A respiratory mask wrong and missing loading detection device based on bidirectional flow collaborative detection

Abstract

The utility model relates to a kind of based on the detection device of wrong and missing installation of respiratory mask of two-way flow collaborative detection, belong to the technical field of respiratory mask production quality detection.It is composed of gas source, detection device, standard flow gas pipe, backflow flow gas pipe, head mould, first flowmeter and second flowmeter.The utility model is based on the principle of two-way collaborative detection of respiratory mask airflow circulation characteristics by optimizing digital-analog conversion link, design multistage filtering algorithm, combined with optical fiber sensor accurate trigger and dynamic threshold configuration, the rapid, accurate detection of respiratory mask wrong and missing installation is realized, and detection efficiency and product pass rate are improved.High detection accuracy, strong data stability and adaptability, without frequent manual operation, detection efficiency is improved by 4 times compared with manual detection, slight wrong and missing installation defects can be effectively identified, wrong and missing installation identification accuracy can reach 99.5%, solve the problems of low efficiency, poor accuracy, weak adaptability, insufficient data stability and weak detection principle pertinence in prior art.

Description

Technical Field

[0001] This utility model relates to a detection device for mis-installation of respiratory masks based on bidirectional flow collaborative detection, belonging to the field of respiratory mask production quality inspection technology. Background Technology

[0002] In the production process of respiratory masks, defects such as misassembly (e.g., misalignment of the mask body and ear loops, or misalignment of the filter layer) and omissions (e.g., missing ear loops, filter layers, or sealing strips) can directly compromise the structural integrity and sealing performance of the mask, leading to a significant reduction in protective effectiveness. Traditional defect detection for respiratory masks mainly relies on manual visual inspection, which suffers from low efficiency (approximately 30 pieces / minute), high labor intensity, and a high error rate (≥5%), making it difficult to meet the inspection needs of large-scale production.

[0003] To address the issues of manual visual inspection, existing technologies include automated inspection equipment. However, most of these devices employ single-sensor detection, such as detecting only the flow of air or flow in a single direction. This type of automated inspection suffers from the following shortcomings: First, the analog-to-digital conversion process is greatly affected by power fluctuations and electromagnetic interference, resulting in severe signal distortion and low flow detection accuracy. Second, the filtering mechanism is simple and cannot effectively eliminate noise caused by instantaneous airflow fluctuations, leading to poor data stability. Third, it lacks adaptability to the structural differences of different models of respiratory masks, resulting in weak detection versatility. Fourth, the triggering mechanism is singular, leading to low detection accuracy when properly installed and prone to false triggering. Fifth, the core detection principle lacks in-depth adaptation to the relationship between the structure of the respiratory mask and airflow characteristics, making it unable to accurately identify minor mis-installation defects.

[0004] Therefore, there is an urgent need for a respiratory mask mis-installation detection device with high-precision and stable digital-to-analog conversion, multi-level filtering mechanism, bidirectional collaborative detection principle based on the airflow characteristics of respiratory masks, and strong adaptability, in order to overcome the shortcomings of existing technologies. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of the existing technology by providing a respiratory mask mis-assembly detection device based on bidirectional flow collaborative detection. This device optimizes the digital-to-analog conversion link, designs a multi-level filtering algorithm, constructs a bidirectional collaborative detection principle based on the airflow characteristics of respiratory masks, and combines precise triggering and dynamic threshold configuration with fiber optic sensors to achieve rapid and accurate detection of mis-assembly and omissions in respiratory masks. This improves production testing efficiency and product qualification rate. The invention also solves the problems of low efficiency, poor accuracy, weak adaptability, insufficient data stability, and weak targeting of the detection principle in the existing technology.

[0006] This utility model achieves the above objectives through the following technical solution: A device for detecting mis-installation of a respiratory mask based on bidirectional flow collaborative detection includes an air source 1, a detection device 2, a standard flow tube 3, a return flow tube 4, a head mold 5, a first flow meter 6, and a second flow meter 7. The device is characterized in that: the air source 1 is connected to the head mold 5 via the standard flow tube 3; the detection device 2 is connected to the head mold 5 via the return flow tube 4; the first flow meter 6 is mounted on the return flow tube 4; and the second flow meter 7 is mounted on the standard flow tube 3.

[0007] The detection device 2 consists of a main control module 201, a unidirectional flow acquisition module 202, a trigger module 203, a threshold configuration module 204, a status indication module 205, a power step-down module 206, a communication module 207, an optical fiber amplifier 209, a power switch 210, and a main control chip 212. The input terminal of the main control module 201 is connected to the power step-down module 206, and the output terminal of the main control module 201 is connected to the unidirectional flow acquisition module 202, the status indication module 205, and the optical fiber amplifier 209. The trigger module 203 communicates with the main control module 201 via TTL. The communication module 207 communicates with the main control chip 212.

[0008] The main control module 201 includes a main control chip 212, which has a built-in 12-bit high-precision ADC module for data acquisition, digital-to-analog conversion, filtering, threshold judgment and status control.

[0009] The unidirectional flow acquisition module 202 includes a first flow acquisition channel and a second flow acquisition channel, wherein: The second flow acquisition channel corresponds to pin IO34 in the circuit, which is connected to the airflow sensor, high-precision voltage divider resistor network and 100nF high-frequency low-ESR filter capacitor. It is used to acquire the airflow analog voltage signal flowing into the head mold, and obtain the reference flow rate after digital-to-analog conversion and flow conversion, which serves as the detection trigger condition. The IO35 pin in the circuit corresponding to the first flow acquisition channel uses the same hardware configuration as the second flow acquisition channel. It is used to acquire the simulated voltage signal of the airflow returning through the breathing mask, and convert it into the detection flow rate, which serves as the basis for qualification judgment.

[0010] The trigger module 203 includes an optical fiber sensor and a button assembly. The optical fiber sensor is connected to the IO19 pin of the main control chip 212, and the button assembly includes a start / stop button, a reset button, a clear button, and an encoder button. The optical fiber sensor is used to identify the installation status of the breathing mask, and the button assembly is used for manual triggering and status switching.

[0011] The threshold configuration module 204 includes two sets of thresholds: a trigger threshold range and a qualified threshold range. The trigger threshold range control requires the reference flow rate collected by the second flow acquisition channel to be within this range, ensuring that the detection is initiated under effective airflow input conditions; The qualified threshold range control requires that the return flow rate collected by the first flow acquisition channel be within the specified range to ensure that the respiratory mask structure is intact and there are no errors or omissions in its installation.

[0012] The status indicator module 205 includes a green LED, a yellow LED, a red LED + a buzzer, and an additional LED, which are used to intuitively reflect the detection status of the device as qualified, under test, unqualified, or waiting through different light states.

[0013] The communication module 207 includes a CH340K chip, which is used for real-time output data detection, data uploading, and remote monitoring.

[0014] The detection device 2 is equipped with a leakage current protector 208 and a cooling fan 211.

[0015] The advantages of this utility model compared with the prior art are as follows: 1. High detection accuracy: Stable digital-to-analog conversion + multi-level filtering ensures that the flow data error is ≤±0.5%. Combined with the bidirectional flow collaborative detection principle and the precise triggering of the fiber optic sensor, the accuracy rate of error and omission identification is ≥99.5%. It can effectively identify minor errors and omissions, solving the problem of high misjudgment rate of traditional detection methods.

[0016] 2. Strong data stability: The multi-level filtering mechanism and optimized digital-to-analog conversion design enable the flow data to remain stable even under industrial environments such as airflow fluctuations and electromagnetic interference. During continuous operation, the flow data fluctuation is ≤±0.5%, and the test results are highly consistent.

[0017] 3. High degree of automation: The fiber optic sensor automatically detects when the breathing mask is installed and triggers the detection. Qualified products are automatically reset to standby mode, eliminating the need for frequent manual operation. The detection efficiency is 4 times higher than that of traditional manual detection, meeting the needs of large-scale production.

[0018] 4. High adaptability: Supports preset thresholds for more than 5 models of respiratory masks and can dynamically update trigger thresholds, adapting to the detection of respiratory masks with different structures and production scenarios, without the need to redesign equipment or significantly adjust parameters.

[0019] 5. Easy to operate: The status indicator light provides intuitive feedback on the test results, and the serial port outputs test data in real time, supporting remote monitoring and data traceability, reducing the operating threshold and facilitating production line management.

[0020] 6. High stability: It features power protection, electromagnetic interference suppression, and multi-stage filtering designs, making it suitable for complex industrial production environments. The failure rate during continuous operation is ≤0.1%, and it has a long service life. Attached Figure Description

[0021] Appendix Figure 1 This is a schematic diagram of a respiratory mask mis-assembly detection device based on bidirectional flow collaborative detection; Appendix Figure 2 This is a top view schematic diagram of a respiratory mask mis-assembly detection device based on bidirectional flow collaborative detection; Appendix Figure 3 This is a schematic diagram of the detection device after it has been cut open.

[0022] In the diagram: 1. Gas source; 2. Detection device; 201. Main control module; 202. Unidirectional flow acquisition module; 203. Trigger module; 204. Threshold configuration module; 205. Status indication module; 206. Power supply step-down module; 207. Communication module; 208. Leakage current protector; 209. Fiber optic amplifier; 210. Power switch; 211. Cooling fan; 212. Main control chip; 3. Standard flow air pipe; 4. Return flow air pipe; 5. Head mold; 6. First flow meter; 7. Second flow meter. Detailed Implementation

[0023] The present invention will be further described below with reference to the accompanying drawings: The core technology of this respiratory mask mis-assembly detection device based on bidirectional flow collaborative detection lies in the integrated design of "stable digital-to-analog conversion + multi-level filtering and noise reduction + bidirectional flow collaboration + adaptation to the airflow characteristics of the respiratory mask" to achieve accurate identification of mis-assembly defects. The device includes an air source 1, a detection device 2, a standard flow air tube 3, a return flow air tube 4, a head mold 5, a first flow meter 6, and a second flow meter 7. The air source 1 is connected to the head mold 5 through the standard flow air tube 3, and the detection device 2 is connected to the head mold 5 through the return flow air tube 4. The first flow meter 6 is installed on the return flow air tube 4, and the second flow meter 7 is installed on the standard flow air tube 3.

[0024] Furthermore, the detection device 2 comprises a main control module 201, a unidirectional flow acquisition module 202, a trigger module 203, a threshold configuration module 204, a status indication module 205, a power step-down module 206, a communication module 207, a leakage protection switch 208, an optical fiber amplifier 209, a power switch 210, and a main control chip 212. The input terminal of the main control module 201 is connected to the power step-down module 206, and the output and input terminals of the main control module 201 are connected to the unidirectional flow acquisition module 202, the status indication module 205, and the optical fiber amplifier 209. The trigger module 203 communicates with the main control module 201 via TTL. The communication module 207 communicates with the main control chip 212. The 220V AC mains power is input through power switch 210, which has a built-in 10A fuse. The current output from power switch 210 is input to leakage current protection switch 208, and then from the output of leakage current protection switch 208 to power step-down module 206. Power step-down module 206 converts 220V to 12V. Simultaneously, the ground pin of power switch 210 is connected to the grounding copper post of power step-down module 206. The current output from power step-down module 206 is input to main control module 201, where current reduction and voltage division are performed to obtain 12V, 5V, and 3.3V voltages. 12V powers unidirectional flow acquisition module 202 and status indication module 205. Unidirectional flow acquisition module 202 communicates directly with the main control chip via analog signals after passing through voltage divider and filter circuits. 5V powers the screen display module, and 3.3V provides stable output power to main control chip 212 and fiber optic amplifier 209. The trigger module 203 communicates with the main control chip via a connector using the TTL method; the trigger button in the trigger module 203 can be reused as the threshold configuration module 204.

[0025] The main control module 201 uses a main control chip 212, which has a built-in 12-bit high-precision ADC module for data acquisition, digital-to-analog conversion, filtering, threshold judgment, and state control. It is the core control unit of the device. The pin connections of the main control chip 212 are as follows: The unidirectional flow acquisition module 202 includes a first flow acquisition channel and a second flow acquisition channel. The second flow acquisition channel corresponds to pin IO34 in the circuit. Pin IO34 is connected to the reference flow acquisition circuit, which connects to the airflow sensor, a high-precision voltage divider resistor network, and a 100nF high-frequency low-ESR filter capacitor. It is used to acquire the simulated voltage signal of the airflow flowing into the head mask, and obtains the reference flow rate through digital-to-analog conversion and flow rate calculation, which serves as the detection trigger condition. The first flow acquisition channel corresponds to pin IO35 in the circuit. Pin IO35 is connected to the return flow acquisition circuit (i.e., airflow sensor → voltage divider resistor R1 / R2 → 100nF filter capacitor → IO35). The first flow acquisition channel uses the same hardware configuration as the second flow acquisition channel to acquire the simulated voltage signal of the airflow returning through the breathing mask, convert it to obtain the detection flow rate, and use it as the basis for qualification judgment.

[0026] The trigger module 203 includes a fiber optic sensor and a button assembly. The fiber optic sensor is connected to the IO19 pin of the main control chip 212. The button assembly includes a start / stop button IO17, a reset button IO18, a clear button IO5, an encoder CLK IO32, an encoder DT IO33, and an encoder button IO13. The fiber optic sensor uses the principle of optical reflection to accurately identify the installation status of the breathing mask. The detection response time is ≤10ms, the trigger accuracy is ≥99.9%, and the detection is triggered by de-jittering when a valid signal is detected to avoid misjudgment caused by improper installation. The button assembly is used for manual triggering and state switching, and the encoder can switch between different detection models.

[0027] The threshold configuration module 204 includes two sets of thresholds: a trigger threshold range and a qualified threshold range. It can preset threshold parameters for various types of respiratory masks and can continue to add more. Each model corresponds to two sets of thresholds. Trigger threshold range: The reference flow rate collected by the second flow acquisition channel must be within this range to ensure that the detection is initiated under effective airflow input conditions; Qualified threshold range: The return flow rate collected by the first flow acquisition channel must be within this range to ensure that the respiratory mask structure is intact and there are no errors or omissions. Supports dynamic updates: When you press and hold the clear button for 3 seconds and the second traffic acquisition channel has an upper limit of valid traffic, the current traffic of the second traffic acquisition channel + 25.0 will be automatically used as the new value, with an upper limit of 195.0, to adapt to airflow fluctuations in different production scenarios.

[0028] The status indication module 205 includes IO16 green LED, IO2 yellow LED, IO4 red LED + buzzer and an additional IO15 LED, which are used to intuitively reflect the detection status of the test as qualified, under test, unqualified and waiting through different light states.

[0029] The power step-down module 206 inputs 12V 7A power to the main control module 201. The main control module 201 then uses a power input protection circuit consisting of a resettable fuse (BSMD2920-700-12V), a TVS diode (SMF12CA), and a PTC thermistor (MF72 10D5) to convert the power to 12V 1A via a DC-DC converter (R-78E12-1.0 / X9). This power is then converted to 5V via an LM7805CT linear regulator and finally to 3.3V (ripple ≤50mV) via an ME6211C33M5G-N LDO regulator. This provides a stable power supply for the unidirectional flow acquisition module, communication module 207, main control chip 212, etc., ensuring the stability of the digital-to-analog conversion from the power source.

[0030] The communication module 207 uses a CH340K chip to achieve serial communication (baud rate 115200), and outputs detection data (flow, count, status, etc. of the first / second flow acquisition channel) in real time, supporting data upload and remote monitoring.

[0031] Furthermore, the detection device 2 is equipped with a leakage current protector 208 and a cooling fan 211.

[0032] The detection principle of this respiratory mask mis-assembly detection device based on bidirectional flow collaborative detection is as follows: The detection principle of this device is based on the strong correlation between the structural integrity of a breathing mask and its airflow characteristics. It combines bidirectional flow collaborative verification, precise triggering, and threshold judgment to achieve accurate identification of mis-installation defects. The specific logic is as follows: 1. Basic airflow characteristics of breathing masks One of the core functions of a breathing mask is to provide protection through structural sealing and a filter layer. In its normal assembled state, it possesses the following airflow characteristics: After the normal breathing mask is installed on the head mold, the edge of the breathing mask forms a tight seal with the surface of the head mold. The filter layer, the main body of the breathing mask and other structures are intact. The airflow forms a fixed flow path from "air source → head mold air inlet → inside of the breathing mask → air outlet inside the head mold", and the airflow resistance is stable. There is a fixed ratio between the airflow flowing into the head mold (the reference flow rate collected by the second flow acquisition channel) and the airflow returning through the breathing mask (the detection flow rate collected by the first flow acquisition channel). This ratio is determined by the structural parameters of the breathing mask (such as the air inlet size, filter layer thickness and sealing method), and this ratio is highly consistent for breathing masks of the same model.

[0033] 2. Incorrect or missing parts during assembly disrupt airflow characteristics: When a breathing mask has defects such as incorrect or missing parts during assembly, its structural integrity or sealing is compromised, directly leading to changes in airflow characteristics. Missing parts defect a: such as missing ear loops, filter layer, or sealing strip, the breathing mask cannot form an effective seal with the head mold, or the airflow path lacks necessary structural constraints, causing airflow to leak from the missing parts, and the flow rate returning to the first flow acquisition channel is greatly reduced, far below the normal threshold lower limit; Misassembly defect b: such as misaligned ear loops, misaligned filter layer installation, skewed assembly of the main body of the breathing mask, uneven force on the sealing surface of the breathing mask and head mold, resulting in local gaps, or the filter layer blocking the air inlet / outlet, causing abnormal airflow resistance (too high or too low), and the return flow either exceeds the upper limit of the normal threshold (too low resistance, airflow leakage) or is lower than the lower limit of the threshold (too high resistance, airflow cannot return normally).

[0034] 3. Detection logic for bidirectional traffic collaborative verification Based on the above characteristics, this device achieves error / missing component detection through a two-way collaborative logic of "baseline flow triggering of the second flow acquisition channel + return flow judgment of the first flow acquisition channel": Step 1: Baseline flow triggering ensures valid detection conditions. The second flow acquisition channel collects the baseline flow rate flowing into the headpiece. The airflow condition for detection is only met when this flow rate falls within the trigger threshold range corresponding to the current model of the respiratory mask. This means the airflow magnitude meets the detection requirements of this model of respiratory mask, avoiding both insufficient detection sensitivity due to insufficient airflow and flow saturation due to excessive airflow. Simultaneously, the fiber optic sensor detects that the respiratory mask is properly installed, ensuring the object being detected is correctly positioned and avoiding false detections due to the absence of a respiratory mask or installation errors. Only when both conditions are met simultaneously will the detection process be initiated, eliminating invalid detection scenarios at the source.

[0035] Step 2: Backflow Flow Judgment to Identify Incorrect or Missing Components. After the detection process starts, the first flow acquisition channel collects backflow flow data every 10ms. After multi-stage filtering, a stable effective flow value is obtained. After continuous detection and acquisition for 1200ms, the final effective flow value is recorded. This final value is then compared with the acceptable threshold range corresponding to the current model of respiratory mask. If the final value is within the acceptable threshold range, it indicates that the breathing mask has a complete structure, good sealing, and airflow characteristics that meet normal requirements, and is judged as "no errors or omissions in assembly". If the final value exceeds the acceptable threshold range, i.e., below the lower limit or above the upper limit, it indicates that the breathing mask has defects such as incorrect assembly or omissions in assembly, and the airflow characteristics are damaged, and is judged as "incorrect or missing assembly".

[0036] 4. The auxiliary role of dynamic threshold adaptation Different models of respiratory masks have different structural parameters (size, filter layer thickness, sealing method), resulting in differences in their baseline flow rate trigger threshold and backflow flow rate qualification threshold. This device can adapt to the testing requirements of different models of respiratory masks by presetting multiple model thresholds and supporting dynamic updates to the upper limit of the trigger threshold, avoiding testing errors caused by model differences and further improving the accuracy of testing.

[0037] Hardware implementation requirements for the respiratory mask mis-assembly detection device based on bidirectional flow collaborative detection Fiber optic sensor connection: The signal output end of the fiber optic sensor is connected to the IO19 pin of the main control chip 212, and the power supply end is connected to the device's 12V stable power supply. The transmitter and receiver are installed on both sides of the head mold with a spacing of 5mm. It detects whether the breathing mask is installed in place through the principle of optical reflection. The detection distance is 0~10mm and the response time is ≤10ms.

[0038] Flow acquisition circuit and digital-to-analog conversion link: The airflow sensor is a high-precision flow sensor with a range of 0~200L / min and an output voltage of 1V~5V, with an error of ≤±1%FS; The voltage divider resistors R1=36kΩ and R2=68kΩ are selected as 0603 packaged metal film resistors with an error of ±1% and a temperature coefficient of ≤50ppm / ℃ to ensure the stability of the voltage division ratio. The filter capacitor is an X7R ceramic capacitor with a 0603 package, a capacitance of 100nF, and an ESR of ≤5Ω, which effectively filters out high-frequency noise. The main control chip 212 module is configured with a resolution of 12 bits, a sampling frequency of 100Hz, a reference voltage of 3.3V (provided by ME6211C33M5G-N), and a converted digital signal range of 0~4095. The formula for converting flow rate is as follows: ①ADC conversion voltage = Digital signal value × 3.3V / 4095; ② Actual voltage = ADC conversion voltage × (R1+R2) / R2 = ADC conversion voltage × (36kΩ+68kΩ) / 68kΩ = ADC conversion voltage × 1.529; ③ Flow rate = (actual voltage - 1.0V) / (5.0V - 1.0V) × 200.0L / min = (actual voltage - 1.0V) × 50.0L / min; After the above conversion, the linear correlation coefficient R² between the flow rate value and the actual airflow flow rate is ≥0.998, ensuring the accuracy of the conversion.

[0039] The 206 power supply step-down module reduces 220V to 12V output, supporting a maximum output of 12V 4A. Input protection circuit: The self-resetting fuse BSMD2920-700-12V has a cutoff current of 7A and an opening time of ≤1s, which can effectively protect the subsequent circuit from overcurrent damage; the TVS diode SMF12CA has a breakdown voltage of 12V and a clamping voltage of 19V, which can absorb surge voltage on the power line; the PTC thermistor MF72 10D5 has a room temperature resistance of 10Ω, and its resistance increases rapidly under overload to limit the current; Voltage regulation circuit: The DC-DC converter R-78E12-1.0 / X9 has a conversion efficiency of ≥85% and an output ripple of ≤100mV; the LM7805CT linear regulator has an output ripple of ≤50mV; and the ME6211C33M5G-N LDO linear regulator has an output ripple of ≤20mV, providing high-quality regulated power for ADC modules and sensors.

[0040] Software implementation requirements for the respiratory mask mis-assembly detection device based on bidirectional flow collaborative detection. 1. Initialization configuration: Serial port configuration: baud rate 115200, data bits 8, stop bits 1, parity none, used for data output and debugging; Memory initialization: 1KB capacity, used to store preset threshold parameters, which are not lost when power is off; ADC module configuration: 12-bit resolution, sampling frequency, reference voltage 3.3V, IO35 and IO34 pins corresponding to the first / second flow acquisition channels are configured as analog input mode; Filter buffer initialization: Create a float buffer of size 10 for the first / second traffic acquisition channel, and initialize all elements to 0; Pin mode configuration: The pins corresponding to the trigger module and the status indicator module are configured as input (with pull-down / pull-up) or output modes respectively, and the indicator lights are initially set to off; Fiber optic sensor initialization: Set the detection trigger level to active high and the sampling interval to 5ms.

[0041] 2. Data acquisition, analog-to-digital conversion, and filtering process: Data acquisition: ADC conversion is triggered every 10ms to acquire the analog voltage signals of the first / second flow acquisition channels and convert them into digital signals of 0~4095; Digital-to-analog conversion: According to the above flow conversion formulas ①~③, the digital signal is converted into the corresponding flow value. During the conversion process, float type data is used to store the data and retain 1 decimal place to avoid errors caused by integer truncation. Hardware filtering: The acquired analog signal has been filtered out of high-frequency noise by a 100nF capacitor in the hardware circuit, and the converted flow rate value has been initially noise-reduced. Software sliding window filtering: ① For the first flow acquisition channel, store the newly converted flow value into the current index position of its filter buffer (the index is initially 0). ② Calculate the cumulative sum of the 10 sets of data in the buffer, divide by 10 to get the average value, and use it as the current effective flow value after filtering; ③ Increment the buffer index by 1; if the index is greater than or equal to 10, reset it to 0 to achieve cyclic coverage. ④ The second flow acquisition channel performs the same sliding window filtering operation; Data change threshold filtering: ① Compare the current filtered effective flow rate value with the previous output flow rate value, and calculate the absolute value of the difference; ② If the difference is greater than 0.2, the output flow rate is updated to the current valid flow rate value; if the difference is less than or equal to 0.2, the previous output flow rate value is maintained. Final output: The flow rate value after multi-stage filtering is used as the final detection data to trigger judgment and pass / fail judgment.

[0042] 3. State machine control: The main loop adopts a task scheduling mechanism, processing input signals (fiber optic sensor status, button status, encoder status) every 5ms, updating indicator light status every 50ms, and performing data acquisition and filtering every 10ms. State transition logic: ① Standby state: All lights are off, waiting to be triggered; when the fiber optic sensor detects that the breathing mask is installed in place (or the start button is manually pressed), it switches to standby state and the additional LED lights up; ② Waiting state: The additional LED is lit, continuously monitoring the flow value of the second flow acquisition channel. When the flow value is within the trigger threshold range of the current model, it switches to test state and the yellow LED lights up; ③ Test status: The yellow LED is on, continuously collecting traffic data from the first traffic acquisition channel and performing multi-level filtering, recording the test start time; when the test duration reaches 1200ms, it switches to the end status; ④ End Status: Based on the comparison between the final flow value of the first flow acquisition channel and the qualified threshold range, if qualified, the green light will light up; if unqualified, the red light and buzzer will start. Qualified products will automatically reset to standby mode after a delay of 800ms, while unqualified products need to press the reset button to return to standby mode.

[0043] 4. Threshold Update: When the clear button is detected to be pressed and held for ≥3 seconds, and the traffic value of the second traffic acquisition channel is >0, the upper limit of the new second traffic acquisition channel is calculated as: current second traffic acquisition channel traffic value + 25.0; If the upper limit of the new second traffic acquisition channel is >195.0, then limit it to 195.0; The updated upper limit value of the second flow acquisition channel is stored in the threshold parameter corresponding to the current model and written to the main control chip 212 memory, which will not be lost when power is off; The serial port outputs a threshold update log, which is convenient for debugging and tracing.

[0044] The steps for using this respiratory mask mis-assembly detection device based on bidirectional flow collaborative detection are as follows: 1. Connect to a 220V power supply. The device will initialize (approximately 1 second). Once completed, it will enter standby mode, and all indicator lights will turn off. 2. Select the model of the breathing mask to be tested through the encoder. The device will automatically read the trigger threshold range and qualified threshold range corresponding to the model from the main control chip 212. If it is necessary to adjust the upper limit of the trigger threshold, press and hold the clear button for 3 seconds. The device will automatically collect the flow value of the current second flow acquisition channel, calculate and update the upper limit threshold of the second flow acquisition channel. 3. Install the breathing mask onto the head mold 5. After the fiber optic sensor detects that the mask is installed in place, the device enters the standby state and the additional LED lights up. 4. When air source 1 is started, airflow is input into head mold 5, and the second flow acquisition channel continuously acquires the reference flow rate and performs multi-stage filtering; when the reference flow rate is within the trigger threshold range of the current model, the device automatically enters the test state and the yellow LED lights up; 5. The test lasts 1.2 seconds. The first flow acquisition channel collects the return flow every 10ms, and after multi-stage filtering, a stable and effective flow value is obtained. After the test, the device enters the end state. If the final flow value of the first flow acquisition channel is within the qualified threshold range, the green light will turn on, the total count and qualified count will increase, and it will automatically reset to standby mode. If the final flow value of the first flow acquisition channel exceeds the qualified threshold range, the red light will turn on and the buzzer will start, and the total count and the unqualified count will increase. You need to press the reset button to return to the standby state. 6. Repeat steps 3 to 5 to perform the next test; during the test, the serial port outputs the flow value, test status, count and other data of the first / second flow acquisition channel in real time, which can be monitored and recorded by the host computer software.

[0045] The above description is merely a specific embodiment of this utility model, intended to enable those skilled in the art to understand or implement this application. The above examples do not impose any limitation on the substantive content of this utility model. Any simple modifications or variations made by those skilled in the art to the above specific embodiments based on the technical essence of this utility model after reading this specification, as well as equivalent embodiments that may utilize the disclosed technical content to make changes or modifications, all fall within the scope of the technical solution of this utility model and do not depart from the essence and scope of this utility model.

Claims

1. A device for detecting mis-installation of a respiratory mask based on bidirectional flow collaborative detection, comprising an air source (1), a detection device (2), a standard flow tube (3), a return flow tube (4), a head mold (5), a first flow meter (6), and a second flow meter (7), characterized in that: The gas source (1) is connected to the head mold (5) through the standard flow gas pipe (3), and the detection device (2) is connected to the head mold (5) through the return flow gas pipe (4); the return flow gas pipe (4) is equipped with a first flow meter (6), and the standard flow gas pipe (3) is equipped with a second flow meter (7).

2. The respiratory mask mis-assembly detection device based on bidirectional flow collaborative detection according to claim 1, characterized in that: The detection device (2) consists of a main control module (201), a one-way flow acquisition module (202), a trigger module (203), a threshold configuration module (204), a status indication module (205), a power step-down module (206), a communication module (207), an optical fiber amplifier (209), a power switch (210), and a main control chip (212). The input terminal of the main control module (201) is connected to the power step-down module (206), and the output terminal of the main control module (201) is connected to the one-way flow acquisition module (202), the status indication module (205), and the optical fiber amplifier (209). The trigger module (203) communicates with the main control module (201) via TTL. The communication module (207) communicates with the main control chip (212).

3. The respiratory mask mis-assembly detection device based on bidirectional flow collaborative detection according to claim 2, characterized in that: The main control module (201) includes a main control chip (212) with a built-in 12-bit high-precision ADC module for data acquisition, digital-to-analog conversion, filtering, threshold judgment and status control.

4. The respiratory mask mis-assembly detection device based on bidirectional flow collaborative detection according to claim 2, characterized in that: The unidirectional flow acquisition module (202) includes a first flow acquisition channel and a second flow acquisition channel, wherein: The second flow acquisition channel corresponds to pin IO34 in the circuit, which is connected to the airflow sensor, high-precision voltage divider resistor network and 100nF high-frequency low-ESR filter capacitor. It is used to acquire the airflow analog voltage signal flowing into the head mold, and obtain the reference flow rate after digital-to-analog conversion and flow conversion, which serves as the detection trigger condition. The IO35 pin in the circuit corresponding to the first flow acquisition channel uses the same hardware configuration as the second flow acquisition channel. It is used to acquire the simulated voltage signal of the airflow returning through the breathing mask, and convert it into the detection flow rate, which serves as the basis for qualification judgment.

5. A respiratory mask mis-assembly detection device based on bidirectional flow collaborative detection according to claim 2, characterized in that: The trigger module (203) includes an optical fiber sensor and a button assembly. The optical fiber sensor is connected to the IO19 pin of the main control chip (212). The button assembly includes a start / stop button, a reset button, a clear button, and an encoder button. The optical fiber sensor is used to identify the installation status of the breathing mask, and the button assembly is used for manual triggering and status switching.

6. The respiratory mask mis-assembly detection device based on bidirectional flow collaborative detection according to claim 2, characterized in that: The threshold configuration module (204) includes two sets of thresholds: a trigger threshold range and a qualified threshold range. The trigger threshold range control requires the reference flow rate collected by the second flow acquisition channel to be within this range, ensuring that the detection is initiated under effective airflow input conditions; The qualified threshold range control requires that the return flow rate collected by the first flow acquisition channel be within the specified range to ensure that the respiratory mask structure is intact and there are no errors or omissions in its installation.

7. A respiratory mask mis-assembly detection device based on bidirectional flow collaborative detection according to claim 2, characterized in that: The status indicator module (205) includes a green LED, a yellow LED, a red LED + a buzzer and an additional LED, which are used to intuitively reflect the detection status of the device as qualified, under test, unqualified, or waiting through different light states.

8. A respiratory mask mis-assembly detection device based on bidirectional flow collaborative detection according to claim 2, characterized in that: The communication module (207) includes a CH340K chip for real-time output data detection, data uploading, and remote monitoring.

9. A respiratory mask mis-assembly detection device based on bidirectional flow collaborative detection according to claim 2, characterized in that: The detection device (2) is equipped with a leakage current protector (208) and a cooling fan (211).

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