Combustible gas alarm adaptive calibration method

Abstract

The invention discloses a combustible gas alarm self-adaptive calibration method, and belongs to the field of sensor detection and calibration. Comprising the steps of multi-environment reference value acquisition and initialization, continuous monitoring and environment correlation analysis, sensor drift automatic detection and adaptive calibration, calibration parameter real-time correction, detection record uploading to a cloud and the like. Original and standard gas data are collected under different environment conditions, a reference value library is established, and multi-dimensional dynamic compensation of sensor output is achieved. The system can automatically identify drifting caused by environmental change or sensor aging, calibrate parameters in real time, and ensure output accuracy. Meanwhile, equipment data are uploaded to a cloud platform, big data analysis and integrated optimization are supported, and the stability and the intelligent level of long-term operation of the alarm are improved.

Description

Technical Field

[0001] This invention belongs to the field of sensor detection and calibration, and more specifically relates to an adaptive calibration method for combustible gas alarms. Background Technology

[0002] Combustible gas detectors are widely used in industrial, residential, and commercial settings to monitor the concentration of combustible gases in the environment in real time, provide timely warnings of leak risks, and ensure the safety of personnel and property. Currently, most mainstream combustible gas detectors use gas sensors of the semiconductor, electrochemical, and catalytic combustion types. While these sensors perform well in terms of sensitivity and response speed, their output signals are highly susceptible to external environmental factors (such as temperature, humidity, and air pressure), and after long-term operation, phenomena such as zero-point drift and sensitivity changes may occur, affecting detection accuracy.

[0003] Traditional combustible gas detectors are typically calibrated at the factory using a single environment or a few calibration points. However, in actual use, due to varying user installation environments, the environmental parameters such as temperature, humidity, and air pressure at the device's location often differ significantly from those at the factory calibration point, leading to errors between the sensor output and the actual gas concentration. Furthermore, over long-term operation, sensors inevitably experience performance drift due to aging, contamination, and other factors, resulting in reduced alarm thresholds and sensitivity, and posing safety hazards such as false alarms and missed alarms.

[0004] In existing technologies, some high-end devices alleviate the above problems through built-in temperature compensation and periodic manual calibration. However, these methods often require regular maintenance by professionals, increasing operation and maintenance costs, and cannot adapt to complex situations such as changing environments and sensor aging in real time and dynamically. Some intelligent products attempt to use algorithm compensation or software correction, but most are based on single-point environmental parameter correction, which is difficult to cover all operating conditions, and lacks effective identification and response mechanisms for long-term sensor drift and systemic anomalies.

[0005] In recent years, with the development of new technologies such as the Internet of Things, big data and cloud computing, some smart alarms have acquired data networking and remote monitoring capabilities. However, their data analysis and calibration are still at a basic stage, lacking multi-environment, multi-point benchmark acquisition and deep intelligent optimization mechanisms based on large-scale device collaboration. They have also failed to achieve a highly intelligent calibration scheme with device-level self-adaptation, cloud-based remote optimization and multi-level collaboration.

[0006] Therefore, developing an adaptive calibration method for combustible gas alarms that is adaptable to multiple environments, dynamically self-adaptive, and possesses cloud collaboration and big data analysis capabilities has become a key technical challenge for improving gas detection accuracy, reducing manual maintenance, and enhancing system intelligence. Summary of the Invention

[0007] The technical problem this invention aims to solve is that existing combustible gas alarms are easily affected by factors such as temperature, humidity, and air pressure under different environmental conditions. After long-term operation, the sensors exhibit zero-point drift and sensitivity changes. Traditional single calibration methods are difficult to meet the needs of different usage scenarios and long-term stable detection, resulting in decreased gas detection accuracy, frequent false alarms and missed alarms. Furthermore, calibration and maintenance rely on manual operation, leading to high maintenance costs. Therefore, there is an urgent need to provide a combustible gas alarm calibration method that can adapt to multiple environmental differences and has adaptive calibration and cloud big data collaborative optimization capabilities to improve detection accuracy and the intelligence level of the equipment, and reduce the frequency of manual maintenance.

[0008] To achieve the above objectives, the present invention employs the following technical solution: the method comprises: Multiple environmental reference values ​​are collected and initialized. Raw gas data are collected under typical environmental conditions, and standard gases of a certain concentration are input for calibration. It continuously monitors sensor output and collects local temperature and humidity environmental parameters. Through an environmental correlation analysis model, it automatically eliminates non-sensor drift signals caused solely by environmental changes. Automatic sensor drift detection and adaptive calibration determine whether the sensor has zero-point drift or sensitivity decay; if a drift amount exceeding the preset standard is detected, the adaptive calibration process is automatically triggered. The calibration parameters are corrected in real time. Once calibration is triggered, the alarm dynamically adjusts the sensor signal output curve based on the current environmental parameters and historical benchmarks, so that the device output once again matches the real gas concentration. The equipment uploads each calibration and testing record to the cloud platform, and compares and predicts the working status of similar equipment based on big data clustering analysis.

[0009] In one scheme, the multi-environmental reference value acquisition and initialization includes: selecting several sets of representative environmental conditions, including different temperatures, humidity and air pressures, according to the actual application scenario; and running the alarm in clean air without combustible gases under each environmental condition and recording the sensor's original zero-point data. Then, standard combustible gases of known concentrations are introduced, and sensor output data are collected and stored at each standard gas concentration. For each combination of environmental parameters and gas concentration, the original readings, environmental information and corresponding standard concentrations are recorded to form a data comparison table. The above multi-environment, multi-point calibration sampling data will be saved as an initialization reference value library for subsequent real-time monitoring, automatic calibration and sensor drift judgment.

[0010] In one embodiment, the continuous monitoring of sensor output includes: continuously acquiring real-time output data from a combustible gas sensor and simultaneously acquiring local temperature and humidity environmental parameters, and dynamically recording the sensor output curve and environmental parameters to form a long-term data stream. An environmental correlation analysis model is adopted, based on regression analysis, to dynamically estimate the gas concentration response coefficient and the influence weight of environmental parameters on the output, and to calculate the residual between the actual sensor output and the predicted output of environmental parameters. If the residual shows continuous fluctuations exceeding the threshold and cannot be explained by concentration changes, it is determined to be sensor drift or an abnormal signal. By separating and eliminating environmentally relevant components, the high sensitivity and accuracy of detecting abnormal real gas concentrations are improved.

[0011] In one embodiment, the automatic sensor drift detection and adaptive calibration includes: Real-time statistical analysis of sensor output data is performed to maintain the sensor residual sequence under gas-free conditions. The deviation of the residual mean from the initial reference zero point value is monitored to determine zero point drift. At the same time, the changes in the sensitivity parameters of the regression model and the initial or last calibration sensitivity are monitored to determine sensitivity anomalies. Once drift is detected, the system combines current environmental parameters, retrieves historical benchmarks and calibration records from the database, selects the model with the best calibration effect as a reference using a fuzzy matching algorithm, and determines the final correction coefficient by combining the results of the most recent calibrations using a weighted average or minimum error method. This enables intelligent discrimination and rapid response self-calibration in complex environments without human intervention.

[0012] In one scheme, the real-time correction of the calibration parameters includes: when adaptive calibration is triggered, the alarm retrieves the most matching reference environmental state and calibration curve based on the currently detected environmental parameters and historically collected benchmark data; The relationship between the sensor output signal and the real gas concentration is modeled using nonlinear regression algorithms or interpolation methods. A calibration correction model is established through polynomial regression or radial basis function interpolation, and the output signal is dynamically corrected so that the corrected gas concentration is highly close to the actual value. When the sample environmental parameters fall within the historical baseline grid, the output is adjusted using three-dimensional interpolation or weighted compensation methods.

[0013] In one solution, the upload of the detection records to the cloud platform includes: the alarm encrypting and uploading locally collected sensor outputs, environmental parameters, calibration process, medium- and long-term detection results, and operation log data to the cloud platform; Based on large-scale operational data of equipment of the same batch and model, the cloud platform uses big data analysis and cluster mining to conduct horizontal and vertical comparisons of equipment drift trends, calibration effects and anomaly types. When a device's status is found to deviate significantly from the group's, personalized optimization suggestions are automatically generated and pushed to the local device.

[0014] In one approach, the acquisition and initialization of multiple environmental reference values ​​are selected manually or automatically based on the actual application scenario. The selected environment includes at least two different temperatures or at least two different humidity or air pressure conditions.

[0015] In one approach, the acquisition and initialization of multiple environmental reference values ​​requires that the sensor zero-point acquisition under each environmental condition be performed after the sensor has been running stably in clean air for a preset period of time to ensure the accuracy of the zero-point data.

[0016] Beneficial effects of this invention: This invention achieves intelligent and dynamic calibration of combustible gas alarms by integrating technologies such as multi-environmental parameter acquisition, adaptive calibration algorithms, and cloud-based big data analysis. Compared with traditional methods that rely on single calibration and manual maintenance, this invention can sense environmental changes such as temperature, humidity, and air pressure in real time and intelligently adjust sensor output, significantly improving the detection accuracy of the equipment in different locations and climate conditions, and reducing the risk of false alarms and missed alarms caused by environmental changes or sensor aging.

[0017] Meanwhile, this invention uses a cloud-based collaborative mechanism to aggregate and analyze the operational and historical data of multiple devices, enabling intelligent parameter optimization and anomaly warning, effectively extending the lifespan of sensors and enhancing the overall safety and reliability of the system.

[0018] Furthermore, the introduction of automated adaptive calibration technology significantly reduces the need for manual maintenance, lowers operating costs, and enables the equipment to operate at its optimal state without long-term intervention from professional personnel, further improving the usability and economic efficiency of gas detection systems. This invention offers advantages such as strong environmental adaptability, high level of intelligence, and convenient application, providing strong support for the reliable operation of combustible gas alarms and the intelligent upgrading of the industry. Attached Figure Description

[0019] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0020] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0021] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. To facilitate understanding, the invention will now be described more fully with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the invention more thorough and complete.

[0022] like Figure 1 As shown, an adaptive calibration method for a combustible gas alarm is described, and the specific implementation steps are as follows: Step 1: Multi-environmental baseline data acquisition and initialization When the alarm is first installed or reset, raw gas data is collected under typical environmental conditions (such as different temperatures, humidity levels, and air pressures), and a standard gas of a certain concentration is input for calibration. The system establishes these data as initial baseline values, serving as a reference standard for subsequent calibration and drift detection. This step ensures that the device establishes multi-environmental baselines, providing a data foundation for subsequent adaptive calibration.

[0023] During the initial installation or reset of a combustible gas alarm, several representative environmental conditions, such as different temperatures, humidity levels, and air pressures, need to be manually or automatically selected based on the actual application scenario to simulate the external environment the alarm may encounter during subsequent use. Under each environmental condition, the alarm is run in clean air without combustible gas for a period of time, and the sensor's raw zero-point data is recorded to reflect its basic signal level under that environment. Subsequently, standard combustible gases (such as methane and hydrogen) of known concentrations are sequentially introduced into the environment surrounding the alarm. After stabilization at each calibrated gas concentration for a period of time, the sensor output data is collected and stored. For each combination of environmental parameters and gas concentration, the system fully records the raw readings, environmental information, and corresponding standard concentration values, forming a data comparison table. Through this multi-environment, multi-point calibration sampling, not only are the sensor's baseline response curves under different external conditions obtained, but the influence of various environmental factors on the sensor output is also effectively captured. Ultimately, these data are saved as an initial baseline library, providing reference standards and data support for subsequent real-time monitoring, automatic calibration, and sensor drift judgment. They also provide a high-quality and diverse raw data foundation for the subsequent adaptive algorithm training and correction modeling of the equipment.

[0024] Step 2: Long-term monitoring and environmental analysis During actual use, the alarm continuously monitors sensor outputs and collects local environmental parameters such as temperature and humidity. When abnormal fluctuations occur in the monitored data, the system automatically eliminates non-sensor drift signals caused solely by environmental changes using an innovative environmental correlation analysis model, thereby improving the accuracy of anomaly detection.

[0025] After the alarm enters normal operation, the system continuously collects real-time output data from the combustible gas sensor, while simultaneously collecting local environmental parameters such as temperature and humidity, ensuring that each gas concentration reading has corresponding environmental background information. Over time, the sensor output curve and environmental parameters are dynamically recorded to form a long-term data stream. To distinguish in real time between the sensor's true response changes and intriguing drift caused by environmental fluctuations, the system introduces an innovative environmental correlation analysis model. This model is based on regression analysis and assumes that the sensor output... It can be decomposed into a gas concentration signal. ,temperature ,humidity The weighting function of environmental parameters and the noise term, i.e.: in, For constant terms, The gas concentration response coefficient, , This indicates the weight of the influence of environmental parameters on the sensor output. This represents the noise term. During operation, the system uses the least squares method to perform a sliding window regression fitting on a segment of historical data, dynamically estimating the coefficients of each parameter. To further eliminate signal bias induced solely by environmental changes, the system calculates in real-time the contribution of environmental parameters to the predicted sensor output: Then, residual calculation is performed between the actual output and the environmental prediction output, i.e.: like If persistent fluctuations exceeding the threshold cannot be explained by concentration changes, it is determined to be sensor drift or an abnormal signal. Through automatic separation and dynamic removal of environmentally relevant components, the alarm significantly improves its sensitivity and detection accuracy to anomalies in real gas concentrations. In practical applications, this correlation analysis model can also periodically self-adjust based on historical benchmark data—the coefficients are continuously optimized during use, enabling the instrument to adapt to long-term environmental evolution.

[0026] Step 3: Automatic Sensor Drift Detection and Adaptive Calibration Using sliding window statistics and an improved drift detection algorithm, the system determines whether the sensor has experienced zero-point drift or sensitivity decay. If a drift exceeding a preset standard is detected, an adaptive calibration process is automatically triggered. Simultaneously, the system can integrate multiple historical calibrations with the current environmental conditions for comprehensive comparison, selecting the optimal calibration model.

[0027] During normal operation of the alarm, the system employs sliding window technology to perform real-time statistical analysis of the sensor output data to monitor zero drift and sensitivity changes. Specifically, the system maintains a set of theoretical values ​​for gas-free conditions (i.e., gas concentration theoretical values) in real-time within a time window of length N. Sensor residual sequence under ) These residuals have been pre-emptively adjusted to exclude the influence of environmental factors. If, over a certain past period, the mean residual... The absolute value of the deviation from the initial reference zero point is greater than the set threshold. ,Right now This initially indicates that the sensor is experiencing zero-point drift. For sensitivity decay, the system monitors the dynamic changes in the response slope using the same method. Assume the regression model slope is estimated as follows: The sensitivity obtained from initialization or the last calibration In comparison, if This means that the current sensitivity has changed abnormally.

[0028] Once the system detects drift using the aforementioned algorithm, it automatically enters the adaptive calibration decision-making process. At this point, the system not only analyzes current environmental parameters but also retrieves multiple sets of historical benchmarks and calibration records stored in the database, calculating the current environmental parameters using a fuzzy matching algorithm. With each historical calibration environment Distance between (in , (Assuming environmental impact is a weight), the model with the closest distance and best calibration performance is selected as a reference, and the final correction coefficient is determined by combining the results of the most recent calibrations using a weighted average or minimum error method. This process effectively reduces the risk of inaccurate judgments due to a single calibration error, while significantly accelerating the calibration response speed, achieving intelligent identification and rapid self-calibration of sensor drift in complex and changing environments.

[0029] Based on the above detection and comparison algorithms, the alarm can make automatic decisions without human intervention, and maintain measurement accuracy and safety response capabilities in a timely manner.

[0030] Step 4: Real-time correction of calibration parameters Once calibration is triggered, the alarm dynamically adjusts the sensor signal output curve based on current environmental parameters and historical benchmarks, using innovative nonlinear regression algorithms or interpolation methods, so that the device output once again closely matches the actual gas concentration.

[0031] When adaptive calibration is triggered by the system, the alarm first retrieves the best-matching reference environmental state and calibration curve based on the currently detected environmental parameters (latest temperature T, humidity H) and historically collected baseline data. To achieve high-precision output correction, the system employs an innovative nonlinear regression algorithm or interpolation method to model the relationship between the sensor output signal and the actual gas concentration. Assuming that the true functional relationship between the sensor signal S, gas concentration C, and environmental parameters is difficult to fully describe through simple linear fitting, quadratic or higher-order polynomial regression can be introduced, or interpolation methods such as radial basis functions (RBF) can be used. Taking polynomial regression as an example, the calibration correction model is expressed as: in, and These are the regression coefficients obtained through training with multi-environment data. This represents the noise term. The system dynamically adjusts the calibration parameters based on the optimal fit result to optimize the current output signal. The corrected gas concentration The values ​​are highly close to the actual values. If the sample environmental parameters fall within the historical baseline grid, the system will also utilize three-dimensional interpolation (such as trilinear interpolation or RBF interpolation) and weighted compensation based on the historical calibration point closest to the current environment. Specifically: in, This represents the K nearest reference calibration concentration values ​​to the current environment. As a distance weighting factor, satisfying .

[0032] For some high-end alarm models, proprietary multi-correction models from the manufacturer can be integrated, such as correction functions based on physical statistical characteristics or the inherent nonlinear response of sensors. These correction functions can be embedded into the aforementioned regression or interpolation models to further improve accuracy. All correction parameters and model data are updated in real time after each adaptive calibration, ensuring that the output signal always maintains a high degree of accuracy and consistency, significantly improving the reliability and safety of the alarm in changing environments.

[0033] Step 5: The device uploads each calibration and testing record to the cloud platform. Based on big data clustering analysis, the platform compares the operating status of similar devices and predicts trends, issuing optimization suggestions or model upgrade packages to individual devices to continuously optimize local calibration and anomaly detection capabilities. For extreme cases where self-calibration is difficult, the platform supports remote intervention or prompts for manual maintenance.

[0034] After the calibration and anomaly detection process is completed, the alarm automatically and encryptedly uploads locally collected sensor outputs, environmental parameters, calibration process data, medium- and long-term test results, and operation logs to the cloud platform. Based on large-scale operational data aggregated from multiple devices of the same batch and model, the cloud platform uses big data analysis and cluster mining to perform horizontal and vertical comparisons of device drift trends, calibration effectiveness, and anomaly types. This not only enables rapid identification of common problems and hidden defects in the overall equipment but also allows observation of long-term behavioral changes in equipment across different regions and environments, forming a multi-dimensional health profile and early warning model.

[0035] When the cloud detects a significant deviation between the working status of a device or a group of devices and the overall system, or when its drift or failure trends do not match the big data prediction model, it automatically generates personalized optimization suggestions or targeted model upgrade packages and pushes them to the local devices via a distribution strategy. The local alarm system automatically replaces or merges existing algorithms based on the received new models or parameters, further improving the accuracy and adaptability of calibration and anomaly detection. For extreme situations that cannot be resolved locally through automatic calibration, such as environmental changes exceeding all historical operating conditions, or core hardware experiencing warning failures, the cloud platform can initiate remote intervention—including issuing temporary policies, modifying sensitivity thresholds, or directly pushing maintenance suggestions, and, if necessary, prompting local maintenance personnel to perform manual repairs.

[0036] Through this self-learning optimization and cloud collaboration mechanism, the alarm system continuously accumulates knowledge and evolves dynamically, which can not only efficiently cope with complex and ever-changing environmental challenges, but also significantly reduce the frequency of manual intervention, providing a strong guarantee for the continuous high-performance operation of intelligent safety monitoring equipment.

[0037] Example: I. Experimental Objective To verify the effectiveness of the adaptive calibration method proposed in this invention in correcting the long-term drift of combustible gas sensors, actual and simulated experiments were conducted under various environmental conditions. The adaptive calibration process was implemented, and the consistency between the sensor output and the actual gas concentration before and after calibration, as well as the system's self-learning capability, were analyzed.

[0038] II. Equipment and Materials 1. Combustible gas alarm: Equipped with a high-sensitivity semiconductor gas sensor, supporting temperature, humidity, and gas pressure data acquisition.

[0039] 2. Standard gas generator: The concentration of methane (CH4) standard gas output can be adjusted.

[0040] 3. Environmental simulation chamber: can accurately control temperature (-10°C~50°C), humidity (20%RH~90%RH), and air pressure (80~110kPa).

[0041] 4. Data Acquisition and Upload Module: Integrates local storage and 4G / Ethernet cloud communication.

[0042] 5. Cloud platform: It has the ability to perform data analysis, cluster comparison, and report generation.

[0043] III. Steps and Processes 1. Multi-environmental baseline acquisition and initialization (1) Based on the common application environment in the petrochemical industry, the following conditions are set: (2) In each environment, clean air without combustible gas is first introduced to allow the sensor to operate stably for 10 minutes, and its zero-point output is recorded. Then, methane standard gas with concentrations of 0, 100, 500, and 1000 ppm is introduced respectively, and the output data is collected.

[0044] Note: Each environmental sample was repeated 5 times, and the above values ​​are the average.

[0045] The benchmark library consists of the above-mentioned multi-environment and multi-concentration sampling data, including the original zero point, output, environmental parameters and corresponding standards.

[0046] 2. Continuous monitoring and environmental correlation analysis After the alarm is put into normal operation, it continuously collects sensor outputs and environmental data. For example, after three months of actual operation, the sampling data for certain periods is as follows: Analysis revealed that in a zero-air environment, the output changed from 141 mV (initial E2) to 157-170 mV, exceeding the reasonable range for environmental compensation (the maximum should be 145 mV). Combining temperature and humidity data, and through three-dimensional interpolation / multivariate regression analysis, environmental factors were automatically eliminated, confirming that the sensor experienced zero-point drift.

[0047] 3. Automatic sensor drift detection and adaptive calibration The system automatically detects zero drift (17~29 mV, exceeding the preset threshold of 10 mV) and triggers adaptive calibration. Calculate the drift amount and calibration coefficient K based on the current environment and the benchmark library; During implementation, historical calibration data is compared with the current output dynamic correction parameters.

[0048] 4. Real-time correction of calibration parameters The system dynamically adjusts the correction coefficients based on real-time environment, current output, and historical benchmarks, and automatically optimizes daily; the table below shows the trend of calibration parameter changes: 5. Uploading and cloud analysis of detection records All calibration processes, outputs, environmental parameters, and operation logs are automatically encrypted and uploaded; the cloud platform clusters big data from the same batch of equipment to analyze systematic drift and individual differences. For example: When the drift of device A1001 is abnormally high, the cloud platform pushes a "replacement recommendation" message to the local device.

[0049] 6. Automatic expansion and maintenance of multiple environmental benchmarks When the equipment operates in special environments (such as high temperature or low pressure) for a long time, the system will prompt you to replenish the benchmark samples in that environment. Manual or automatic replenishment will be carried out to enrich the benchmark library and improve subsequent adaptive capabilities.

[0050] This device has been running continuously in the laboratory and field for 6 months, with automatic calibration triggered 8 times. After cloud comparison, no abnormal sensitivity loss was found, and the data accuracy has been improved by more than 15%.

[0051] The calibration error has been kept within ±5% for a long time, which is significantly better than traditional periodic manual calibration (±10~20%).

[0052] This embodiment systematically demonstrates the entire process of adaptive calibration for combustible gas alarms: from multi-environmental benchmark acquisition, continuous drift detection, automatic calibration to cloud-based cluster analysis. Dynamic acquisition and the establishment of data comparison tables enable the device to adaptively correct itself even if the sensor drifts or its sensitivity decreases, ensuring alarm accuracy and long-term reliability.

[0053] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0054] It should be understood that the above detailed description of the technical solutions of the present invention with reference to preferred embodiments is illustrative and not restrictive. Those skilled in the art can modify the technical solutions described in the embodiments or make equivalent substitutions for some of the technical features based on reading this specification; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An adaptive calibration method for a combustible gas alarm, characterized in that: The method includes: Multiple environmental reference values ​​are collected and initialized. Raw gas data are collected under typical environmental conditions, and standard gases of a certain concentration are input for calibration. It continuously monitors sensor output and collects local temperature and humidity environmental parameters. Through an environmental correlation analysis model, it automatically eliminates non-sensor drift signals caused solely by environmental changes. Automatic sensor drift detection and adaptive calibration determine whether the sensor has zero-point drift or sensitivity decay; if a drift amount exceeding the preset standard is detected, the adaptive calibration process is automatically triggered. The calibration parameters are corrected in real time. Once calibration is triggered, the alarm dynamically adjusts the sensor signal output curve based on the current environmental parameters and historical benchmarks, so that the device output once again matches the real gas concentration. The equipment uploads each calibration and testing record to the cloud platform, and compares and predicts the working status of similar equipment based on big data clustering analysis.

2. The adaptive calibration method for a combustible gas alarm according to claim 1, characterized in that: The multi-environmental reference value acquisition and initialization includes: selecting several representative environmental conditions based on the actual application scenario, including different temperatures, humidity and air pressures, and running the alarm in clean air without combustible gases under each environmental condition and recording the sensor's original zero-point data. Then, standard combustible gases of known concentrations are introduced, and sensor output data are collected and stored at each standard gas concentration. For each combination of environmental parameters and gas concentration, the original readings, environmental information and corresponding standard concentrations are recorded to form a data comparison table. The aforementioned multi-environment, multi-point calibration sampling data will be saved as an initialization reference value library for subsequent real-time monitoring, automatic calibration, and sensor drift judgment.

3. The adaptive calibration method for a combustible gas alarm according to claim 1, characterized in that: The continuous monitoring of sensor output includes: continuously collecting real-time output data from the combustible gas sensor and simultaneously collecting local temperature and humidity environmental parameters, and dynamically recording the sensor output curve and environmental parameters to form a long-term data stream. An environmental correlation analysis model is adopted, based on regression analysis, to dynamically estimate the gas concentration response coefficient and the influence weight of environmental parameters on the output, and to calculate the residual between the actual sensor output and the predicted output of environmental parameters. If the residual shows continuous fluctuations exceeding the threshold and cannot be explained by concentration changes, it is determined to be sensor drift or an abnormal signal. By separating and eliminating environmentally relevant components, the high sensitivity and accuracy of detecting abnormal real gas concentrations are improved.

4. The adaptive calibration method for a combustible gas alarm according to claim 1, characterized in that: The aforementioned automatic sensor drift detection and adaptive calibration includes: Real-time statistical analysis of sensor output data is performed to maintain the sensor residual sequence under gas-free conditions. The deviation of the residual mean from the initial reference zero point value is monitored to determine zero point drift. At the same time, the changes in the sensitivity parameters of the regression model and the initial or last calibration sensitivity are monitored to determine sensitivity anomalies. Once drift is detected, the system combines current environmental parameters, retrieves historical benchmarks and calibration records from the database, selects the model with the best calibration effect as a reference using a fuzzy matching algorithm, and determines the final correction coefficient by combining the results of the most recent calibrations using a weighted average or minimum error method. This enables intelligent discrimination and rapid response self-calibration in complex environments without human intervention.

5. The adaptive calibration method for a combustible gas alarm according to claim 1, characterized in that: The real-time correction of the calibration parameters includes: when adaptive calibration is triggered, the alarm retrieves the most matching reference environmental state and calibration curve based on the currently detected environmental parameters and historically collected benchmark data; [0001][0001][0001] The relationship between the sensor output signal and the real gas concentration is modeled by nonlinear regression algorithm or interpolation method. A calibration correction model is established by polynomial regression or radial basis function interpolation, and the output signal is dynamically corrected so that the corrected gas concentration is equal to the actual value. When the sample environmental parameters fall within the historical baseline grid, the output is adjusted using three-dimensional interpolation or weighted compensation methods.

6. The adaptive calibration method for a combustible gas alarm according to claim 1, characterized in that: The aforementioned detection records are uploaded to the cloud platform, including: the alarm encrypting and uploading locally collected sensor outputs, environmental parameters, calibration process, medium- and long-term detection results, and operation log data to the cloud platform; Based on large-scale operational data of equipment of the same batch and model, the cloud platform uses big data analysis and cluster mining to conduct horizontal and vertical comparisons of equipment drift trends, calibration effects and anomaly types. When a device's status is found to deviate significantly from the group's, personalized optimization suggestions are automatically generated and pushed to the local device.

7. The adaptive calibration method for a combustible gas alarm according to claim 1, characterized in that: The multi-environmental reference value acquisition and initialization are selected manually or automatically according to the actual application scenario. The selected environment includes at least two different temperatures or at least two different humidity or air pressure conditions.

8. The adaptive calibration method for a combustible gas alarm according to claim 1, characterized in that: The aforementioned multi-environmental reference value acquisition and initialization requires that the sensor zero-point acquisition under each environmental condition be performed after the sensor has been running stably in clean air for a preset period of time to ensure the accuracy of the zero-point data.

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