Foam extinguishing agent quality detection method and system and storage medium

Abstract

The invention discloses a foam extinguishing agent quality detection method and system and a storage medium. The foam extinguishing agent quality detection method comprises the following steps: S1, selecting a plurality of qualified samples and to-be-detected samples of a foam extinguishing agent, collecting near infrared absorption spectrums of the samples, and processing the near infrared absorption spectrums to obtain pretreatment spectrums; s2, calculating the overall absorbance difference of all the samples according to the pretreatment spectrum, screening qualified samples meeting requirements by adopting a Glubbs test method on the basis of the overall absorbance difference, and taking the pretreatment spectrum of the qualified samples as a verification spectrum set; s3, independently adding the near infrared absorption spectrum of each to-be-detected sample into the verification spectrum set, and calculating the Grubbs statistical magnitude of the to-be-detected sample; s4, calculating the matching degree of the to-be-detected sample and the qualified sample according to the maximum Glubbs statistical magnitude of the qualified sample screened in the step S2 and the Glubbs statistical magnitude of the to-be-detected sample in the step S3, and judging that the quality of the to-be-detected sample is qualified if a set condition is met; whether the foam extinguishing agent is qualified or not can be rapidly and accurately detected.

Description

Technical Field

[0001] The invention relates to foam fire extinguishing agent quality detection, in particular to a foam fire extinguishing agent quality detection method, system and storage medium. Background Art

[0002] Fixed fire extinguishing systems are the last safety barrier for large oil-filled equipment such as transformers. The quality of the foam fire extinguishing agent used in the fire extinguishing system directly determines the system's fire extinguishing efficiency, which is crucial to the safe operation of substations (converter stations). However, the national standard testing method for foam fire extinguishing agents generally requires a 2-month testing cycle, which is difficult to match the progress of on-site projects, and even the fire extinguishing agent has been put into service with "diseases". At the same time, the performance degradation mechanism of foam fire extinguishing agents is still unclear, and environmental factors such as ultraviolet rays, high temperature, and humidity often cause the backup fire extinguishing agent to fail prematurely.

[0003] Although there have been studies on the use of near-infrared spectroscopy technology to detect the quality of fire extinguishing agents, the difference in the quality of foam fire extinguishing agents in near-infrared spectral spectra is relatively small. Directly judging whether the foam fire extinguishing agent is qualified by the difference in near-infrared spectral spectra often leads to many unqualified fire extinguishing agent samples being misjudged as qualified samples, thus causing uncontrollable hazards. Summary of the invention

[0004] Purpose of the invention: The purpose of the present invention is to provide a foam fire extinguishing agent quality detection method, system and storage medium that can quickly and accurately detect whether the quality of the foam fire extinguishing agent is qualified.

[0005] Technical solution: The foam fire extinguishing agent quality detection method of the present invention comprises the following steps:

[0006] S1, selecting a plurality of qualified samples of foam fire extinguishing agent and samples to be tested, collecting their near infrared absorption spectra and performing smoothing to obtain pre-processed spectra;

[0007] S2. Calculate the overall absorbance difference of all samples based on the preprocessed spectra, and use the Grubbs test method to screen out qualified samples that meet the requirements based on the overall absorbance difference, and use their preprocessed spectra as the verification spectrum set;

[0008] S3, adding the near-infrared absorption spectrum of each sample to be tested to the verification spectrum set to calculate its overall absorbance difference, and using the average and standard deviation of the overall absorbance difference of all qualified samples in the original verification spectrum set to calculate the Grubbs statistic of the sample to be tested;

[0009] S4. Calculate the matching degree between the sample to be tested and the qualified sample according to the maximum Grubbs statistic of the qualified sample after screening in step S2 and the Grubbs statistic of the sample to be tested in step S3, and judge the sample to be tested whose matching degree meets the set conditions as qualified.

[0010] Based on the above technical method, steps S1 and S2 collect the near-infrared absorption spectrum of the foam fire extinguishing agent sample and calculate the overall absorbance difference of all samples. The overall absorbance difference can reflect the fluctuation of the absorbance of the sample at each wavelength, which is equivalent to a characteristic value of the spectrum of each sample; the qualified samples that meet the requirements are screened out by the Grubbs test method based on the overall absorbance difference, and the qualified samples with obviously abnormal overall absorbance difference in the qualified samples can be eliminated, so that the overall absorbance difference of all qualified samples corresponding to the verification spectrum set, that is, the spectral characteristics, are within a set reasonable range, which ensures that the quality of these qualified samples is not much different, so that the quality of the sample to be tested can be judged by the degree of deviation between the sample to be tested and all qualified samples in the verification spectrum set in the subsequent step; step S3 adds each sample to the verification spectrum set as a new whole to calculate the overall absorbance difference of the sample to be tested in this new whole, and adds the average value and standard deviation of the overall absorbance difference of all qualified samples in the verification spectrum set to calculate the Grubbs statistic of the sample to be tested, which reflects the E of the sample to be tested. j Compared with the average value of the overall difference in absorbance of all qualified samples in the verification spectrum set, the degree of deviation is equivalent to the degree of deviation of the spectral characteristics of the sample to be tested compared with the average value of the spectral characteristics of all qualified samples in the verification spectrum set, and the spectral characteristics correspond to the mass of the foam fire extinguishing agent, which reflects the degree of deviation between the mass of the sample to be tested and the average mass of the qualified samples. Similarly, the Grubbs statistic of each qualified sample in the verification spectrum set actually reflects the degree of deviation between the mass of the qualified sample and the average mass of all qualified samples; step S4 calculates the matching value according to the maximum Grubbs statistic of all qualified samples in the verification spectrum set and the Grubbs statistic of the sample to be tested in step S3, which is equivalent to calculating the degree of deviation between the mass of the sample to be tested and the average mass of all qualified samples in the verification spectrum set and the maximum degree of deviation between the mass of the qualified samples in the verification spectrum set and the average value. If the two match and meet the requirements, that is, the difference is not large, it can be determined that the mass of the sample to be tested is close enough to the mass of the qualified samples in the verification spectrum set, and the quality of the sample to be tested is qualified.

[0011] The above method can determine whether the quality of the sample to be tested is qualified only by collecting the near-infrared absorption spectrum of the sample and performing relevant calculations. The process is fast and simple, and there is no need to wait for a long time. In addition, the difference in the sample spectra is not directly compared, but the Grubbs statistic of the qualified sample and the sample to be tested is calculated, and then the matching degree of the two is calculated. The matching degree is used to determine whether the quality of the sample to be tested is qualified. Compared with the intuitive comparison of the spectra, it is more accurate and reliable, because the intuitive comparison of the spectra alone cannot make an effective difference judgment from the overall perspective, and it is easy to miss small differences and easily make misjudgments. In general, this method can quickly and accurately detect whether the quality of the foam fire extinguishing agent is qualified.

[0012] Preferably, in step S1, the near-infrared absorption spectrum of each sample is repeatedly collected multiple times, and the spectra collected multiple times are averaged and smoothed to obtain a pre-processed spectrum of the sample.

[0013] The near-infrared absorption spectrum of each sample is collected multiple times and then averaged to obtain a spectrum. Compared with a single collection, the error existing in a single collection can be reduced through averaging.

[0014] Preferably, in step S1, a Savitzky-Golay filter, an Adjacent Averaging algorithm or an FFT Filtering algorithm is used to smooth the near-infrared absorption spectrum.

[0015] Preferably, the overall absorbance difference in step S2 is calculated according to the following formula:

[0016]

[0017] in, is the overall difference in absorbance of the ith sample, represents the absorbance value of the i-th sample at the k-th wavelength, For calculation The average absorbance value of all samples in the sample set at the kth wavelength, where n is the total number of wavelengths of light used for measurement.

[0018] Preferably, the step S2 of screening out qualified samples that meet the requirements by using the Grubbs test method based on the overall absorbance difference comprises the following sub-steps:

[0019] S2.1. Calculate the Grubbs statistic of qualified samples according to the following formula:

[0020]

[0021] in, is the Grubbs statistic of the i-th qualified sample, is the overall absorbance difference of the i-th qualified sample in the set of all qualified samples, is the average value of the overall absorbance difference of all qualified samples, is the standard deviation of the overall absorbance difference of all qualified samples;

[0022] S2.2. Confirm whether it exists Greater than critical value If there are any, remove these qualified samples and return to step S2.1 until there are no more Greater than critical value .

[0023] Preferably, the Grubbs statistic of the sample to be tested in step S3 is calculated according to the following formula:

[0024]

[0025] in, is the Grubbs statistic of the jth sample to be tested, is the overall absorbance difference of the jth sample to be tested in the verification spectrum set.

[0026] Preferably, the matching degree between the pre-processed spectrum of the sample to be tested and the verification spectrum set in step S4 is calculated according to the following formula:

[0027]

[0028] in, is the matching degree between the preprocessed spectrum of the jth sample to be tested and the verification spectrum set, ;

[0029] when The sample to be tested is judged as unqualified when When the sample is determined to need further testing, The sample to be tested is judged to be qualified.

[0030] Preferably, the cuvette used for collecting the spectrum in step S1 is made of quartz or optical glass.

[0031] Preferably, the spectrum acquisition wavelength range in step S1 is 800-3300 nm.

[0032] The foam fire extinguishing agent quality detection system of the present invention comprises:

[0033] Spectrum acquisition and processing module: used to collect near-infrared absorption spectra of multiple qualified samples and samples to be tested of foam fire extinguishing agent and perform smoothing to obtain pre-processed spectra;

[0034] Spectral screening module: used to calculate the overall absorbance difference of all samples based on the pre-processed spectra, and use the Grubbs test method to screen out qualified samples that meet the requirements and use their pre-processed spectra as the verification spectrum set;

[0035] Grubbs statistic calculation module for samples to be tested: used to add each sample to be tested to the verification spectrum set to calculate its overall absorbance difference, and use the average value and standard deviation of the overall absorbance difference of all qualified samples in the original verification spectrum set to calculate the Grubbs statistic of the sample to be tested;

[0036] Detection module: used to calculate the matching degree between the tested sample and the qualified sample by using the maximum Grubbs statistic of the qualified samples after screening by the spectral screening module and the Grubbs statistic of the tested sample in step S3, and judge the tested sample whose matching degree meets the set conditions as qualified.

[0037] The computer-readable storage medium storing one or more programs of the present invention includes instructions, and when the instructions are executed by a computing device, the computing device executes any one of the above methods.

[0038] Beneficial effect: Compared with the prior art, the present invention has the following significant effects: by collecting the near-infrared absorption spectrum of the sample, calculating the overall difference in the sample absorbance is equivalent to extracting the characteristic value of the entire spectrum, and then screening the sample according to the overall difference and calculating the matching degree to judge whether the quality of the sample to be tested is qualified. The whole process is fast and simple, and compared with directly comparing the spectra, the result determined by the overall characteristics extracted from the spectrum is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a spectrum comparison diagram of the sample to be tested and the qualified sample after screening in Example 1;

[0040] Figure 2 This is a spectrum comparison diagram of the sample to be tested and the qualified samples after screening in Example 2. DETAILED DESCRIPTION

[0041] As shown in the figure, the foam fire extinguishing agent quality detection method of the present invention comprises the following steps:

[0042] S1, selecting a plurality of qualified samples of foam fire extinguishing agent and samples to be tested, collecting their near infrared absorption spectra and performing smoothing to obtain pre-processed spectra;

[0043] The cuvette used for collecting spectra is made of quartz or optical glass, and the spectrum collection wavelength range is 800~3300nm. The wavelength range does not affect the detection effect of this method, and other wavelength ranges can be used for collection according to actual needs.

[0044] The near-infrared absorption spectrum of each sample is repeatedly collected multiple times, and the spectra collected multiple times are averaged and smoothed to obtain the pre-processed spectrum of the sample; the near-infrared absorption spectrum is smoothed using a Savitzky-Golay filter, an Adjacent Averaging algorithm, or an FFT Filtering algorithm.

[0045] S2. Calculate the overall absorbance difference of all samples based on the preprocessed spectra, and use the Grubbs test method to screen out qualified samples that meet the requirements based on the overall absorbance difference, and use their preprocessed spectra as the verification spectrum set;

[0046] The overall difference in absorbance is calculated according to the following formula

[0047]

[0048] in is the overall difference in absorbance of the ith sample, represents the absorbance value of the i-th sample at the k-th wavelength, For calculation The average absorbance value of all samples in the sample set at the kth wavelength, n is the total number of wavelengths of light used for measurement; here is used to calculate The sample set may contain only qualified samples, or a mixture of qualified samples and samples to be tested.

[0049] The overall difference in absorbance reflects the overall deviation of the absorbance of a sample from the average absorbance of all other samples under all wavelengths of light. It is equivalent to a characteristic value of the near-infrared absorption spectrum of a sample, and reflects the overall characteristics of the spectrum through numerical values.

[0050] The Grubbs test method is used to screen out qualified samples that meet the requirements based on the overall difference in absorbance, including the following steps:

[0051] S2.1. Calculate the Grubbs statistic of qualified samples according to the following formula:

[0052]

[0053] in, is the Grubbs statistic of the i-th qualified sample, is the overall absorbance difference of the i-th qualified sample in the set consisting of all qualified samples. The sample set used to calculate the overall absorbance difference here is the set consisting of all qualified samples; is the average value of the overall absorbance difference of all qualified samples, is the standard deviation of the overall absorbance difference of all qualified samples;

[0054] S2.2. Confirm whether it exists Greater than critical value If there are any, remove these qualified samples and return to step S2.1 until there are no more Greater than critical value After returning to step S2.1, the sample set used to calculate the overall absorbance difference should remove the qualified samples that have been eliminated to form a new sample set for calculation, including and All calculations are performed based on a new set of samples.

[0055] The near-infrared absorption spectra of qualified samples screened in steps S2.1 and S2.2 are constructed into a verification spectrum set.

[0056] S3, adding the near-infrared absorption spectrum of each sample to be tested to the verification spectrum set to calculate its overall absorbance difference, and using the average and standard deviation of the overall absorbance difference of all qualified samples in the original verification spectrum set to calculate the Grubbs statistic of the sample to be tested;

[0057] To better illustrate step S3, assuming that the near-infrared absorption spectrum of the jth sample to be tested is added to the verification spectrum set alone is equivalent to constructing an independent sample set J for the jth sample to be tested. Set J consists of the near-infrared absorption spectra of all qualified samples in the verification spectrum set and the jth sample to be tested. Set J can be used to calculate the overall difference in absorbance of the jth sample to be tested. .

[0058] The Grubbs statistic of the sample to be tested is calculated using the average and standard deviation of the overall absorbance difference of all qualified samples in the original validation spectrum set. This means that when calculating the Grubbs statistic of the jth sample to be tested, the average and standard deviation of the overall absorbance difference of all qualified samples in the original validation spectrum set are still used. and To calculate the Grubbs statistic of the sample to be tested, instead of using the newly constructed set J to calculate the new and , so as to reflect more realistically With the validation spectrum set degree of deviation.

[0059] Specifically, the Grubbs statistic of the sample to be tested is calculated according to the following formula:

[0060]

[0061] in, is the Grubbs statistic of the jth sample to be tested, is the overall difference in absorbance of the jth sample to be tested in the verification spectrum set, that is, the overall difference in absorbance of the jth sample to be tested in set J.

[0062] S4. Calculate the matching degree between the sample to be tested and the qualified sample according to the maximum Grubbs statistic of the qualified sample after screening in step S2 and the Grubbs statistic of the sample to be tested in step S3, and judge the sample to be tested whose matching degree meets the set conditions as qualified.

[0063] The matching degree between the pre-processed spectrum of the sample to be tested and the verification spectrum set is calculated according to the following formula:

[0064]

[0065] in, is the matching degree between the preprocessed spectrum of the jth sample to be tested and the verification spectrum set, , that is This is twice the maximum overall difference in absorbance of the qualified samples in the validation spectral set.

[0066] when The sample to be tested is judged as unqualified when When the sample is determined to need further testing, The sample to be tested is judged to be qualified.

[0067] The foam fire extinguishing agent quality detection system of the present invention comprises:

[0068] Spectrum acquisition and processing module: used to collect near-infrared absorption spectra of multiple qualified samples and samples to be tested of foam fire extinguishing agent and perform smoothing to obtain pre-processed spectra;

[0069] Spectral screening module: used to calculate the overall absorbance difference of all samples based on the pre-processed spectra, and use the Grubbs test method to screen out qualified samples that meet the requirements and use their pre-processed spectra as the verification spectrum set;

[0070] Grubbs statistic calculation module for samples to be tested: used to add each sample to be tested to the verification spectrum set separately, and calculate the Grubbs statistic of the sample to be tested using the average value and standard deviation of the overall difference of absorbance of all qualified samples in the verification spectrum set;

[0071] Detection module: used to calculate the matching degree between the sample to be tested and the qualified sample by using the maximum Grubbs statistic of the qualified sample after screening by the spectral screening module and the Grubbs statistic of the sample to be tested in step S3. If the matching degree meets the set conditions, the sample to be tested is judged to be qualified.

[0072] The computer-readable storage medium storing one or more programs of the present invention includes instructions, and when the instructions are executed by a computing device, the computing device executes the above method.

[0073] In order to better illustrate the method of the present invention, two specific embodiments are described below:

[0074] Example 1: Establishment of the optimal validation set spectral library based on five 6% AFFF qualified samples and quality evaluation of unknown samples to be tested

[0075] The specific steps are as follows:

[0076] (1) Using an absorption cell with an optical path of 0.1 cm, a near-infrared spectrometer was used to test the near-infrared absorption spectrum of the sample between 1200 nm and 2400 nm. The scanning interval was set to 1.0 nm, with a total of 1201 wavelengths. Each sample was sampled three times (to reduce the error caused by internal inhomogeneity of the sample), and three near-infrared absorption spectra were tested continuously for each sample. Finally, all spectra were averaged and converted into one spectrum, and the original spectrum was smoothed using the Savitzky-Golay convolution smoothing method. The window points were set to 10 and the polynomial order was set to 2 to obtain the preprocessed standard spectrum of the qualified sample. Using the same test and preprocessing conditions as step 1, the preprocessed spectra of 5 6% AFFF qualified samples were obtained and used as the initial verification set spectra.

[0077] (2) Calculate the overall difference in near-infrared spectral absorbance of the initial validation set samples , the calculation model is as follows:

[0078]

[0079] Where i is a natural number in [1,+∞), is the overall difference of near-infrared spectral absorbance of the i-th qualified sample, is the absorbance value of the i-th qualified sample under the k-th wavelength condition, is the average absorbance value of all qualified samples in the initial validation set under the kth wavelength condition, n is the total number of wavelengths of light used for measurement, n=1201.

[0080] After calculation, the overall difference in near-infrared spectral absorbance of the five qualified samples of 6% AFFF is As shown in Table 1:

[0081] Table 1 Overall difference in near infrared spectral absorbance of 5 qualified samples of 6% AFFF

[0082] Sample No. 1 2 3 4 5 <![CDATA[E i ]]> 0.2717 0.3698 0.3269 0.3669 0.4223

[0083] (3) Calculate the initial validation set samples The Grubbs statistic is calculated as follows:

[0084]

[0085] in, is the Grubbs statistic of the i-th qualified sample, is the average value of the overall difference in near-infrared spectral absorbance of all qualified samples, and S is the standard deviation of the overall difference in near-infrared spectral absorbance of all qualified samples.

[0086] Because in this embodiment The sample set used only contains qualified samples, so there is no To be distinguished.

[0087] The calculation formula of S is as follows:

[0088]

[0089] Where N is the total number of qualified samples in the initial validation set.

[0090] After calculation, 6% AFFF qualified samples of 5 initial validation sets Grubbs statistic As shown in Table 2:

[0091] Table 2 5 initial validation set 6% AFFF qualified samples Grubbs statistic

[0092] Sample No. 1 2 3 4 5 <![CDATA[G i ]]> 1.5890 0.3654 0.4908 0.3077 1.4101

[0093] After analysis, 5 qualified samples The Grubbs statistic is less than the critical value , , so no further optimization is required and it can be used as the optimal validation spectrum set;

[0094] The same near-infrared spectrum test and pretreatment method as the qualified samples were used to obtain the pre-processed near-infrared absorption spectrum of the sample 6 to be tested. The near-infrared absorption spectrum of the sample 6 to be tested was added to the optimal verification spectrum set, and the overall difference of the near-infrared spectrum absorbance of the sample 6 to be tested was calculated using the same calculation method as the initial verification set samples. To be replaced by the average absorbance value of all samples including the sample to be tested under the kth wavelength condition, we finally get .

[0095] Then the Grubbs statistic of the sample to be tested is calculated according to the following formula:

[0096]

[0097] in, is the average value of the overall difference in near-infrared spectral absorbance of all qualified samples in the optimal verification spectral set, and S is the standard deviation of the overall difference in near-infrared spectral absorbance of all qualified samples in the optimal verification spectral set. .

[0098] (5) Calculate the consistency spectrum threshold of the optimal verification spectrum set , the calculation model is as follows:

[0099] , Max (G i ) = 1.5890,

[0100] After calculation, = 3.1780;

[0101] (6) Calculate the matching degree between the near infrared spectrum of the sample 6 to be tested and the spectrum of the optimal validation set. The calculation model is as follows:

[0102]

[0103] After calculation, the matching degree R of the sample 6 to be tested is 54.3186, which is much less than 90%, so the sample 6 to be tested is judged as an unqualified sample. Figure 1 Directly comparing the near-infrared absorption spectrum of the sample 6 to be tested with the spectra of the five validation set samples, there are obvious differences between the two spectra.

[0104] Example 2: Establishment of the optimal validation set spectral library based on 36 3% AFFF qualified samples and quality evaluation of unknown samples to be tested

[0105] The specific steps include:

[0106] (1) Using an absorption cell with an optical path of 0.2 cm, a near-infrared spectrometer was used to test the near-infrared absorption spectrum of the sample between 1200 nm and 2400 nm. The scanning interval was set to 1.0 nm, with a total of 1201 wavelengths. The near-infrared spectrum of each sample was collected three times, and three spectra were collected continuously each time. Finally, all spectra were averaged and converted into one spectrum. The original spectrum was smoothed using the Savitzky-Golay convolution smoothing method. The window points were set to 10 and the polynomial order was set to 2 to obtain the preprocessed standard spectrum of the qualified sample. Using the same test and preprocessing conditions as step 1, the preprocessed spectra of 36 3% AFFF qualified samples were obtained and used as the initial verification set spectrum.

[0107] (2) Calculate the overall difference in near-infrared spectral absorbance of the initial validation set samples , the calculation model is as follows:

[0108]

[0109] Where i is a natural number in [1,+∞), is the overall difference of near-infrared spectral absorbance of the i-th qualified sample, is the absorbance value of the i-th qualified sample under the k-th wavelength condition, is the average absorbance value of all qualified samples in the initial validation set under the kth wavelength condition, n is the total number of wavelengths of light used for measurement, n=1201.

[0110] After calculation, the overall difference in near-infrared spectral absorbance of 39 qualified samples of 3% AFFF is As shown in Table 3:

[0111] Table 3 Overall difference in near infrared spectral absorbance of 39 qualified samples of 3% AFFF

[0112] Sample No. <![CDATA[E i ]]> Sample No. <![CDATA[E i ]]> Sample No. <![CDATA[E i ]]> Sample No. <![CDATA[E i ]]> 1 0.2197 11 0.1521 21 0.1396 31 0.1419 2 0.2321 12 0.1786 22 0.0841 32 0.1533 3 0.1832 13 0.0985 23 0.1289 33 0.1425 4 0.2046 14 0.1704 24 0.1673 34 0.1919 5 0.162 15 0.1187 25 0.1271 35 0.1488 6 0.1983 16 0.1532 26 0.1752 36 0.1896 7 0.1863 17 0.1869 27 0.1125 37 0.1932 8 0.1339 18 0.1082 28 0.1433 38 0.1585 9 0.1358 19 0.1614 29 0.1102 39 0.1843 10 0.1286 20 0.1246 30 0.1539

[0113] (3) Calculate the initial validation set samples The Grubbs statistic is calculated as follows:

[0114]

[0115] in, is the Grubbs statistic of the i-th qualified sample, is the average value of the overall difference in near-infrared spectral absorbance of all qualified samples, and S is the standard deviation of the overall difference in near-infrared spectral absorbance of all qualified samples.

[0116] The calculation formula of S is as follows:

[0117]

[0118] Where N is the total number of qualified samples in the initial validation set.

[0119] After calculating 39 initial validation set 3%AFFF qualified samples Grubbs statistic As shown in Table 4:

[0120] Table 4 39 initial validation set 3% AFFF qualified samples Grubbs statistic

[0121] Sample No. <![CDATA[G i ]]> Sample No. <![CDATA[G i ]]> Sample No. <![CDATA[G i ]]> Sample No. <![CDATA[G i ]]> 1 1.9009 11 0.1152 21 0.4897 31 0.422 2 2.2719 12 0.6756 22 2.1468 32 0.0815 3 0.812 13 1.7179 23 0.8103 33 0.4045 4 1.4506 14 0.4311 24 0.3368 34 1.072 5 0.1802 15 1.1128 25 0.862 35 0.2155 6 1.2631 16 0.0831 26 0.5744 36 1.0024 7 0.9036 17 0.9231 27 1.2988 37 1.1099 8 0.6594 18 1.4266 28 0.3801 38 0.0754 9 0.6041 19 0.1609 29 1.3684 39 0.8455 10 0.8189 20 0.9385 30 0.0632

[0122] After analysis, among the 39 qualified samples, samples No. 2 and No. 22 Grubbs statistic All of them are greater than the critical value of 2.0 and should be discarded; after discarding samples No. 2 and No. 22, the remaining 37 qualified sample validation sets are further optimized;

[0123] After calculation, the overall difference in near-infrared spectral absorbance of 37 qualified samples of 3% AFFF is As shown in Table 5:

[0124] Table 5 Overall difference in near infrared spectral absorbance of 37 qualified samples of 3% AFFF

[0125] Sample No. <![CDATA[E i ]]> Sample No. <![CDATA[E i ]]> Sample No. <![CDATA[E i ]]> Sample No. <![CDATA[E i ]]> 1 0.2226 11 0.1555 21 0.1413 31 0.1411 2 Abandoned 12 0.1802 22 Abandoned 32 0.1498 3 0.1867 13 0.0993 23 0.1273 33 0.141 4 0.2073 14 0.1699 24 0.1646 34 0.1886 5 0.1657 15 0.1213 25 0.1269 35 0.1474 6 0.2011 16 0.1527 26 0.1727 36 0.1872 7 0.1899 17 0.1875 27 0.1099 37 0.189 8 0.1365 18 0.1086 28 0.1433 38 0.1557 9 0.1383 19 0.1604 29 0.1079 39 0.1807 10 0.1298 20 0.1243 30 0.1505

[0126] After calculating 37 initial validation set 3%AFFF qualified samples Grubbs statistic As shown in Table 6:

[0127] Table 6 37 initial validation set 3%AFFF qualified samples Grubbs statistic

[0128] Sample No. <![CDATA[G i ]]> Sample No. <![CDATA[G i ]]> Sample No. <![CDATA[G i ]]> Sample No. <![CDATA[G i ]]> 1 2.2283 11 0.0093 21 0.4821 31 0.4879 2 Abandoned 12 0.8163 22 Abandoned 32 0.1981 3 1.0332 13 1.8825 23 0.9468 33 0.493 4 1.7166 14 0.4718 24 0.2961 34 1.0939 5 0.3328 15 1.1492 25 0.9604 35 0.2798 6 1.5131 16 0.1006 26 0.5663 36 1.0481 7 1.1396 17 1.0577 27 1.5276 37 1.1092 8 0.6408 18 1.5707 28 0.416 38 0.003 9 0.5806 19 0.1566 29 1.5958 39 0.8309 10 0.8633 20 1.0472 30 0.176

[0129] After analysis, among the 37 qualified samples, sample No. Grubbs statistic If the sample is greater than the critical value of 2.0, it should be discarded. After discarding sample No. 1, repeat operations (2) and (3) to further optimize the remaining 36 qualified sample validation sets.

[0130] After calculation, the overall difference in near-infrared spectral absorbance of the remaining 36 3% AFFF qualified samples is As shown in Table 7:

[0131] Table 7 Overall difference in near infrared spectral absorbance of 36 qualified samples of 3% AFFF

[0132] Sample No. <![CDATA[E i ]]> Sample No. <![CDATA[E i ]]> Sample No. <![CDATA[E i ]]> Sample No. <![CDATA[E i ]]> 1 Abandoned 11 0.1578 21 0.1431 31 0.1405 2 Abandoned 12 0.1838 22 Abandoned 32 0.1474 3 0.19 13 0.1001 23 0.1258 33 0.1405 4 0.2103 14 0.1674 24 0.165 34 0.1858 5 0.1681 15 0.1234 25 0.1269 35 0.1443 6 0.2025 16 0.1548 26 0.1719 36 0.186 7 0.1927 17 0.1857 27 0.1088 37 0.1857 8 0.1384 18 0.1094 28 0.1433 38 0.1532 9 0.1399 19 0.1586 29 0.1062 39 0.1766 10 0.1303 20 0.1245 30 0.1476

[0133] After calculating 36 initial validation set 3%AFFF qualified samples Grubbs statistic As shown in Table 8:

[0134] Table 8 36 initial validation set 3%AFFF qualified samples Grubbs statistic

[0135] Sample No. <![CDATA[G i ]]> Sample No. <![CDATA[G i ]]> Sample No. <![CDATA[G i ]]> Sample No. <![CDATA[G i ]]> 1 Abandoned 11 0.1395 21 0.3756 31 0.4694 2 Abandoned 12 1.0563 22 Abandoned 32 0.2267 3 1.2754 13 1.8945 23 0.986 33 0.4704 4 1.9931 14 0.4799 24 0.3942 34 1.1265 5 0.5034 15 1.0734 25 0.9469 35 0.3353 6 1.718 16 0.0359 26 0.6373 36 1.1358 7 1.3715 17 1.1257 27 1.5862 37 1.1258 8 0.5425 18 1.5664 28 0.3712 38 0.0203 9 0.4895 19 0.1679 29 1.6787 39 0.8048 10 0.8275 20 1.0337 30 0.2183

[0136] After analysis, 36 qualified samples Grubbs statistic All of them are less than the critical value of 2.0, so no further optimization is required, and the near-infrared spectra of the remaining 36 qualified samples are selected as the optimal verification spectrum set;

[0137] (5) The same near-infrared spectrum test and preprocessing method as the samples in the spectral set are used to obtain the preprocessed near-infrared spectrum of the sample X1 to be tested, and the same calculation method as the validation set samples is used to calculate the overall difference of the near-infrared spectrum absorbance of the sample X1 to be tested. , but in the calculation process To be replaced by the average absorbance value of all 37 samples including the sample X1 to be tested and 36 qualified samples under the kth wavelength condition, we finally get

[0138] Then the Grubbs statistic of the sample to be tested is calculated according to the following formula:

[0139]

[0140] in, is the average value of the overall difference in near-infrared spectral absorbance of all 36 qualified samples in the optimal verification spectral set, and S is the standard deviation of the overall difference in near-infrared spectral absorbance of all 36 qualified samples in the optimal verification spectral set. .

[0141] (6) Calculate the consistency spectrum threshold of the qualified sample verification spectrum set , the calculation model is as follows:

[0142] , Max(Ti) = 1.9931,

[0143] After calculation, = 3.9862;

[0144] (7) Calculate the matching degree between the near infrared spectrum of the sample X1 to be tested and the spectrum of the optimal validation set. The calculation model is as follows:

[0145]

[0146] After calculation, the matching degree of the sample X1 to be tested is R = 90.24. When the matching degree R∈[90%~95%] is between, the sample needs to be further tested for fire extinguishing performance. By directly comparing the near-infrared spectrum of the sample X1 to be tested with the spectrum of the 36 validation set grid samples, the difference between the two spectra is small. Only after the spectrum is partially enlarged can the slight difference be seen (see Appendix). Figure 2 ), it is easy to misjudge whether the sample is qualified directly by the spectrum.

[0147] The national standard method was used to analyze the various indicators of sample X1. The specific results are shown in Table 9:

[0148] Table 9 Physical indicators and fire extinguishing performance of 3% AFFF unknown sample X1

[0149] Serial number Test items Unqualified sample test data 1 density 1.056 2 Surface tension 22.0 3 Interfacial tension 3.5 4 Diffusion coefficient -0.5 5 Freezing point -10 6 Foaming multiple (11.4 L tube gun) 7.5 7 25% liquid separation time (11.4L tube gun) 3’10’’ 8 90% fire control time 2’40’’ 9 Fire extinguishing time 3' Fire not completely extinguished 10 Burning time /

[0150] The national standard analysis results show that the physical indicators "surface tension" and "diffusion coefficient" of the 3% AFFF unknown sample X1, as well as the fire extinguishing performance indicators "90% fire control time" and "fire extinguishing time" are unqualified, which is consistent with the prediction results provided by this method, indicating that this method has high accuracy.

Claims

1. A method for testing the quality of a foam fire extinguishing agent, characterized in that: The following steps are involved: S1, selecting a plurality of qualified samples of foam fire extinguishing agent and samples to be tested, collecting their near infrared absorption spectra and performing smoothing to obtain pre-processed spectra; S2. Calculate the overall absorbance difference of all samples based on the preprocessed spectra, and use the Grubbs test method to screen out qualified samples that meet the requirements based on the overall absorbance difference, and use their preprocessed spectra as the verification spectrum set; S3, adding the near-infrared absorption spectrum of each sample to be tested to the verification spectrum set to calculate its overall absorbance difference, and using the average and standard deviation of the overall absorbance difference of all qualified samples in the original verification spectrum set to calculate the Grubbs statistic of the sample to be tested; S4. Calculate the matching degree between the sample to be tested and the qualified sample according to the maximum Grubbs statistic of the qualified sample after screening in step S2 and the Grubbs statistic of the sample to be tested in step S3, and judge the sample to be tested whose matching degree meets the set conditions as qualified.

2. The foam fire extinguishing agent quality detection method according to claim 1, characterized in that: In step S1, the near-infrared absorption spectrum of each sample is repeatedly collected multiple times, and the spectra collected multiple times are averaged and smoothed to obtain a pre-processed spectrum of the sample.

3. The foam fire extinguishing agent quality detection method according to claim 1, characterized in that: In the step S1, a Savitzky-Golay filter, an Adjacent Averaging algorithm or an FFT Filtering algorithm is used to smooth the near-infrared absorption spectrum.

4. The foam fire extinguishing agent quality detection method according to claim 1, characterized in that: The overall difference in absorbance in step S2 is calculated according to the following formula in, is the overall difference in absorbance of the ith sample, represents the absorbance value of the i-th sample at the k-th wavelength, For calculation The average absorbance value of all samples in the sample set at the kth wavelength, where n is the total number of wavelengths of light used for measurement.

5. The foam fire extinguishing agent quality detection method according to claim 4, characterized in that: In step S2, the method of using the Grubbs test method to screen out qualified samples that meet the requirements based on the overall absorbance difference includes the following sub-steps: S2.

1. Calculate the Grubbs statistic of qualified samples according to the following formula: in, is the Grubbs statistic of the i-th qualified sample, is the overall absorbance difference of the i-th qualified sample in the set of all qualified samples, is the average value of the overall absorbance difference of all qualified samples, is the standard deviation of the overall absorbance difference of all qualified samples; S2.

2. Confirm whether it exists Greater than critical value If there are any, remove these qualified samples and return to step S2.1 until there are no more Greater than critical value .

6. The foam fire extinguishing agent quality detection method according to claim 5, characterized in that: The Grubbs statistic of the sample to be tested in step S3 is calculated according to the following formula: in, is the Grubbs statistic of the jth sample to be tested, is the overall absorbance difference of the jth sample to be tested in the verification spectrum set.

7. The foam fire extinguishing agent quality detection method according to claim 6, characterized in that: The matching degree between the pre-processed spectrum of the sample to be tested and the verification spectrum set in step S4 is calculated according to the following formula: in, is the matching degree between the jth sample to be tested and the qualified sample, ; when The sample to be tested is judged as unqualified when When the sample is determined to need further testing, The sample to be tested is judged to be qualified.

8. The foam fire extinguishing agent quality detection method according to claim 1, characterized in that: The material of the cuvette used for collecting the spectrum in step S1 is quartz or optical glass.

9. A foam fire extinguishing agent quality detection system, characterized in that: The system comprises: Spectrum acquisition and processing module: used to collect near-infrared absorption spectra of multiple qualified samples and samples to be tested of foam fire extinguishing agent and perform smoothing to obtain pre-processed spectra; Spectral screening module: used to calculate the overall absorbance difference of all samples based on the pre-processed spectra, and use the Grubbs test method to screen out qualified samples that meet the requirements and use their pre-processed spectra as the verification spectrum set; Grubbs statistic calculation module for samples to be tested: used to add each sample to be tested to the verification spectrum set to calculate its overall absorbance difference, and use the average value and standard deviation of the overall absorbance difference of all qualified samples in the original verification spectrum set to calculate the Grubbs statistic of the sample to be tested; Detection module: used to calculate the matching degree between the tested sample and the qualified sample by using the maximum Grubbs statistic of the qualified samples after screening by the spectral screening module and the Grubbs statistic of the tested sample in step S3, and judge the tested sample whose matching degree meets the set conditions as qualified.

10. A computer-readable storage medium storing one or more programs, characterized in that : The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform any of the methods according to claims 1 to 8.

Images