A mass spectrum fingerprint construction method, device, medium and equipment

Abstract

This invention provides a method, apparatus, medium, and device for constructing a mass spectrometry fingerprint spectrum, relating to the field of pollution source tracing technology. The method includes: constructing several relative abundance maps corresponding to several target devices; determining a stability coefficient for each target device based on the several relative abundance maps and a first target formula; determining a difference coefficient between the target devices based on the several relative abundance maps and a second target formula; and constructing a mass spectrometry fingerprint spectrum based on the stability coefficient, the difference coefficient, and an evaluation model. This approach considers both the stability within the device and the differences between devices, improving the accuracy of the mass spectrometry fingerprint spectrum. Furthermore, the mass spectrometry fingerprint spectrum can be used to investigate the leakage and diffusion status and distribution of pollutants, enabling timely identification of leaking units and tracing of pollution emission sources, effectively reducing the environmental and economic losses caused by pollutant emissions.

Description

Technical Field

[0001] This invention relates to the field of pollution source tracing technology, and more specifically, to a method, apparatus, medium, and equipment for constructing mass spectrometry fingerprints. Background Technology

[0002] Petrochemical enterprises are a pillar industry of the national economy, but they are also major sources of pollutant emissions. Therefore, it is particularly important to investigate the leakage, diffusion, and distribution of pollutants from petrochemical enterprises and to trace the sources of pollution emissions.

[0003] In existing technologies, only methods for establishing fingerprint spectra are disclosed, but methods for establishing mass spectrometry fingerprint spectra are not. Furthermore, the fingerprint spectra construction process only considers the similarity between contaminant sources or samples within the device, resulting in poor accuracy of the constructed fingerprint spectra.

[0004] To address the problems of existing technologies, this invention provides a method, apparatus, medium, and device for constructing mass spectrometry fingerprints. Summary of the Invention

[0005] To address the problems of existing technologies, this invention provides a method, apparatus, medium, and device for constructing mass spectrometry fingerprints, the method comprising:

[0006] Construct several relative abundance maps corresponding to several target devices;

[0007] Based on the aforementioned relative abundance maps and the first target formula, the stability coefficient corresponding to each of the target devices is determined;

[0008] Based on the aforementioned relative abundance maps and the second target formula, the difference coefficients among the target devices are determined;

[0009] Based on the stability coefficient, the difference coefficient, and the evaluation model, a mass spectrometry fingerprint is constructed.

[0010] According to one embodiment of the present invention, the horizontal axis of the plurality of relative abundance plots is the mass-to-charge ratio, and the vertical axis of the plurality of relative abundance plots is the percentage of response intensity.

[0011] According to an embodiment of the present invention, the first target formula is:

[0012]

[0013] Where SC refers to the stability coefficient; S hji The percentage of response intensity corresponding to a mass-to-charge ratio of j in the i-th relative abundance map of the target device; The value of m is the average percentage of the response intensity corresponding to a mass-to-charge ratio of j in several relative abundance maps of the target device; m is the number of relative abundance maps in the target device; n is the number of mass-to-charge ratios in the relative abundance maps; d is the number of target devices; and K is a constant.

[0014] According to one embodiment of the present invention, the second target formula is:

[0015]

[0016] Wherein, DC refers to the coefficient of difference; Refers to each target device The average value.

[0017] According to an embodiment of the present invention, the mass spectrometry fingerprint is constructed through the following steps:

[0018] Input the stability coefficient and the difference coefficient into the evaluation model, and output the evaluation coefficient;

[0019] With the evaluation coefficient as the objective, the target mass-to-charge ratio and the target response intensity percentage corresponding to the target mass-to-charge ratio are determined in the several relative abundance maps to construct the mass spectrometry fingerprint.

[0020] According to one embodiment of the present invention, the evaluation model is:

[0021] EC=α×SC+β×DC

[0022] Where EC refers to the evaluation coefficient; α and β refer to the weighting coefficients.

[0023] According to an embodiment of the present invention, the plurality of relative abundance maps are constructed by means of the following steps:

[0024] Obtain several mass spectrometry detection data corresponding to several gas samples;

[0025] Interference data in the mass spectrometry detection data are deleted, and different mass-to-charge ratios in the mass spectrometry detection data are selected to construct the relative abundance maps.

[0026] According to one embodiment of the present invention, the mass spectrometry detection data is detection data obtained by analyzing the plurality of gas samples using a mass spectrometer, including mass-to-charge ratio and response intensity.

[0027] According to one embodiment of the present invention, the interference data is mass spectrometry detection data corresponding to non-target devices among the plurality of gas samples.

[0028] According to one embodiment of the present invention, the target device is an apparatus for constructing a mass spectrometry fingerprint.

[0029] According to another aspect of the present invention, an electronic device is also provided, comprising:

[0030] A memory on which computer programs are stored;

[0031] A processor for executing the computer program in the memory to implement the steps of the method as described in any of the preceding methods.

[0032] According to another aspect of the invention, a storage medium is also provided, comprising a series of instructions for performing the steps of the method as described in any of the preceding claims.

[0033] According to another aspect of the present invention, a mass spectrometry fingerprinting apparatus is also provided, which performs the method as described in any of the preceding claims, the apparatus comprising:

[0034] The construction module is used to construct several relative abundance maps corresponding to several target devices;

[0035] The first determining module is used to determine the stability coefficient corresponding to each of the target devices based on the plurality of relative abundance maps and the first target formula;

[0036] The second determining module is used to determine the difference coefficients between the target devices based on the plurality of relative abundance maps and the second target formula;

[0037] The evaluation module is used to construct a mass spectrometry fingerprint based on the stability coefficient, the difference coefficient, and the evaluation model.

[0038] This invention provides a method, apparatus, medium, and device for constructing mass spectrometry fingerprints, which have the following advantages compared with the prior art:

[0039] This invention first constructs several relative abundance maps corresponding to several target devices. Then, based on these relative abundance maps and a first target formula, it determines the stability coefficient of the target devices. Next, based on these relative abundance maps and a second target formula, it determines the difference coefficient between the target devices. Finally, based on the stability coefficient, the difference coefficient, and the evaluation model, it constructs a mass spectrometry fingerprint spectrum. This approach considers both the stability within the device and the differences between devices, improving the accuracy of the mass spectrometry fingerprint spectrum. Furthermore, the mass spectrometry fingerprint spectrum can be used to investigate the leakage and diffusion status and distribution of pollutants, enabling timely identification of leaking units and tracing of pollution emission sources, effectively reducing the environmental and economic losses caused by pollutant emissions.

[0040] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description, claims, and drawings. Attached Figure Description

[0041] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0042] Figure 1 A flowchart of a method for constructing a mass spectrometry fingerprint spectrum according to an embodiment of the present invention is shown;

[0043] Figure 2 A block diagram of a mass spectrometry fingerprinting apparatus according to an embodiment of the present invention is shown.

[0044] In the accompanying drawings, the same parts use the same reference numerals. Also, the drawings are not drawn to scale. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0046] To effectively investigate the leakage and diffusion of pollutants within enterprise facilities, identify key leakage units, and trace the source of pollution emissions, some enterprises have deployed intelligent mobile monitoring systems that use robots as carriers and are equipped with online detection equipment such as PTR-MS, SPI-MS, and EI-MS.

[0047] However, while the single-mass spectrometer detection equipment on the intelligent mobile monitoring system can detect and provide real-time feedback on the concentration of gas components, it cannot accurately detect the qualitative and quantitative aspects of gas components. This results in low accuracy of the constructed fingerprint spectrum, failing to meet the data accuracy requirements of fingerprint spectra and the needs of enterprise mobile monitoring and traceability scenarios. Furthermore, the fingerprint spectrum construction process only considers the similarity between pollution sources or samples within the device, which is not comprehensive.

[0048] The existing technology (CN108760912A) mentions a method and application for odor pollutant tracing based on odor fingerprinting, including: A. Identifying suspected pollution sources: Based on odor complaints (including complaint occurrence time, odor quality, odor intensity, odor frequency, and duration), suspected odor pollution sources are screened around the complaint point; B. Pollution source emission sampling and analysis: Based on information such as the production process, production patterns, and emission characteristics of the pollution sources, typical odor gas samples emitted by each pollution source are collected, including exhaust port samples and fugitive emission samples. Qualitative and quantitative analysis is performed using chromatography or chromatographic mass spectrometry to obtain the material composition and concentration of the pollution source gas samples; C. Environmental odor gas sampling... Sample Analysis: Upon receiving a complaint, odorous gas samples are rapidly collected from the environment. Qualitative and quantitative analysis is performed using the same method as in step B to obtain the composition and concentration of the odorous gas samples. D. Screening of Odor Pollutants: Based on the pollutants detected from the pollution source and the odor threshold, substances with low odor thresholds—that is, substances that can cause unpleasant sensations and odor pollution at low concentrations—are screened as odor pollutants. E. Construction of Odor Fingerprint Maps for Pollution Sources and the Environment: Based on the test and analysis results of the pollution source and the odorous gas, the screened odor pollutants are used as fingerprints, and odor fingerprint maps for each pollution source and the odorous gas are established using the odor activity values ​​of the odor pollutants. F. Calculation of Similarity Using a Source Tracing Model. These steps differ from those of the present invention.

[0049] The existing technology (CN103065198A) mentions a method for fine source apportionment of atmospheric odor pollution, including: 1) determining the odor pollution source investigation area; 2) conducting odor pollution source investigations and establishing a reliable and comprehensive odor pollution information database; 3) based on the odor source investigation, analyzing the environmental impact of pollution sources under different conditions, and determining the main odor pollution sources affecting the investigation area; 4) identifying odor characteristic markers of various emission sources; 5) establishing an odor fingerprint spectrum that reflects the emission characteristics of pollution sources; 6) applying a fuzzy clustering model method for fingerprint spectrum identification, and using Mat... Lab software programming enables rapid and accurate comparison of spectra between computer-generated images; 7) Constructing a refined source apportionment model for atmospheric odor pollution sources: First, when a pollution incident occurs or a public complaint is received, characteristic odor pollutants are identified through on-site sampling and analysis. Combined with meteorological conditions at the time of pollution, potential pollution sources in the upwind direction are searched in the odor pollution source information database. Then, fingerprint spectral recognition technology is further applied to accurately screen and identify the sources of odor. Finally, an atmospheric diffusion model is used to simulate the migration and diffusion process of odor pollutants, quantifying and assessing the scope and degree of impact of the pollution sources. This differs from the steps of this invention.

[0050] The prior art (CN113514568A) mentions a method for constructing fingerprint spectra of petrochemical plants based on VOCs similarity, including: acquiring at least one gas sample from the target plant; performing GC-MS analysis on all gas samples to obtain the total ion chromatogram corresponding to each gas sample, and obtaining the compound component data of the corresponding gas sample based on the total ion chromatogram; performing principal component analysis on all gas samples based on the compound component data of the gas samples, and extracting all gas samples whose compound component data reach a preset similarity, wherein the compound component data includes the physical property values ​​of each compound, and the physical property values ​​of the compounds include the response value or concentration of the compound. The process involves several steps: First, calculating the root mean square (RMS) values ​​of the physical properties of all compounds in gas samples whose component data reach a preset similarity. Then, generating a basic gas sample spectrum for the target device based on these RMS values. Next, extracting common peaks from the total ion chromatograms of all gas samples reaching the preset similarity. Then, identifying the common peaks whose response values ​​on the total ion chromatogram rank in the top N% and whose corresponding compound concentration values ​​rank in the top n% among all compounds, as characteristic peaks. Finally, using the compounds corresponding to the extracted characteristic peaks as fingerprint compounds for the target device, and generating a fingerprint spectrum for the target device based on the fingerprint compounds and the basic gas sample spectrum. These steps differ from those of the present invention.

[0051] Example 1: In view of the above-mentioned defects of the prior art, the present invention provides a method, apparatus, medium and device for constructing mass spectrometry fingerprint spectrum. Figure 1 A flowchart of a method for constructing a mass spectrometry fingerprint according to an embodiment of the present invention is shown. The method includes:

[0052] S101, construct several relative abundance maps corresponding to several target devices respectively;

[0053] S102, based on several relative abundance maps and the first target formula, determine the stability coefficient corresponding to each target device;

[0054] S103, based on several relative abundance maps and the second target formula, determine the difference coefficient between target devices;

[0055] S104. Based on the stability coefficient, the difference coefficient, and the evaluation model, a mass spectrometry fingerprint spectrum is constructed.

[0056] This solution is applicable to petrochemical enterprises. The target device can be any device requiring the construction of a mass spectrometry fingerprint; the target device may contain a source of pollution.

[0057] This invention first constructs several relative abundance maps corresponding to several target devices. Then, based on these relative abundance maps and a first target formula, it determines the stability coefficient of the target devices. Next, based on these relative abundance maps and a second target formula, it determines the difference coefficient between the target devices. Finally, based on the stability coefficient, the difference coefficient, and the evaluation model, it constructs a mass spectrometry fingerprint spectrum. This approach considers both the stability within the device and the differences between devices, improving the accuracy of the mass spectrometry fingerprint spectrum. Furthermore, the mass spectrometry fingerprint spectrum can be used to investigate the leakage and diffusion status and distribution of pollutants, enabling timely identification of leaking units and tracing of pollution emission sources, effectively reducing the environmental and economic losses caused by pollutant emissions.

[0058] In one possible embodiment, the horizontal axis of several relative abundance plots represents the mass-to-charge ratio, and the vertical axis of several relative abundance plots represents the percentage of response intensity.

[0059] In this way, the relative abundance map uses mass-to-charge ratio and response intensity percentage as parameters, eliminating the need to obtain the types and concentrations of gas components in petrochemical plant facilities. This solves the problem of inaccurate fingerprint construction caused by inaccurate gas component types, improves the accuracy of mass spectrometry fingerprints, and meets the needs of enterprises in mobile traceability scenarios.

[0060] In one possible embodiment, the first objective formula is:

[0061]

[0062] Where SC refers to the stability coefficient; S hji The percentage of response intensity corresponding to a mass-to-charge ratio of j in the i-th relative abundance map of the target device; The value of m is the average percentage of the response intensity corresponding to a mass-to-charge ratio of j in several relative abundance maps of the target device; m is the number of relative abundance maps in the target device; n is the number of mass-to-charge ratios in the relative abundance maps; d is the number of target devices; and K is a constant.

[0063] In Equation 1, m>1, n>d, S hji and The maximum value of the square of the difference; K can be 1; the range of SC can be in the interval [0, 1]. The more stable the relative abundance maps of the target device are, the closer SC is to 1.

[0064] For example, the first objective formula can also be:

[0065]

[0066] In Equation 2, x refers to the mass-to-charge ratio in the target device when it is j, S hji and The maximum absolute value of the difference between them, where y refers to the maximum value of x corresponding to different target devices, is expressed as follows:

[0067]

[0068] After that, Replace Formula 2 The first objective formula is obtained.

[0069] In Equations 3 and 4, the percentage of response intensity S corresponding to the mass-to-charge ratio j in the m relative abundance plots corresponding to the target device h is... hji The mean percentage of response intensity corresponding to a mass-to-charge ratio of j in the m relative abundance plots The difference between The S-value of the target device h at a mass-to-charge ratio of j was characterized. hji Compared to The fluctuations.

[0070] In Equation 2, due to the different mass-to-charge ratios in the relative abundance diagram and the differences between different target devices... The calculated values ​​differ significantly. To avoid overlooking this during the calculation process... The impact of insignificant data will Multiply by the weighting coefficient y / x to ensure that the difference is approximately in the same interval.

[0071] In addition, to ensure that the stability coefficient SC and the variability coefficient DC are on the same dimension and thus comparable, the stability coefficient is normalized to ensure that the value of the stability coefficient is within the range [0, 1].

[0072] In this way, based on the different mass-to-charge ratios in the relative abundance diagram and the first target formula, the stability coefficient corresponding to the target device can be obtained. The stability of the target device is considered in the calculation of the mass spectrometry fingerprint spectrum, providing a basis for the construction of the mass spectrometry fingerprint spectrum.

[0073] In one possible embodiment, the second objective formula is:

[0074]

[0075] Wherein, DC refers to the coefficient of difference; Refers to each target device The average value.

[0076] In Equation 5, When the mass-to-charge ratio is j, and The maximum value of the square of the difference between them, DC can take the value in the interval [0, 1]. The greater the difference in the relative abundance maps between the target devices, the closer DC is to 1.

[0077] Among them, the average percentage of response intensity corresponding to the mass-to-charge ratio of j in the target device h. The average percentage of response intensity corresponding to a mass-to-charge ratio of j in d target devices The difference This characterizes the percentage of response intensity corresponding to a target device with a mass-to-charge ratio of j, compared to the mean of different target devices. The fluctuations.

[0078] Similarly, for Weighting and normalization processes are performed to ensure that the difference coefficient (DC) and stability coefficient (SC) are on the same dimension and are comparable.

[0079] Furthermore, both the stability coefficient SC and the variability coefficient DC reflect the stability within the target device and the variability between target devices. Moreover, the coefficients are constructed using an approximate data processing method, and the stability coefficient SC and the variability coefficient DC under the same numerical conditions can be approximately equivalent in reflecting the stability within the target device and the variability between target devices.

[0080] In this way, based on the different mass-to-charge ratios in the relative abundance diagram and the second target formula, the difference coefficient between target devices can be determined. The difference between target devices is considered in the calculation of the mass spectrometry fingerprint spectrum, providing a basis for constructing the mass spectrometry fingerprint spectrum.

[0081] In one possible embodiment, a mass spectrometry fingerprint is constructed through the following steps:

[0082] Input the stability coefficient and the variability coefficient into the evaluation model, and output the evaluation coefficient;

[0083] With the goal of maximizing the evaluation coefficient, the target mass-to-charge ratio and the target response intensity percentage corresponding to the target mass-to-charge ratio are determined in several relative abundance maps to construct a mass spectrometry fingerprint.

[0084] The target response intensity percentage can be the average of the response intensity percentages corresponding to multiple sets of relative abundance maps at the target mass-to-charge ratio in the target device. For example, the target response intensity percentage can be determined using the following formula:

[0085]

[0086] Among them, S hji This refers to the percentage of response intensity corresponding to the mass-to-charge ratio of the i-th relative abundance map in the target device h when it is j. This refers to the percentage of the target response intensity corresponding to the target device h when the mass-to-charge ratio is j.

[0087] As shown in Equation 6, after determining the maximum value of the evaluation coefficient, the target mass-to-charge ratio in the relative abundance map corresponding to the evaluation coefficient is determined. Then, the response intensity percentage corresponding to the target mass-to-charge ratio is found in several relative abundance maps, and the average value of the response intensity percentage is calculated as the target response intensity percentage. After that, a mass spectrometry fingerprint is constructed based on the target mass-to-charge ratio and the target response intensity percentage.

[0088] In this way, based on the stability coefficient, the difference coefficient, and the evaluation model, the evaluation coefficient is obtained. Thus, the evaluation coefficient takes into account both the stability within the target device and the difference between target devices. At the same time, the mass spectrometry fingerprint spectrum is constructed using the target mass-to-charge ratio and the target response intensity percentage corresponding to the maximum evaluation coefficient, thereby improving the accuracy of the mass spectrometry fingerprint spectrum.

[0089] In one possible implementation, the evaluation model is:

[0090] EC = α × SC + β × DC (Equation 7)

[0091] Where EC refers to the evaluation coefficient; α and β refer to the weighting coefficients.

[0092] In Equation 7, α and β are both greater than 0, and α + β = 1. Since the stability within the target device and the differences between target devices play equally important roles in the construction of mass spectrometry fingerprints, α and β are both set to 0.5. The value of EC can range from [0, 1], and the higher the evaluation of the mass spectrometry fingerprint, the closer EC is to 1.

[0093] In this way, based on the stability coefficient, the difference coefficient, and the evaluation model, the evaluation coefficient can be determined, providing a basis for the establishment of mass spectrometry fingerprinting.

[0094] In one possible embodiment, several relative abundance maps are constructed through the following steps:

[0095] Obtain several mass spectrometry detection data corresponding to several gas samples;

[0096] Interference data in several mass spectrometry detection data sets were removed, and different mass-to-charge ratios in several mass spectrometry detection data sets were selected to construct several relative abundance maps.

[0097] For example, mass spectrometry detection data may include mass-to-charge ratio and response intensity.

[0098] In this process, different mass-to-charge ratios are selected from several mass spectrometry detection data to form a mass-to-charge ratio set. Based on the mass-to-charge ratio set and the response intensity corresponding to each mass-to-charge ratio in the mass-to-charge ratio set, the percentage of response intensity corresponding to the mass-to-charge ratio set is determined to construct several relative abundance maps.

[0099] Among them, the percentage of response intensity S is determined.j The formula is as follows:

[0100]

[0101] In Equation 8, I j The mass-to-charge ratio (MTCR) refers to the response intensity corresponding to the MTCR of the set of mass-to-charge ratios, where n is the number of MTCRs in the set of MTCRs.

[0102] Specifically, the number of relative abundance maps corresponding to any target device should be consistent with the number of mass spectrometry detection data of that target device.

[0103] In this way, several relative abundance maps were constructed, providing a basis for the establishment of stability coefficients, difference coefficients, and mass spectrometry fingerprints.

[0104] In one possible embodiment, the mass spectrometry detection data is the detection data obtained by analyzing several gas samples using a mass spectrometer.

[0105] For example, several gas samples can reflect the emission characteristics of pollutants in the target device.

[0106] In this way, the constructed relative abundance map reflects the characteristics of pollutants, providing a basis for the establishment of mass spectrometry fingerprinting, and also providing a basis for investigating the leakage and diffusion status and distribution of pollutants, leakage units, and tracing the source of pollution emissions.

[0107] In one possible embodiment, the interference data is mass spectrometry detection data corresponding to non-target devices in several gas samples.

[0108] Specifically, interference data generally includes the mass-to-charge ratio and response intensity of components in ambient air, such as oxygen and nitrogen.

[0109] In this way, removing interfering data from the mass spectrometry detection data reduces the impact of interfering data and improves the accuracy of the relative abundance map.

[0110] Example 2: The mass spectrometry fingerprinting method provided by this invention can also be used in conjunction with a computer-readable storage medium. The storage medium stores a computer program, and executing the computer program runs the mass spectrometry fingerprinting method. The computer program can execute computer instructions, which include computer program code. The computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc.

[0111] Computer-readable storage media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0112] It should be noted that the contents of computer-readable storage media may be appropriately added to or subtracted from the contents according to the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media may not include electrical carrier signals and telecommunication signals.

[0113] Example 3: The mass spectrometry fingerprinting method provided by this invention can also be used in conjunction with an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program in the memory to run the mass spectrometry fingerprinting method. The computer program can execute computer instructions, which include computer program code. The computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc.

[0114] Example 4: According to another aspect of the present invention, a mass spectrometry fingerprinting apparatus is also provided, which performs a mass spectrometry fingerprinting method. Figure 2 A block diagram of a mass spectrometry fingerprinting apparatus according to an embodiment of the present invention is shown. The apparatus includes:

[0115] Module 510 is used to construct several relative abundance maps corresponding to several target devices;

[0116] The first determining module 520 is used to determine the stability coefficient corresponding to each target device based on several relative abundance maps and the first target formula.

[0117] The second determining module 530 is used to determine the difference coefficient between target devices based on several relative abundance maps and the second target formula;

[0118] Evaluation module 540 is used to construct a mass spectrometry fingerprint based on the stability coefficient, the difference coefficient, and the evaluation model.

[0119] Example 5: This invention provides a method for constructing a mass spectrometry fingerprint evaluation model for mobile traceability in petrochemical enterprises. This example specifically includes the following steps:

[0120] Step 1: Obtain the relative abundance map in the target device.

[0121] The target device is a petrochemical enterprise where a mass spectrometry fingerprint spectrum needs to be constructed. The target device contains a pollution source. The horizontal axis of the relative abundance plot represents the mass-to-charge ratio, and the vertical axis represents the percentage of response intensity S for each mass-to-charge ratio.

[0122] Step 2: Construct the stability coefficient SC within the target device.

[0123] The formula for calculating the stability coefficient SC is as follows:

[0124]

[0125] In the formula, S hji The percentage of response intensity corresponds to the mass-to-charge ratio of the i-th relative abundance map of device h being j, d refers to the number of target devices, m refers to the number of relative abundance maps acquired by the target devices, and n refers to the number of mass-to-charge ratios in the relative abundance maps, where m>1 and n>d. This refers to the average percentage of response intensity corresponding to a mass-to-charge ratio of j on the m relative abundance maps acquired by the target device h. When the mass-to-charge ratio of the target device h is j, S hji and The maximum value of the squared difference between the values ​​is SC, which is the stability coefficient in the interval [0, 1]. The more stable the multiple relative abundance maps of the target device are, the closer SC is to 1.

[0126] Specifically, for the m relative abundance maps acquired by the target device h, for a certain mass-to-charge ratio j, the percentage of response intensity S hji Mean of the percentage of response intensity corresponding to m relative abundance plots The difference between S reflects the percentage of response intensity of the target device h at a mass-to-charge ratio of j. hji Compared to the mean The fluctuations.

[0127] Specifically, due to the differences between the m mass-to-charge ratios of the target device h and between different target devices... The calculated values ​​differ significantly. To avoid overlooking this in the statistical calculation process... The impact of insignificant data will Multiply by the weighting coefficient y / x to ensure that the difference is approximately in the same interval.

[0128] The stability coefficient SC of the target device can be:

[0129]

[0130] Where x refers to the target device h with a mass-to-charge ratio of j, S hji and The maximum absolute value of the difference between them; y refers to the maximum value of x under different target devices and mass-to-charge ratios. The specific expression is as follows:

[0131]

[0132] Replacing the absolute value of the difference in the stability coefficient SC with the squared difference, the stability coefficient SC of the target device is:

[0133]

[0134] Specifically, to ensure that the stability coefficient SC and the variability coefficient DC are on the same dimension and thus comparable, the stability coefficient is normalized to ensure that the value of the stability coefficient is within the range [0, 1].

[0135] Step 3: Construct the difference coefficient DC between the target devices.

[0136] The formula for calculating the coefficient of difference (DC) is as follows:

[0137]

[0138] In the formula, For d target devices The mean, When the mass-to-charge ratio is j, and The maximum value of the square of the difference between them, DC is the difference coefficient in the interval [0, 1]. The greater the difference in the relative abundance maps between the target devices, the closer DC is to 1.

[0139] Specifically, for the m relative abundance maps acquired by the target device h, for a given mass-to-charge ratio j, the mean of the percentage of response intensity corresponding to the m relative abundance maps. This represents the percentage of the response intensity corresponding to the target device h.

[0140] Specifically, d target devices can generate d relative abundance maps with m mass-to-charge ratios. The percentage of response intensity corresponds to target device h with a mass-to-charge ratio of j. The mean percentage of response intensity corresponding to a mass-to-charge ratio of j in d target devices The difference This reflects the percentage of response intensity corresponding to a mass-to-charge ratio of j in each target device compared to the mean. The fluctuations.

[0141] Similarly, for the difference Weighting and normalization processes are performed to ensure that the difference coefficient (DC) and stability coefficient (SC) are on the same dimension and are comparable.

[0142] Furthermore, both the stability coefficient SC and the difference coefficient DC reflect the stability of the mass spectrometry within the target device and the difference between the mass spectrometry of the target devices using the difference value. Moreover, the coefficients are constructed using an approximate data processing method. Under the same numerical conditions, the stability coefficient SC and the difference coefficient DC can be approximately equivalent in reflecting the stability of the mass spectrometry within the target device and the difference between the mass spectrometry of the target devices.

[0143] Step 4: Construct the EC mass spectrometry fingerprint evaluation model for mobile traceability.

[0144] The formula for calculating EC in the mass spectrometry fingerprint evaluation model is shown below:

[0145] EC=α×SC+β×DC

[0146] In the formula, α and β refer to the weighting coefficients of SC and DC, where α and β are both greater than 0, and α + β = 1. Due to the stability within the target device and the differences between target devices, both play equally important roles in the construction of mass spectrometry fingerprints, α = β = 0.5. Generally, EC is the mass spectrometry fingerprint evaluation coefficient in the interval [0, 1]. The better the mass spectrometry fingerprint evaluation, the closer EC is to 1.

[0147] In summary, this invention provides a method, apparatus, medium, and device for constructing mass spectrometry fingerprints, which have the following advantages compared with the prior art:

[0148] This invention first constructs several relative abundance maps corresponding to several target devices. Then, based on these relative abundance maps and a first target formula, it determines the stability coefficient of the target devices. Next, based on these relative abundance maps and a second target formula, it determines the difference coefficient between the target devices. Finally, based on the stability coefficient, the difference coefficient, and the evaluation model, it constructs a mass spectrometry fingerprint spectrum. This approach considers both the stability within the device and the differences between devices, improving the accuracy of the mass spectrometry fingerprint spectrum. Furthermore, the mass spectrometry fingerprint spectrum can be used to investigate the leakage and diffusion status and distribution of pollutants, enabling timely identification of leaking units and tracing of pollution emission sources, effectively reducing the environmental and economic losses caused by pollutant emissions.

[0149] It should be understood that the embodiments disclosed herein are not limited to the specific structures, processing steps, or materials disclosed herein, but should be extended to equivalent substitutions of these features as understood by those skilled in the art. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0150] In the description of this invention, unless otherwise stated, "a plurality of" means two or more; the terms "upper," "lower," "left," "right," "inner," "outer," "front end," "rear end," "head," "tail," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0151] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0152] Certain terms are used throughout this application to refer to specific system components. As those skilled in the art will recognize, the same components may often be referred to by different names, and therefore this application is not intended to distinguish those components that differ only in name and not in function. In this application, the terms “comprise,” “include,” and “have” are used in an open-ended manner and should therefore be interpreted as meaning “including, but not limited to…”. Furthermore, the terms “substantially,” “materially,” or “approximately” as used herein refer to industry-accepted tolerances for the corresponding terms. The term “coupling,” as may be used herein, includes direct coupling and indirect coupling via additional components, elements, circuits, or modules, wherein, for indirect coupling, the intermediate component, element, circuit, or module does not alter the information of the signal but may adjust its current level, voltage level, and / or power level. Inferred coupling (e.g., one element is inferredly coupled to another element) includes direct and indirect coupling between two elements in the same manner as “coupling.”

[0153] The phrase "an embodiment" or "an embodiment" used in this specification means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Therefore, the phrase "an embodiment" or "an embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment.

[0154] The embodiments of the present invention are given for illustrative and descriptive purposes only, and are not intended to be exhaustive or to limit the invention to the forms disclosed. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of the invention, and to enable those skilled in the art to understand the invention and to design various embodiments with various modifications suitable for a particular purpose.

[0155] While the embodiments disclosed in this invention are as described above, the content is merely for the purpose of facilitating understanding of the invention and is not intended to limit the invention. Any person skilled in the art to which this invention pertains may make any modifications and variations in form and detail of the implementation without departing from the spirit and scope disclosed herein; however, the scope of patent protection for this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A method for constructing a mass spectrometry fingerprint, characterized in that, The method includes: Construct several relative abundance maps corresponding to several target devices; Based on the aforementioned relative abundance maps and the first target formula, the stability coefficient corresponding to each of the target devices is determined; Based on the aforementioned relative abundance maps and the second target formula, the difference coefficients among the target devices are determined; Based on the stability coefficient, the difference coefficient, and the evaluation model, a mass spectrometry fingerprint is constructed.

2. The method as described in claim 1, characterized in that, The horizontal axis of the relative abundance plots represents the mass-to-charge ratio, and the vertical axis of the relative abundance plots represents the percentage of response intensity.

3. The method as described in claim 2, characterized in that, The first target formula is: Where SC refers to the stability coefficient; S hji The percentage of response intensity corresponding to a mass-to-charge ratio of j in the i-th relative abundance map of the target device; The value of m is the average percentage of the response intensity corresponding to a mass-to-charge ratio of j in several relative abundance maps of the target device; m is the number of relative abundance maps in the target device; n is the number of mass-to-charge ratios in the relative abundance maps; d is the number of target devices; and K is a constant.

4. The method as described in claim 3, characterized in that, The second objective formula is: Wherein, DC refers to the coefficient of difference; Refers to each target device The average value.

5. The method as described in claim 4, characterized in that, The mass spectrometry fingerprint is constructed using the following steps: Input the stability coefficient and the difference coefficient into the evaluation model, and output the evaluation coefficient; With the evaluation coefficient as the objective, the target mass-to-charge ratio and the target response intensity percentage corresponding to the target mass-to-charge ratio are determined in the several relative abundance maps to construct the mass spectrometry fingerprint.

6. The method as described in claim 5, characterized in that, The evaluation model is as follows: EC=α×SC+β×DC Where EC refers to the evaluation coefficient; α and β refer to the weighting coefficients.

7. The method according to any one of claims 1-6, characterized in that, The relative abundance maps are constructed using the following steps: Obtain several mass spectrometry detection data corresponding to several gas samples; Interference data in the mass spectrometry detection data are deleted, and different mass-to-charge ratios in the mass spectrometry detection data are selected to construct the relative abundance maps.

8. The method as described in claim 7, characterized in that, The mass spectrometry detection data are the detection data obtained by analyzing the gas samples using a mass spectrometer, including the mass-to-charge ratio and response intensity.

9. The method as described in claim 7 or 8, characterized in that, The interference data refers to the mass spectrometry detection data corresponding to the non-target device among the gas samples.

10. The method according to any one of claims 1-9, characterized in that, The target device is a device for which a mass spectrometry fingerprint spectrum needs to be constructed.

11. A storage medium, characterized in that, It includes a series of instructions for performing the method steps as described in any one of claims 1-10.

12. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method according to any one of claims 1-10.

13. A mass spectrometry fingerprinting device, characterized in that, The apparatus for performing the method as described in any one of claims 1-10 includes: The construction module is used to construct several relative abundance maps corresponding to several target devices; The first determining module is used to determine the stability coefficient corresponding to each of the target devices based on the plurality of relative abundance maps and the first target formula; The second determining module is used to determine the difference coefficients between the target devices based on the plurality of relative abundance maps and the second target formula; The evaluation module is used to construct a mass spectrometry fingerprint based on the stability coefficient, the difference coefficient, and the evaluation model.

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