Gas alarm gas-permeable membrane blockage identification method and system

Abstract

A kind of gas alarm gas-permeable membrane blockage identification method and system, it is related to the field of testing materials by means of determining the physical properties of materials, in the method, reference benchmark value is determined according to the output signal collected by photosensitive element;The first number group of real-time output signals is converted into the first number group of real-time light intensity data;The real-time maximum light intensity value and the real-time minimum light intensity value are screened out;The difference between the real-time maximum light intensity value and the real-time minimum light intensity value is divided into the second number of intervals, and the proportion in the corresponding real-time light intensity data is calculated;The cumulative sum of the second number of intervals and the proportion is calculated, to obtain real-time determination value;In the case where it is determined that real-time determination value is greater than the preset multiple of reference benchmark value, it is determined that the gas-permeable membrane is not blocked;In the case where it is determined that real-time determination value is not greater than the preset multiple of reference benchmark value, it is determined that the gas-permeable membrane is blocked.The present application is used to improve the accuracy of gas alarm gas-permeable membrane blockage identification.

Description

Technical Field

[0001] This application belongs to the field of testing materials by measuring their physical properties, and particularly relates to a method and system for identifying blockage of the permeable membrane in a gas alarm. Background Technology

[0002] Gas detectors are primarily used to detect gases in the air, and the sensor needs to be in direct contact with the air. However, in real-world environments, impurities such as moisture and dust can enter the sensor, affecting its performance. To address this issue, gas detectors are typically designed with a waterproof and breathable membrane to isolate moisture and block dust while ensuring the normal passage of gas.

[0003] In related technologies, a pressure difference detection system is typically used to monitor the condition of waterproof and breathable membranes. This system measures the pressure difference generated when gas passes through the membrane by installing pressure sensors on both sides. When the pressure difference exceeds a set range, it indicates that the waterproof and breathable membrane may be blocked, thus prompting maintenance.

[0004] However, temperature changes can cause the sensitivity of pressure sensors to drift, affecting their measurement accuracy; while humidity changes can cause performance changes in the internal components of the pressure sensor, resulting in fluctuations in the measured values. By the time the predetermined pressure difference is reached, the breathable membrane has been blocked for a considerable period, during which time it can no longer meet the ventilation requirements of the alarm. In other words, the product has been operating with a defect for an extended period, making it difficult for the pressure difference method to provide early warning. These environmental factors make it difficult for the blockage judgment method based on a fixed threshold to accurately reflect the actual state of the breathable membrane, reducing the accuracy of the gas alarm's breathable membrane blockage identification. Summary of the Invention

[0005] This application provides a method and system for identifying blockages in the permeable membrane of a gas alarm, which improves the accuracy of identifying blockages in the permeable membrane of a gas alarm.

[0006] In a first aspect, this application provides a method for identifying blockage of the vent membrane of a gas alarm. A reference value is determined based on the output signal collected by a photosensitive element at a preset time interval. The photosensitive element is located at the light convergence point inside the vent membrane of the gas alarm. The light convergence point is the intersection point after parallel light rays are reflected by a concave mirror installed inside the vent membrane.

[0007] Collect a preset first number of real-time output signals and convert the preset first number of real-time output signals into a preset first number of real-time light intensity data;

[0008] Filter out the real-time maximum light intensity value and the real-time minimum light intensity value of the preset first group;

[0009] The difference between the real-time maximum light intensity value and the real-time minimum light intensity value is divided into a preset second number of intervals, and the proportion of the data volume in the preset second number of intervals in the corresponding real-time light intensity data is calculated.

[0010] Calculate the sum of the interval and percentage of the preset second quantity to obtain the real-time judgment value;

[0011] If the real-time judgment value is greater than a preset multiple of the reference benchmark value, it is determined that the breathable membrane is not blocked;

[0012] If the real-time judgment value is determined to be no greater than a preset multiple of the reference benchmark value, the air permeable membrane is determined to be blocked.

[0013] By employing the above technical solution, a concave mirror is placed on the inner side of the breathable membrane, and the photosensitive element is located at the convergence point of light rays. This allows parallel light to converge at the photosensitive element after reflection by the concave mirror, thus expanding the light signal collection area. Since multiple sets of data are continuously collected at preset time intervals and converted into light intensity data, instantaneous interference from ambient light, temperature, and other factors can be reduced. By calculating the difference between the real-time maximum and minimum light intensity values ​​and dividing the data into intervals, and combining the cumulative sum of the data volume proportions in each interval, a mathematical model is established to characterize the actual light transmittance state of the breathable membrane. This method uses the ratio of the real-time judgment value to the reference value as the judgment criterion. Through the adjustable parameter of a preset multiple, both the accuracy of detection and the adjustability of detection sensitivity are improved. This detection method based on light intensity distribution characteristics improves the reliability and adaptability of identifying the clogging state of the breathable membrane.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, a reference value is determined based on the output signal acquired by the photosensitive element at preset time intervals, specifically including:

[0015] The output signal collected by the photosensitive element at a preset time interval is received to obtain a preset first number of sets of output signals;

[0016] The output signals of the first preset number of groups are converted into light intensity data of the first preset number of groups, and the maximum and minimum light intensity values ​​of the first preset number of groups are selected.

[0017] The difference between the maximum and minimum light intensity values ​​is divided into a preset second number of intervals, and the proportion of data within the preset second number of intervals in the corresponding light intensity data is calculated.

[0018] The light intensity data of each group are weighted according to the proportion to obtain the weighted average of the first preset number of data.

[0019] Calculate the average of the weighted averages of the first preset number of values ​​to obtain the reference benchmark value.

[0020] By employing the aforementioned technical solution, a data model reflecting the light intensity distribution characteristics of the breathable membrane under normal conditions is established through light intensity conversion and interval division of the output signals of a preset first number of groups. By calculating the proportion of data in each interval and performing weighted calculations, the final reference value includes both the overall trend information of light intensity changes and the local characteristic information of data distribution. The final reference value is obtained by averaging the weighted average of the preset first number of groups again, further reducing the impact of occasional factors on the reference value. This multi-level data processing method makes the reference value highly representative and stable, reducing the probability of misjudgment caused by fluctuations in environmental factors.

[0021] In conjunction with some embodiments of the first aspect, in some embodiments, before determining a reference value based on the output signal acquired by the photosensitive element at preset time intervals, the method further includes:

[0022] Determine whether the ambient light intensity is greater than a preset threshold;

[0023] If it is greater than that, then the reference reference value is determined based on the output signal collected by the photosensitive element at preset time intervals;

[0024] If the value is not greater than the preset installation point of the gas alarm, then turn on the auxiliary light source installed at the preset installation point of the gas alarm.

[0025] Receive several comparison signal values ​​collected by the photosensitive element;

[0026] If the number of consecutive comparison signal values ​​is less than the preset light transmittance threshold signal value for the third time, it is determined that the breathable membrane is blocked.

[0027] If the comparison signal value is not less than the preset light transmittance threshold signal value, it is determined that the breathable membrane is not blocked.

[0028] By adopting the above technical solution, and by judging the ambient light intensity and taking corresponding detection strategies, the auxiliary light source is actively activated for detection when the light is insufficient, thus expanding the applicability of the detection method. When the ambient light is sufficient, a detection method based on light intensity distribution characteristics is used; when the ambient light is insufficient, the blockage status is determined by continuously collecting and comparing the signal values ​​with a preset light transmittance threshold. This dual-mode detection method overcomes the limitations of a single detection method under different lighting conditions, enabling the method to continuously perform detection under various lighting conditions. By introducing a preset third continuous judgment mechanism, misjudgments caused by instantaneous fluctuations are avoided, improving the reliability of detection results in low-light environments.

[0029] In conjunction with some embodiments of the first aspect, in some embodiments, after turning on the auxiliary light source installed at a preset mounting point of the gas alarm, the method further includes:

[0030] Record the output signal of the photosensitive element at the first moment when the auxiliary light source is turned on;

[0031] Record the output signal of the photosensitive element at the second moment when the auxiliary light source is off;

[0032] The difference between the output signal at the first moment and the output signal at the second moment is calculated to obtain the change in light intensity.

[0033] Multiple light intensity change values ​​are acquired at preset time intervals;

[0034] If the light intensity changes for a predetermined number of consecutive cycles remain constant, it is determined that the auxiliary light source is not blocked.

[0035] Control the heating device located inside the auxiliary light source to maintain the preset heating frequency and preset heating power.

[0036] By employing the above technical solution, the output signal of the photosensitive element is collected when the auxiliary light source is on and off, and the difference is calculated to obtain the light intensity change value. By continuously monitoring the stability of multiple light intensity change values, it is possible to promptly detect whether the auxiliary light source is blocked. When it is confirmed that the auxiliary light source is not blocked, the heating equipment is controlled to operate at a preset heating frequency and power, ensuring the normal operating temperature of the auxiliary light source, extending its service life, realizing real-time monitoring of the auxiliary light source's operating status, and ensuring the continuous effectiveness of the auxiliary light source's detection function in low-light environments.

[0037] In conjunction with some embodiments of the first aspect, in some embodiments, after acquiring multiple light intensity change values ​​at preset time intervals, the method further includes:

[0038] If the light intensity change value decreases continuously for a predetermined number of consecutive times, it is determined that the auxiliary light source is blocked.

[0039] The heating equipment is controlled by a preset heating power and continues for a preset duration until the light intensity change value remains constant.

[0040] By adopting the above technical solution, the problem of whether the auxiliary light source is blocked can be determined by judging whether the light intensity change value decreases continuously for a preset number of consecutive times. When blocked, the heating equipment is controlled to run for a preset time with a preset heating power until the light intensity change value remains unchanged. This can promptly detect and solve the problem of the auxiliary light source being blocked, so that the auxiliary light source can work continuously and stably, and ensure the reliability of the detection of air permeable membrane blockage.

[0041] In conjunction with some embodiments of the first aspect, in some embodiments, after determining that the breathable membrane is blocked, the method further includes:

[0042] Within a preset observation period, the output signal of the photosensitive element is recorded at preset time intervals to obtain multiple sets of output signal sequences;

[0043] The signal attenuation rate is obtained by calculating the ratio of the difference in signal values ​​between two adjacent sets of output signal sequences to the adjacent acquisition time interval.

[0044] The signal attenuation rate is compared with a preset attenuation rate reference range to determine the clogging level of the breathable membrane. The preset attenuation rate reference range includes multiple clogging levels.

[0045] When the blockage level is level one, the vibration device is controlled to vibrate at a first vibration frequency for a first duration; when the blockage level is level two, the vibration device is controlled to vibrate at a second vibration frequency for a second duration; when the blockage level is level three, the vibration device is controlled to vibrate at a third vibration frequency until the output signal of the photosensitive element returns to the preset normal signal range.

[0046] By employing the above technical solution, the output signal sequence of the photosensitive element is recorded within a preset observation period. The signal attenuation rate is calculated and compared with a preset attenuation rate reference range to determine the specific clogging level of the breathable membrane. Different vibration frequencies and durations are then applied according to different clogging levels. Analysis of the signal attenuation rate accurately reflects the actual degree of clogging in the breathable membrane, avoiding the use of a uniform treatment method. Different vibration parameters are used for different clogging levels: low-frequency short-duration vibration for mild clogging and high-frequency continuous vibration for severe clogging. This differentiated treatment method ensures cleaning effectiveness while avoiding unnecessary mechanical stress on the breathable membrane. It achieves accurate identification and graded treatment of the clogging degree, improves cleaning efficiency, extends the service life of the breathable membrane, and reduces equipment energy consumption.

[0047] In conjunction with some embodiments of the first aspect, in some embodiments, the signal attenuation rate is compared with a preset attenuation rate reference range to determine the clogging level of the breathable membrane, specifically including:

[0048] Calculate the average signal attenuation rate to obtain the average attenuation rate;

[0049] Calculate the difference between the maximum and minimum values ​​of the signal attenuation rate to obtain the attenuation fluctuation amplitude;

[0050] The blockage characteristic value is obtained by weighting the average decay rate and the decay fluctuation amplitude.

[0051] The blockage characteristic value is compared with the preset attenuation rate reference range. When the blockage characteristic value is less than the first blockage threshold, the blockage level is determined to be Level 1; when the blockage characteristic value is not less than the first blockage threshold and is less than the second blockage threshold, the blockage level is determined to be Level 2; when the blockage characteristic value is not less than the second blockage threshold, the blockage level is determined to be Level 3.

[0052] By employing the above technical solution, the average signal attenuation rate and attenuation fluctuation amplitude are calculated, and then weighted to obtain a clogging characteristic value. The specific clogging level is determined based on the magnitude of the clogging characteristic value. This clogging level determination method comprehensively considers the overall trend and fluctuation of signal attenuation, and can more comprehensively reflect the clogging status of the breathable membrane, improving the accuracy and reliability of clogging level determination.

[0053] Secondly, embodiments of this application provide a gas alarm vent membrane blockage identification system, which includes: one or more processors and a memory; the memory is coupled to one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the system to perform the method described in the first aspect and any possible implementation thereof.

[0054] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a system, cause the system to perform the method described in the first aspect and any possible implementation thereof.

[0055] Fourthly, embodiments of this application provide a computer program product that, when run on a system, causes the system to execute the method described in any possible implementation of the first aspect.

[0056] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0057] 1. This application provides a method for identifying clogging of the breathable membrane in a gas alarm. By placing a concave mirror on the inner side of the breathable membrane and utilizing the characteristic that the photosensitive element is located at the convergence point of light, parallel light is reflected by the concave mirror and converges at the photosensitive element, thus expanding the light signal acquisition area. Since multiple sets of data are continuously collected at preset time intervals and converted into light intensity data, instantaneous interference from factors such as ambient light and temperature can be reduced. By calculating the difference between the real-time maximum and minimum light intensity values ​​and dividing the data into intervals, and combining the cumulative sum of the data volume proportions in each interval, a mathematical model is established to characterize the actual light transmittance state of the breathable membrane. This method uses the ratio of the real-time judgment value to the reference value as the judgment criterion. Through the adjustable parameter of a preset multiple, both the accuracy of detection and the adjustability of detection sensitivity are improved. This detection method based on light intensity distribution characteristics improves the reliability and adaptability of identifying the clogging state of the breathable membrane.

[0058] 2. This application provides a method for identifying blockages in the permeable membrane of a gas alarm. By judging the ambient light intensity and adopting corresponding detection strategies, it actively activates an auxiliary light source for detection when the light is insufficient, thus expanding the applicability of the detection method. When the ambient light is sufficient, a detection method based on light intensity distribution characteristics is used; when the ambient light is insufficient, the blockage status is determined by continuously collecting and comparing the signal values ​​with a preset light transmittance threshold. This dual-mode detection method overcomes the limitations of a single detection method under different lighting conditions, enabling continuous detection under various lighting conditions. By introducing a preset third continuous judgment mechanism, misjudgments caused by instantaneous fluctuations are avoided, improving the reliability of detection results in low-light environments.

[0059] 3. This application provides a method for identifying clogging of the vent membrane in a gas alarm. By recording the output signal sequence of the photosensitive element within a preset observation period, calculating the signal attenuation rate, and comparing it with a preset attenuation rate reference range, the specific clogging level of the vent membrane is determined. Different vibration frequencies and durations are then applied according to different clogging levels. Analysis of the signal attenuation rate accurately reflects the actual degree of clogging of the vent membrane, avoiding the use of a uniform processing method. Different vibration parameters are used for different clogging levels: low-frequency short-duration vibration for mild clogging and high-frequency continuous vibration for severe clogging. This differentiated processing method ensures cleaning effectiveness while avoiding unnecessary mechanical stress on the vent membrane, achieving accurate identification and graded processing of the clogging degree, improving cleaning efficiency, extending the service life of the vent membrane, and reducing equipment energy consumption. Attached Figure Description

[0060] Figure 1 This is a flowchart illustrating a method for identifying blockage of the permeable membrane in a gas alarm according to an embodiment of this application.

[0061] Figure 2 This is a flowchart illustrating a method for identifying and processing congestion levels based on signal attenuation characteristics, as described in an embodiment of this application.

[0062] Figure 3 This is a schematic diagram of the physical device structure of a gas alarm vent membrane blockage identification system provided in an embodiment of this application. Detailed Implementation

[0063] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0064] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0065] Gas detectors are used to detect the gas being tested in the air. Their sensors must be open to the air, but moisture, dust, and other impurities in the environment can enter the sensor and cause changes in its performance. Therefore, gas detectors are generally designed with a waterproof and breathable membrane to isolate moisture and also block dust.

[0066] Since gas detectors are used in various places, if the waterproof and breathable membrane is blocked by dust, dirt or grease, it will inevitably affect the air intake effect, and thus affect the parameter performance of the detector. If the waterproof and breathable membrane is completely blocked, the detector will lose its gas alarm function.

[0067] Therefore, the need for gas alarms with waterproof and breathable membranes to actively identify whether the membrane is effective has become an industry requirement.

[0068] This application provides a method for identifying clogging of the breathable membrane in a gas alarm, using a waterproof and breathable membrane as the main component. Different gas alarms have different structures, resulting in varying positions of the waterproof and breathable membrane relative to the sensor, and consequently, different breathability. Gas alarms are limited by gas response time, generally requiring good breathability. The waterproof and breathable membrane used in this application is a PE polymer breathable membrane, which has a certain degree of light transmittance. This is a hardware characteristic prerequisite for implementing the gas alarm clogging identification method provided in this application.

[0069] The following example is used in conjunction with Figure 1 The present application describes a method for identifying blockage of the permeable membrane in a gas alarm:

[0070] Please see Figure 1This is a flowchart illustrating a method for identifying blockage of the permeable membrane in a gas alarm according to an embodiment of this application.

[0071] S101. Determine whether the ambient light intensity is greater than the preset threshold.

[0072] The system determines whether the current ambient light intensity exceeds a preset threshold. This preset threshold can be set according to the actual application scenario and requirements to ensure that the identification of air membrane blockage is performed under suitable lighting conditions. Ambient light intensity can be measured using various light sensors or photosensitive elements, such as photoresistors, photodiodes, and phototransistors. The system can collect ambient light intensity data in real time or at regular time intervals and compare it with the preset threshold to determine whether the lighting conditions are met for further air membrane blockage identification. The setting of the preset threshold can be comprehensively considered based on factors such as the lighting characteristics of the actual application scenario and the installation environment of the gas alarm to ensure that subsequent processing steps are performed under appropriate lighting conditions. The specific value of the preset threshold is not limited here.

[0073] S102. Determine the reference value based on the output signal collected by the photosensitive element at preset time intervals;

[0074] If the value is greater than the preset time interval, the system determines a reference benchmark value based on the output signals collected by the photosensitive element at preset time intervals. The photosensitive element is located at the light convergence point inside the breathable membrane of the gas alarm. The light convergence point is the intersection point after parallel light rays are reflected by a concave mirror installed inside the breathable membrane. Specifically, determining the reference benchmark value based on the output signals collected by the photosensitive element at preset time intervals involves: the system receiving the output signals collected by the photosensitive element at preset time intervals to obtain a preset first number of output signals; converting the preset first number of output signals into preset first number of light intensity data, and filtering out the maximum and minimum light intensity values ​​of the preset first number of groups; dividing the difference between the maximum and minimum light intensity values ​​into a preset second number of intervals, and calculating the proportion of data within the preset second number of intervals in the corresponding light intensity data; weighting each group of light intensity data according to the proportion to obtain a preset first number of weighted average values; and calculating the average of the preset first number of weighted average values ​​to obtain the reference benchmark value.

[0075] The system determines a reference value based on the output signals collected by the photosensitive element at preset time intervals. The photosensitive element is located at the light convergence point inside the breathable membrane of the gas alarm; the light convergence point is the intersection of parallel light rays after reflection by a concave mirror installed inside the breathable membrane. The system receives the output signals collected by the photosensitive element at preset time intervals, obtaining a preset first number of output signals. Then, the preset first number of output signals are converted into preset first number of light intensity data, and the maximum and minimum light intensity values ​​of the preset first number of groups are selected. Next, the difference between the maximum and minimum light intensity values ​​is divided into preset second number of intervals, and the proportion of data within the preset second number of intervals in the corresponding light intensity data is calculated. Based on the proportion, each group of light intensity data is weighted to obtain a preset first number of weighted average values. Finally, the average of the preset first number of weighted average values ​​is calculated to obtain the reference value. The preset time interval, the preset first number, and the preset second number can be set according to actual application requirements and are not limited here.

[0076] The photosensitive element can be a photoresistor, photodiode, phototransistor, etc., and its output signal has a certain functional relationship with the received light intensity. The system can establish a conversion function between the output signal and light intensity based on the model and characteristics of the photosensitive element, converting a preset first number of output signals into corresponding light intensity data. Then, a simple sorting algorithm is used to find the maximum and minimum light intensity values, calculate their difference, and divide the difference into a preset second number of intervals. For each interval, the number of light intensity data points falling within that interval is counted, and their proportion in the total data volume is calculated. A weighted average is then calculated for each group of light intensity data based on its proportion. Finally, the arithmetic mean of all weighted averages is calculated to obtain a reference benchmark value.

[0077] To improve the stability and reliability of the reference value, the system can use a sliding window approach for data processing. A fixed-size data window is set. Each time a new output signal is acquired, it is converted into light intensity data and added to the window, while the oldest data in the window is deleted. All data within the window are processed in step S102 to obtain a reference value. As new data is continuously added and old data is continuously deleted, the reference value is dynamically updated to adapt to possible changes in ambient light.

[0078] S103. Collect the real-time output signals of a preset first number of groups and convert the real-time output signals of the preset first number of groups into real-time light intensity data of the preset first number of groups.

[0079] The system acquires a preset first set of real-time output signals and converts them into a preset first set of real-time light intensity data. The real-time output signal refers to the light intensity signal received by the photosensitive element at the current moment, which can be converted into a voltage or current signal by a photoelectric conversion circuit. The system continuously acquires a preset first set of real-time output signals (e.g., 10 signals) at preset time intervals (e.g., 0.1 seconds), forming a time series. Then, based on the sensitivity and linearity characteristics of the photosensitive element, each real-time output signal is converted into corresponding real-time light intensity data, resulting in a preset first set of real-time light intensity data sequences. The preset first set can be set according to actual needs and computing resources, and is not limited here.

[0080] The system can convert the analog output signal of the photosensitive element into a digital signal using an analog-to-digital converter (ADC), and then convert the digital signal into real-time light intensity data using a lookup table method or a polynomial fitting method. To improve the accuracy and speed of the conversion, a light intensity-signal correspondence table can be pre-established, or a polynomial function can be fitted based on the photosensitive element's datasheet. When acquiring the real-time output signal, the system can directly look up the data in the correspondence table or substitute it into the polynomial function to obtain the real-time light intensity data, avoiding complex real-time calculations.

[0081] To reduce data transmission and storage overhead, the system can perform real-time light intensity data calculation and compression at the photosensitive element. The photosensitive element can integrate a microprocessor or dedicated signal processing circuit, which immediately converts and compresses the acquired real-time output signal to obtain a set of compressed real-time light intensity data. The compressed data is then transmitted to the main controller for further processing. This distributed computing approach reduces data transmission volume and latency, improving the system's real-time performance and scalability.

[0082] S104. Filter out the real-time maximum light intensity value and the real-time minimum light intensity value of the preset first number group;

[0083] The system filters out the maximum and minimum real-time light intensity values ​​from a preset first set. The maximum real-time light intensity value refers to the highest value in the preset first set of real-time light intensity data, and the minimum real-time light intensity value refers to the lowest value. By identifying the maximum and minimum values ​​in the real-time light intensity data sequence, the intensity range of the current lighting environment can be determined, providing a reference for subsequent light intensity change analysis and threshold judgment. Simultaneously, the difference between the maximum and minimum values ​​reflects the degree of light intensity fluctuation, which can be used to assess the stability of the lighting environment.

[0084] S105. Divide the difference between the real-time maximum light intensity value and the real-time minimum light intensity value into a preset second number of intervals, and calculate the proportion of the data volume in the preset second number of intervals in the corresponding real-time light intensity data.

[0085] The system divides the difference between the real-time maximum and minimum light intensity values ​​into a preset second number of intervals and calculates the proportion of data within each of these intervals in the corresponding real-time light intensity data. The difference between the real-time maximum and minimum light intensity values ​​reflects the dynamic range of the current light intensity. Dividing it into a preset second number of intervals (e.g., 10) allows for a more detailed characterization of the light intensity distribution. For each interval, the number of real-time light intensity data points falling within that interval is counted, and their proportion in the total data volume is calculated, resulting in a histogram or probability density function reflecting the light intensity distribution. This light intensity distribution information can be used for subsequent analysis tasks such as pattern recognition and anomaly detection.

[0086] The system can employ an equal-width partitioning method, dividing the difference between the real-time maximum and minimum light intensity values ​​into a predetermined second number of intervals. The width of each interval is the difference divided by the predetermined second number. Then, it iterates through a predetermined first number of groups of real-time light intensity data, incrementing a counter for each interval based on its location. Finally, the counter value for each interval is divided by the total data volume to obtain the proportion of that interval. This equal-width partitioning method is simple, intuitive, and easy to implement and understand.

[0087] To more accurately depict the details of light intensity distribution, the system can employ adaptive partitioning, dynamically adjusting the width and number of intervals based on the distribution characteristics of real-time light intensity data. Common methods include equal-frequency partitioning, clustering partitioning, and entropy maximization partitioning. Equal-frequency partitioning divides the data into several groups with equal data volume in each group; clustering partitioning uses clustering algorithms (such as k-means) to divide the data into several clusters, where data within each cluster has high similarity and data between clusters has large differences; entropy maximization partitioning optimizes the boundaries of intervals by maximizing the information entropy of the interval partitioning. These adaptive partitioning methods can better capture the local features of light intensity distribution, improving the accuracy and effectiveness of proportion calculation.

[0088] S106. Calculate the sum of the interval and percentage of the preset second quantity to obtain the real-time judgment value;

[0089] The system calculates the cumulative sum of the light intensity values ​​and their corresponding percentages for a preset second number of intervals to obtain a real-time judgment value. This real-time judgment value is a comprehensive indicator reflecting the current lighting environment. It is calculated by multiplying the light intensity value of each interval by its corresponding percentage, then summing these values ​​to obtain a weighted average. This weighted averaging method considers both the absolute magnitude and relative distribution of light intensity, providing a more accurate description of the overall characteristics of the lighting environment than a simple arithmetic average. The magnitude of the real-time judgment value can be used for subsequent threshold comparisons and trend analysis to determine whether the current lighting meets specific conditions or exceeds the normal range.

[0090] S107. If the real-time judgment value is determined to be greater than a preset multiple of the reference benchmark value, it is determined that the breathable membrane is not blocked.

[0091] The system determines that the breathable membrane is not blocked if the real-time judgment value is greater than a preset multiple of the reference benchmark value. The reference benchmark value is a baseline value reflecting a normal lighting environment, calculated according to step S102. The preset multiple is a coefficient greater than 1, used to set the threshold for judging whether the breathable membrane is blocked. If the real-time judgment value exceeds the preset multiple of the reference benchmark value, it indicates that the current light intensity is significantly higher than the normal level, the breathable membrane is not blocked, and light can pass through normally. This threshold judgment method can effectively detect abnormal light intensity caused by breathable membrane blockage, and promptly identify and handle faults.

[0092] S108. If the real-time judgment value is determined to be no greater than a preset multiple of the reference benchmark value, the air permeable membrane is determined to be blocked.

[0093] The system determines that the breathable membrane is blocked if the real-time judgment value is not greater than a preset multiple of the reference value. Conversely, in step S107, if the real-time judgment value does not exceed a preset multiple of the reference value, it indicates that the current light intensity is lower than or close to normal levels, and the breathable membrane may be blocked, hindering normal light transmission. In this case, the system marks the breathable membrane as blocked, indicating that cleaning or maintenance is required. Timely detection and handling of breathable membrane blockage is crucial for ensuring the normal operation and safety monitoring of the gas alarm.

[0094] S109. Turn on the auxiliary light source installed at the preset installation point of the gas alarm;

[0095] If the judgment result in step S101 is not greater than, the system turns on the auxiliary light source installed at the preset installation point of the gas alarm. Further, the system records the output signal of the photosensitive element at the first moment when the auxiliary light source is on; records the output signal of the photosensitive element at the second moment when the auxiliary light source is off; calculates the difference between the output signal at the first moment and the output signal at the second moment to obtain the light intensity change value; acquires multiple light intensity change values ​​at preset time intervals; if a preset number of consecutive light intensity change values ​​remain unchanged, it determines that the auxiliary light source is not blocked; and controls the heating device located inside the auxiliary light source to maintain a preset heating frequency and preset heating power. If a preset number of consecutive light intensity change values ​​decrease, it determines that the auxiliary light source is blocked; and controls the heating device to operate at a preset heating power for a preset duration until the light intensity change value remains unchanged.

[0096] In step S109, if the ambient light intensity is not greater than a preset threshold, the system will turn on the auxiliary light source installed at the preset installation point of the gas alarm. To identify whether the auxiliary light source is blocked, the system records the output signal of the photosensitive element in the on and off states of the auxiliary light source, i.e., the output signal at the first moment and the second moment. Then, the difference between the output signals at these two moments is calculated to obtain the light intensity change value. Multiple light intensity change values ​​are obtained at preset time intervals. If a preset number of consecutive light intensity change values ​​remain unchanged, it is determined that the auxiliary light source is not blocked, and the heating device located inside the auxiliary light source is controlled to operate at a preset heating frequency and preset heating power. If a preset number of consecutive light intensity change values ​​decrease, it is determined that the auxiliary light source is blocked, and the heating device is controlled to operate at a preset heating power for a preset duration until the light intensity change value no longer changes. The preset time interval, preset number, preset heating frequency, preset heating power, and preset duration can be set according to actual needs and experience, and are not limited here.

[0097] The system can automatically activate an auxiliary light source when the ambient light intensity is insufficient. The auxiliary light source can be an LED lamp, whose brightness and spectral characteristics must match the sensitivity of the photosensitive element. The system alternately turns the auxiliary light source on and off at preset time intervals (e.g., 1 second). After each state switch, it records the output signal of the photosensitive element and calculates the difference between two adjacent output signals, i.e., the light intensity change value. A preset number (e.g., 3) of consecutive light intensity change values ​​form an observation window. The system determines whether the auxiliary light source is blocked based on the trend of the light intensity change values ​​within the window. If the light intensity change value remains constant, the preset operating state of the heating equipment is maintained; if the light intensity change value decreases, the power of the heating equipment is increased to a preset value and maintained for a preset duration until the light intensity change value stabilizes.

[0098] To improve the reliability of auxiliary light source obstruction detection, the system can employ multiple photosensitive elements and multiple auxiliary light sources. Several auxiliary light sources and photosensitive elements are evenly distributed along the perimeter of the gas alarm's permeable membrane, forming a ring array. Each auxiliary light source is sequentially turned on and off, and the corresponding photosensitive element records the output signal and calculates the light intensity change value. The system comprehensively analyzes the light intensity change values ​​of all photosensitive elements, and based on their consistency and spatial distribution characteristics, determines whether the auxiliary light source is partially or completely obstructed. This multi-sensor fusion approach can effectively improve the coverage and accuracy of obstruction detection, and reduce missed detections caused by partial obstruction.

[0099] S110, Receive several comparison signal values ​​collected by the photosensitive element;

[0100] The system receives several comparison signal values ​​collected by the photosensitive element. These comparison signal values ​​are a set of reference signal values ​​output by the photosensitive element under specific conditions, used to compare with real-time signal values ​​to determine whether the gas membrane is blocked. Typically, the comparison signal values ​​are collected under conditions where the gas membrane is not blocked and illumination is normal, reflecting the light intensity level when the gas alarm is working normally. The system can acquire comparison signal values ​​during equipment installation, commissioning, and periodic calibration, storing them in non-volatile memory as a reference for subsequent judgments. The number of comparison signal values ​​and the acquisition time interval can be set according to actual needs and equipment characteristics, and are not limited here.

[0101] The system can control a photosensitive element to continuously collect signal values ​​at preset time intervals (e.g., 1 second) under stable lighting conditions and without clogging the breathable membrane, obtaining a set of comparison signal values. To improve the representativeness and reliability of the comparison signal values, multiple sets of data can be collected, and each set can be preprocessed, such as removing outliers and calculating averages. The processed comparison signal values ​​are then stored in the system's parameter table or configuration file for later use. In practical applications, the comparison signal values ​​can also be updated periodically according to changes in environmental conditions to adapt to different operating states.

[0102] S111. If the number of consecutive preset comparison signal values ​​is less than the preset light transmittance threshold signal value, it is determined that the breathable membrane is blocked.

[0103] The system determines that the breathable membrane is blocked if the comparison signal value is lower than a preset light transmittance threshold signal value for a third consecutive preset number of times. The preset light transmittance threshold signal value is a threshold set based on comparison signal values ​​and empirical data to judge whether the breathable membrane is blocked. When the real-time signal value is lower than this threshold value multiple times consecutively, it indicates a significant decrease in the light transmittance performance of the breathable membrane, suggesting possible blockage. The condition of a third consecutive preset number of times is to avoid misjudgments caused by occasional short-term interference or noise, thus improving the reliability of the judgment. Only when the real-time signal value is lower than the preset light transmittance threshold signal value for a certain number of consecutive times (e.g., 5 times) is the breathable membrane considered to be indeed blocked, requiring immediate attention.

[0104] S112. If the comparison signal value is not less than the preset light transmittance threshold signal value, it is determined that the breathable membrane is not blocked.

[0105] If the system determines that the comparison signal value is not less than the preset light transmittance threshold signal value, it indicates that the breathable membrane is not blocked. Conversely, in step S111, if the real-time signal value is not less than the preset light transmittance threshold signal value, it means that the light transmittance performance of the breathable membrane is normal and there is no blockage; the gas alarm can continue to operate normally. In this case, the system marks the breathable membrane as unblocked, and no special processing is required. By monitoring the relationship between the comparison signal value and the preset light transmittance threshold signal value in real time, abnormalities such as breathable membrane blockage can be detected promptly, ensuring the reliability and safety of the gas alarm.

[0106] In the above embodiments, by setting a concave mirror on the inner side of the breathable membrane and utilizing the characteristic that the photosensitive element is located at the light convergence point, parallel light is reflected by the concave mirror and converges at the photosensitive element, thus expanding the light signal collection area. Since multiple sets of data are continuously collected at preset time intervals and converted into light intensity data, instantaneous interference from factors such as ambient light and temperature can be reduced. By calculating the difference between the real-time maximum and minimum light intensity values ​​and dividing the data into intervals, and combining the cumulative sum of the data volume proportions in each interval, a mathematical model is established to characterize the actual light transmittance state of the breathable membrane. This method uses the ratio of the real-time judgment value to the reference benchmark value as the judgment criterion. Through the adjustable parameter of a preset multiple, both the accuracy of detection and the adjustability of detection sensitivity are improved. This detection method based on light intensity distribution characteristics improves the reliability and adaptability of identifying the clogging state of the breathable membrane.

[0107] By assessing ambient light intensity and implementing corresponding detection strategies, the method actively activates auxiliary light sources for detection when light is insufficient, thus expanding its applicability. When ambient light is sufficient, a detection method based on light intensity distribution characteristics is employed; when ambient light is insufficient, the blockage status is determined by continuously acquiring and comparing signal values ​​with a preset light transmittance threshold. This dual-mode detection method overcomes the limitations of single-mode detection under varying lighting conditions, enabling continuous detection under diverse lighting conditions. The introduction of a preset third continuous judgment mechanism avoids misjudgments caused by instantaneous fluctuations, improving the reliability of detection results in low-light environments.

[0108] After determining that the breathable membrane is blocked, in order to take appropriate measures and achieve accurate determination of the degree of blockage, this application embodiment also provides a blockage level identification and processing method based on signal attenuation characteristics. This method analyzes the change characteristics of the output signal of the photosensitive element during the blockage process of the breathable membrane to accurately classify the degree of blockage and take corresponding processing measures according to different levels. The following is a combination of... Figure 2 This application describes a method for identifying and processing congestion levels based on signal attenuation characteristics:

[0109] Please see Figure 2This is a flowchart illustrating a method for identifying and processing congestion levels based on signal attenuation characteristics in an embodiment of this application.

[0110] S201. Within a preset observation period, record the output signal of the photosensitive element at preset time intervals to obtain multiple sets of output signal sequences;

[0111] Within a preset observation period, the system records the output signal of the photosensitive element at preset time intervals, obtaining multiple sets of output signal sequences. The preset observation period refers to a fixed time interval for continuous monitoring of the clogging status of the breathable membrane, which can be set according to actual needs and experience, such as 1 hour, 12 hours, or 24 hours. Within the preset observation period, the system periodically records the output signal of the photosensitive element at preset time intervals (such as 1 minute or 5 minutes), forming a time series. By continuously recording the output signals at multiple time points, dynamic data reflecting the clogging process of the breathable membrane can be obtained, providing a basis for subsequent clogging level identification. The specific values ​​of the preset observation period and preset time intervals can be flexibly set according to factors such as actual application scenarios, equipment characteristics, and monitoring requirements; no limitations are imposed here.

[0112] The system can trigger data acquisition tasks at preset time intervals within a preset observation period using a timer or real-time clock. Each trigger causes the photosensitive element to sample a signal, and the sampled value, along with the corresponding timestamp, is recorded in memory to form a data point. Over time, the system continuously acquires and records data points, obtaining a sequence of output signals reflecting changes in the clogging state of the permeable membrane. To save storage space and reduce data transmission overhead, the raw data can be preprocessed, such as compressed or downsampled, improving data storage and processing efficiency.

[0113] The system can also employ an adaptive sampling strategy, dynamically adjusting the preset time interval based on the changing characteristics of the breathable membrane's clogging state. When the clogging is mild or changes slowly, the sampling interval can be appropriately extended to reduce the amount of data; conversely, when the clogging worsens or changes intensify, the sampling interval can be appropriately shortened to improve the temporal resolution of the data. This adaptive sampling method optimizes system resource utilization efficiency while ensuring data quality. Furthermore, an event-triggered mechanism can be introduced to automatically initiate high-frequency sampling when an anomaly is detected (such as a sudden change in the output signal), capturing crucial details of the change.

[0114] S202. Calculate the ratio of the difference in signal values ​​between two adjacent sets of output signal sequences to the adjacent acquisition time interval to obtain the signal attenuation rate.

[0115] The system calculates the ratio of the difference in signal values ​​between two adjacent output signal sequences to the time interval between adjacent acquisitions to obtain the signal attenuation rate. The signal attenuation rate reflects how quickly the output signal of the photosensitive element changes over time during the clogging process of the breathable membrane; it is a dynamically changing indicator. By calculating the difference in signal values ​​between two adjacent sampling times and dividing it by the corresponding time interval, the average rate of change of the output signal within that time period, i.e., the signal attenuation rate, can be obtained. Since the clogging of the breathable membrane is a continuous process, the signal attenuation rate also changes continuously over time. The system continuously calculates the latest signal attenuation rate using a sliding window or recursive method to track the changing trend of the clogging state of the breathable membrane in real time.

[0116] The system can calculate the signal attenuation rate using a two-point difference method. For each newly acquired data point, the signal value is subtracted from the previous data point to obtain the signal change between the two adjacent points. Then, the signal change is divided by the sampling time interval between the two adjacent points to obtain the signal attenuation rate within that time interval. By continuously updating the latest data points, a series of signal attenuation rate values ​​can be obtained, reflecting the dynamic changes in the clogging state of the permeable membrane. To smooth out short-term fluctuations and noise, the signal attenuation rate can also be subjected to moving average or exponential smoothing to obtain a more stable and reliable trend indicator.

[0117] S203. Compare the signal attenuation rate with the preset attenuation rate reference range to determine the clogging level of the breathable membrane.

[0118] The system compares the signal attenuation rate with a preset attenuation rate reference range to determine the clogging level of the breathable membrane. Specifically, this includes: calculating the average signal attenuation rate to obtain the average attenuation rate; calculating the difference between the maximum and minimum signal attenuation rates to obtain the attenuation fluctuation amplitude; obtaining the clogging characteristic value based on the weighted sum of the average attenuation rate and the attenuation fluctuation amplitude; comparing the clogging characteristic value with the preset attenuation rate reference range; determining the clogging level as Level 1 when the clogging characteristic value is less than a first clogging threshold; determining the clogging level as Level 2 when the clogging characteristic value is not less than the first clogging threshold and is less than a second clogging threshold; and determining the clogging level as Level 3 when the clogging characteristic value is not less than the second clogging threshold.

[0119] The system compares the signal attenuation rate with a preset attenuation rate reference range to determine the clogging level of the breathable membrane. The preset attenuation rate reference range is a set of thresholds pre-set based on empirical data and theoretical analysis to classify different clogging levels. By comparing the real-time calculated signal attenuation rate with the reference range, the current degree of clogging of the breathable membrane can be determined, and a corresponding level assessment can be given.

[0120] Specifically, the system first calculates the average signal attenuation rate over a period of time, obtaining the average attenuation rate, which reflects the overall trend and degree of clogging of the breathable membrane. Then, it calculates the difference between the maximum and minimum signal attenuation rates within the same period, obtaining the attenuation fluctuation amplitude, reflecting the dynamic range of change during the clogging process. Next, the system obtains a comprehensive clogging characteristic value based on the weighted sum of the average attenuation rate and the attenuation fluctuation amplitude, taking into account both the static level and dynamic fluctuations of clogging. Finally, the clogging characteristic value is compared with a preset attenuation rate reference range. When the clogging characteristic value is less than a first clogging threshold, the clogging level is determined to be Level 1, indicating a slight clogging degree; when the clogging characteristic value is not less than the first clogging threshold and less than a second clogging threshold, the clogging level is determined to be Level 2, indicating a moderate clogging degree; when the clogging characteristic value is not less than the second clogging threshold, the clogging level is determined to be Level 3, indicating a severe clogging degree. The number of clogging levels and the threshold settings can be adjusted according to actual needs and experience, and are not limited here.

[0121] S204. When the blockage level is Level 1, the vibration device is controlled to vibrate at a first vibration frequency for a first duration; when the blockage level is Level 2, the vibration device is controlled to vibrate at a second vibration frequency for a second duration; when the blockage level is Level 3, the vibration device is controlled to vibrate at a third vibration frequency until the output signal of the photosensitive element returns to the preset normal signal range.

[0122] The system takes corresponding measures based on the identified level of clogging in the breathable membrane. When the clogging level is Level 1, indicating a minor blockage, the system controls the vibration device to vibrate at a first vibration frequency for a first duration. This moderate vibration loosens and removes dust and impurities adhering to the membrane surface, restoring breathability. When the clogging level is Level 2, indicating a more severe blockage, the system controls the vibration device to vibrate at a second vibration frequency for a second duration. Increasing the vibration frequency and duration more effectively removes the blockage material and improves breathability. When the clogging level is Level 3, indicating a severe blockage that may be difficult to clear with simple vibration, the system controls the vibration device to vibrate continuously at a third vibration frequency while monitoring the output signal of the photosensitive element. Vibration stops only when the output signal returns to the preset normal range, ensuring the clogging problem is completely resolved. The vibration frequency and duration can be set according to actual conditions and equipment characteristics; specific limitations are not specified here. After identifying the blockage level, the corresponding blockage level and related information can be displayed on a display terminal connected to the gas alarm. This display terminal can be a computer monitor, a mobile phone monitor, or other display terminals, which are not limited here.

[0123] The system applies mechanical vibration to the breathable membrane by controlling a vibrating motor, piezoelectric element, or horn, causing the blockage material to dislodge. Different control parameters, such as vibration frequency, amplitude, and duration, are set according to the degree of blockage. When a change in the blockage level is detected, the vibration parameters are adjusted accordingly to achieve an adaptive cleaning effect. Simultaneously, the system can record the effect of each vibration and the recovery status of the breathable membrane, serving as a basis for optimizing the control strategy.

[0124] In addition to mechanical vibration, the system can be combined with other cleaning methods, such as airflow purging and ultrasonic cleaning, to form a comprehensive breathable membrane cleaning solution. Depending on the level of blockage and the nature of the blockage material, the most suitable cleaning method or a combination of methods can be selected to improve cleaning efficiency and effectiveness. For example, airflow purging can be used first, followed by mechanical vibration. This combined cleaning approach can provide a more comprehensive and effective solution for different types and degrees of blockage problems.

[0125] In the above embodiments, by recording the output signal sequence of the photosensitive element within a preset observation period, calculating the signal attenuation rate, and comparing it with a preset attenuation rate reference range, the specific clogging level of the breathable membrane is determined. Different vibration frequencies and durations are then applied according to different clogging levels. Analysis of the signal attenuation rate accurately reflects the actual degree of clogging of the breathable membrane, avoiding the use of a uniform processing method. Different vibration parameters are used for different clogging levels: low-frequency short-duration vibration for mild clogging and high-frequency continuous vibration for severe clogging. This differentiated processing method ensures cleaning effectiveness while avoiding unnecessary mechanical stress on the breathable membrane. It achieves accurate identification and graded processing of the clogging degree of the breathable membrane, improves cleaning efficiency, extends the service life of the breathable membrane, and reduces equipment energy consumption.

[0126] The system in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference needed]. Figure 3 This is a schematic diagram of the physical device structure of a gas alarm vent membrane blockage identification system provided in an embodiment of this application.

[0127] It should be noted that, Figure 3 The structure of the system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0128] like Figure 3As shown, the system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on a program stored in Read-Only Memory (ROM) 302 or a program loaded from storage portion 308 into Random Access Memory (RAM) 303, such as executing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0129] The following components are connected to I / O interface 305: input section 306 including a camera, infrared sensor, etc.; output section 307 including a liquid crystal display (LCD) and speakers, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card and a modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0130] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0131] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. The transmitted data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0132] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0133] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the system described in the above embodiments; or it may exist independently and not assembled into the system. The storage medium carries one or more computer programs that, when executed by a processor of a system, cause the system to implement the methods provided in the above embodiments.

[0134] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0135] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0136] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0137] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for identifying blockage of the permeable membrane in a gas alarm, characterized in that, include: The reference value is determined based on the output signal collected by the photosensitive element at a preset time interval. The photosensitive element is located at the light convergence point inside the gas alarm's breathable membrane. The light convergence point is the intersection point of parallel light rays after being reflected by a concave mirror installed inside the breathable membrane. Collect a preset first number of real-time output signals and convert the preset first number of real-time output signals into the preset first number of real-time light intensity data; The real-time maximum light intensity value and the real-time minimum light intensity value of the preset first number group are selected. The difference between the real-time maximum light intensity value and the real-time minimum light intensity value is divided into a preset second number of intervals, and the proportion of the data volume in the preset second number of intervals in the corresponding real-time light intensity data is calculated. Calculate the sum of the preset second quantity interval and the percentage to obtain the real-time judgment value; If the real-time judgment value is determined to be greater than a preset multiple of the reference benchmark value, it is determined that the breathable membrane is not blocked; If the real-time determination value is not greater than a preset multiple of the reference benchmark value, the breathable membrane is determined to be blocked.

2. The method according to claim 1, characterized in that, The step of determining the reference value based on the output signal collected by the photosensitive element at preset time intervals specifically includes: The output signal collected by the photosensitive element at the preset time interval is received to obtain the preset first number of output signals; The output signals of the preset first number of groups are converted into light intensity data of the preset first number of groups, and the maximum and minimum light intensity values ​​of the preset first number of groups are filtered out. The difference between the maximum light intensity value and the minimum light intensity value is divided into a preset second number of intervals, and the proportion of the data in the preset second number of intervals in the corresponding light intensity data is calculated. The light intensity data of each group are weighted according to the proportion to obtain the preset first number of weighted average values. Calculate the average of the preset first number of weighted averages to obtain the reference benchmark value.

3. The method according to claim 1, characterized in that, Before determining the reference value based on the output signal acquired by the photosensitive element at preset time intervals, the method further includes: Determine whether the ambient light intensity is greater than a preset threshold; If it is greater than that, then the process of determining the reference value based on the output signal collected by the photosensitive element at preset time intervals is executed. If the value is not greater than the preset installation point of the gas alarm, then turn on the auxiliary light source installed at the preset installation point of the gas alarm. Receive several comparison signal values ​​collected by the photosensitive element; If the comparison signal value is determined to be less than a preset light transmittance threshold signal value for a third consecutive preset number of times, it is determined that the breathable membrane is blocked. If the comparison signal value is determined to be not less than the preset light transmittance threshold signal value, it is determined that the breathable membrane is not blocked.

4. The method according to claim 3, characterized in that, After the auxiliary light source installed at the preset mounting point of the gas alarm is turned on, the method further includes: Record the output signal of the photosensitive element at the first moment when the auxiliary light source is turned on; Record the output signal of the photosensitive element at the second moment when the auxiliary light source is off; The difference between the output signal at the first moment and the output signal at the second moment is calculated to obtain the change in light intensity; Multiple light intensity change values ​​are obtained according to the preset time interval; If the light intensity change value remains unchanged for a consecutive preset number of times, it is determined that the auxiliary light source is not blocked; The heating device located inside the auxiliary light source is controlled to maintain a preset heating frequency and preset heating power.

5. The method according to claim 4, characterized in that, After acquiring multiple light intensity change values ​​according to the preset time interval, the method further includes: If the light intensity change value decreases for a consecutive preset number of times, it is determined that the auxiliary light source is blocked; The heating device is controlled to operate with the preset heating power for a preset duration until the light intensity change value remains constant.

6. The method according to claim 1, characterized in that, After determining that the breathable membrane is blocked, the method further includes: Within a preset observation period, the output signal of the photosensitive element is recorded at each preset time interval to obtain multiple sets of output signal sequences; The signal attenuation rate is obtained by calculating the ratio of the difference in signal values ​​between two adjacent sets of output signal sequences to the adjacent acquisition time interval. The signal attenuation rate is compared with a preset attenuation rate reference range to determine the clogging level of the breathable membrane. The preset attenuation rate reference range includes multiple clogging levels. When the blockage level is level one, the vibration device is controlled to vibrate at a first vibration frequency for a first duration; when the blockage level is level two, the vibration device is controlled to vibrate at a second vibration frequency for a second duration; when the blockage level is level three, the vibration device is controlled to vibrate at a third vibration frequency until the output signal of the photosensitive element returns to the preset normal signal range.

7. The method according to claim 6, characterized in that, The step of comparing the signal attenuation rate with a preset attenuation rate reference range to determine the clogging level of the breathable membrane specifically includes: Calculate the average value of the signal attenuation rate to obtain the average attenuation rate; Calculate the difference between the maximum and minimum values ​​of the signal attenuation rate to obtain the attenuation fluctuation amplitude; The blockage characteristic value is obtained by weighting the average decay rate and the decay fluctuation amplitude. The blockage feature value is compared with a preset attenuation rate reference range. When the blockage feature value is less than a first blockage threshold, the blockage level is determined to be Level 1; when the blockage feature value is not less than the first blockage threshold and less than a second blockage threshold, the blockage level is determined to be Level 2; when the blockage feature value is not less than the second blockage threshold, the blockage level is determined to be Level 3.

8. A gas alarm vent membrane blockage detection system, characterized in that, The system includes: One or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on the system, the system performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the system, the system performs the method as described in any one of claims 1-7.

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