. The carbonate auxiliary electrode 8, the reference electrode 9, the sensitive electrode A12, the NASICON, and the Al2O3 ceramic tube 10 are each provided in two groups and are symmetrically arranged linearly on both sides of the heating coil 11. The carbonate auxiliary electrode 8 is connected to the main control MCU unit (connected to the main control module 4) via a wire B. Its basic principle is to use the mobile ion characteristics of the NASICON solid electrolyte as a solid electrolyte. After the heating coil 11 heats the internal temperature of the sensor to a certain degree, the sensitive electrode A12 is exposed to the atmospheric environment, so that the concentration (or partial pressure) of carbon dioxide is converted into an electrical signal through an electrochemical reaction. The concentration value of carbon dioxide in the corresponding environment can be obtained by measuring the size of the electrical signal on the wire led by the sensitive electrode A12.

[0067] Specifically, the gas sensor is a flexible special metal oxide semiconductor gas sensor, which includes a sensitive electrode B13, a heating electrode 14, an insulating layer 15, a main body A16, a micro-hotplate 17, and a main body B18. The sensitive electrode B13, the heating electrode 14, the insulating layer 15, the main body A16, the micro-hotplate 17, and the main body B18 are arranged linearly in sequence, and the sensitive electrode B13 is connected to the main control MCU unit via a wire A3. In the present invention, the micro-hotplate 17 of the special metal oxide semiconductor gas sensor is made of Si as the substrate, the main body A16 and the main body B18 are made of SiO2, the heating electrode 14 is made of Pt, SiO2 (APCVD) is used as the insulating layer 15 between the sensitive electrode B13 and the heating electrode 14, the sensitive electrode B13 is made of precious metal Au, the substrate material of the micro-hotplate 17 is Si, and an inverted trapezoidal cavity is used inside. The advantage of this is that the temperature and power characteristics of the sensor micro-hotplate 17 are better. The sensor's principle is to utilize sensitive materials on the surface of the sensor device. When they come into contact with a specific gas, they react, causing a change in their conductivity or resistivity. This, in turn, causes a corresponding change in the current or voltage across the sensor electrodes. By measuring the voltage or current, the gas concentration or composition can be determined. Certain diseases can alter the composition of exhaled gas. Research has shown that measuring the composition of exhaled gas can be used to assess a person's health, making it a popular method for disease monitoring in recent years.

[0068] Specifically, the flexible pressure sensor includes a PDMS film 19, interdigital electrodes 20, PZT nanofibers 21, and a PET substrate 22. The PDMS film 19, interdigital electrodes 20, PZT nanofibers 21, and PET substrate 22 are arranged linearly in sequence, and the interdigital electrodes 20 are connected to the main control MCU unit via a wire A3. The principle of a general flexible pressure sensor is to indicate the magnitude of the applied pressure by measuring the magnitude of the electrical signal at the output electrode. In the present invention, when an external force is applied to the flexible pressure sensor, the nanofibers will deform, thereby generating a potential difference. The interdigital electrodes 20 bound to the nanofibers will reflect the change in potential difference. After the material is selected, the potential difference of the interdigital electrodes 20 will change with the application of the external force. In this way, the magnitude of the potential difference between the two ends of the electrodes can be detected to calculate the magnitude of the applied external force. When the flexible sensor is placed in a suitable position on the mask, the airflow will cause the surface of the flexible sensor to continuously deform when the user wears it and breathes, causing the waveform of the flexible sensor output current to change periodically. Therefore, the output current data of the sensor can be converted and calculated to obtain the user's breathing rate data 32.

[0069] Specifically, the flexible thermoelectric module 6 includes thermoelectric devices 26 (not shown) and a flexible board (not shown). Several thermoelectric devices 26 are evenly embedded in a rectangular shape on the flexible board. A flexible high-thermal-conductivity material 27 is connected to the flexible board. The flexible high-thermal-conductivity material 27 is filled between the thermoelectric devices 26. The thermoelectric devices 26 are electrically connected. The thermoelectric devices 26 include a heat-collecting layer 23, a thermocouple 24, and a heat-dissipating layer 25. The heat-collecting layer 23, the thermocouple 24, and the heat-dissipating layer 25 are arranged linearly in sequence. The thermocouple 24 is connected to the energy collection and conversion circuit via a wire B5. This arrangement is designed to absorb human body heat energy, convert it into electrical energy, and transmit it to the energy collection and conversion circuit via a wire A3 for storage within the energy storage module. The filling of the flexible high-thermal-conductivity material improves overall thermal conductivity, making the module durable and capable of bending, twisting, and stretching. It can maintain full functionality even in a stretched state, thus being able to break through the ductility limitations of the skin, providing very ideal properties for wearable electronic devices and soft robots. The principle of power generation in thermoelectric modules is the Seebeck effect: when there is a temperature difference between the two ends of a conductor, the electrons in the conductor will move from the hot end to the cold end, and finally gather at the cold end, thereby forming a potential difference inside the conductor. At the same time, under the action of this potential difference, a reverse charge flow is generated. When the charge flow of thermal motion reaches a dynamic equilibrium with the internal electric field, a stable thermoelectric electromotive force is formed at both ends of the semiconductor, and power generation can be achieved by means of this principle. In the present invention, a temperature difference is formed by the different temperatures on both sides of the heat collection layer 23 and the heat dissipation layer 25, resulting in a potential difference between the two ends of the thermocouple 24, causing the flow of ions at both ends of the thermocouple 24 to form an electric current. When sufficient thermoelectric devices 26 are placed on a piece of material, sufficient current will be generated to meet the power supply requirements.

[0070] Specifically, the photovoltaic module is made of flexible photovoltaic cell material. Its power generation principle is the photoelectric effect: light creates a potential difference between different parts of an uneven semiconductor or a semiconductor-metal bond, thereby generating an electric current. In this invention, the photovoltaic module is mounted on the outer surface of the mask, converting ambient light energy into electrical energy to power the entire system.

[0071] Specifically, the wireless communication module utilizes a mainstream low-power Bluetooth module. It transmits the data collected by the sensors to the user's mobile app, which then generates a daily health report based on the collected data for the user to use as a reference for their daily health status. This module also features relatively low power consumption, making it suitable for the application scenarios of the present invention.

[0072] Specifically, the voice announcement module utilizes a low-power, mainstream voice announcement module on the market. Prepared voice messages are stored in the module, and then controlled by the aforementioned main control MCU unit. Voice prompts are generated based on the different calculation results of the main control MCU unit to alert users to current problems, allowing them to take timely measures to resolve them. This module also features low power consumption and ease of use, making it suitable for the application scenarios of the present invention.

[0073] Specifically, the mask body 1 is provided with a flexible nose clip, which is made of a composite flexible material. The flexible nose clip is soft and does not irritate the skin. It can fit on the nose clip of the mask, which will reduce the pressure of the mask on the nose bridge when we wear the mask, and increase the wearing comfort.

[0074] Specifically, the BP neural network algorithm includes an input layer 28 , a hidden layer 29 and an output layer 30 . The input layer 28 takes carbon dioxide concentration data 31 and respiratory rate data 32 as input data and outputs rest need data output 33 and normal condition data output 34 in the output layer 30 .

[0075] Specifically, a BP neural network is used to calculate the collected data to determine the user's hypoxia status. The BP neural network structure includes an input layer 28, a hidden layer 29, and an output layer 30. In the present invention, carbon dioxide concentration data 31 and respiratory rate data 32 collected by the sensor are used as network inputs. After the overall network calculations, a rest-needed data output 33 and a normal condition data output 34 are obtained, indicating whether the user's current condition requires removing the mask for proper rest. Generally, a portion of physiological data is collected in advance as data samples for neural network training. After the neural network is built and trained on a computer, it is transplanted to the main control MCU unit for use.

[0076] Specifically, the BP neural network adopts the gradient descent method to obtain the objective function during training. During the forward propagation of data, the output H of the hidden layer 29 needs to be calculated first, and then the weight w between the input layer 28 and the hidden layer 29 needs to be calculated. ij , hidden layer 29 threshold a, get the output H of hidden layer 29 j for:

[0077]

[0078] Wherein, j=1, 2, 3...; Then, the connection weight between the hidden layer 29 and the output layer 30, w jk The output layer 30 threshold b can be obtained by outputting the output layer 30 output O k for:

[0079]

[0080] Where k = 1, 2, 3..., m; Since there is an error between the output value and the expected value, the error needs to be propagated backwards to update the weights and thresholds. k And the expected output y can get the prediction error e k :

[0081] e k =y k -O k

[0082] The two updated weights w ij 、w jk for:

[0083]

[0084] Where i = 1, 2, 3, ..., n, j = 1, 2, 3, ..., l, k = 1, 2, 3, ..., m; η represents the learning rate; then the threshold is updated:

[0085]

[0086] Finally, the threshold is updated to the neural network structure, and the above process is repeated until the error falls within the ideal range, completing the neural network training. In actual use, the relevant model code files are ported to the main control MCU unit and run. The main control MCU unit will then run the network and input the carbon dioxide concentration data 31 and respiratory rate data 32 collected by the actual sensor. After calculation, the specific physical condition corresponding to this set of data is obtained, enabling accurate pre-determination of whether the user is about to experience discomfort. If discomfort is about to occur, the relevant instructions will be executed and a corresponding voice prompt will be issued, reminding the user to take measures to avoid the situation, thus achieving the intended function of the invention.

[0087] In the embodiment of the present invention, first, the mask body 1 is worn normally on the face, the flexible photoelectric module 2 is installed on the outer surface of the mask, the flexible thermoelectric module 6 is installed on one side of the user's chest, the system main control module 4 is installed on the other side of the user's chest, and the mask part, the flexible thermoelectric module 6 and the system main board are connected with a wire; secondly, the relevant personnel use the main control module 4 to detect whether the output voltage of the energy collection and conversion circuit reaches the normal working voltage of 3.3V. After reaching it, the carbon dioxide concentration sensor and the flexible pressure sensor are used in turn to collect carbon dioxide concentration data 31 and respiratory rate data 32, and the above two data are input into the BP preset in the main control MCU unit. The neural network performs calculations and determines whether the user needs to take off the mask for proper rest. If rest is required, a voice prompt will be given through the voice broadcast module; secondly, the gas exhaled by the user is collected by the gas sensor and its composition data is detected to determine whether the user's health status is abnormal. If abnormal, a voice prompt will be given through the voice broadcast module; finally, all the collected physiological indicator data are sent to the user's mobile phone APP with the help of the wireless communication module. The APP will process the received data and generate a daily health report for the user to use as a reference for his or her own health status. After completing the above work, the entire system will work again when the output voltage of the energy collection and conversion circuit reaches the standard, and repeat the above process.

[0088] The self-powered smart mask detection device mentioned in the present invention can analyze the user's breathing frequency, carbon dioxide concentration in the exhaled gas, and exhaled gas composition to judge the user's physical condition and give voice prompts. At the same time, the photoelectric module and thermoelectric module in the present invention can collect light energy in the environment and heat energy on the user's body and convert them into current, and provide electrical energy for the operation of the entire system through energy collection and conversion circuits, so that the present invention has the ability to be self-powered. Compared with the traditional lithium battery power supply method, the present invention does not require additional power supply and is more convenient to use.

[0089] A self-powered intelligent mask detection method, based on the above-mentioned self-powered intelligent mask detection device, includes the following steps:

[0090] S1. Wear the mask body 1 normally on the face, install the flexible photoelectric module 2 on the outer surface of the mask, install the flexible thermoelectric module 6 on one side of the user's chest, install the system main control module 4 on the other side of the user's chest, and connect the flexible photoelectric module 2, the flexible thermoelectric module 6 and the energy collection and conversion unit (main control module 4) with wires, and use the flexible photoelectric module 2 and the flexible thermoelectric module 6 to power the main control module 4;

[0091] S2. Use the main control module 4 to detect whether the output voltage of the energy collection and conversion circuit reaches the normal operating voltage of 3.3V. If it reaches the normal operating voltage, use the carbon dioxide concentration sensor and the flexible pressure sensor to collect carbon dioxide concentration data 31 and respiratory rate data 32 in sequence, input the above two data into the BP neural network preset in the main control MCU unit for calculation and determine whether the user needs to take off the mask for proper rest. If rest is required, a voice prompt is given through the voice broadcast module;

[0092] S3. Use the gas sensor to collect the gas exhaled by the user and detect its composition data to determine whether the user's health condition is abnormal. If abnormal, a voice prompt is given through the voice broadcast module;

[0093] S4. All collected physiological indicator data are sent to the user's mobile phone APP via the wireless communication module. The APP will process the received data and generate a daily health report for the user to use as a reference for his or her health status;

[0094] S5: Wait until the output voltage of the energy collection and conversion circuit reaches the standard, and repeat the process of S1-4.

[0095] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0096] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A self-powered intelligent mask detection device, characterized in that: Including the mask body, also includes: The inner layer is detachably connected to the main body of the mask and is used to carry components; A carbon dioxide concentration sensor is embedded in the inner layer and is used to detect the carbon dioxide concentration; A flexible pressure sensor, embedded in the inner layer, is used to obtain the user's breathing rate; A gas sensor, embedded in the inner layer, is used to analyze the composition of the user's exhaled gas; A flexible thermoelectric module, which can be removably mounted on the human chest and used to convert thermal energy into electrical energy; A flexible photoelectric module connected to the mask body for converting light energy into electrical energy; and The main control module is connected to the carbon dioxide concentration sensor, flexible pressure sensor, gas sensor, flexible thermoelectric module and flexible photoelectric module for device control and data processing; The main control module includes a main control board and a main control MCU unit, an energy collection and conversion circuit, a wireless communication module, and a voice broadcast module installed on the main control board. The energy collection and conversion circuit, the wireless communication module, and the voice broadcast module are all connected to the main control MCU unit, the wireless communication module and the voice broadcast module are connected to the energy collection and conversion module, the energy collection and conversion circuit is connected to the flexible thermoelectric module through a wire B, the energy collection and conversion circuit is connected to the flexible photoelectric module through a wire C, the main control MCU unit is connected to the carbon dioxide concentration sensor, the flexible pressure sensor, and the gas sensor through a wire A, and a BP neural network is provided in the main control MCU unit; The BP neural network algorithm includes an input layer, a hidden layer and an output layer. The input layer takes the carbon dioxide concentration data and the respiratory rate data as input data and outputs the need for rest data output and the normal situation data output at the output layer. The BP neural network uses the gradient descent method to obtain the objective function during training. Specifically, during the forward propagation of data, the hidden layer output H must be calculated first, and then the weights w between the input layer and the hidden layer are calculated. ij , hidden layer threshold a, we can get the output H of the hidden layer j for: Among them, j = 1, 2, 3...l; then the connection weight w between the hidden layer and the output layer jk , the output layer threshold b can get the output layer output O k for: Where k = 1, 2, 3..., m; Since there is an error between the output value and the expected value, the error needs to be propagated backwards to update the weights and thresholds. k And the expected output y can get the prediction error e k : and k =and k -EITHER k The two updated weights w ij 、w jk for: Where i = 1, 2, 3, ..., n, j = 1, 2, 3, ..., l, k = 1, 2, 3, ..., m; η represents the learning rate; then the threshold is updated: Finally, the threshold is updated to the neural network structure, and the above process is repeated until the error drops to the ideal range, and the neural network training is completed.

2. The self-powered intelligent mask detection device according to claim 1, characterized in that: The energy collection and conversion circuit includes a micro energy collection and storage circuit and a DC-DC current conversion circuit. The DC-DC current conversion circuit is connected to the micro energy collection and storage circuit. The DC-DC current conversion circuit is connected to the wireless communication module, the voice broadcast module and the main control MCU unit. The micro energy collection and storage circuit is connected to the flexible thermoelectric module through wire B, and the micro energy collection and storage circuit is connected to the flexible photoelectric module through wire C.

3. The self-powered intelligent mask detection device according to claim 1, characterized in that: The carbon dioxide concentration sensor includes a carbonate auxiliary electrode, a reference electrode, a sensitive electrode A, a NASICON, an Al2O3 ceramic tube, and a heating coil. The carbonate auxiliary electrode, the reference electrode, the sensitive electrode A, the NASICON, and the Al2O3 ceramic tube are each provided in two groups and are symmetrically arranged linearly on both sides of the heating coil. The carbonate auxiliary electrode is connected to the main control module via a wire A.

4. The self-powered intelligent mask detection device according to claim 1, characterized in that: The gas sensor is a flexible special metal oxide semiconductor gas sensor, which includes a sensitive electrode B, a heating electrode, an insulating layer, a main body A, a micro hot plate and a main body B. The sensitive electrode B, the heating electrode, the insulating layer, the main body A, the micro hot plate and the main body B are arranged linearly in sequence, and the sensitive electrode B is connected to the main control MCU unit through a wire A; The micro-hotplate substrate of the flexible special metal oxide semiconductor gas sensor is selected as Si, the main body A and the main body B are made of SiO2, the heating electrode material is selected as Pt, SiO2 is used as the insulating layer between the sensitive electrode B and the heating electrode, and the sensitive electrode B is made of precious metal Au. The substrate material of the micro-hotplate is Si and an inverted trapezoidal cavity is used inside.

5. The self-powered intelligent mask detection device according to claim 1, characterized in that: The flexible pressure sensor includes a PDMS film, interdigital electrodes, PZT nanofibers and a PET substrate, wherein the PDMS film, interdigital electrodes, PZT nanofibers and PET substrate are linearly arranged in sequence, and the interdigital electrodes are connected to a main control module via a wire A.

6. The self-powered intelligent mask detection device according to claim 1, characterized in that: The mask body is provided with a flexible nose clip, which is made of a composite flexible material.

7. A self-powered intelligent mask detection method, characterized in that: The self-powered intelligent mask detection device according to any one of claims 1 to 6 comprises the following steps: S1. Wear the mask body normally on the face, install the flexible photoelectric module on the outer surface of the mask, install the flexible thermoelectric module on one side of the user's chest, install the system main control module on the other side of the user's chest, and connect the flexible photoelectric module, flexible thermoelectric module and energy collection and conversion unit with wires, and use the flexible photoelectric module and flexible thermoelectric module to power the main control module; S2. Use the main control module to detect whether the output voltage of the energy collection and conversion circuit reaches the normal operating voltage of 3.3V. If it reaches the normal operating voltage, use the carbon dioxide concentration sensor and the flexible pressure sensor to collect carbon dioxide concentration data and respiratory rate data in turn. Input the above two data into the BP neural network preset in the main control MCU unit for calculation and determine whether the user needs to take off the mask for proper rest. If rest is required, a voice prompt is given through the voice broadcast module; S3. Use the gas sensor to collect the gas exhaled by the user and detect its composition data to determine whether the user's health condition is abnormal. If abnormal, a voice prompt is given through the voice broadcast module; S4. All collected physiological indicator data are sent to the user's mobile phone APP via the wireless communication module. The APP will process the received data and generate a daily health report for the user to use as a reference for his or her health status; S5: Wait until the output voltage of the energy collection and conversion circuit reaches the standard, and repeat the process of S1-4.

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