Anti-cross interference gas detection method based on acousto-optic and electrochemical dual mechanisms

Abstract

The invention discloses an anti-cross interference gas detection method based on acousto-optic and electrochemical dual mechanisms, and relates to the technical field of anti-interference gas detection, and the method comprises the following steps: constructing a strong interference factor comparison table, and recording strong interference factors of target gas; the method comprises the following steps: detecting target gas through electrochemical detection, acoustic surface wave detection and photoacoustic spectrum detection, and judging whether a detection result is interfered or not; building an anti-interference calibration model, and carrying out joint calibration on a detection result; if the detection result is interfered, giving credibility to electrochemical detection, surface acoustic wave detection and photoacoustic spectrum detection by referring to a strong interference factor comparison table, and performing joint calibration on the detection result by combining an anti-interference calibration model; the method is used for solving the problems that an existing anti-interference gas detection technology is high in use limitation, and anti-interference detection cannot be achieved in part of detection environments.

Description

Technical Field

[0001] This invention relates to the field of anti-interference gas detection technology, specifically to an anti-cross-interference gas detection method based on a combination of acoustic-optical and electrochemical mechanisms. Background Technology

[0002] Anti-interference gas detection technology refers to solutions that significantly suppress or eliminate the influence of non-target gases and environmental variables on the detection accuracy of target gases through specific design methods or system integration. Its core objective is to achieve highly selective and stable gas concentration measurement in complex and ever-changing real environments.

[0003] In some environments requiring gas detection, traditional gas detection methods are susceptible to interference from other factors, necessitating the use of anti-interference gas detection technologies. However, existing anti-interference gas detection technologies typically employ a single detection method, which has significant limitations regardless of the calibration method used. A single detection technique may fail completely in certain environments, thus limiting the applicability of existing anti-interference gas detection technologies. For example, patent application CN116183537A discloses an "Anti-interference NDIR Mixed Gas Detection Method and System Based on Differential Elimination," which uses a single detection method to detect mixed gases and performs anti-interference calculations through differential elimination. However, this single detection method has many limitations, making it unusable in some scenarios and exhibiting significant limitations. Therefore, existing anti-interference gas detection technologies still suffer from significant limitations in application and cannot achieve anti-interference detection in certain detection environments. Summary of the Invention

[0004] This invention aims to at least partially address one of the technical problems in the prior art. It constructs a strong interference factor comparison table to record the strong interference factors of the target gas. Then, it detects the target gas using electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy to determine if the detection results are interfered with. Prior to this, historical test data is obtained via the internet or through testing, and an anti-interference calibration model is constructed to jointly calibrate the detection results of electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy. Then, the strong interference factor comparison table is used to assign credibility to electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy. Next, the anti-interference calibration model is used to analyze the predicted deviations of EC concentration, SAW concentration, and PAS concentration. Finally, based on a comprehensive analysis of EC concentration, SAW concentration, PAS concentration, predicted deviations, and credibility, the detection results are jointly calibrated. This addresses the problem that existing anti-interference gas detection technologies still have significant limitations and cannot achieve anti-interference detection in certain detection environments.

[0005] To achieve the above objectives, this application provides a method for detecting gases with anti-cross-interference based on acousto-optic and electrochemical dual mechanisms, comprising the following steps: Construct a table of strong interference factors and record the strong interference factors of the target gas; The target gas is detected by electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy to determine whether the detection results are interfered with. An anti-interference calibration model is built based on historical test data to jointly calibrate the detection results when the detection results of electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy are interfered with. If the detection results are interfered with, the reliability of electrochemical detection, surface acoustic wave detection and photoacoustic spectroscopy detection is assigned by referring to the strong interference factor comparison table, and the detection results are jointly calibrated by combining the anti-interference calibration model.

[0006] Furthermore, a strong interference factor comparison table is constructed, and the strong interference factors of the target gas are recorded, including the following sub-steps: A strong interference factor comparison table is constructed. The strong interference factor comparison table records the factors that affect the detection results when the target gas is detected by electrochemical detection, the factors that affect the detection results when the target gas is detected by surface acoustic wave detection, and the factors that affect the detection results when the target gas is detected by photoacoustic spectroscopy. These factors are collectively referred to as strong interference factors. The strong interfering factors of electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy detection are named electrochemical interference factors, surface acoustic wave interference factors, and photoacoustic spectroscopy interference factors, respectively.

[0007] Furthermore, the target gas is detected using electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy. Determining whether the detection results are interfered with includes the following sub-steps: The target gas is detected by electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy to obtain EC concentration, SAW concentration, and PAS concentration, respectively. The EC concentration, SAW concentration, and PAS concentration are the target gas concentrations obtained by the three detection methods: electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy. The preset detection error refers to the detection results, which are EC concentration, SAW concentration and PAS concentration. The absolute value of the difference between any two detection results is calculated and named as the detection deviation. If all the detection deviations formed by the three detection results are less than or equal to the detection error, the error allowable signal is output; otherwise, the error large signal is output. If the output error is acceptable, the average value of EC concentration, SAW concentration, and PAS concentration is calculated as the actual concentration of the target gas; if the output error is large, an anti-interference calibration model is executed.

[0008] Furthermore, an anti-interference calibration model is built based on historical test data. When the detection results of electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy are interfered with, the joint calibration of the detection results includes the following sub-steps: Obtain historical test data through the internet or testing and build an anti-interference calibration model; The detection results from electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy are jointly calibrated.

[0009] Furthermore, acquiring historical test data via the internet or testing and constructing an anti-interference calibration model includes the following sub-steps: The historical test data includes data type, test concentration, and actual concentration, and the historical test data is provided by the manufacturer. The data types include electrochemical data, surface acoustic wave data, and photoacoustic spectroscopy data; Construct an anti-interference calibration model and input historical test data into the anti-interference calibration model.

[0010] Furthermore, the joint calibration of the detection results from electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy includes the following sub-steps: Historical test data of data types electrochemical data, surface acoustic wave data, and photoacoustic spectroscopy data are named as first data, second data, and third data, respectively, and first data, second data, and third data are collectively referred to as classified data; For any classification of data, the test concentration is labeled as TC and the true concentration is labeled as RC. The result of the calculation, |RC-TC| / RC, is labeled as concentration deviation. A one-dimensional coordinate system was established with the concentration deviation as the X-axis, named the Interference Deviation Relationship Chart. The concentration deviations calculated from the test concentration and the true concentration in the classification data were entered into the Interference Deviation Relationship Chart according to the number of strong interferences. Cluster analysis is performed on the interference deviation relationship diagram to obtain different relationship clusters. The number of relationship clusters is counted and named as the total number of clusters. It is determined whether the total number of clusters is three. If it is, a clustering completion signal is output; otherwise, a clustering correction signal is output. If the clustering correction signal is output, the average value of the test concentration in the relation cluster is calculated and named the cluster level. The maximum and minimum values ​​of the cluster level are found and named the maximum level and minimum level, respectively. The median of the maximum level and minimum level is calculated and named the cluster median. The cluster level that is closest to the cluster median is found and named the median level. For any clustering level, calculate the difference between the clustering level and the maximum, minimum, and median levels, and name them as the first difference, the second difference, and the third difference, respectively. If the first difference is less than the second and third differences, the relationship clustering corresponding to the clustering level is classified as the first biased clustering. If the third difference is less than the first and second differences, the relationship clustering corresponding to the clustering level is classified as the second biased clustering. If the second difference is less than the first and third differences, the relationship clustering corresponding to the clustering level is classified as the third biased clustering. If the clustering completion signal is output, the relationship cluster with the highest clustering level is classified as the first biased cluster, the relationship cluster with the lowest clustering level is classified as the third biased cluster, and the remaining relationship clusters are classified as the second biased cluster. Calculate the average concentration deviation in the first deviation cluster and name it the first deviation. Calculate the average concentration deviation in the second deviation cluster and name it the second deviation. Calculate the average concentration deviation in the third deviation cluster and name it the third deviation. The first deviation > the second deviation > the third deviation.

[0011] Furthermore, if the detection results are interfered with, the confidence level of electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy detection is assigned with reference to the strong interference factor comparison table. At the same time, the detection results are jointly calibrated in conjunction with the anti-interference calibration model, including the following sub-steps: The reliability of electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy detection is attributed by referring to a table of strong interference factors. The prediction biases of EC, SAW, and PAS concentrations were analyzed using an anti-interference calibration model. Based on a comprehensive analysis of EC concentration, SAW concentration, PAS concentration, prediction bias, and reliability, the test results are jointly calibrated.

[0012] Furthermore, assigning confidence levels to electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy detection by referring to the strong interference factor checklist includes the following sub-steps: The user determines the number of strong interferences in the current detection environment for electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy by referring to the strong interference factor comparison table, and marks them as SQ1, SQ2, and SQ3, respectively. The number of strong interferences represents the number of strong interference factors present during the detection of electrochemical detection, surface acoustic wave detection, or photoacoustic spectroscopy. Calculate 1-SQ1 / (SQ1+SQ2+SQ3), and label the result as KQ1. Calculate 1-SQ2 / (SQ1+SQ2+SQ3), and label the result as KQ2. Calculate 1-SQ3 / (SQ1+SQ2+SQ3), and label the result as KQ3. Calculate KQ1 / (KQ1+KQ2+KQ3) and name the result as electrochemical confidence level; calculate KQ2 / (KQ1+KQ2+KQ3) and name the result as surface acoustic wave confidence level; calculate KQ3 / (KQ1+KQ2+KQ3) and name the result as photoacoustic spectroscopy confidence level. The electrochemical reliability, surface acoustic wave reliability, and photoacoustic spectroscopy reliability are collectively referred to as reliability.

[0013] Furthermore, the analysis of the predicted biases of EC, SAW, and PAS concentrations using an anti-interference calibration model includes the following sub-steps: The confidence levels of electrochemical, surface acoustic wave, and photoacoustic spectroscopy are ranked from largest to smallest and named as first confidence level, second confidence level, and third confidence level, respectively. The data types corresponding to the first confidence level, second confidence level, and third confidence level are named as first confidence data, second confidence data, and third confidence data, respectively. The third deviation of the first reliable data is obtained and named the first prediction deviation; the second deviation of the second reliable data is obtained and named the second prediction deviation; the first deviation of the third reliable data is obtained and named the third prediction deviation. The first prediction deviation, the second prediction deviation, and the third prediction deviation are collectively referred to as prediction deviations.

[0014] Furthermore, based on a comprehensive analysis of EC concentration, SAW concentration, PAS concentration, prediction bias, and reliability, the joint calibration of the detection results includes the following sub-steps: The EC concentration, SAW concentration, or PAS concentration of the first, second, and third reliable data are named the first concentration, the second concentration, and the third concentration, respectively, and are represented by the symbols C1, C2, and C3. Determine the relative sizes of C2 and C1. If C2 > C1, set the second prediction deviation to a negative number; otherwise, set the second prediction deviation to a positive number. Determine the relative values ​​of C3 and C1. If C3 > C1, set the third prediction deviation to a negative number; otherwise, set the third prediction deviation to a positive number. Label the set second and third prediction deviations as ED2 and ED3 respectively, calculate C2×(1+ED2), label the calculation result as F2, calculate C3×(1+ED3), label the calculation result as F3; Determine the magnitudes of C1, F2, and F3. If C1 > F2 and C1 > F3, set the first prediction deviation to a negative number; if C1 < F2 and C1 < F3, set the first prediction deviation to a positive number; if C1 > C2 and C1 < C3, or C1 < C2 and C1 > C3, set the first prediction deviation to a positive number. Simultaneously calculate C1 × (1 + ED1) and C1 × (1 - ED1), and label the results as G1 and G2 respectively. Calculate |G1 - F2| + |G1 - F3| and |G2 - F2| + |G2 - F3|, and label the results as H1 and H2 respectively. Determine the magnitudes of H1 and H2. If H1 > H2, set the first prediction deviation to a negative number; otherwise, set the first prediction deviation to a positive number. The first predicted deviation after setting is labeled as ED1, and the first confidence level, second confidence level, and third confidence level are labeled as RY1, RY2, and RY3, respectively. Calculate C1×(1+ED1), and mark the result as F1. Calculate F1×RY1+F2×RY2+F3×RY3, and name the result as calibration concentration. The calibration concentration is the concentration of the target gas after joint calibration.

[0015] The beneficial effects of this invention are as follows: By constructing a strong interference factor comparison table to record the strong interference factors of the target gas, the target gas is then detected by electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy to determine whether the detection results are interfered with. Before this, historical test data is obtained through the Internet or testing and an anti-interference calibration model is constructed to jointly calibrate the detection results of electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy. The advantage is that by detecting the target gas through three modes of electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy, the gas detection can cover most or even all detection environments, thus improving the applicability of anti-interference gas detection.

[0016] This invention assigns credibility to electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy by referring to a strong interference factor comparison table. It then analyzes the predicted deviations of EC, SAW, and PAS concentrations using an anti-interference calibration model. Finally, based on a comprehensive analysis of EC, SAW, PAS concentrations, predicted deviations, and credibility, the detection results are jointly calibrated. The advantage lies in that, considering the strong interference factors in electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy under different environments, the calibrated concentrations are more biased towards detection results with less interference. Furthermore, by calibrating the predicted deviations based on the magnitude and credibility of EC, SAW, and PAS concentrations before calibrating the detection results, this multi-faceted joint calibration method achieves strong anti-interference gas detection, improving the accuracy and effectiveness of anti-interference gas detection. Attached Figure Description

[0017] Figure 1 This is a flowchart of the steps of the method of the present invention; Figure 2 This is a diagram showing the interference deviation relationship of the present invention; Figure 3 This is a schematic diagram of the relation clustering method of the present invention; Figure 4 This is a schematic diagram of the first bias clustering, the second bias clustering, and the third bias clustering of the present invention. Detailed Implementation

[0018] 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.

[0019] Example 1, please refer to Figure 1 As shown, this application provides a method for detecting gases with anti-cross-interference based on acousto-optic and electrochemical dual mechanisms, including the following steps: Step S1: Construct a strong interference factor comparison table and record the strong interference factors of the target gas; Step S1 includes the following sub-steps: Step S101: Construct a strong interference factor comparison table. The strong interference factor comparison table records the factors that affect the detection results when the target gas is detected by electrochemical detection, the factors that affect the detection results when the target gas is detected by surface acoustic wave detection, and the factors that affect the detection results when the target gas is detected by photoacoustic spectroscopy. These are collectively referred to as strong interference factors. Step S102: The strong interfering factors of electrochemical detection, surface acoustic wave detection and photoacoustic spectroscopy detection are named electrochemical interference factors, surface acoustic wave interference factors and photoacoustic spectroscopy interference factors, respectively. In specific implementation, assuming the target gas in this embodiment is benzene, the following table shows a comparison of strong interfering factors: Table 1 Comparison of Strong Interference Factors Detection type Strong interference factors Electrochemical detection <![CDATA[Ethanol, H2S, temperature drift, and humidity drift]]> Surface acoustic wave detection Toluene, xylene, oil aerosols, temperature drift, and humidity drift Photoacoustic spectroscopy detection Toluene, water vapor, mechanical vibration and airflow noise In this embodiment, strong interference factors must have a strong interference effect on the detection results. If the interference effect on the detection results is weak and the error is small, it can be ignored. The strength of interference is determined by the tester and is not specifically described in this embodiment.

[0020] Step S2 involves detecting the target gas using electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy to determine if the detection results are interfered with. Step S2 includes the following sub-steps: Step S201: The target gas is detected by electrochemical detection, surface acoustic wave detection and photoacoustic spectroscopy to obtain EC concentration, SAW concentration and PAS concentration. EC concentration, SAW concentration and PAS concentration are the target gas concentrations obtained by the three detection methods of electrochemical detection, surface acoustic wave detection and photoacoustic spectroscopy, respectively. Step S202: Preset detection error. EC concentration, SAW concentration and PAS concentration are collectively referred to as detection results. Calculate the absolute value of the difference between any two detection results and name it as detection deviation. If all detection deviations formed by the three detection results are less than or equal to the detection error, then output the error allowable signal; otherwise, output the error large signal. Step S203: If the output error is an allowable signal, calculate the average value of EC concentration, SAW concentration and PAS concentration as the actual concentration of the target gas; if the output error is a large signal, execute the anti-interference calibration model. In practice, the detected EC, SAW, and PAS concentrations were 0.028 ppm, 0.034 ppm, and 0.025 ppm, respectively. The detection error was a preset value set by the user. The user could adjust the detection error according to their accuracy requirements. If the errors between the EC, SAW, and PAS concentrations did not exceed the detection error, it meant that the detection results were less affected by interference and the results from the three detection methods were similar. The average value could be directly taken as the actual concentration of the target gas. However, if the detection deviation between any two detection results exceeded the detection error, it meant that there were strong interference factors in the detection environment, which seriously interfered with the detection results and required calibration. For example, the detection deviations calculated by pairing the EC, SAW, and PAS concentrations listed above included 0.006 ppm, 0.009 ppm, and 0.003 ppm, respectively. In this embodiment, the detection error was set to 0.005 ppm. 0.006 ppm and 0.009 ppm both exceeded 0.005 ppm, resulting in a large output error signal, requiring the execution of an anti-interference calibration model.

[0021] Step S3 involves building an anti-interference calibration model based on historical test data to perform joint calibration of the detection results when the results of electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy are interfered with. Step S3 includes the following sub-steps: Step S301: Obtain historical test data via the Internet or testing and construct an anti-interference calibration model; Step S301 includes the following sub-steps: Step S301.1: Historical test data includes data type, test concentration, and actual concentration. Historical test data is provided by the manufacturer. Step S301.2, the data types include electrochemical data, surface acoustic wave data, and photoacoustic spectroscopy data; Step S301.3: Construct an anti-interference calibration model and input historical test data into the anti-interference calibration model; In practice, the historical test data is provided by the manufacturer, and this embodiment will not provide a specific explanation of the source of the data. Step S302: Perform joint calibration on the detection results of electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy. Step S302 includes the following sub-steps: Step S302.1: The historical test data with data types of electrochemical data, surface acoustic wave data and photoacoustic spectroscopy data are named as first data, second data and third data respectively, and the first data, second data and third data are collectively referred to as classified data; Step S302.2: Analyze the data for any classification, label the test concentration as TC, label the true concentration as RC, calculate |RC-TC| / RC, and label the calculation result as concentration deviation; Please see Figure 2 As shown in step S302.3, a one-dimensional coordinate system is established with the concentration deviation as the X-axis and named the interference deviation relationship diagram. The concentration deviation calculated from the test concentration and the true concentration in the classification data is entered into the interference deviation relationship diagram according to the number of strong interferences. In specific implementation, taking electrochemical data as an example, specifically the first data point, for instance, if the tested concentration TC is 0.028 ppm and the actual concentration RC is 0.02 ppm, the calculated concentration deviation is 0.4. Since there are many first data points, the calculated concentration deviations are numerous. Therefore, this embodiment does not list every concentration deviation; instead, it provides a simple explanation using an interference deviation relationship diagram. The constructed interference deviation relationship diagram is shown below. Figure 2 As shown; Please see Figure 3 As shown, in step S302.4, cluster analysis is performed on the interference deviation relationship diagram to obtain different relationship clusters. The number of relationship clusters is counted and named as the total number of clusters. It is determined whether the total number of clusters is three. If yes, the clustering completion signal is output; otherwise, the clustering correction signal is output. Step S302.5: If the clustering correction signal is output, calculate the average value of the test concentration in the relation cluster and name it as the cluster level. Find the maximum and minimum values ​​of the cluster level and name them as the maximum level and minimum level, respectively. Calculate the median of the maximum level and minimum level and name it as the cluster median. Find the cluster level that is closest to the cluster median and name it as the median level. In practice, cluster analysis is used to obtain relation clusters, such as... Figure 3 As shown, Figure 3Each hollow circle represents a relation cluster, resulting in a total of 9 clusters, not 3. Therefore, the data clustering calibration signal is calculated according to the relation clusters from left to right, yielding cluster levels of 0.09, 0.15, 0.21, 0.3, 0.41, 0.46, 0.55, 0.62, and 0.68. The maximum level is 0.68, and the minimum level is 0.09. The calculated cluster median is 0.385, and the cluster level closest to 0.395 is 0.41. Therefore, 0.41 is marked as the median level. Please see Figure 4 As shown, in step S302.6, for any clustering level, calculate the difference between the clustering level and the maximum level, minimum level, and median level, and name them as the first difference, the second difference, and the third difference, respectively. If the first difference is less than the second difference and the third difference, the relationship clustering corresponding to the clustering level is classified as the first biased clustering. If the third difference is less than the first difference and the second difference, the relationship clustering corresponding to the clustering level is classified as the second biased clustering. If the second difference is less than the first difference and the third difference, the relationship clustering corresponding to the clustering level is classified as the third biased clustering. Step S302.7: If the clustering completion signal is output, the relationship cluster with the highest clustering level is classified as the first bias cluster, the relationship cluster with the lowest clustering level is classified as the third bias cluster, and the remaining relationship clusters are classified as the second bias cluster. Step S302.8: Calculate the average concentration deviation in the first deviation cluster and name it the first deviation; calculate the average concentration deviation in the second deviation cluster and name it the second deviation; calculate the average concentration deviation in the third deviation cluster and name it the third deviation; first deviation > second deviation > third deviation. In practical implementation, taking a clustering level of 0.15 as an example, calculating the first, second, and third differences is actually to find which of the three clustering levels—maximum, minimum, and median—the clustering level is closest to. Figure 3 It is easy to see that the clustering level of 0.15 is closest to the minimum level. Therefore, the relation clustering with a clustering level of 0.15 is included in the third biased clustering. The relation clustering corresponding to the minimum level also belongs to the third biased clustering. Finally, the analysis yields the first biased clustering, second biased clustering, and third biased clustering as follows: Figure 4 As shown, the calculated first deviation is 0.59, the second deviation is 0.38, and the third deviation is 0.14.

[0022] Step S4: If the detection results are interfered with, assign confidence levels to electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy detection by referring to the strong interference factor comparison table, and simultaneously perform joint calibration of the detection results using an anti-interference calibration model; Step S4 includes the following sub-steps: Step S401: Assign credibility to electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy detection by referring to the strong interference factor comparison table; Step S401 includes the following sub-steps: Step S401.1: The user determines the number of strong interferences in the current detection environment for electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy by referring to the strong interference factor comparison table, and marks them as SQ1, SQ2, and SQ3 respectively. The number of strong interferences represents the number of strong interference factors present during the detection of electrochemical detection, surface acoustic wave detection, or photoacoustic spectroscopy. Step S401.2: Calculate 1-SQ1 / (SQ1+SQ2+SQ3), and mark the result as KQ1; calculate 1-SQ2 / (SQ1+SQ2+SQ3), and mark the result as KQ2; calculate 1-SQ3 / (SQ1+SQ2+SQ3), and mark the result as KQ3. Step S401.3: Calculate KQ1 / (KQ1+KQ2+KQ3) and name the calculation result as electrochemical confidence level; calculate KQ2 / (KQ1+KQ2+KQ3) and name the calculation result as surface acoustic wave confidence level; calculate KQ3 / (KQ1+KQ2+KQ3) and name the calculation result as photoacoustic spectroscopy confidence level. Step S401.4, the electrochemical confidence level, surface acoustic wave confidence level, and photoacoustic spectroscopy confidence level are collectively referred to as confidence level; In practice, in a fixed environment, the types of strong interference factors are usually fixed, so the number of strong interferences can be updated periodically. For example, at this time, the number of strong interferences SQ1, SQ2, and SQ3 for electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy detection are 2, 4, and 1, respectively. The calculated KQ1, KQ2, and KQ3 are 5 / 7, 3 / 7, and 6 / 7, respectively. Further calculations show that the reliability of electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy are 5 / 14, 3 / 14, and 6 / 14, respectively. This is because, generally, the fewer the number of strong interferences, the smaller the impact on the detection results, and vice versa. Therefore, the reliability of electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy detection is confirmed by the number of strong interferences. The higher the reliability, the closer the detection results are to the true values, and the larger the proportion that can be referenced. Step S402: Analyze the predicted deviations of EC concentration, SAW concentration, and PAS concentration using an anti-interference calibration model; Step S402 includes the following sub-steps: Step S402.1: Sort the electrochemical confidence level, surface acoustic wave confidence level and photoacoustic spectroscopy confidence level by order, and name them as first confidence level, second confidence level and third confidence level in descending order. Name the data types corresponding to the first confidence level, second confidence level and third confidence level as first confidence data, second confidence data and third confidence data respectively. Step S402.2: Obtain the third deviation of the first reliable data and name it the first prediction deviation; obtain the second deviation of the second reliable data and name it the second prediction deviation; obtain the first deviation of the third reliable data and name it the third prediction deviation. Step S402.3, the first prediction deviation, the second prediction deviation and the third prediction deviation are collectively referred to as prediction deviation; In practice, the first confidence level, the second confidence level and the third confidence level were obtained by sorting and were 6 / 14, 5 / 14 and 3 / 14 respectively. Thus, the first confidence data were obtained as surface acoustic wave data, electrochemical data and photoacoustic spectral data. The first prediction deviation was 0.2, the second prediction deviation was 0.38 and the third prediction deviation was 0.41. Step S403: Based on a comprehensive analysis of EC concentration, SAW concentration, PAS concentration, prediction bias, and confidence level, the detection results are jointly calibrated. Step S403 includes the following sub-steps: Step S403.1: The EC concentration, SAW concentration or PAS concentration of the first reliable data, the second reliable data and the third reliable data are named as the first concentration, the second concentration and the third concentration respectively, and are represented by the symbols C1, C2 and C3 respectively. Step S403.2: Determine the size of C2 and C1. If C2 > C1, set the second prediction deviation to a negative number; otherwise, set the second prediction deviation to a positive number. Step S403.3: Determine the magnitude of C3 and C1. If C3 > C1, set the third prediction deviation to a negative number; otherwise, set the third prediction deviation to a positive number. Step S403.4: Mark the set second and third estimated deviations as ED2 and ED3 respectively, calculate C2×(1+ED2), mark the calculation result as F2, calculate C3×(1+ED3), and mark the calculation result as F3; In practice, since the first reliable data is photoacoustic spectral data, corresponding to PAS concentration, C1 is 0.025 ppm. Similarly, C2 and C3 are 0.028 ppm and 0.034 ppm, respectively. Since C1 is closest to the true concentration of the target gas, it is necessary to use C1 as the basis for analysis. It is determined that C2 and C3 are both greater than C1. Therefore, the second and third prediction deviations are both set to negative numbers, resulting in ED2 and ED3 of -0.38 and -0.41, respectively. F2 and F3 are calculated to be 0.01736 ppm and 0.02006 ppm, respectively. Step S403.5: Determine the magnitudes of C1, F2, and F3. If C1 > F2 and C1 > F3, set the first prediction deviation to a negative number; if C1 < F2 and C1 < F3, set the first prediction deviation to a positive number; if C1 > C2 and C1 < C3, or C1 < C2 and C1 > C3, set the first prediction deviation to a positive number. Simultaneously calculate C1 × (1 + ED1) and C1 × (1 - ED1), and label the calculation results as G1 and G2 respectively. Calculate |G1 - F2| + |G1 - F3| and |G2 - F2| + |G2 - F3|, and label the calculation results as H1 and H2 respectively. Determine the magnitudes of H1 and H2. If H1 > H2, set the first prediction deviation to a negative number; otherwise, set the first prediction deviation to a positive number. Step S403.6: Mark the first predicted deviation after setting as ED1, and mark the first confidence level, the second confidence level and the third confidence level as RY1, RY2 and RY3 respectively; Step S403.7: Calculate C1×(1+ED1), mark the calculation result as F1, calculate F1×RY1+F2×RY2+F3×RY3, name the calculation result as calibration concentration, which is the concentration of the target gas after joint calibration. In practice, it was determined that C1 > F2 and C1 > F3, so the first estimated deviation was set to a negative number, resulting in ED1 of -0.2. From this, F1 was calculated to be 0.02 ppm. RY1, RY2, and RY3 were 6 / 14, 5 / 14, and 3 / 14, respectively. The calculated calibration concentration was 0.02 × 6 / 14 + 0.01736 × 5 / 14 + 0.02006 × 3 / 14 = 0.01907 ppm. However, the actual concentration of the target gas during this test was 0.19 ppm. Therefore, the error was calculated to be 0.00007 ppm, which is only about 0.04% and can be ignored.

[0023] 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, and the processor can call these instructions. When the processor executes a computer-readable instruction, it performs steps such as those in the anti-cross-interference gas detection method based on a combination of acoustic-optical and electrochemical mechanisms to achieve the following functions: constructing a strong interference factor reference table to record strong interference factors of the target gas; detecting the target gas using electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy to determine if the detection results are interfered with; building an anti-interference calibration model to jointly calibrate the detection results; if the detection results are interfered with, assigning credibility to electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy with reference to the strong interference factor reference table, and simultaneously performing joint calibration of the detection results using the anti-interference calibration model.

[0024] 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.

[0025] 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 the anti-cross-interference gas detection method based on the dual mechanisms of acoustic-optical and electrochemical methods provided by the above methods. The method includes: constructing a strong interference factor comparison table to record the strong interference factors of the target gas; detecting the target gas through electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy to determine whether the detection results are interfered with; building an anti-interference calibration model to jointly calibrate the detection results; if the detection results are interfered with, assigning credibility to electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy with reference to the strong interference factor comparison table, and simultaneously combining the anti-interference calibration model to jointly calibrate the detection results.

[0026] 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 of the above-described anti-cross-interference gas detection method based on acousto-optic and electrochemical dual mechanisms to achieve the following functions: constructing a strong interference factor reference table to record strong interference factors of the target gas; detecting the target gas through electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy to determine whether the detection results are interfered with; building an anti-interference calibration model to jointly calibrate the detection results; if the detection results are interfered with, assigning credibility to electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy with reference to the strong interference factor reference table, and simultaneously performing joint calibration of the detection results in conjunction with the anti-interference calibration model.

[0027] 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.

[0028] 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.

[0029] 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. A method for detecting gases with anti-cross-interference based on acousto-optic and electrochemical dual mechanisms, characterized in that, Includes the following steps: Construct a table of strong interference factors and record the strong interference factors of the target gas; The target gas is detected by electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy to determine whether the detection results are interfered with. An anti-interference calibration model is built based on historical test data to jointly calibrate the detection results when the detection results of electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy are interfered with. If the detection results are interfered with, the reliability of electrochemical detection, surface acoustic wave detection and photoacoustic spectroscopy detection is assigned by referring to the strong interference factor comparison table, and the detection results are jointly calibrated by combining the anti-interference calibration model.

2. The method for detecting gas against cross-interference based on acousto-optic and electrochemical dual mechanisms according to claim 1, characterized in that, Constructing a strong interference factor comparison table and recording the strong interference factors of the target gas includes the following sub-steps: A strong interference factor comparison table is constructed. The strong interference factor comparison table records the factors that affect the detection results when the target gas is detected by electrochemical detection, the factors that affect the detection results when the target gas is detected by surface acoustic wave detection, and the factors that affect the detection results when the target gas is detected by photoacoustic spectroscopy. These factors are collectively referred to as strong interference factors. The strong interfering factors of electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy detection are named electrochemical interference factors, surface acoustic wave interference factors, and photoacoustic spectroscopy interference factors, respectively.

3. The method for detecting gas with anti-cross-interference based on acousto-optic and electrochemical dual mechanisms according to claim 2, characterized in that, The detection of the target gas using electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy, and the determination of whether the detection results are interfered with, includes the following sub-steps: The target gas is detected by electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy to obtain EC concentration, SAW concentration, and PAS concentration, respectively. The EC concentration, SAW concentration, and PAS concentration are the target gas concentrations obtained by the three detection methods: electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy. The preset detection error refers to the detection results, which are EC concentration, SAW concentration and PAS concentration. The absolute value of the difference between any two detection results is calculated and named as the detection deviation. If all the detection deviations formed by the three detection results are less than or equal to the detection error, the error allowable signal is output; otherwise, the error large signal is output. If the output error is acceptable, the average value of EC concentration, SAW concentration, and PAS concentration is calculated as the actual concentration of the target gas; if the output error is large, an anti-interference calibration model is executed.

4. The method for detecting gas with anti-cross-interference based on acousto-optic and electrochemical dual mechanisms according to claim 3, characterized in that, An anti-interference calibration model is built based on historical test data. When the detection results of electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy are interfered with, the joint calibration of the detection results includes the following sub-steps: Obtain historical test data through the internet or testing and build an anti-interference calibration model; The detection results from electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy are jointly calibrated.

5. The method for detecting gas with anti-cross-interference based on acousto-optic and electrochemical dual mechanisms according to claim 4, characterized in that, Obtaining historical test data and building an anti-interference calibration model through the internet or testing includes the following sub-steps: The historical test data includes data type, test concentration, and actual concentration, and the historical test data is provided by the manufacturer. The data types include electrochemical data, surface acoustic wave data, and photoacoustic spectroscopy data; Construct an anti-interference calibration model and input historical test data into the anti-interference calibration model.

6. The method for detecting gas with anti-cross-interference based on acousto-optic and electrochemical dual mechanisms according to claim 5, characterized in that, The joint calibration of detection results from electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy includes the following sub-steps: Historical test data of data types electrochemical data, surface acoustic wave data, and photoacoustic spectroscopy data are named as first data, second data, and third data, respectively, and first data, second data, and third data are collectively referred to as classified data; For any classification of data, the test concentration is labeled as TC and the true concentration is labeled as RC. The result of the calculation, |RC-TC| / RC, is labeled as concentration deviation. A one-dimensional coordinate system was established with the concentration deviation as the X-axis, named the Interference Deviation Relationship Chart. The concentration deviations calculated from the test concentration and the true concentration in the classification data were entered into the Interference Deviation Relationship Chart according to the number of strong interferences. Cluster analysis is performed on the interference deviation relationship diagram to obtain different relationship clusters. The number of relationship clusters is counted and named as the total number of clusters. It is determined whether the total number of clusters is three. If it is, a clustering completion signal is output; otherwise, a clustering correction signal is output. If the clustering correction signal is output, the average value of the test concentration in the relation cluster is calculated and named the cluster level. The maximum and minimum values ​​of the cluster level are found and named the maximum level and minimum level, respectively. The median of the maximum level and minimum level is calculated and named the cluster median. The cluster level that is closest to the cluster median is found and named the median level. For any clustering level, calculate the difference between the clustering level and the maximum, minimum, and median levels, and name them as the first difference, the second difference, and the third difference, respectively. If the first difference is less than the second and third differences, the relationship clustering corresponding to the clustering level is classified as the first biased clustering. If the third difference is less than the first and second differences, the relationship clustering corresponding to the clustering level is classified as the second biased clustering. If the second difference is less than the first and third differences, the relationship clustering corresponding to the clustering level is classified as the third biased clustering. If the clustering completion signal is output, the relationship cluster with the highest clustering level is classified as the first biased cluster, the relationship cluster with the lowest clustering level is classified as the third biased cluster, and the remaining relationship clusters are classified as the second biased cluster. Calculate the average concentration deviation in the first deviation cluster and name it the first deviation. Calculate the average concentration deviation in the second deviation cluster and name it the second deviation. Calculate the average concentration deviation in the third deviation cluster and name it the third deviation. The first deviation > the second deviation > the third deviation.

7. The method for detecting gas with anti-cross-interference based on acousto-optic and electrochemical dual mechanisms according to claim 6, characterized in that, If the detection results are interfered with, the confidence level of electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy detection is assigned by referring to the strong interference factor comparison table. At the same time, the detection results are jointly calibrated by combining the anti-interference calibration model, including the following sub-steps: The reliability of electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy detection is attributed by referring to a table of strong interference factors. The prediction biases of EC, SAW, and PAS concentrations were analyzed using an anti-interference calibration model. Based on a comprehensive analysis of EC concentration, SAW concentration, PAS concentration, prediction bias, and reliability, the test results are jointly calibrated.

8. The method for detecting gas with anti-cross-interference based on acousto-optic and electrochemical dual mechanisms according to claim 7, characterized in that, Assigning confidence levels to electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy detection by referring to the strong interference factor checklist includes the following sub-steps: The user determines the number of strong interferences in the current detection environment for electrochemical detection, surface acoustic wave detection, and photoacoustic spectroscopy by referring to the strong interference factor comparison table, and marks them as SQ1, SQ2, and SQ3, respectively. The number of strong interferences represents the number of strong interference factors present during the detection of electrochemical detection, surface acoustic wave detection, or photoacoustic spectroscopy. Calculate 1-SQ1 / (SQ1+SQ2+SQ3), and label the result as KQ1. Calculate 1-SQ2 / (SQ1+SQ2+SQ3), and label the result as KQ2. Calculate 1-SQ3 / (SQ1+SQ2+SQ3), and label the result as KQ3. Calculate KQ1 / (KQ1+KQ2+KQ3) and name the result as electrochemical confidence level; calculate KQ2 / (KQ1+KQ2+KQ3) and name the result as surface acoustic wave confidence level; calculate KQ3 / (KQ1+KQ2+KQ3) and name the result as photoacoustic spectroscopy confidence level. The electrochemical reliability, surface acoustic wave reliability, and photoacoustic spectroscopy reliability are collectively referred to as reliability.

9. The method for detecting gas with anti-cross-interference based on acousto-optic and electrochemical dual mechanisms according to claim 8, characterized in that, Analyzing the prediction bias of EC, SAW, and PAS concentrations using an anti-interference calibration model includes the following sub-steps: The confidence levels of electrochemical, surface acoustic wave, and photoacoustic spectroscopy are ranked from largest to smallest and named as first confidence level, second confidence level, and third confidence level, respectively. The data types corresponding to the first confidence level, second confidence level, and third confidence level are named as first confidence data, second confidence data, and third confidence data, respectively. The third deviation of the first reliable data is obtained and named the first prediction deviation; the second deviation of the second reliable data is obtained and named the second prediction deviation; the first deviation of the third reliable data is obtained and named the third prediction deviation. The first prediction deviation, the second prediction deviation, and the third prediction deviation are collectively referred to as prediction deviations.

10. The method for detecting gas against cross-interference based on acousto-optic and electrochemical dual mechanisms according to claim 9, characterized in that, Based on a comprehensive analysis of EC concentration, SAW concentration, PAS concentration, prediction bias, and reliability, the joint calibration of the detection results includes the following sub-steps: The EC concentration, SAW concentration, or PAS concentration of the first, second, and third reliable data are named the first concentration, the second concentration, and the third concentration, respectively, and are represented by the symbols C1, C2, and C3. Determine the relative sizes of C2 and C1. If C2 > C1, set the second prediction deviation to a negative number; otherwise, set the second prediction deviation to a positive number. Determine the relative values ​​of C3 and C1. If C3 > C1, set the third prediction deviation to a negative number; otherwise, set the third prediction deviation to a positive number. Label the set second and third prediction deviations as ED2 and ED3 respectively, calculate C2×(1+ED2), label the calculation result as F2, calculate C3×(1+ED3), label the calculation result as F3; Determine the relative values ​​of C1, F2, and F3. If C1 > F2 and C1 > F3, then set the first prediction deviation to a negative number. If C1 < F2 and C1 < F3, then the first prediction deviation is set to a positive number; if C1 > C2 and C1 < C3, or C1 < C2 and C1 > C3, then the first prediction deviation is set to a positive number. At the same time, C1 × (1 + ED1) and C1 × (1 - ED1) are calculated, and the calculation results are marked as G1 and G2 respectively. |G1 - F2| + |G1 - F3| and |G2 - F2| + |G2 - F3| are calculated, and the calculation results are marked as H1 and H2 respectively. The magnitudes of H1 and H2 are judged. If H1 > H2, then the first prediction deviation is set to a negative number; otherwise, the first prediction deviation is set to a positive number. The first predicted deviation after setting is labeled as ED1, and the first confidence level, second confidence level, and third confidence level are labeled as RY1, RY2, and RY3, respectively. Calculate C1×(1+ED1), and mark the result as F1. Calculate F1×RY1+F2×RY2+F3×RY3, and name the result as calibration concentration. The calibration concentration is the concentration of the target gas after joint calibration.

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