An adaptive environmental compensation and failure prediction algorithm for a gas detection instrument

Abstract

The application discloses a kind of adaptive environmental compensation and fault prediction algorithm for gas detection instrument, it is related to gas detection compensation and fault prediction technical field, including the following steps: constructing detection test space, record test results;Analysis of the environmental influence duration of gas detection instrument and time sequence correction to test results, obtain experimental data;Adaptive environmental compensation analysis is carried out to gas detection instrument, and adaptive environmental compensation algorithm is formed;Response chamber is set, and the response characteristics of response curve are extracted;Based on the time sequence change of response characteristics, fault prediction is carried out to gas detection instrument;The application is used to solve the problem that the existing gas detection compensation and fault prediction technology is not comprehensive enough for the influence factors of detection accuracy and is difficult to solve response curve in daily use, leading to the problem that the concentration compensation of gas and the fault prediction of instrument are not accurate enough.

Description

Technical Field

[0001] This invention relates to the field of gas detection compensation and fault prediction technology, specifically to an adaptive environmental compensation and fault prediction algorithm for gas detection instruments. Background Technology

[0002] Gas detection compensation and fault prediction technology refers to a comprehensive technical system that uses algorithms and data processing methods to correct and compensate for measurement errors generated by gas sensors under various environmental conditions in real time. At the same time, it analyzes the sensor's working status data, historical performance and operating characteristics, and uses machine learning, artificial intelligence and data mining methods to predict possible sensor failures or performance degradation in advance, thereby realizing error correction and preventive maintenance.

[0003] Existing gas detection compensation and fault prediction technologies typically employ fixed compensation functions when compensating for gas concentration, failing to consider the impact of different environments on the accuracy of gas detection instruments. Consequently, the compensation accuracy using traditional fixed compensation is low. Furthermore, existing technologies struggle to accurately predict faults in gas detection instruments because fault diagnosis relies on response curves, which are difficult to assess in daily use. For instance, patent application CN114217021A discloses a "concentration compensation method and detection method and device for gas detection," which uses a fixed compensation function to compensate for gas concentration, neglecting the impact of different environments on the accuracy of gas detection instruments. These existing technologies also suffer from insufficient consideration of factors affecting detection accuracy and difficulty in solving response curves in daily use, leading to inaccurate gas concentration compensation and instrument fault prediction. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in the prior art. It constructs a detection and testing space, randomly changes the environmental parameters within the space at first test intervals, records the test results, analyzes the duration of environmental influence on the gas detector, performs time-series calibration on the test results based on the duration of environmental influence, obtains experimental data, analyzes the influence of temperature and humidity on detection accuracy based on the experimental data, obtains the influence relationships of temperature and humidity on accuracy, further divides these relationships into baseline and calibration relationships, and then performs adaptive environmental compensation analysis on the gas detector based on the baseline and calibration relationships to form an adaptive environmental compensation algorithm. A reaction chamber is set up, and the response curve of the gas detector is tested in the reaction chamber. The response characteristics of the response curve are extracted, and finally, the health status of the gas detector is monitored based on the response characteristics. Fault prediction of the gas detector is performed based on the time-series changes of the response characteristics. This addresses the problems of existing gas detection compensation and fault prediction technologies, which do not comprehensively consider the factors affecting detection accuracy and are difficult to solve for response curves in daily use, resulting in inaccurate gas concentration compensation and instrument fault prediction.

[0005] To achieve the above objectives, this application provides an adaptive environmental compensation and fault prediction algorithm for gas detection instruments, comprising the following steps:

[0006] Construct a testing space, randomly change the environmental parameters within the testing space every first test duration, and record the test results;

[0007] The duration of environmental impact on gas detection instruments was analyzed, and the test results were time-series calibrated based on the duration of environmental impact to obtain experimental data.

[0008] Based on experimental data analysis, the influence of temperature and humidity on detection accuracy is analyzed, and an adaptive environmental compensation algorithm is formed for the gas detection instrument.

[0009] A reaction chamber is set up, and the response curve of the gas detection instrument is tested through the reaction chamber to extract the response characteristics of the response curve.

[0010] The health status of gas detection instruments is monitored based on response characteristics, and fault prediction of gas detection instruments is performed based on the time-series changes of response characteristics.

[0011] Furthermore, a detection test space is constructed, and the environmental parameters within the detection test space are randomly changed every first test duration. The test results are recorded, including the following sub-steps:

[0012] A detection and testing space is constructed, which is a closed space that can change environmental parameters and contains an air inlet and an air outlet to maintain a stable concentration of the test gas.

[0013] Introduce a test gas at the first test concentration into the test space and keep the test gas at the first test concentration at all times;

[0014] The environmental parameters within the test space are randomly changed every first test duration, including ambient temperature and ambient humidity.

[0015] A gas detection instrument is installed in the testing space to detect the concentration of the test gas in real time, which is named the detection concentration. The detection time is also recorded. The detection time and the detection concentration are the test results.

[0016] Furthermore, the duration of environmental impact on the gas detection instrument is analyzed, and the test results are time-series calibrated based on the duration of environmental impact. The experimental data obtained includes the following sub-steps:

[0017] Set the environmental parameters in the test space to the first test state. After the first test duration, detect the gas concentration of the test gas and name it the calibration auxiliary concentration. Continuously record the first test duration and calculate the range of the calibration auxiliary concentration within the first test duration, which is named the calibration concentration range.

[0018] Change the environmental parameters in the detection test space, record the calibration auxiliary concentration in real time and time it. When the calibration auxiliary concentration exceeds the calibration concentration range, stop the timing and obtain the duration of the timing, which is named the environmental impact duration.

[0019] The detection time in the test results is advanced by the duration of environmental influence to obtain the calibration time. The ambient temperature and humidity corresponding to the calibration time are named calibration temperature and calibration humidity, respectively. The calibration temperature and calibration humidity are correlated with the detection concentration to form experimental data, which includes detection concentration, calibration time, calibration temperature, and calibration humidity.

[0020] Furthermore, based on experimental data analysis of the influence of temperature and humidity on detection accuracy, and adaptive environmental compensation analysis of the gas detection instrument is performed, forming an adaptive environmental compensation algorithm including the following sub-steps:

[0021] Based on experimental data analysis, the relationship between temperature and humidity on detection accuracy is obtained, and the relationship between temperature and humidity on accuracy is obtained.

[0022] The influence of temperature accuracy and humidity accuracy is divided into reference relationship and calibration relationship;

[0023] Based on the benchmark relationship and calibration relationship, an adaptive environmental compensation analysis is performed on the gas detection instrument to form an adaptive environmental compensation algorithm.

[0024] Furthermore, based on the experimental data analysis, the relationship between temperature and humidity on detection accuracy was obtained, including the following sub-steps:

[0025] Label the detected concentration as P1, label the first test concentration as P2, and calculate (P1-P2) / P2 to obtain the detection deviation.

[0026] A two-dimensional coordinate system is established with calibration temperature and calibration humidity as the X-axis and detection deviation as the Y-axis, respectively named Temperature Accuracy Influence Analysis Chart and Humidity Accuracy Influence Analysis Chart. The detection deviation is entered into the Temperature Accuracy Influence Analysis Chart and Humidity Accuracy Influence Analysis Chart according to calibration temperature and calibration humidity, respectively. The coordinate points in the Temperature Accuracy Influence Analysis Chart and Humidity Accuracy Influence Analysis Chart are named Temperature Accuracy Influence Analysis Point and Humidity Accuracy Influence Analysis Point, respectively.

[0027] Regression analysis was performed on the temperature accuracy influence diagram and the humidity accuracy influence diagram to obtain the temperature accuracy influence curve and the humidity accuracy influence curve.

[0028] The temperature accuracy influence curve and its temperature accuracy influence analysis points represent the temperature accuracy influence relationship, and the humidity accuracy influence curve and its humidity accuracy influence analysis points represent the humidity accuracy influence relationship.

[0029] Furthermore, dividing the influence of temperature accuracy and humidity accuracy into reference relationships and calibration relationships includes the following sub-steps:

[0030] Obtain the standard deviations of the temperature accuracy influence curve and the humidity accuracy influence curve, and name them temperature standard deviation and humidity standard deviation, respectively.

[0031] If the temperature standard deviation is less than or equal to the humidity standard deviation, the temperature accuracy influence analysis chart is named the baseline analysis chart, and the humidity accuracy influence analysis chart is named the calibration analysis chart; if the temperature standard deviation is greater than the humidity standard deviation, the humidity accuracy influence analysis chart is named the baseline analysis chart, and the temperature accuracy influence analysis chart is named the calibration analysis chart.

[0032] The temperature accuracy influence analysis point or humidity accuracy influence analysis point corresponding to the baseline analysis chart is named the baseline analysis point, the temperature accuracy influence analysis point or humidity accuracy influence analysis point corresponding to the calibration analysis chart is named the calibration analysis point, the temperature accuracy influence curve or humidity accuracy influence curve corresponding to the baseline analysis chart is named the baseline influence curve, and the temperature accuracy influence curve or humidity accuracy influence curve corresponding to the calibration analysis chart is named the calibration influence curve.

[0033] The benchmark analysis diagram, benchmark analysis points, and benchmark influence curve constitute the benchmark relationship, and the calibration analysis diagram, calibration analysis points, and calibration influence curve constitute the calibration relationship.

[0034] Furthermore, based on the benchmark relationship and calibration relationship, an adaptive environmental compensation analysis is performed on the gas detection instrument to form an adaptive environmental compensation algorithm, which includes the following sub-steps:

[0035] The gas concentration detected in real time by the gas detector is named the real-time concentration. The ambient temperature and humidity at the current time are named the effective temperature and effective humidity, respectively.

[0036] If the baseline analysis plot is a temperature accuracy influence analysis plot, then the effective temperature is named the baseline parameter, and the effective humidity is named the calibration parameter. If the baseline analysis plot is a humidity accuracy influence analysis plot, then the effective humidity is named the baseline parameter, and the effective temperature is named the calibration parameter.

[0037] The benchmark analysis point whose X-axis is equal to the benchmark parameter in the benchmark analysis graph is named the benchmark reference point. The value of the benchmark reference point on the Y-axis is named the benchmark reference difference. The calibration humidity or calibration temperature corresponding to the benchmark reference point is named the calibration reference parameter.

[0038] The calibration reference parameters are numbered using the symbol CH. i This indicates that, where i is a non-zero natural number and i is the index of CH, CH i Substitute the calibration analysis curve into the equation to obtain the value of CH on the X-axis of the calibration analysis curve. i The Y-axis value of the coordinate point is named the calibration reference difference, and is indicated by the symbol CR. i It means that CH i The corresponding benchmark difference is marked as CD. i ;

[0039] Obtain the value of the point on the Y-axis where the X-axis of the calibration analysis curve equals the calibration parameter. Name this value the calibration anchorage difference and represent it using the symbol CM.

[0040] Assuming the baseline analysis point corresponding to the effective temperature and effective humidity environment is the first hypothetical point, and labeling the value of the first hypothetical point on the Y-axis as F, the following relationship exists. Through formula Solve for F, where F is the prediction bias. Increasing the proportion of the prediction bias can achieve adaptive environmental compensation for the real-time concentration, and finally obtain the actual concentration.

[0041] Furthermore, a reaction chamber is set up, and the response curve of the gas detection instrument is tested through the reaction chamber. The response characteristics of the response curve are extracted, including the following sub-steps:

[0042] A reaction chamber is installed in a gas detection instrument. The reaction chamber includes a reaction box and a storage box. Baking soda is placed in the reaction box, and edible citric acid is injected into the storage box. The reaction box and the storage box are connected by a pipe and a smart valve.

[0043] At the first interval of each cycle, the intelligent control valve is opened, and a test volume of edible citric acid is injected into the reaction box. At this time, baking soda and edible citric acid react chemically to produce carbon dioxide.

[0044] The response curve of the gas detection instrument in the reaction chamber is used to extract the response characteristics of the response curve.

[0045] Furthermore, the process of extracting response features from the response curve of the test gas detector in the reaction chamber includes the following sub-steps:

[0046] Before the reaction occurs, the carbon dioxide concentration is recorded and named the normal concentration. After the reaction occurs, the change of carbon dioxide concentration over time is recorded in real time. When the carbon dioxide concentration returns to the normal concentration, the recording stops and the response data is obtained. The response data is specifically a two-dimensional coordinate system with time on the X-axis and carbon dioxide concentration on the Y-axis. The curve formed by the change of carbon dioxide concentration over time is recorded in the system, which is the response curve.

[0047] Obtain the highest point of the response curve and name it the response point. Obtain the time taken for the carbon dioxide concentration to reach the response point from the normal concentration and name it the response time.

[0048] The time taken for the carbon dioxide concentration to reach the normal concentration from the response point is called the recovery time. The response time and the recovery time are the response characteristics.

[0049] Furthermore, monitoring the health status of the gas detection instrument based on response characteristics and predicting faults in the gas detection instrument based on the time-series changes of the response characteristics includes the following sub-steps:

[0050] Obtain health criteria for response characteristics, wherein the health criteria include response criteria and recovery criteria;

[0051] Record the response characteristics obtained from each test, number the response characteristics in chronological order, and use the symbol H. j This indicates that j is a non-zero natural number and j is the index of H;

[0052] With j as the horizontal axis, H j A two-dimensional coordinate system is established for the vertical axis, named the fault prediction coordinate system. This fault prediction coordinate system includes a response fault prediction coordinate system and a recovery fault prediction coordinate system. H... jThe response time in the input is entered into the response fault prediction coordinate system according to j, and H is set to... j The recovery time is entered into the recovery fault prediction coordinate system according to j;

[0053] Regression analysis is performed on the response fault prediction coordinate system and the recovery fault prediction coordinate system to obtain the response fault prediction function and the recovery fault prediction function. The expected prediction time is obtained. The expected prediction time is substituted into the response fault prediction function and the recovery fault prediction function to solve for the response prediction time and the recovery prediction time.

[0054] If the response prediction time is greater than or equal to the response standard or the recovery prediction time is greater than or equal to the recovery standard, an instrument fault signal will be output.

[0055] The beneficial effects of this invention are as follows: This invention constructs a detection and testing space, randomly changes the environmental parameters within the space every first test duration, records the test results, analyzes the duration of environmental influence on the gas detection instrument, performs time-series calibration on the test results based on the duration of environmental influence, obtains experimental data, analyzes the influence relationship between temperature and humidity on detection accuracy based on the experimental data, obtains the influence relationship between temperature and humidity accuracy, further divides the influence relationship between temperature and humidity accuracy into a benchmark relationship and a calibration relationship, and then performs adaptive environmental compensation analysis on the gas detection instrument based on the benchmark relationship and the calibration relationship to form an adaptive environmental compensation algorithm. The advantage is that ambient temperature and humidity have a certain impact on the detection accuracy of gas concentration. If only a fixed compensation function is used for uniform compensation, the error of the compensated gas concentration is still relatively large. However, by including temperature and humidity in the reference range, and dividing the influence of temperature and humidity on detection accuracy into a benchmark relationship and a calibration relationship, and using the benchmark relationship as a standard, and then calibrating the benchmark relationship with the calibration relationship, more accurate compensation for gas concentration under various temperature and humidity conditions can be achieved, improving the accuracy and rationality of gas concentration detection compensation.

[0056] This invention sets up a reaction chamber to test the response curve of a gas detection instrument, extracts the response characteristics of the response curve, and finally monitors the health status of the gas detection instrument based on the response characteristics. Furthermore, it predicts the faults of the gas detection instrument based on the temporal changes of the response characteristics. The advantage is that the reaction chamber solves the problem of difficulty in collecting response curves during daily use, thereby improving the accuracy and effectiveness of fault prediction for gas detection instruments. Attached Figure Description

[0057] Figure 1 This is a flowchart illustrating the steps of the method of the present invention;

[0058] Figure 2This is a schematic diagram of the influence of temperature accuracy on the present invention.

[0059] Figure 3 This is a schematic diagram of the humidity accuracy impact analysis diagram of the present invention;

[0060] Figure 4 This is a schematic diagram of the reference point for the present invention;

[0061] Figure 5 This is a schematic diagram of the response data of the present invention;

[0062] Figure 6 This is a schematic diagram of the response fault prediction coordinate system of the present invention. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] Example 1, please refer to Figure 1 As shown, this application provides an adaptive environmental compensation and fault prediction algorithm for gas detection instruments, including the following steps:

[0065] Step S1 involves constructing a detection test space, randomly changing the environmental parameters within the test space every first test duration, and recording the test results. Step S1 includes the following sub-steps:

[0066] Step S101: Construct a detection test space. The detection test space is a closed space that can change environmental parameters, and it contains an air inlet and an air outlet to maintain a stable concentration of the test gas.

[0067] Step S102: Introduce a test gas of the first test concentration into the detection test space and keep the test gas at the first test concentration at all times.

[0068] Step S103: The environmental parameters in the test space are randomly changed every first test duration. The environmental parameters include ambient temperature and ambient humidity.

[0069] Step S104: Install a gas detection instrument in the test space, and use the gas detection instrument to detect the concentration of the test gas in real time, which is named the detection concentration. At the same time, record the detection time. The detection time and the detection concentration are the test results.

[0070] In practice, the testing space only needs to ensure control over the concentration of the test gas and the internal temperature and humidity. The test gas should be a non-toxic and harmless safe gas. In this embodiment, oxygen is selected as the test gas. The first test concentration is only for analyzing the deviation of the gas concentration and can be any value set by the tester. In this embodiment, the first test concentration is 20.9%, because the oxygen content in the air is also close to 20.9%. If the testing space accidentally exchanges gas with the outside world, it will not have a significant impact on the test results. The setting of the first test duration should be combined with the duration of environmental impact in the subsequent analysis process. In analyzing the duration of environmental impact, the initial test duration is typically set to at least 2 hours to ensure the gas detection instrument is sufficiently affected by environmental parameters. The initial test duration is then adjusted based on the obtained environmental impact duration. This saves time in subsequent tests. Since the environmental impact duration has not yet been determined, it will not be listed here but will be explained in detail during the analysis. Every first test duration, the environmental parameters within the test space are randomly changed to simulate the detection results of the gas detection instrument under different temperature and humidity conditions. For example, at 18:33:46 on May 26, 2025, the ambient temperature was 12℃ and the humidity was 25%. The detected oxygen concentration in the test space was 20.7%, meaning the detected concentration was 20.7%. The corresponding ambient temperature and humidity were 12℃ and 25%, respectively. It is important to note that the ambient temperature and humidity correspond to the detection time, not the detected concentration.

[0071] Step S2 involves analyzing the duration of environmental impact on the gas detection instrument, performing time-series calibration on the test results based on the duration of environmental impact, and obtaining experimental data. Step S2 includes the following sub-steps:

[0072] Step S201: Set the environmental parameters in the test space to the first test state. After the first test duration, detect the gas concentration of the test gas and name it as the calibration auxiliary concentration. Continuously record the first test duration and calculate the range of the calibration auxiliary concentration within the first test duration, which is named the calibration concentration range.

[0073] Step S202: Change the environmental parameters in the detection test space, record the calibration auxiliary concentration in real time and time it. When the calibration auxiliary concentration exceeds the calibration concentration range, stop the timing and obtain the duration of the timing, which is named the environmental impact duration.

[0074] Step S203: The detection time in the test results is advanced by the duration of environmental influence to obtain the calibration time. The ambient temperature and humidity corresponding to the calibration time are named calibration temperature and calibration humidity, respectively. The calibration temperature and calibration humidity are correlated with the detection concentration to form experimental data. The experimental data includes detection concentration, calibration time, calibration temperature and calibration humidity.

[0075] In practice, the first test state can be set randomly, as this tests the duration required for environmental parameters to affect the readings of the gas detector. The specific values ​​of the environmental parameters are irrelevant. In this embodiment, the first test state is set to an ambient temperature of 18°C ​​and an ambient humidity of 30%. The temperature and humidity in the test space are set to 18°C ​​and 30%, respectively. The initial test duration is temporarily set to 2 hours, as 2 hours is a relatively long time, and the gas detector will be fully affected by the environmental parameters. After 2 hours, the oxygen concentration in the test space is measured again to obtain the calibration auxiliary concentration. At this point, the calibration auxiliary concentration is completely affected by the environmental parameters, causing a reading deviation. However, since the environmental parameters remain unchanged, the calibration auxiliary concentration will be within a stable range, i.e., the calibration concentration range. For example, after 2 hours, the test space is measured during the 3rd and 4th hours. The oxygen concentration remained within the range of [20.78%, 20.91%], so the calibration concentration range was [20.78%, 20.91%]. Then, the environmental parameters were changed to an ambient temperature of 34℃ and an ambient humidity of 40%. For a short time, the calibration auxiliary concentration remained within the calibration range because the gas detector had not yet been fully affected by the environmental parameters. However, 16 seconds after the environmental parameters were changed, the calibration auxiliary concentration was found to be 20.95%, outside the calibration range. This indicates that the gas detector was affected by the changed environmental parameters, meaning the environmental influence duration was 16 seconds. This means that the change in environmental parameters required 16 seconds to affect the gas detector's reading. The detection time in the test results is then adjusted to account for this environmental influence duration. For example, in step S1, the detection concentration was 20.7%, and the detection time was 2025.5.26. At 18:33:46, the corresponding ambient temperature was 12℃ and the ambient humidity was 25%. Advancing the detection time by 16 seconds yielded a calibration time of 2025.5.26 18:33:30. This indicates that the gas concentration detected at 2025.5.26 18:33:46 was actually influenced by the environmental parameters at 2025.5.26 18:33:30, not the real-time environmental parameters. Therefore, the ambient temperature and humidity at 2025.5.26 18:33:30 were used as the calibration temperature and humidity. The final calibration time was 2025.5.26 18:33:30, the detected concentration was 20.7%, the calibration temperature was 15℃, and the calibration humidity was 30%. This constitutes one experimental data point. Calibrating all test results yielded different experimental data.

[0076] Step S3 involves analyzing the influence of temperature and humidity on detection accuracy based on experimental data, and performing adaptive environmental compensation analysis on the gas detection instrument to form an adaptive environmental compensation algorithm. Step S3 includes the following sub-steps:

[0077] Step S301: Analyze the influence of temperature and humidity on detection accuracy based on experimental data to obtain the influence relationship of temperature on accuracy and humidity on accuracy.

[0078] Step S301 includes the following sub-steps:

[0079] Step S3011: Label the detection concentration as P1, label the first test concentration as P2, and calculate (P1-P2) / P2 to obtain the detection deviation;

[0080] Please see Figures 2 to 3 As shown, in step S3012, a two-dimensional coordinate system is established with the calibration temperature and calibration humidity as the X-axis and the detection deviation as the Y-axis, respectively named the Temperature Accuracy Influence Analysis Chart and the Humidity Accuracy Influence Analysis Chart. The detection deviation is entered into the Temperature Accuracy Influence Analysis Chart and the Humidity Accuracy Influence Analysis Chart according to the calibration temperature and calibration humidity, respectively. The coordinate points in the Temperature Accuracy Influence Analysis Chart and the Humidity Accuracy Influence Analysis Chart are named the Temperature Accuracy Influence Analysis Point and the Humidity Accuracy Influence Analysis Point, respectively.

[0081] Step S3013: Perform regression analysis on the temperature accuracy influence diagram and the humidity accuracy influence diagram to obtain the temperature accuracy influence curve and the humidity accuracy influence curve.

[0082] Step S3014: The temperature accuracy influence curve and its temperature accuracy influence analysis point are the temperature accuracy influence relationship; the humidity accuracy influence curve and its humidity accuracy influence analysis point are the humidity accuracy influence relationship.

[0083] In specific implementation, for example, in the experimental data listed in step S2, the detected concentration is 20.7%, while the first test concentration is 20.9%. The calculated detection deviation is -0.0096, and the result is rounded to four decimal places. This represents the proportion by which the gas concentration detected by the gas detector is -0.0096 lower than the actual concentration. That is, when the ambient temperature is 15℃ and the ambient humidity is 30%, the gas concentration detected by the gas detector is -0.0096 lower than the actual concentration. The temperature accuracy influence analysis diagram is constructed as follows. Figure 2 As shown in the figure, the influence of humidity accuracy is analyzed. Figure 3 As shown, Figure 2 and Figure 3 The curves in the figure are the temperature accuracy influence curve and the humidity accuracy influence curve, from which the influence relationship of temperature accuracy and humidity accuracy can be obtained.

[0084] Step S302: Divide the influence relationship of temperature accuracy and humidity accuracy into reference relationship and calibration relationship;

[0085] Step S302 includes the following sub-steps:

[0086] Step S3021: Obtain the standard deviations of the temperature accuracy influence curve and the humidity accuracy influence curve, and name them temperature standard deviation and humidity standard deviation, respectively.

[0087] Step S3022: If the temperature standard deviation is less than or equal to the humidity standard deviation, the temperature accuracy influence analysis chart is named the baseline analysis chart, and the humidity accuracy influence analysis chart is named the calibration analysis chart; if the temperature standard deviation is greater than the humidity standard deviation, the humidity accuracy influence analysis chart is named the baseline analysis chart, and the temperature accuracy influence analysis chart is named the calibration analysis chart.

[0088] Step S3023: Name the temperature accuracy influence analysis point or humidity accuracy influence analysis point corresponding to the benchmark analysis chart as the benchmark analysis point, name the temperature accuracy influence analysis point or humidity accuracy influence analysis point corresponding to the calibration analysis chart as the calibration analysis point, name the temperature accuracy influence curve or humidity accuracy influence curve corresponding to the benchmark analysis chart as the benchmark influence curve, and name the temperature accuracy influence curve or humidity accuracy influence curve corresponding to the calibration analysis chart as the calibration influence curve.

[0089] Step S3024: The benchmark analysis diagram, benchmark analysis points, and benchmark influence curve constitute the benchmark relationship; the calibration analysis diagram, calibration analysis points, and calibration influence curve constitute the calibration relationship.

[0090] In specific implementation, by Figure 2 and Figure 3 It is readily apparent that the fitting effect of the humidity accuracy influence curve is far superior to that of the temperature accuracy influence curve, meaning that the temperature standard deviation is smaller than the humidity standard deviation. This indicates that ambient humidity has a greater impact on the readings of gas detectors. Therefore, it is necessary to use ambient humidity as the baseline for analysis, with ambient temperature as an auxiliary factor. Consequently, the baseline analysis chart is the humidity accuracy influence analysis chart, the calibration analysis chart is the temperature accuracy influence analysis chart, the baseline analysis points are the humidity accuracy influence analysis points, the calibration analysis points are the temperature accuracy influence analysis points, the baseline influence curve is the humidity accuracy influence curve, and the calibration influence curve is the temperature accuracy influence curve.

[0091] Step S303: Based on the reference relationship and calibration relationship, perform adaptive environmental compensation analysis on the gas detection instrument to form an adaptive environmental compensation algorithm;

[0092] Step S303 includes the following sub-steps:

[0093] Step S3031: Obtain the gas concentration detected in real time by the gas detection instrument and name it the real-time concentration; obtain the ambient temperature and ambient humidity at the current time of the previous environmental influence duration and name them the effective temperature and effective humidity, respectively.

[0094] Step S3032: If the benchmark analysis chart is a temperature accuracy influence analysis chart, then the effective temperature is named the benchmark parameter and the effective humidity is named the calibration parameter. If the benchmark analysis chart is a humidity accuracy influence analysis chart, then the effective humidity is named the benchmark parameter and the effective temperature is named the calibration parameter.

[0095] Please see Figure 4 As shown in step S3033, the benchmark analysis point whose X-axis is equal to the benchmark parameter in the benchmark analysis graph is named the benchmark reference point, the value of the benchmark reference point on the Y-axis is named the benchmark reference difference, and the calibration humidity or calibration temperature corresponding to the benchmark reference point is named the calibration reference parameter.

[0096] Step S3034: Number the calibration reference parameters using the symbol CH. i This indicates that, where i is a non-zero natural number and i is the index of CH, CH i Substitute the calibration analysis curve into the equation to obtain the value of CH on the X-axis of the calibration analysis curve. i The Y-axis value of the coordinate point is named the calibration reference difference, and is indicated by the symbol CR. i It means that CH i The corresponding benchmark difference is marked as CD. i ;

[0097] Step S3035: Obtain the value of the Y-axis of the coordinate point on the calibration analysis curve where the X-axis is equal to the calibration parameter, and name it as the calibration anchorage difference, represented by the symbol CM;

[0098] In specific implementation, assuming the gas detection instrument in this embodiment detects a real-time indoor oxygen concentration of 19.8% at 12:26:44 on July 18, 2025, the ambient temperature and humidity are acquired in the 16 seconds prior to 12:26:44, i.e., the ambient temperature and humidity at 12:26:28 on July 18, 2025 are acquired as the effective temperature and effective humidity, resulting in an effective temperature of 10℃ and an effective humidity of 50%. At this time, the baseline parameter is 50%, and the calibration parameter is 10℃. The baseline analysis point where the X-axis of the baseline analysis graph equals 50% is acquired as the baseline reference point, resulting in the baseline reference point as shown below. Figure 4As shown, the reference points include (50, 0.02) and (50, 0.039), where 0.02 and 0.039 are the reference differences. Taking the reference point (50, 0.02) as an example, this point was obtained from experimental data showing a concentration of 20.482%, a calibration temperature of 35℃, and a calibration humidity of 50%. The detection deviation corresponding to a concentration of 20.482% is [missing value]. Therefore, the calibration reference parameter for this reference point is 35℃. Similarly, the calibration reference parameter for (50, 0.039) is 15℃. The numbers are then assigned to CH. 1 and CH2, i.e. CH1 is 35℃ and CH2 is 15℃. Find the Y-axis value of the coordinate point on the calibration analysis curve where the X-axis equals 35℃, and get CR1 as 0.048. Find the Y-axis value of the coordinate point on the calibration analysis curve where the X-axis equals 15℃, and get CR2 as -0.019. The CD1 of CH1 is 0.02 and the CD2 of CH2 is 0.039. The calibration parameter is 10℃. The coordinate point on the calibration analysis curve where the X-axis equals 10℃ is (10, -0.045), i.e., the calibration anchoring difference CM is -0.045.

[0099] Step S3036: Assuming the reference analysis point corresponding to the effective temperature and effective humidity environment is the first hypothetical point, the value of the first hypothetical point on the Y-axis is marked as F, and the relationship exists. Through formula Solve for F, which is the prediction bias. Increasing the real-time concentration by the proportion of the prediction bias will complete the adaptive environmental compensation for the real-time concentration and finally obtain the actual concentration.

[0100] In practice, assuming an ambient temperature of 10℃ and an ambient humidity of 50%, the detection deviation of the gas detector is F. Substituting this into the benchmark analysis chart yields the first hypothetical point. At this point, the X-axis of the first hypothetical point is 50, but the Y-axis value of F is unknown. The detection deviation is mainly affected by the ambient humidity, but also slightly influenced by the ambient temperature. Therefore, the actual detection deviation is distributed around the upper and lower sides of the benchmark analysis curve, rather than within the curve itself. The approximate trend of the detection deviation with changing ambient temperature, obtained from the calibration analysis chart, is shown. Taking the benchmark reference point (50, 0.02) as an example, its CR1 and CD1 are 0.048 and 0.02 respectively, while CM is -0.045. CR1 represents the approximate distribution of the detection deviation when the ambient temperature is 15℃, while CM represents the approximate distribution when the ambient temperature is 10℃. Since the deviation of each benchmark reference point from the benchmark influence curve is affected by the ambient temperature, the upper and lower positions of the first hypothetical point relative to each benchmark reference point in the benchmark analysis chart should correspond to the positions of CM and CR1. i The proportions are similar, meaning there is a relationship. , conversion to obtain Although the relationship is approximately equal, a more accurate result can be obtained by using the positional relationship between the first assumed point and multiple benchmark reference points. Among them, CR1 is 0.048, CD1 is 0.02, CM is -0.045, CR2 is -0.019, CD2 is 0.039, and max() is the maximum value operator. Finally, F is found to be 0.0054, while the real-time concentration is 19.8%. The proportion of increasing by 0.0054 is 19.8% × (1 + 0.0054) = 19.90692%, which is 19.9% ​​when rounded to one decimal place.

[0101] Step S4: Set up the reaction chamber, test the response curve of the gas detection instrument in the reaction chamber, and extract the response characteristics of the response curve; Step S4 includes the following sub-steps:

[0102] Step S401: Install a reaction chamber in the gas detection instrument. The reaction chamber includes a reaction box and a storage box. Put baking soda powder in the reaction box and inject edible citric acid into the storage box. The reaction box and the storage box are connected by a pipe and a smart control valve.

[0103] Step S402: At the first interval of each cycle, open the intelligent control valve and inject a test volume of edible citric acid into the reaction box. At this time, baking soda and edible citric acid react chemically to produce carbon dioxide.

[0104] In practice, neither baking soda nor citric acid poses any safety hazards, and the small amount of carbon dioxide produced by the chemical reaction will not affect the environment or the human body. However, it can cause the carbon dioxide concentration in a small area near the gas detector to rise rapidly and then fall, thereby testing the response curve of the gas detector. At the same time, baking soda and citric acid can be replaced after they are completely consumed. The first cycle is set by the tester. Since the change in the response curve of the gas detector needs to be observed over a long period of time, the first cycle can be extended, for example, once a day or once every two days. This embodiment does not make specific requirements. This embodiment takes a first cycle of 1 day as an example.

[0105] Step S403: Based on the response curve of the test gas detection instrument in the reaction chamber, extract the response characteristics of the response curve;

[0106] Step S403 includes the following sub-steps:

[0107] Please see Figure 5As shown, in step S4031, before the reaction occurs, the carbon dioxide concentration is recorded and named the normal concentration. After the reaction occurs, the change of carbon dioxide concentration over time is recorded in real time. When the carbon dioxide concentration returns to the normal concentration, the recording stops and the response data is obtained. The response data is specifically a two-dimensional coordinate system with time on the X-axis and carbon dioxide concentration on the Y-axis. The curve formed by the change of carbon dioxide concentration over time is recorded in the data, which is the response curve.

[0108] Step S4032: Obtain the highest point of the response curve and name it the response point; obtain the time taken for the carbon dioxide concentration to reach the response point from the normal concentration and name it the response time.

[0109] Step S4033: Obtain the time taken for the carbon dioxide concentration to reach the normal concentration from the response point, and name it the recovery time. The response time and the recovery time are the response characteristics.

[0110] In practice, before the reaction occurs, the normal concentration is recorded as 0.04%, representing the typical carbon dioxide concentration in the air under normal conditions. After the reaction occurs, the change in carbon dioxide concentration over time is recorded in real time. Recording stops when the carbon dioxide concentration returns to its normal level, and the response data is obtained as follows: Figure 5 As shown, the curve is the response curve, and the highest point on the response curve is the response point. The response time was 8 seconds, and the recovery time was 16 seconds. The response time represents the response speed of the gas detector to changes in gas concentration, while the recovery time represents the response speed of the gas detector to return to normal levels after the gas concentration recovers.

[0111] Step S5 involves monitoring the health status of the gas detector based on its response characteristics and predicting faults in the gas detector based on the temporal changes in these response characteristics. Step S5 includes the following sub-steps:

[0112] Step S501: Obtain the health criteria for the response characteristics, including response criteria and recovery criteria;

[0113] Step S502: Record the response features obtained from each test, number the response features in chronological order, and use the symbol H. j This indicates that j is a non-zero natural number and j is the index of H;

[0114] Please see Figure 6 As shown, in step S503, with j as the horizontal axis, H j Establish a two-dimensional coordinate system for the vertical axis, named the fault prediction coordinate system. The fault prediction coordinate system includes the response fault prediction coordinate system and the recovery fault prediction coordinate system. H j The response time in the input is entered into the response fault prediction coordinate system according to j, and H is set to...j The recovery time is entered into the recovery fault prediction coordinate system according to j;

[0115] Step S504: Perform regression analysis on the response fault prediction coordinate system and the recovery fault prediction coordinate system to obtain the response fault prediction function and the recovery fault prediction function, obtain the expected prediction time, substitute the expected prediction time into the response fault prediction function and the recovery fault prediction function, and solve to obtain the response prediction duration and the recovery prediction duration.

[0116] Step S505: If the response prediction time is greater than or equal to the response standard or the recovery prediction time is greater than or equal to the recovery standard, then output an instrument fault signal.

[0117] In practice, the response standard is within 15 seconds, and the recovery standard is within 20 seconds, i.e., [0, 15 seconds] and [0, 20 seconds]. By comparison, if the response time and recovery time are both within the response standard, it means that the gas detection instrument is currently functioning normally. The response characteristics are then numbered according to the chronological order, for example, if response characteristics for a total of 100 days are monitored, they are numbered from H1 to H2. 100 Construct a response fault prediction coordinate system and restore the fault prediction coordinate system and H j The response time is entered into the response fault prediction coordinate system according to j, and H is also entered into the system. j The recovery time is entered into the recovery fault prediction coordinate system according to j. Taking the response fault prediction coordinate system as an example, the response fault prediction coordinate system is constructed as follows: Figure 6 As shown, j is actually the number of days. Figure 6 The curve in the figure is the curve corresponding to the response fault prediction function. The curve shows that the response time exceeds 15 seconds on the 176th day, which means that the response time of the gas detection instrument will be unqualified on the 176th day and maintenance is required.

[0118] Example 2: This application provides an electronic device, which may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory stores computer-readable instructions. The processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, steps such as those in an adaptive environmental compensation and fault prediction algorithm for a gas detection instrument are performed to achieve the following functions: constructing a detection test space and recording test results; analyzing the duration of environmental impact on the gas detection instrument and performing time-series calibration on the test results to obtain experimental data; performing adaptive environmental compensation analysis on the gas detection instrument to form an adaptive environmental compensation algorithm; setting up a reaction chamber and extracting the response characteristics of the response curve; and predicting faults in the gas detection instrument based on the time-series changes in the response characteristics.

[0119] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0120] Example 3: This application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute an adaptive environmental compensation and fault prediction algorithm for a gas detection instrument provided by the methods described above. The method includes: constructing a detection test space and recording test results; analyzing the duration of environmental impact on the gas detection instrument and performing time-series calibration on the test results to obtain experimental data; performing adaptive environmental compensation analysis on the gas detection instrument to form an adaptive environmental compensation algorithm; setting up a reaction chamber and extracting the response characteristics of the response curve; and predicting faults in the gas detection instrument based on the time-series changes of the response characteristics.

[0121] Example 4: This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the steps described above in the adaptive environmental compensation and fault prediction algorithm for gas detection instruments to achieve the following functions: constructing a detection test space and recording test results; analyzing the duration of environmental impact on the gas detection instrument and performing time-series calibration on the test results to obtain experimental data; performing adaptive environmental compensation analysis on the gas detection instrument to form an adaptive environmental compensation algorithm; setting up a reaction chamber and extracting the response characteristics of the response curve; and predicting faults in the gas detection instrument based on the time-series changes in the response characteristics.

[0122] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the above technical solutions, in essence or in terms of their contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0123] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. 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 spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An adaptive ambient compensation and failure prediction method for a gas detection instrument, characterized in that, Includes the following steps: Construct a testing space, randomly change the environmental parameters within the testing space every first test duration, and record the test results; The duration of environmental impact on gas detection instruments was analyzed, and the test results were time-series calibrated based on the duration of environmental impact to obtain experimental data. Based on experimental data analysis, the influence of temperature and humidity on detection accuracy is analyzed, and an adaptive environmental compensation algorithm is formed for the gas detection instrument. A reaction chamber is set up, and the response curve of the gas detection instrument is tested through the reaction chamber to extract the response characteristics of the response curve. The health status of gas detection instruments is monitored based on response characteristics, and fault prediction of gas detection instruments is performed based on the time-series changes of response characteristics. Based on experimental data analysis of the influence of temperature and humidity on detection accuracy, and adaptive environmental compensation analysis of the gas detection instrument, the adaptive environmental compensation algorithm includes the following sub-steps: Based on experimental data analysis, the relationship between temperature and humidity on detection accuracy is obtained, and the relationship between temperature and humidity on accuracy is obtained. The influence of temperature accuracy and humidity accuracy is divided into reference relationship and calibration relationship; Based on the benchmark relationship and calibration relationship, an adaptive environmental compensation analysis is performed on the gas detection instrument to form an adaptive environmental compensation algorithm. Based on experimental data analysis, the relationship between temperature and humidity on detection accuracy was obtained, including the following sub-steps: Label the detected concentration as P1, label the first test concentration as P2, and calculate (P1-P2) / P2 to obtain the detection deviation. A two-dimensional coordinate system is established with calibration temperature and calibration humidity as the X-axis and detection deviation as the Y-axis, respectively named Temperature Accuracy Influence Analysis Chart and Humidity Accuracy Influence Analysis Chart. The detection deviation is entered into the Temperature Accuracy Influence Analysis Chart and Humidity Accuracy Influence Analysis Chart according to calibration temperature and calibration humidity, respectively. The coordinate points in the Temperature Accuracy Influence Analysis Chart and Humidity Accuracy Influence Analysis Chart are named Temperature Accuracy Influence Analysis Point and Humidity Accuracy Influence Analysis Point, respectively. Regression analysis was performed on the temperature accuracy influence diagram and the humidity accuracy influence diagram to obtain the temperature accuracy influence curve and the humidity accuracy influence curve. The temperature accuracy influence curve and its temperature accuracy influence analysis points represent the temperature accuracy influence relationship, and the humidity accuracy influence curve and its humidity accuracy influence analysis points represent the humidity accuracy influence relationship. The influence of temperature accuracy and humidity accuracy is divided into baseline relationships and calibration relationships, including the following sub-steps: Obtain the standard deviations of the temperature accuracy influence curve and the humidity accuracy influence curve, and name them temperature standard deviation and humidity standard deviation, respectively. If the temperature standard deviation is less than or equal to the humidity standard deviation, the temperature accuracy influence analysis chart is named the baseline analysis chart, and the humidity accuracy influence analysis chart is named the calibration analysis chart; if the temperature standard deviation is greater than the humidity standard deviation, the humidity accuracy influence analysis chart is named the baseline analysis chart, and the temperature accuracy influence analysis chart is named the calibration analysis chart. The temperature precision influence analysis point or the humidity precision influence analysis point corresponding to the reference analysis graph is named as a reference analysis point, the temperature precision influence analysis point or the humidity precision influence analysis point corresponding to the calibration analysis graph is named as a calibration analysis point, the temperature precision influence curve or the humidity precision influence curve corresponding to the reference analysis graph is named as a reference influence curve, and the temperature precision influence curve or the humidity precision influence curve corresponding to the calibration analysis graph is named as a calibration influence curve; The reference analysis graph, the reference analysis point and the reference influence curve are the reference relationship, and the calibration analysis graph, the calibration analysis point and the calibration influence curve are the calibration relationship; The adaptive environmental compensation analysis of the gas detection instrument is performed based on the reference relationship and the calibration relationship to form an adaptive environmental compensation algorithm, including the following sub-steps: The gas concentration detected by the gas detection instrument in real time is obtained and named as a real-time concentration, and the environmental temperature and the environmental humidity at the previous environmental influence time length of the current time are obtained and named as an effective temperature and an effective humidity respectively; If the reference analysis graph is a temperature precision influence analysis graph, the effective temperature is named as a reference parameter, and the effective humidity is named as a calibration parameter, and if the reference analysis graph is a humidity precision influence analysis graph, the effective humidity is named as a reference parameter, and the effective temperature is named as a calibration parameter; A reference analysis point in the reference analysis graph, in which the X axis is equal to the reference parameter, is obtained and named as a reference reference point, the value of the reference reference point on the Y axis is named as a reference reference difference value, and the corresponding calibration humidity or calibration temperature of the reference reference point is obtained and named as a calibration reference parameter; The calibration reference parameter is numbered by a symbol CH i , wherein i is a non-zero natural number and i is the serial number of CH i , CH i is substituted into the calibration analysis curve to obtain the value of the Y axis of the coordinate point on the calibration analysis curve whose X axis is equal to CH i , which is named as a calibration reference difference value and is represented by a symbol CR i , and the corresponding reference reference difference value is marked as CD i ; The value of a coordinate point on the Y axis of the calibration analysis curve, in which the X axis is equal to the calibration parameter, is obtained and named as a calibration anchor difference value, and is represented by a symbol CM; Assuming that the corresponding reference analysis point in the effective temperature and effective humidity environment is the first assumption point, the value of the first assumption point on the Y axis is marked as F, and there is a relationship Solve F by formula The F is the prediction deviation, and increasing the proportion of the prediction deviation can complete the adaptive environment compensation of the real-time concentration, and finally the actual concentration is obtained.

2. The adaptive environmental compensation and failure prediction method for a gas detection instrument according to claim 1, wherein, The detection test space is constructed, the environmental parameters in the detection test space are randomly changed every first test time length, and the test results are recorded, including the following sub-steps: The detection test space is constructed, the detection test space is a closed space capable of changing environmental parameters, and an air inlet and an air outlet are arranged therein to maintain the concentration of the test gas stable; The test gas of the first test concentration is put into the detection test space, and the test gas is kept at the first test concentration at all times; The environmental parameters in the detection test space are randomly changed every first test time length, and the environmental parameters include the environmental temperature and the environmental humidity; The gas detection instrument is installed in the detection test space, the concentration of the test gas is detected in real time by the gas detection instrument and named as a detection concentration, and the detection time is recorded, and the detection time and the detection concentration are the test results.

3. The adaptive environmental compensation and failure prediction method for a gas detection instrument according to claim 2, wherein, The environmental influence time length of the gas detection instrument is analyzed, the test results are time-sequentially calibrated based on the environmental influence time length, and experimental data are obtained, including the following sub-steps: The environmental parameters in the detection test space are set to the first test state, the gas concentration of the test gas is detected after the first test time length, named as a calibration auxiliary concentration, the first test time length is continuously recorded, the range of the calibration auxiliary concentration in the first test time length is calculated, and named as a calibration concentration range; The environmental parameters in the test space are changed, the calibration auxiliary concentration is recorded in real time, and the time is counted. When the calibration auxiliary concentration exceeds the calibration concentration range, the time counting is stopped, and the time length obtained by the time counting is obtained, which is named as environmental influence time length; The detection time in the test result is advanced by the environmental influence time length to obtain a calibration time. The environmental temperature corresponding to the calibration time is named as calibration temperature, and the environmental humidity corresponding to the calibration time is named as calibration humidity. The calibration temperature and the calibration humidity are associated with the detection concentration to form experimental data. The experimental data include the detection concentration, the calibration time, the calibration temperature and the calibration humidity.

4. The adaptive environmental compensation and failure prediction method for a gas detection instrument according to claim 3, wherein, A reaction chamber is arranged, and a response curve of the gas detection instrument is tested by the reaction chamber. The response characteristics of the response curve include the following sub-steps: A reaction chamber is arranged, and a response curve of the gas detection instrument is tested by the reaction chamber. The response characteristics of the response curve include the following sub-steps: Before the reaction occurs, the carbon dioxide concentration is recorded and named as normal concentration. After the reaction occurs, the carbon dioxide concentration is recorded in real time with time. When the carbon dioxide concentration returns to the normal concentration, the recording is stopped to obtain response data. The response data are specifically a two-dimensional coordinate system with time as the X axis and carbon dioxide concentration as the Y axis, wherein a curve formed by the change of the carbon dioxide concentration with time is recorded, which is the response curve. The highest point of the response curve is obtained and named as a response point. The time length taken by the carbon dioxide concentration from the normal concentration to the response point is obtained and named as a response time length.

5. The adaptive environmental compensation and failure prediction method for a gas detection instrument according to claim 4, wherein, The time length taken by the carbon dioxide concentration from the response point to the normal concentration is obtained and named as a recovery time length. The response time length and the recovery time length are the response characteristics. Based on the response characteristics, the health state of the gas detection instrument is monitored, and based on the time sequence change of the response characteristics, the gas detection instrument is fault predicted, including the following sub-steps: The health standard of the response characteristics is obtained. The health standard includes a response standard and a recovery standard. Regression analysis is performed on the response fault prediction coordinate system and the recovery fault prediction coordinate system to obtain a response fault prediction function and a recovery fault prediction function. An expected prediction time is obtained, the expected prediction time is substituted into the response fault prediction function and the recovery fault prediction function, and the response prediction time length and the recovery prediction time length are obtained by solving.

6. The adaptive environmental compensation and failure prediction method for a gas detection instrument according to claim 5, wherein, If the response prediction time length is greater than or equal to the response standard or the recovery prediction time length is greater than or equal to the recovery standard, an instrument fault signal is output. ​ The response characteristics obtained in each test are recorded, the response characteristics are numbered in the order of time, and the response characteristics are represented by symbols H j , where j is a non-zero natural number and j is the serial number of H. A two-dimensional coordinate system is established with j as the horizontal axis and H j as the vertical axis, and is named as a failure prediction coordinate system, which includes a response failure prediction coordinate system and a recovery failure prediction coordinate system, and the response duration in H j is recorded in the response failure prediction coordinate system according to j, and the recovery duration in H j is recorded in the recovery failure prediction coordinate system according to j. ​ ​

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