A method and system for spectral analysis and elimination of water vapor interference of a laser sensor

Abstract

The application provides a spectrum analysis and elimination method and system for water vapor interference of a laser sensor, and the method comprises the following steps: scanning a spectrum interval containing a methane characteristic absorption peak to obtain an original light intensity signal curve A composed of N discrete wavelength points; selecting M wavelength point data outside the range of the methane characteristic absorption peak in the original light intensity signal curve A, and generating a reference signal curve B through a linear regression algorithm; performing point-by-point difference ratio operation on the original light intensity signal curve A and the reference signal curve B to obtain an absorption intensity curve C; counting the number of wavelength points whose absorption intensity values in the absorption intensity curve C continuously exceed a preset threshold T within the range of the methane characteristic absorption peak; and determining that methane gas exists when the number of wavelength points continuously exceeding the threshold T reaches a set number K. In an environment where methane gas and water vapor are mixed together, the accuracy of the sensor detection result of the methane concentration can be effectively improved, and the false positive rate of the sensor in a pure water vapor interference environment can be reduced.

Description

Technical Field

[0001] This invention relates to the field of gas detection technology, specifically a method and system for spectral analysis and elimination of water vapor interference in laser sensors. Background Technology

[0002] Laser detection currently mainly employs tunable diode laser absorption spectroscopy (TDLAS) technology, which uses the characteristic absorption peak near 1653 nm to invert concentration.

[0003] Laser sensors are primarily used to detect leaks of toxic or flammable gases. They are typically installed in locations where gas leaks are likely to occur. However, if the humidity at the detection site is high, the resulting water vapor inevitably contaminates and affects the sensor's optical path. This can lead to decreased sensor sensitivity or false alarms and missed alarms, especially when there is only water vapor interference or when the flammable or toxic gas being detected is mixed with water vapor. Water molecules continuously absorb light in the 1550-1750nm wavelength range, and water vapor condensation causes light intensity attenuation.

[0004] Currently, there are two common solutions: one is to add a hydrophobic filter membrane, and the other is to set up a gas chamber heating device. However, adding a hydrophobic filter membrane causes the permeability of the gas to be detected to decrease as the humidity increases. When the water vapor concentration is too high, water droplets are easily condensed on the surface of the filter screen, which prevents the gas to be detected from entering the gas chamber. Adding a gas chamber heating device increases power consumption, which in turn shortens the life of the sensor.

[0005] In summary, passive protection cannot solve the spectral overlap between water vapor and the gas being detected, leading to significant errors in the detection results. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for spectral analysis and elimination of water vapor interference in laser sensors, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] Spectral analysis and elimination methods for water vapor interference in laser sensors include:

[0009] Scan the spectral region containing the characteristic absorption peak of methane to obtain the original light intensity signal curve A, which consists of N discrete wavelength points;

[0010] Select M wavelength points outside the methane characteristic absorption peak range in the original light intensity signal curve A, and generate a reference signal curve B using a linear regression algorithm;

[0011] The absorption intensity curve C is obtained by performing point-by-point difference ratio calculation on the original light intensity signal curve A and the reference signal curve B;

[0012] The number of wavelength points in the absorption intensity curve C where the absorption intensity value continuously exceeds a preset threshold T within the range of the methane characteristic absorption peak is counted.

[0013] When the number of wavelength points that continuously exceed the threshold T reaches a set number K, it is determined that methane gas is present.

[0014] Optionally, in the step of generating the baseline signal curve B using a linear regression algorithm, specifically the least squares method, the slope k and intercept b of the baseline signal curve B are calculated using the following formulas:

[0015]

[0016] Where x is the wavelength point number, y is the corresponding light intensity value, and N≥20;

[0017] Using the above formula and the known acquired data, the linear formula for the reference signal curve B can be obtained:

[0018] y = kx + b.

[0019] Optionally, the step involves performing a point-by-point difference ratio calculation on the original light intensity signal curve A and the reference signal curve B to obtain the absorption intensity curve C.

[0020] The absorption intensity at the i-th wavelength point is C i :

[0021]

[0022] Optionally, the step involves counting the number of wavelength points in the absorption intensity curve C where the absorption intensity value continuously exceeds a preset threshold T within the range of the methane characteristic absorption peak.

[0023] The preset threshold T is a dynamically adjustable parameter. After setting the initial default value, it will adaptively adjust based on the ambient humidity sensor data.

[0024] Optionally, the rule for adaptive adjustment based on ambient humidity sensor data is as follows:

[0025] When the relative humidity of the environment is ≥80%, T should be increased by 10% to 20%.

[0026] When the relative humidity is ≤30%, adjust temperature (T) by 5% to 10%.

[0027] Optionally, the method further includes:

[0028] When the condition is met for the first time, K consecutive wavelength points are detected, and the concentration of methane gas is calculated.

[0029] If the methane concentration values ​​obtained from P consecutive calculations are all greater than zero and the fluctuation range is ≤15%, then a methane alarm is confirmed.

[0030] Optionally, a tunable diode laser can be used and controlled to scan a spectral region containing the characteristic absorption peak of methane to obtain a raw light intensity signal curve A consisting of N discrete wavelength points.

[0031] A spectral analysis and elimination system for water vapor interference in a laser sensor, comprising:

[0032] The original light intensity signal curve A generation module is used to scan the spectral range containing the characteristic absorption peak of methane to obtain the original light intensity signal curve A composed of N discrete wavelength points;

[0033] The reference signal curve B generation module is used to select M wavelength points in the original light intensity signal curve A that are outside the range of the methane characteristic absorption peak, and generate the reference signal curve B through a linear regression algorithm.

[0034] The calculation module is used to perform point-by-point difference ratio calculation on the original light intensity signal curve A and the reference signal curve B to obtain the absorption intensity curve C;

[0035] The statistics module is used to count the number of wavelength points in which the absorption intensity curve C continuously exceeds a preset threshold T within the range of the methane characteristic absorption peak.

[0036] The judgment module is used to determine the presence of methane gas when the number of wavelength points that continuously exceed the threshold T reaches a set number K.

[0037] Optionally, it also includes: an environmental compensation module, which has a built-in temperature and humidity sensor and a threshold adaptive adjustment algorithm, used to perform:

[0038] When the relative humidity of the environment is ≥80%, T should be increased by 10% to 20%.

[0039] When the relative humidity is ≤30%, adjust temperature (T) by 5% to 10%.

[0040] Optionally, it also includes: a historical data comparison module for storing a typical water vapor interference pattern feature library, wherein the data in the typical water vapor interference pattern feature library includes: humidity value and threshold T corresponding to the current humidity value, and the preset threshold T is adjusted according to the humidity value detected by the humidity sensor.

[0041] Compared with existing technologies, this application provides a method and system for eliminating water vapor interference in methane detection. The method includes: scanning a spectral range containing the characteristic absorption peak of methane to obtain an original light intensity signal curve A composed of N discrete wavelength points; selecting M wavelength points in the original light intensity signal curve A that are outside the range of the characteristic absorption peak of methane, and generating a reference signal curve B through a linear regression algorithm; performing point-by-point difference ratio calculations on the original light intensity signal curve A and the reference signal curve B to obtain an absorption intensity curve C; counting the number of wavelength points in the absorption intensity curve C whose absorption intensity value continuously exceeds a preset threshold T within the range of the characteristic absorption peak of methane; and determining the presence of methane gas when the number of wavelength points continuously exceeding the threshold T reaches a set number K. Through the above method, in an environment where methane gas and water vapor are mixed, the accuracy of the sensor's methane concentration detection can be effectively improved; and the false alarm rate of the sensor can be reduced in an environment with only water vapor interference. Attached Figure Description

[0042] Figure 1 This is a comparison chart of the original signal and the fitted curve.

[0043] Figure 2 This is a schematic diagram of the absorption intensity curve generation.

[0044] Figure 3 This is a schematic diagram of a typical water vapor interference mode.

[0045] Figure 4 The distribution of effective points under different methane concentrations is shown.

[0046] Figure 5 Flowchart of a method for eliminating water vapor interference in methane detection

[0047] Figure 6 This is a graph showing the methane concentration data measured by the sensor in an environment where methane gas and water vapor are mixed together.

[0048] Figure 7 This is a graph showing the methane concentration data measured by the sensor under a simple water vapor interference environment. Detailed Implementation

[0049] 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.

[0050] Furthermore, elements in this invention are referred to as being "fixed to" or "set on" another element, which may be directly on the other element or may also include an intervening element. When an element is considered to be "connected" to another element, it may be directly connected to the other element or may also include an intervening element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementations.

[0051] It should be noted that the technical solution provided in this application is applicable to the handling of water vapor interference by any gas sensor, such as methane, hydrogen, acetylene, ethylene, ammonia, hydrogen sulfide, etc. This application takes methane as an example to elaborate on the technical solution of this application. The technical solution of this application is also applicable to the detection of other gases.

[0052] Specifically, taking methane as an example, in order to solve the problem of false alarms or missed alarms of household methane laser sensors under water vapor interference, and to ensure that the sensor can work normally under various water vapor interference conditions, the technical solution of this application is proposed as follows:

[0053] Please see Figure 1 - Figure 7 This invention provides a method for spectral analysis and elimination of water vapor interference in an alkyl laser sensor, comprising: steps S101-S105:

[0054] Step S101. Scan the spectral range containing the characteristic absorption peak of methane to obtain the original light intensity signal curve A composed of N discrete wavelength points.

[0055] In this embodiment, scanning the spectral range containing the characteristic absorption peak of methane can be achieved using a tunable diode laser, for example: laser: NELNLK1 B6EAAA (center wavelength 1653.7nm), detector: Hamamatsu G12180-010A (response bandwidth 10MHz); utilizing the characteristics and principles of methane absorption of specific spectra, data can be collected at the sensor receiver end, such as... Figure 1 The original light intensity signal curve A, as shown Figure 1 As shown, the waveform of the original light intensity signal curve A is actually obtained by plotting the voltage signal data corresponding to 100 scanning points at different wavelengths collected by the laser receiver. By controlling the laser's operating current and temperature, the methane absorption wavelength range is controlled between 40 and 60 scanning points.

[0056] The original light intensity signal curve A is the signal waveform after methane gas is present in the sensor's optical path and absorbs a specific wavelength. The higher the methane concentration, the greater the absorption intensity, and the more obvious the concave part of the original light intensity signal curve A is.

[0057] Step S102. Select M wavelength points in the original light intensity signal curve A that are outside the range of the methane characteristic absorption peak, and generate a reference signal curve B using a linear regression algorithm.

[0058] In this embodiment, the reference signal curve B without methane absorption interference is obtained by fitting the data actually collected in step S101. Figure 1 As shown, the reference signal curve B consists of the straight-line portions at both ends of the original light intensity signal curve A, i.e. Figure 1 The data from points less than 40 and greater than 60 on the X-axis were obtained through fitting, representing the signal waveform curves when there is no methane gas absorption in the optical path.

[0059] In this embodiment, after each acquisition of the original light intensity signal curve A, the data is fitted through step S102 to obtain the reference signal curve B without methane interference. Thus, the data acquisition in each detection process will eventually yield two curve data, namely the original light intensity signal curve A and the reference signal curve B.

[0060] Step S103. Perform point-by-point difference ratio calculation on the original light intensity signal curve A and the reference signal curve B to obtain the absorption intensity curve C.

[0061] In this embodiment, through Figure 1 The original light intensity signal curve A and the reference signal curve B correspond to each data point on the Y-axis, and are obtained through the formula: The absorption intensity value at the i-th wavelength point is calculated to be C. i And generate absorption intensity curve C, to Figure 1 For example, combined with Figure 1 For each data point on the Y-axis corresponding to the original light intensity signal curve A and the reference signal curve B, the following is obtained: Figure 2 The absorption intensity curve shown is shown.

[0062] Figure 2 The absorption intensity value corresponding to each point on the curve shown is Figure 1 The difference or ratio between the original light intensity signal curve A and the reference signal curve B at each current scanning point on the Y-axis is converted into this absorption intensity curve. The purpose of this conversion is to make subsequent data processing and related condition judgments more intuitive and convenient.

[0063] In this embodiment, Figure 1 The absorption intensity value corresponding to each data point on the original light intensity signal curve A is in Figure 2 The current scanning points correspond one-to-one.

[0064] In this embodiment, as Figure 3As shown: the number of data points collected in the X-axis range of 40 to 60 points (i.e., near the absorption peak) is relatively small compared to the number of discrete points below the fitted curve, and all water vapor interference data basically conform to this characteristic. Figure 4 As shown, the number of data points collected in the X-axis range of 40 to 60 points (i.e., near the absorption peak) increases regularly relative to the number of discrete points below the fitted curve as the methane gas concentration increases.

[0065] Step S104. Count the number of wavelength points in the absorption intensity curve C where the absorption intensity value continuously exceeds the preset threshold T within the range of methane characteristic absorption peak.

[0066] use Figure 3 Figure 4 The water vapor interference detection pattern shown is analyzed and eliminated using the following method in this embodiment, including steps S1041-S1042:

[0067] Step S1041. Determine whether the interference or the presence of measuring gas is due to the number of points below the fitted curve.

[0068] Step S1042. Figure 2 For example, the absorption intensity values ​​corresponding to the data points at positions 40 to 60 on the X-axis of the absorption intensity curve are compared sequentially with the absorption intensity threshold (e.g., 50) to determine whether the number of points below the fitted curve is valid, and the number of valid points is recorded at the same time.

[0069] Step S105. When the number of wavelength points that continuously exceed the threshold T reaches a set number K, it is determined that methane gas is present.

[0070] In this embodiment, the number of valid points is used as the criterion. When the number of valid points is continuously counted to reach or meet the set valid point threshold (e.g., 6), it is considered that methane gas is present and subsequent processing is performed. Otherwise, it is determined to be water vapor interference.

[0071] In this embodiment, the ratio of the difference in absorption intensity curves within the absorption wavelength range (absorption intensity) is positive. If it is greater than a certain threshold (e.g., 50, which is a value given in actual testing based on the test results and can be flexibly set, the reason for introducing a certain value instead of 0 is to exclude the inherent error between the data curve A collected when there is no methane and the fitted curve B), it indicates that the actual measured data at that point is below the fitted curve data. If the number of points greater than the threshold continuously exceeds the set number, it indicates that methane is being absorbed. Otherwise, it indicates water vapor interference or no methane.

[0072] In this embodiment, see Figure 5As shown, firstly, the laser receiver acquires actual waveform curve A data; based on the acquired curve A data, a standard data curve B without methane absorption is obtained through fitting; the above two sets of data curves A and B are used to obtain the absorption intensity waveform curve C through specific calculations, and the X-axis point marker field PointAddr = 40; [The text abruptly ends here, likely due to an incomplete translation or a formatting error.] Figure 2 The absorption intensity value corresponding to the X-axis point marker field PointAddr is compared with a threshold (e.g., 50):

[0073] If the number of valid points exceeds the threshold of 50, the Count field of the valid point count flag is incremented by 1, and the PointAddr field of the X-axis position flag is incremented by 1.

[0074] If the number is less than the threshold of 50, the valid point count flag field Count is cleared to zero and the count restarts from 0, and the X-axis point position flag field PointAddr is incremented by 1;

[0075] Determine if the X-axis point marker field PointAddr is greater than 60:

[0076] If it is less than 60, then the return step will be as follows: Figure 2 The absorption intensity value corresponding to the X-axis point marker field PointAddr is compared with a threshold (e.g., 50).

[0077] If the value is greater than 60, the task of comparing the absorption intensity and threshold at points 40 to 60 ends.

[0078] Determine if the consecutive valid point count (Count) is greater than 6:

[0079] If Count is greater than 6, it means that there is a target gas to be measured, and subsequent data processing and calculation are performed to obtain the effective concentration data value. The effective concentration value field MetCount is incremented by 1 (it stays at 5 and will not be incremented by 1 again).

[0080] If Count is less than 6, it means there is no target gas to be measured. The subsequent processing and calculation will be terminated, and the MetCount field of the effective concentration value will be cleared to zero.

[0081] Determine if the MetCount field, representing the effective concentration value, is greater than or equal to 5:

[0082] If MetCount is greater than or equal to 5, output the measured concentration value and end the program;

[0083] If MetCount is less than 5, the concentration output will be zero and the program will terminate.

[0084] In one specific implementation, in the step of generating the reference signal curve B using a linear regression algorithm, the linear regression algorithm is the least squares method, and the slope k and intercept b of the reference signal curve B are calculated using the following formulas:

[0085]

[0086] Where x is the wavelength point number, y is the corresponding light intensity value, and N≥20;

[0087] Using the above formula and the known acquired data, the linear formula for the reference signal curve B can be obtained:

[0088] y = kx + b.

[0089] In this embodiment, the least squares linear fitting algorithm is used: the least squares method is a mathematical optimization technique that finds the best function match for the data by minimizing the sum of squared errors. In linear fitting, the least squares method is used to find a straight line such that the sum of the squared perpendicular distances from all data points to that line is minimized. As described above, the original acquired waveform ( Figure 1 The data from the straight sections at both ends of the original light intensity signal curve A) are used to solve for the slope k and intercept b of the standard straight-line waveform (reference signal curve B) without methane interference using the least squares formula. This yields the straight-line formula and the data for each point on the line. The specific application formula is as follows:

[0090]

[0091] In the above formula, the amount of data N = 80 (below) Figure 1 (100 scan points minus 20 points in the middle range of 40-60);

[0092] x = down Figure 1 Current scan point values ​​(0-39, 60-99);

[0093] y = down Figure 1 The current scanning point value corresponds to the voltage value on the Y-axis;

[0094] Using the above formula and the known collected data, the linear formula for the reference signal curve B can be obtained: y = kx + b.

[0095] In this embodiment, combined with the appendix Figure 1 Appendix Figure 2 Explanation of the fitting calculation for the absorption intensity curve C:

[0096] Will Figure 1 and Figure 2 Each point on the curve is denoted by (X, Y), where X represents the x-coordinate and Y represents the y-coordinate. Then, we know that:

[0097] The data for each point on the original light intensity signal curve A are A(0, Y0), A(1, Y1), A(2, Y2)...A(97, Y97), A(98, Y98), A(99, Y99);

[0098] The data for each point on the reference signal curve B are B(0, Y0), B(1, Y1), B(2, Y2)...B(97, Y97), B(98, Y98), B(99, Y99);

[0099] The data for each point on the absorption intensity curve C is calculated using formula 1 – A / B as follows:

[0100] C(0,Y0)=1–A(Y0) / B(Y0);

[0101] C(1,Y1)=1–A(Y1) / B(Y1);

[0102] C(2,Y2)=1-A(Y2) / B(Y2);

[0103] …

[0104] C(97, Y97)=1-A(Y97) / B(Y97);

[0105] C(98, Y98)=1-A(Y98) / B(Y98);

[0106] C(99, Y99)=1-A(Y99) / B(Y99);

[0107] The absorption intensity curve 0-99 scanning current point data can be obtained: C(Y0), C(Y1), C(Y2)...C(Y97), C(Y98), C(Y99);

[0108] but:

[0109] C(Y0) = the ratio of the difference between A(Y0) and B(Y0) at point 0;

[0110] C(Y1) = the ratio of the difference between A(Y1) and B(Y1) at point 1;

[0111] C(Y2) = the ratio of the differences between A(Y2) and B(Y2) at point 2;

[0112] …

[0113] C(Y97) = the ratio of the difference between A(Y97) and B(Y97) at point 97;

[0114] C(Y98) = the ratio of the difference between A(Y98) and B(Y98) at point 98;

[0115] C(Y99) = the ratio of the difference between A(Y99) and B(Y99) at the 99th point;

[0116] The data values ​​C(Y0), C(Y1), C(Y2)...C(Y97), C(Y98), C(Y99) at each point on the absorption intensity curve represent the ratio of the difference between the actual acquired data line and the standard methane-free data line at each scanning point. The calculated data is then represented on a coordinate graph, which yields... Figure 2 ;

[0117] Therefore, through Figure 1 and Figure 2 It can be seen that the difference ratio is relatively large in the absorption wavelength range, and the difference ratio is the largest at the midpoint of the absorption wavelength range. This difference ratio can be regarded as the absorption intensity of methane at that point. Therefore, the absorption intensity near the absorption wavelength increases with the increase of methane concentration.

[0118] In one specific implementation, the step involves counting the number of wavelength points in which the absorption intensity curve C continuously exceeds a preset threshold T within the range of the methane characteristic absorption peak. The preset threshold T is a dynamically adjustable parameter, and after setting an initial default value, it is adaptively adjusted based on the environmental humidity sensor data.

[0119] In this embodiment, the aforementioned preset threshold T is an adjustment of a dynamically adjustable parameter. Specifically, the rule for adaptive adjustment based on ambient humidity sensor data is as follows:

[0120] When the relative humidity of the environment is ≥80%, T should be increased by 10% to 20%.

[0121] When the relative humidity is ≤30%, adjust temperature (T) by 5% to 10%.

[0122] Understandably, the preset threshold T is increased when the humidity is higher and decreased when the humidity is lower, so as to better reduce the impact of water vapor on the accuracy of the test results.

[0123] In one specific embodiment, the method further includes:

[0124] When the condition is met for the first time, K consecutive wavelength points are detected, and the concentration of methane gas is calculated.

[0125] If the methane concentration values ​​obtained from P consecutive calculations are all greater than zero and the fluctuation range is ≤15%, then a methane alarm is confirmed.

[0126] In this embodiment, to eliminate the extremely rare false alarms caused by water vapor or other factors, an additional processing logic is added to count the effective concentration value (the calculated concentration value is not zero) multiple times. The validity of the measured concentration value is determined based on whether the count exceeds a set threshold (e.g., 5), and then the value is reported. When the effective concentration value has been counted 5 times, the count is stopped, and the next effective concentration value is reported directly. If the count reaches, for example, 3 or 4 times (less than 5 times), and a zero concentration value is detected, the count is reset to zero, and the count restarts when a valid concentration value appears again. In other words, this processing logic only performs a counting judgment on the first valid concentration value. If the gas to be measured is always present, the concentration value after more than 5 counts is directly output without affecting the real-time performance of the measurement data.

[0127] The spectral analysis and elimination method for water vapor interference in laser sensors provided in this application makes the sensor more accurate in detecting methane concentration in an environment where methane gas and water vapor are mixed together. The false alarm rate of the sensor can be reduced in a simple water vapor interference environment. Currently, the measurement is performed under conditions of high water vapor concentration or humidity. If used in a normal environment, its relevant concentration detection index will be higher or the false alarm rate will be lower.

[0128] Secondly, this embodiment provides a spectral analysis and elimination system for water vapor interference in a laser sensor, comprising:

[0129] The original light intensity signal curve A generation module is used to scan the spectral range containing the characteristic absorption peak of methane to obtain the original light intensity signal curve A composed of N discrete wavelength points;

[0130] The reference signal curve B generation module is used to select M wavelength points in the original light intensity signal curve A that are outside the range of the methane characteristic absorption peak, and generate the reference signal curve B through a linear regression algorithm.

[0131] The calculation module is used to perform point-by-point difference ratio calculation on the original light intensity signal curve A and the reference signal curve B to obtain the absorption intensity curve C;

[0132] The statistics module is used to count the number of wavelength points in which the absorption intensity curve C continuously exceeds a preset threshold T within the range of the methane characteristic absorption peak.

[0133] The judgment module is used to determine the presence of methane gas when the number of wavelength points that continuously exceed the threshold T reaches a set number K.

[0134] In one specific embodiment, it further includes: an environmental compensation module, which has a built-in temperature and humidity sensor and a threshold adaptive adjustment algorithm, for performing:

[0135] When the relative humidity of the environment is ≥80%, T should be increased by 10% to 20%.

[0136] When the relative humidity is ≤30%, adjust temperature (T) by 5% to 10%.

[0137] By making the above adjustments, the threshold T in this application can be adaptively adjusted according to changes in ambient relative humidity, thereby reducing the false alarm rate of methane leak detection.

[0138] In one specific embodiment, it further includes: a historical data comparison module for storing a typical water vapor interference pattern feature library, wherein the data in the typical water vapor interference pattern feature library includes: humidity value and a threshold T corresponding to the current humidity value, and the preset threshold T is adjusted according to the humidity value detected by the humidity sensor.

[0139] In this embodiment, different humidity environments are simulated in the laboratory to record the fingerprint characteristics of water vapor interference; the current ambient humidity is obtained through a humidity sensor; the threshold T corresponding to the current ambient humidity is matched in the feature library of typical water vapor interference patterns, and the matched threshold T is used as the preset threshold T.

[0140] For example: Scenario: Steam is generated when boiling water in the kitchen. At this time, the humidity will rise sharply (e.g., the humidity increases by 60%). The light intensity signal fluctuates due to the interference of the steam. The interference features under 60% humidity are retrieved from the feature library. It is found that the current signal fluctuation matches the "high humidity water vapor interference" mode. The threshold is automatically increased from 50 to 65. At this time, the spectral analysis and elimination method of water vapor interference of laser sensor provided in this application (steps S101-S105) can be used to detect methane leaks under the condition of a preset threshold T of 65, thereby reducing the false alarm rate of methane leak detection.

[0141] It is evident that the solution proposed in this application can avoid misidentifying steam as methane leakage. Furthermore, compared to the fixed threshold solution, the technical solution proposed in this application reduces the false alarm rate for methane leakage detection.

[0142] Experimental example (see Figure 6 , Figure 7 )

[0143] Figure 6 The data shows the methane concentration measured by the sensor in an environment where methane gas and water vapor are mixed. There are a total of 3,527 continuous detection concentration data points, of which 3,507 are valid and 20 are invalid. Therefore, the percentage of valid measured concentrations is 99.4%.

[0144] Figure 7The data results of methane concentration measured by the sensor under a simple water vapor interference environment are as follows: There are a total of 2404 continuous detection concentration data. 2404 data points correctly identified no methane gas concentration (i.e., the correct concentration measurement value is 0% LEL). There are 0 false alarms of concentration values. Therefore, the water vapor interference elimination rate is 100%.

[0145] According to one embodiment of the present invention, a server is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute a method for managing credit system data.

[0146] According to one embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored, wherein the computer program instructions, when executed by a processor, implement a method for managing credit system data.

[0147] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0148] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0149] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for spectral analysis and elimination of water vapor interference in laser sensors, characterized in that, include: Scan the spectral region containing the characteristic absorption peak of methane to obtain the original light intensity signal curve A, which consists of N discrete wavelength points; Select M wavelength points outside the methane characteristic absorption peak range in the original light intensity signal curve A, and generate a reference signal curve B using a linear regression algorithm; The absorption intensity curve C is obtained by performing point-by-point difference ratio calculation on the original light intensity signal curve A and the reference signal curve B; The number of wavelength points in the absorption intensity curve C where the absorption intensity value continuously exceeds a preset threshold T within the range of the methane characteristic absorption peak is counted. When the number of wavelength points that continuously exceed the threshold T reaches a set number K, it is determined that methane gas is present; The step involves counting the number of wavelength points in the absorption intensity curve C where the absorption intensity value continuously exceeds a preset threshold T within the range of the methane characteristic absorption peak. The preset threshold T is a dynamically adjustable parameter. After setting the initial default value, it adaptively adjusts based on the ambient humidity sensor data. The rules for adaptive adjustment based on ambient humidity sensor data are as follows: When the relative humidity is ≥80%, adjust temperature (T) by 10% to 20%. When the relative humidity is ≤30%, adjust temperature (T) by 5% to 10%. The method further includes: When the condition is met for the first time, K consecutive wavelength points are detected, and the concentration of methane gas is calculated. If the methane concentration values ​​obtained from P consecutive calculations of methane gas concentration are all greater than zero and the fluctuation range is ≤15%, then a methane alarm is confirmed. The step involves performing a point-by-point difference ratio calculation between the original light intensity signal curve A and the reference signal curve B to obtain the absorption intensity curve C. The absorption intensity at the i-th wavelength point is C i : 。 2. The method for spectral analysis and elimination of water vapor interference in laser sensors according to claim 1, characterized in that, In the step of generating the baseline signal curve B using a linear regression algorithm, specifically the least squares method, the slope k and intercept b of the baseline signal curve B are calculated using the following formulas: Where x is the wavelength point number, y is the corresponding light intensity value, and N≥20; Using the above formula and the known acquired data, the linear formula for the reference signal curve B can be obtained: y = kx + b.

3. The method for spectral analysis and elimination of water vapor interference in laser sensors according to claim 1, characterized in that, A tunable diode laser is used and controlled to scan a spectral region containing the characteristic absorption peak of methane to obtain the original light intensity signal curve A, which consists of N discrete wavelength points.

4. A spectral analysis and elimination system for water vapor interference in a laser sensor, characterized in that, include: The original light intensity signal curve A generation module is used to scan the spectral range containing the characteristic absorption peak of methane to obtain the original light intensity signal curve A composed of N discrete wavelength points; The reference signal curve B generation module is used to select M wavelength points in the original light intensity signal curve A that are outside the range of the methane characteristic absorption peak, and generate the reference signal curve B through a linear regression algorithm. The calculation module is used to perform point-by-point difference ratio calculation on the original light intensity signal curve A and the reference signal curve B to obtain the absorption intensity curve C, wherein; The statistics module is used to count the number of wavelength points in which the absorption intensity curve C continuously exceeds a preset threshold T within the range of the methane characteristic absorption peak. The judgment module is used to determine the presence of methane gas when the number of wavelength points that continuously exceed the threshold T reaches a set number K; when the condition is met for the first time, the concentration of methane gas is calculated; if the methane concentration values ​​obtained from P consecutive calculations are all greater than zero and the fluctuation range is ≤15%, then a methane alarm is confirmed. Among the statistics, the number of wavelength points in which the absorption intensity curve C continuously exceeds a preset threshold T within the range of methane characteristic absorption peaks is included. The preset threshold T is a dynamically adjustable parameter. After setting the initial default value, it adaptively adjusts based on the ambient humidity sensor data. The rules for adaptive adjustment based on ambient humidity sensor data are as follows: When the relative humidity is ≥80%, adjust temperature (T) by 10% to 20%. When the relative humidity is ≤30%, adjust temperature (T) by 5% to 10%. By performing point-by-point difference scaling on the original light intensity signal curve A and the reference signal curve B, the absorption intensity curve C is obtained. The absorption intensity at the i-th wavelength point is C i : 。 5. The spectral analysis and elimination system for water vapor interference in laser sensors according to claim 4, characterized in that, Also includes: The environmental compensation module, with its built-in temperature and humidity sensor and threshold adaptive adjustment algorithm, is used to perform: When the relative humidity is ≥80%, adjust temperature (T) by 10% to 20%. When the relative humidity is ≤30%, T should be reduced by 5% to 10%.

6. The spectral analysis and elimination system for water vapor interference in laser sensors according to claim 5, characterized in that, Also includes: The historical data comparison module is used to store a feature library of typical water vapor interference patterns. The data in the feature library of typical water vapor interference patterns includes: humidity value and threshold T corresponding to the current humidity value. The preset threshold T is adjusted according to the humidity value detected by the humidity sensor.

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