Method and device for detecting acetone molecules in transformer oil, terminal and medium

Abstract

The invention provides a method and device for detecting acetone molecules in transformer oil, a terminal and a medium, and the method comprises the following steps: establishing an acetone molecule model, and obtaining an acetone molecule standard Raman spectrogram; carrying out simulation calculation on the acetone molecule model according to Gaussian software, and determining an optimal simulation Raman spectrogram and an optimal simulation combination; establishing a plurality of acetone single noble metal atomic complex models, and according to Gaussian software, the optimal simulation Raman spectrogram, the optimal simulation combination and the plurality of acetone single noble metal atomic complex models, determining the optimal noble metal, and obtaining a simulation Raman spectrogram; acquiring an actual Raman spectrum chart of Raman spectrum detection on the to-be-detected mixture; and matching the actual Raman spectrum with the simulated Raman spectrum to determine whether acetone molecules exist in the transformer oil. The sensitivity of detecting the acetone molecules in the transformer oil is improved, and the accuracy of detecting the acetone molecules in the transformer oil is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a method, device, terminal and medium for detecting acetone molecules in transformer oil. Background Art

[0002] Transformers account for a high proportion in power equipment, and their aging problem is a key factor related to the safe operation of the power grid. The rapid development of the power grid has put forward high requirements for the assessment of the aging state of transformers. The service life of power transformers is generally related to the deterioration of insulating materials. After being used for many years, the transformer oil-paper insulation system will be aged under the action of thermal stress and electrical stress, affecting the insulation performance of the transformer. Insulating oil and insulating paper decompose to produce substances such as carbon monoxide, carbon dioxide, furfural, methanol, acetone, etc. that reflect the nature of the fault and the degree of aging, which are dissolved in the oil. Since insulating oil contains rich aging information, the detection of insulating oil is of great significance. In order to evaluate the aging state of oil-immersed power equipment, test results such as the content of furfural in oil, dissolved gases in oil, and the degree of polymerization of insulating paper are often used. However, due to the complex steps and difficult sampling of these methods, they are often difficult to be used for on-site rapid aging state assessment.

[0003] Acetone has the characteristics of stable dissolution in oil, not easy to adsorb, not easy to be affected by environmental temperature and air, etc. Taking acetone as an aging characteristic quantity of transformer oil-paper insulation is of great significance in the aging assessment of transformer oil-paper insulation. The commonly used quantitative method for aging characteristic substances in transformer oil is the linear regression model (ULR). However, in the surface-enhanced Raman quantitative analysis of acetone, the fitting effect in the low-concentration range is not ideal. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technology, the present invention proposes a method for detecting acetone molecules in transformer oil, aiming to improve the sensitivity and accuracy of detecting acetone molecules in transformer oil.

[0005] In a first aspect, an embodiment of the present application provides a method for detecting acetone molecules in transformer oil, including:

[0006] Establish an acetone molecule model and obtain a standard Raman spectrogram of acetone molecules;

[0007] Perform simulation calculations on the acetone molecule model according to Gaussian software, and compare the results of the simulation calculations with the standard Raman spectrogram to determine the optimal simulated Raman spectrogram and the optimal simulation combination; the optimal simulation combination is the combination formed by density functional theory and split valence basis set;

[0008] Establish multiple acetone single noble metal atom complex models, and determine the optimal noble metal and obtain the simulated Raman spectrum according to Gaussian software, the optimal simulated Raman spectrum diagram, the optimal simulation combination, and multiple of the acetone single noble metal atom complex models; the simulated Raman spectrum diagram is obtained by performing simulation calculations on a complex model formed by acetone and at least one optimal noble metal;

[0009] Obtain the actual Raman spectrum of the mixture to be detected by Raman spectroscopy; the mixture to be detected is a mixture obtained by mixing pretreated transformer oil with optimal noble metal nanoparticles;

[0010] Match the actual Raman spectrum with the simulated Raman spectrum to determine whether acetone molecules exist in the transformer oil.

[0011] Optionally, the establishment of multiple acetone single noble metal atom complex models, and the determination of the optimal noble metal and the obtaining of the simulated Raman spectrum according to Gaussian software, the optimal simulated Raman spectrum diagram, the optimal simulation combination, and multiple of the acetone single noble metal atom complex models, includes:

[0012] Establish an acetone gold atom complex model, an acetone silver atom complex model, and an acetone copper atom complex model;

[0013] Optimize the acetone gold atom complex model, the acetone silver atom complex model, and the acetone copper atom complex model in the optimal simulation combination according to Gaussian software, and perform simulation calculations on the optimized acetone gold atom complex model, the acetone silver atom complex model, and the acetone copper atom complex model to obtain an acetone gold atom simulated Raman spectrum diagram, an acetone silver atom simulated Raman spectrum diagram, and an acetone copper atom simulated Raman spectrum diagram;

[0014] Compare and analyze the acetone gold atom simulated Raman spectrum diagram, the acetone silver atom simulated Raman spectrum diagram, and the acetone copper atom simulated Raman spectrum diagram with the optimal simulated Raman spectrum diagram respectively to determine the optimal noble metal;

[0015] Establish an acetone bimetallic complex model and an acetone trimetallic complex model according to the optimal noble metal, and perform simulation calculations on the acetone bimetallic complex model and the acetone trimetallic complex model in the optimal simulation combination to obtain the simulated Raman spectrum diagram.

[0016] Optionally, the optimization of the acetone gold atom complex model, the acetone silver atom complex model, and the acetone copper atom complex model in the optimal simulation combination according to Gaussian software includes:

[0017] According to the Gaussian09W Gaussian software, the acetone molecules in the acetone-gold atom complex model, acetone-silver atom complex model, and acetone-copper atom complex model are optimized respectively in the optimal simulation combination;

[0018] According to the Gaussian09W Gaussian software, the gold atom in the acetone-gold atom complex model, the silver atom in the acetone-silver atom complex model, and the copper atom in the acetone-copper atom complex model are optimized in the density functional theory and Lanl2dz basis set of the optimal simulation combination.

[0019] Optionally, the comparing and analyzing the acetone-gold atom simulated Raman spectrum, acetone-silver atom simulated Raman spectrum, and acetone-copper atom simulated Raman spectrum with the optimal simulated Raman spectrum respectively to determine the optimal noble metal includes:

[0020] Read the characteristic peak values and the characteristic peak intensities corresponding to the characteristic peaks from the acetone-gold atom simulated Raman spectrum, acetone-silver atom simulated Raman spectrum, acetone-copper atom simulated Raman spectrum, and the optimal simulated Raman spectrum respectively to form the first data set, the second data set, the third data set, and the fourth data set;

[0021] Compare the characteristic peak intensity corresponding to each characteristic peak in the fourth data set with the characteristic peak intensities corresponding to each characteristic peak in the first data set, the second data set, and the third data set respectively. Screen out the data set in which the characteristic peak intensity corresponding to each characteristic peak in the first data set, the second data set, and the third data set is larger than the characteristic peak intensity corresponding to each characteristic peak in the fourth data set, and the intensity weighted average value obtained by weighted averaging the characteristic peak intensities corresponding to each characteristic peak in the first data set, the second data set, and the third data set with a preset weight is the largest. The noble metal corresponding to the screened data set is used as the optimal noble metal.

[0022] Optionally, establishing the acetone-bimetallic complex model and acetone-trimetallic complex model according to the optimal noble metal, and performing simulation calculations on the acetone-bimetallic complex model and acetone-trimetallic complex model in the optimal simulation combination according to the Gaussian software to obtain the simulated Raman spectrum, including:

[0023] Establish the acetone-bimetallic complex model and acetone-trimetallic complex model of acetone and two and three optimal noble metal atoms;

[0024] According to the Gaussian09W Gaussian software, the acetone molecules in the acetone-bimetallic complex model and acetone-trimetallic complex model are optimized respectively in the optimal simulation combination;

[0025] Optimize the optimal noble metal atoms in the acetone bimetallic complex model and the acetone trimetallic complex model according to the Gaussian 09W Gaussian software in the density functional theory and the Lanl2dz basis set in the optimal simulation combination;

[0026] Perform simulation calculations on the optimized acetone bimetallic complex model and the acetone trimetallic complex model to obtain the acetone bimetallic simulation Raman spectrogram and the acetone trimetallic simulation Raman spectrogram, and combine the acetone monometallic atom simulation Raman spectrogram, the acetone bimetallic simulation Raman spectrogram, and the acetone trimetallic simulation Raman spectrogram corresponding to the optimal noble metal to obtain the simulated Raman spectrogram.

[0027] Optionally, the simulation calculation of the acetone molecular model is performed according to the Gaussian software, and the result of the simulation calculation is compared with the standard Raman spectrogram to determine the optimal simulation Raman spectrogram and the optimal simulation combination, including:

[0028] Perform Raman spectral data simulation calculations on the acetone molecular model through the Gaussian software in the combination formed by various density functional theories and various split valence bond basis sets to obtain a set of simulated Raman spectrograms;

[0029] Compare each simulated Raman spectrogram in the set of simulated Raman spectrograms with the standard Raman spectrogram respectively to obtain the optimal simulated Raman spectrogram;

[0030] Take the combination formed by the density functional theory and the split valence bond basis set corresponding to the optimal simulated Raman spectrogram as the optimal simulation combination.

[0031] Optionally, the step of comparing each simulated Raman spectrogram in the set of simulated Raman spectrograms with the standard Raman spectrogram respectively to obtain the optimal simulated Raman spectrogram includes:

[0032] Obtain the highest simulated Raman characteristic peak value of each simulated Raman spectrogram from each simulated Raman spectrogram;

[0033] Obtain the highest standard Raman characteristic peak value from the standard Raman spectrogram;

[0034] Take the absolute value of the difference obtained by subtracting the highest standard Raman value from the highest simulated Raman characteristic peak value in each simulated Raman spectrogram to obtain the frequency shift amount;

[0035] Take the simulated Raman spectrogram with the smallest frequency shift amount and the highest similarity to the standard Raman spectrogram as the optimal simulated Raman spectrogram.

[0036] Optionally, the density functional theory includes: BPV86 functional, HCTH functional, PBEPBE functional, BY3LP functional, and CAM - B3LYP functional, and the split valence basis set includes 3 - 21G basis set, 6 - 31G basis set, 6 - 311G basis set, 6 - 311G+ basis set;

[0037] Performing Raman spectral data simulation calculations on the acetone molecular model through Gaussian software in combinations formed by various density functional theories and various split valence basis sets to obtain a simulated Raman spectral atlas, including:

[0038] Performing simulation calculations on the acetone molecular model through the BPV86 functional in Gaussian09W software in combinations of 3 - 21G basis set, 6 - 31G basis set, 6 - 311G basis set, and 6 - 311G+ basis set respectively to obtain the first simulated Raman spectral atlas;

[0039] Performing simulation calculations on the acetone molecular model through the HCTH functional in Gaussian09W software in combinations of 3 - 21G basis set, 6 - 31G basis set, 6 - 311G basis set, and 6 - 311G+ basis set respectively to obtain the second simulated Raman spectral atlas;

[0040] Performing simulation calculations on the acetone molecular model through the PBEPBE functional in Gaussian09W software in combinations of 3 - 21G basis set, 6 - 31G basis set, 6 - 311G basis set, and 6 - 311G+ basis set respectively to obtain the third simulated Raman spectral atlas;

[0041] Performing simulation calculations on the acetone molecular model through the BY3LP functional in Gaussian09W software in combinations of 3 - 21G basis set, 6 - 31G basis set, 6 - 311G basis set, and 6 - 311G+ basis set respectively to obtain the fourth simulated Raman spectral atlas;

[0042] Performing simulation calculations on the acetone molecular model through the CAM - B3LYP functional in Gaussian09W software in combinations of 3 - 21G basis set, 6 - 31G basis set, 6 - 311G basis set, and 6 - 311G+ basis set respectively to obtain the fifth simulated Raman spectral atlas;

[0043] Merging the first simulated Raman spectral atlas, the second simulated Raman spectral atlas, the third simulated Raman spectral atlas, the fourth simulated Raman spectral atlas, and the fifth simulated Raman spectral atlas to obtain the simulated Raman spectral atlas.

[0044] In a second aspect, an acetone molecule detection device in transformer oil provided by an embodiment of the present application includes:

[0045] A model - building module, configured to build an acetone molecular model and obtain a standard Raman spectral map of the acetone molecule;

[0046] The optimal combination determination module is used to perform simulation calculations on the acetone molecular model using Gaussian software, compare the results of the simulation calculations with the standard Raman spectrogram, and determine the optimal simulated Raman spectrogram and the optimal simulation combination; the optimal simulation combination is the combination formed by density functional theory and split valence basis set;

[0047] The simulated spectrum acquisition module is used to establish multiple acetone single noble metal atom complex models, and determine the optimal noble metal and obtain the simulated Raman spectrogram according to Gaussian software, the optimal simulated Raman spectrogram, the optimal simulation combination, and the multiple acetone single noble metal atom complex models; the simulated Raman spectrogram is obtained by performing simulation calculations on a complex model formed by acetone and at least one optimal noble metal;

[0048] The actual spectrum acquisition module is used to acquire the actual Raman spectrogram of the to-be-detected mixture subjected to Raman spectroscopic detection; the to-be-detected mixture is a mixture obtained by mixing the pretreated transformer oil with the optimal noble metal nanoparticles;

[0049] The result determination module is used to match the actual Raman spectrum with the simulated Raman spectrum to determine whether acetone molecules exist in the transformer oil.

[0050] In a third aspect, an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for detecting acetone molecules in transformer oil as described in any one of the above first aspects is implemented.

[0051] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method for detecting acetone molecules in transformer oil as described in any one of the above first aspects is implemented.

[0052] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on a terminal device, the terminal device is enabled to execute the method for detecting acetone molecules in transformer oil as described in any one of the above first aspects.

[0053] The beneficial effects of the embodiments of the present application compared with the prior art are:

[0054] In the embodiments of the present application, by establishing an acetone molecular model and multiple acetone single noble metal atom complex models, the optimal noble metal is determined and a simulated Raman spectrum is obtained; the actual Raman spectrum is matched with the simulated Raman spectrum to determine whether acetone molecules exist in the transformer oil; the sensitivity of detecting acetone molecules in the transformer oil is improved, and the accuracy of detecting acetone molecules in the transformer oil is improved. The surface-enhanced laser Raman spectroscopy technology can directly perform non-contact and rapid detection on the transformer oil sample, can meet the requirements of on-line monitoring, and can overcome the problems of insufficient sensitivity and poor accuracy of traditional detection methods. The laser Raman spectroscopy has good detection stability. Description of the Drawings

[0055] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts do not necessarily draw according to the actual scale.

[0056] Figure 1 It is a schematic flowchart of a method for detecting acetone molecules in transformer oil provided by an embodiment of the present application;

[0057] Figure 2 It is a schematic diagram of an acetone molecular model in the method for detecting acetone molecules in transformer oil of the present invention;

[0058] Figure 3 It is a standard Raman spectrum diagram of acetone molecules in the method for detecting acetone molecules in transformer oil of the present invention;

[0059] Figure 4 It is a schematic flowchart of the second embodiment of the method for detecting acetone molecules in transformer oil of the present invention;

[0060] Figure 5 It is a Raman spectrum comparison diagram of the BPV86 functional in the method for detecting acetone molecules in transformer oil of the present invention;

[0061] Figure 6 It is a Raman spectrum comparison diagram of the HCTH functional in the method for detecting acetone molecules in transformer oil of the present invention;

[0062] Figure 7 It is a Raman spectrum comparison diagram of the PBEPBE functional in the method for detecting acetone molecules in transformer oil of the present invention;

[0063] Figure 8 It is a Raman spectrum comparison diagram of the B3LYP functional in the method for detecting acetone molecules in transformer oil of the present invention;

[0064] Figure 9It is a comparative Raman spectrum diagram of the CAM - B3LYP functional in the method for detecting acetone molecules in transformer oil of the present invention;

[0065] Figure 10 It is a schematic flow diagram of the third embodiment of the method for detecting acetone molecules in transformer oil of the present invention;

[0066] Figure 11 It is a comparative diagram of the simulated Raman spectrum of acetone molecules and the Raman enhancement with the addition of one gold atom in the method for detecting acetone molecules in transformer oil of the present invention;

[0067] Figure 12 It is a comparative diagram of the simulated Raman spectrum of acetone molecules and the Raman enhancement with the addition of one silver atom in the method for detecting acetone molecules in transformer oil of the present invention;

[0068] Figure 13 It is a comparative diagram of the simulated Raman spectrum of acetone molecules and the Raman enhancement with the addition of one copper atom in the method for detecting acetone molecules in transformer oil of the present invention;

[0069] Figure 14 It is a comparative diagram of the calculated Raman spectrum of acetone molecules and the Raman enhancement with the addition of multiple copper atoms in the method for detecting acetone molecules in transformer oil of the present invention;

[0070] Figure 15 It is a comparative diagram of the calculated Raman spectrum of acetone molecules and the Raman enhancement with the addition of multiple gold atoms in the method for detecting acetone molecules in transformer oil of the present invention;

[0071] Figure 16 It is a comparative diagram of the calculated Raman spectrum of acetone molecules and the Raman enhancement with the addition of multiple silver atoms in the method for detecting acetone molecules in transformer oil of the present invention;

[0072] Figure 17 It is a schematic structural diagram of the device for detecting acetone molecules in transformer oil provided by the embodiment of the present application;

[0073] Figure 18 It is a schematic structural diagram of the terminal device provided by the embodiment of the present application. Detailed implementation manners

[0074] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well - known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0075] It should be understood that when used in the specification of this application and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0076] It should also be understood that the term "and / or" used in the specification of this application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0077] The execution subject of the method for detecting acetone molecules in transformer oil provided by the embodiments of this application can be a device for detecting acetone molecules in transformer oil. The device for detecting acetone molecules in transformer oil establishes an acetone molecule model and obtains a standard Raman spectrogram of acetone molecules; performs simulation calculations on the acetone molecule model according to Gaussian software, and compares the results of the simulation calculations with the standard Raman spectrogram to determine the optimal simulated Raman spectrogram and the optimal simulation combination; the optimal simulation combination is a combination formed by density functional theory and split valence basis set; establishes a plurality of acetone single noble metal atom complex models, and determines the optimal noble metal and obtains a simulated Raman spectrogram according to Gaussian software, the optimal simulated Raman spectrogram, the optimal simulation combination and the plurality of acetone single noble metal atom complex models; the simulated Raman spectrogram is obtained by performing simulation calculations on a complex model formed by acetone and at least one optimal noble metal; obtains an actual Raman spectrogram of the mixture to be detected by Raman spectroscopy; the mixture to be detected is a mixture obtained by mixing pretreated transformer oil with optimal noble metal nanoparticles; matches the actual Raman spectrum with the simulated Raman spectrum to determine whether there are acetone molecules in the transformer oil.

[0078] Among them, in the surface-enhanced Raman quantitative analysis of acetone by ULR, the fitting effect in the low concentration range is not ideal. This is because when using the least squares method for linear regression, the weight of the low concentration is relatively low, and the sensitivity to acetone is insufficient, resulting in a poor fitting effect. At the same time, ULR ignores the rich information contained in multiple characteristic peaks and even the entire spectrum. The intensity of a characteristic peak used for modeling is not stable, and the prediction result is inaccurate, which limits its application in acetone quantification.

[0079] Figure 1 Shows a schematic flowchart of the method for detecting acetone molecules in transformer oil provided by the embodiments of this application. As an example but not a limitation, this method can be applied to the above-mentioned device for detecting acetone molecules in transformer oil, or it can also be a method for a user or operator to perform operations and judgments on the device for detecting acetone molecules in transformer oil. As Figure 1 shown, this method may include:

[0080] S10. Establish an acetone molecule model and obtain the standard Raman spectrogram of the acetone molecule;

[0081] The acetone molecule detection device in transformer oil establishes an acetone molecule model and obtains the standard Raman spectrogram of the acetone molecule. The acetone molecule detection device in transformer oil, the user or the operator uses Gauss View software to establish a model of an acetone molecule, as Figure 2 shown; wherein, Figure 2 the red small balls represent oxygen atoms, the gray small balls represent carbon atoms, and the white small balls represent hydrogen atoms; the acetone molecule detection device in transformer oil, the user or the operator obtains the standard Raman spectrogram of the acetone molecule stored in the acetone molecule detection device in transformer oil or in the cloud server. The standard Raman spectrogram of the acetone molecule is obtained by performing experimental Raman spectroscopy on the acetone molecule, specifically as Figure 3 shown.

[0082] In a possible implementation, obtaining the standard Raman spectrogram of the acetone molecule can be: the operator places the standard acetone substance on the Raman spectrometer, the operator performs detection through the Raman spectrometer to obtain the standard Raman spectrogram, and the operator sends the standard Raman spectrogram to the acetone molecule detection device in transformer oil; then the acetone molecule detection device in transformer oil obtains it in its memory.

[0083] S20. Perform simulation calculations on the acetone molecule model according to Gaussian software, and compare the results of the simulation calculations with the standard Raman spectrogram to determine the optimal simulated Raman spectrogram and the optimal simulation combination; the optimal simulation combination is the combination formed by the density functional theory and the split valence basis set;

[0084] After the acetone molecule detection device in transformer oil establishes the acetone molecule model and obtains the standard Raman spectrogram of the detected molecule, it performs simulation calculations on the acetone molecule model according to Gaussian software, and compares the results of the simulation calculations with the standard Raman spectrogram to determine the optimal simulated Raman spectrogram and the optimal simulation combination; the optimal simulation combination is the combination formed by the density functional theory and the split valence basis set.

[0085] Further, referring to Figure 4 Figure 4 is the flowchart of the second embodiment of the method for detecting acetone molecules in transformer oil of the present invention. Based on the above Figure 4 shown embodiment, the specific step flowchart of performing simulation calculations on the acetone molecule model according to Gaussian software and comparing the results of the simulation calculations with the standard Raman spectrogram to determine the optimal simulated Raman spectrogram and the optimal simulation combination specifically includes:

[0086] ​S21. Use Gaussian software to perform Raman spectroscopy data simulation calculations on the acetone molecular model in combinations formed by various density functional theories and various split valence basis sets to obtain a simulated Raman spectroscopy atlas.

[0087] Among them, the density functional theories include: BPV86 functional, HCTH functional, PBEPBE functional, BY3LP functional, and CAM - B3LYP functional. The split valence basis sets include 3 - 21G basis set, 6 - 31G basis set, 6 - 311G basis set, and 6 - 311G+ basis set.

[0088] After the acetone molecule detection device in transformer oil has established an acetone molecular model and obtained the standard Raman spectroscopy atlas of the detected molecule, perform simulation calculations on the acetone molecular model through the BPV86 functional in Gaussian09W software in combinations of 3 - 21G basis set, 6 - 31G basis set, 6 - 311G basis set, and 6 - 311G+ basis set to obtain the first simulated Raman spectroscopy atlas; perform simulation calculations on the acetone molecular model through the HCTH functional in Gaussian09W software in combinations of 3 - 21G basis set, 6 - 31G basis set, 6 - 311G basis set, and 6 - 311G+ basis set to obtain the second simulated Raman spectroscopy atlas; perform simulation calculations on the acetone molecular model through the PBEPBE functional in Gaussian09W software in combinations of 3 - 21G basis set, 6 - 31G basis set, 6 - 311G basis set, and 6 - 311G+ basis set to obtain the third simulated Raman spectroscopy atlas; perform simulation calculations on the acetone molecular model through the BY3LP functional in Gaussian09W software in combinations of 3 - 21G basis set, 6 - 31G basis set, 6 - 311G basis set, and 6 - 311G+ basis set to obtain the fourth simulated Raman spectroscopy atlas; perform simulation calculations on the acetone molecular model through the CAM - B3LYP functional in Gaussian09W software in combinations of 3 - 21G basis set, 6 - 31G basis set, 6 - 311G basis set, and 6 - 311G+ basis set to obtain the fifth simulated Raman spectroscopy atlas; merge the first simulated Raman spectroscopy atlas, the second simulated Raman spectroscopy atlas, the third simulated Raman spectroscopy atlas, the fourth simulated Raman spectroscopy atlas, and the fifth simulated Raman spectroscopy atlas to obtain the simulated Raman spectroscopy atlas.

[0089] Specifically, before the simulation calculation of the acetone molecule detection device in transformer oil or by the operator, five density functional theories, namely BPV86, HCTH, PBEPBE, B3LYP, and CAM-B3LYP, are defined as density functional theories 1, 2, 3, 4, and 5; before the simulation calculation of the acetone molecule detection device in transformer oil or by the operator, the basis sets of 3-21G, 6-31G, 6-311G, and 6-311G+(3d) are defined as a, b, c, and d; the acetone molecule detection device in transformer oil or the operator uses the basis set combinations of 1-a, 1-b, 1-c, and 1-d in Gaussian09W software to perform simulation calculations to obtain the first simulated Raman spectrum atlas, as shown in Figure 5 ; the acetone molecule model is simulated using the basis set combinations of 2-a, 2-b, 2-c, and 2-d in Gaussian09W software to obtain the second simulated Raman spectrum atlas, as shown in Figure 6 ; the acetone molecule model is simulated using the basis set combinations of 3-a, 3-b, 3-c, and 3-d in Gaussian09W software to obtain the third simulated Raman spectrum atlas, as shown in Figure 7 ; the acetone molecule model is simulated using the basis set combinations of 4-a, 4-b, 4-c, and 4-d in Gaussian09W software to obtain the fourth simulated Raman spectrum atlas, as shown in Figure 8 ; the acetone molecule model is simulated using the basis set combinations of 5-a, 5-b, 5-c, and 5-d in Gaussian09W software to obtain the fifth simulated Raman spectrum atlas, as shown in Figure 9 ; the first simulated Raman spectrum atlas, the second simulated Raman spectrum atlas, the third simulated Raman spectrum atlas, the fourth simulated Raman spectrum atlas, and the fifth simulated Raman spectrum atlas are merged to obtain the simulated Raman spectrum atlas.

[0090] S22. Each simulated Raman spectrum in the simulated Raman spectrum atlas is compared with the standard Raman spectrum to obtain the optimal simulated Raman spectrum;

[0091] After the acetone molecule detection device in transformer oil obtains the simulated Raman spectrogram atlas, the highest simulated Raman characteristic peak value of each simulated Raman spectrogram is obtained from each simulated Raman spectrogram; the highest standard Raman characteristic peak value is obtained from the standard Raman spectrogram; the absolute value of the difference obtained by subtracting the highest standard Raman value from the highest simulated Raman characteristic peak value in each simulated Raman spectrogram is taken to obtain the frequency shift amount; the simulated Raman spectrogram with the smallest frequency shift amount and the highest similarity to the standard Raman spectrogram is used as the optimal simulated Raman spectrogram. As shown in Table 1, among the simulation results using the B3LYP functional and the 6-311G+(3d) basis set, the frequency shift amounts of the two highest Raman characteristic peaks and the two measured Raman characteristic peaks are the smallest, and the similarity between this Raman spectrogram and the measured Raman spectrogram is the highest. Therefore, the B3LYP functional and the 6-311G+(d) basis set are the optimal combination for the simulation calculation of a single acetone molecule.

[0092] Table 1 Result Summary Table

[0093]

[0094] In a possible implementation, after the acetone molecule detection device in transformer oil obtains the simulated Raman spectrogram atlas, each simulated Raman spectrogram in the simulated Raman spectrogram atlas is compared with the standard Raman spectrogram respectively, and the simulated Raman spectrogram with the highest similarity to the standard Raman spectrogram atlas is selected as the optimal simulated Raman spectrogram.

[0095] S23, take the combination formed by the density functional theory and the split valence bond basis set corresponding to the optimal simulated Raman spectrogram as the optimal simulation combination.

[0096] After the acetone molecule detection device in transformer oil obtains the optimal simulated Raman spectrogram, take the combination formed by the density functional theory and the split valence bond basis set that are used in the simulation calculation to obtain the optimal simulated Raman spectrogram as the optimal simulation combination. As shown in Table 1, among the simulation results using the B3LYP functional and the 6-311G+(3d) basis set, the frequency shift amounts of the two highest Raman characteristic peaks and the two measured Raman characteristic peaks are the smallest, and the similarity between this Raman spectrogram and the measured Raman spectrogram is the highest. Therefore, the B3LYP functional and the 6-311G+(3d) basis set are the optimal combination for the simulation calculation of a single acetone molecule.

[0097] In this implementation, the acetone molecule detection device in transformer oil takes the combination formed by the density functional theory and the split valence bond basis set in the optimal simulated Raman spectrogram obtained by performing simulation calculations in a combination formed by a density functional theory and a split valence bond basis set through Gaussian software as the optimal simulation combination.

[0098] This application used 20 kinds (5 functional methods, 4 basis sets) of different basis set combinations (a total of 5 groups), and grouped and analyzed and compared them with the measured data of acetone molecules, thus obtaining the conclusion that the basis set combination 4-d (the 6-311+g 3d basis set in the B3LYP method) is the closest to the experimental data. Then, the Raman spectrum and the vibration modes of the Raman spectral peaks were analyzed to determine the optimal unit for simulating the Raman spectrum to detect acetone molecules.

[0099] S30. Establish multiple acetone single-precious-metal atom complex models, and determine the optimal precious metal and obtain the simulated Raman spectrum according to Gaussian software, the optimal simulated Raman spectrum, the optimal simulation combination, and the multiple acetone single-precious-metal atom complex models; the simulated Raman spectrum is obtained by performing simulation calculations on a complex model formed by acetone and at least one optimal precious metal.

[0100] After determining the optimal simulation combination, the acetone molecule detection device in transformer oil establishes multiple acetone single-precious-metal atom complex models, and determines the optimal precious metal and obtains the simulated Raman spectrum according to Gaussian software, the optimal simulated Raman spectrum, the optimal simulation combination, and the multiple acetone single-precious-metal atom complex models.

[0101] Further, referring to Figure 10 , Figure 10 is a schematic flowchart of the third embodiment of the method for detecting acetone molecules in transformer oil of the present invention. Based on the above Figure 10 shown embodiment, the specific step flowchart of establishing multiple acetone single-precious-metal atom complex models and determining the optimal precious metal and obtaining the simulated Raman spectrum according to Gaussian software, the optimal simulated Raman spectrum, the optimal simulation combination, and the multiple acetone single-precious-metal atom complex models specifically includes:

[0102] S31. Establish an acetone-gold atom complex model, an acetone-silver atom complex model, and an acetone-copper atom complex model.

[0103] After determining the optimal simulation combination, the acetone molecule detection device in transformer oil respectively constructs an acetone-gold atom complex model, an acetone-silver atom complex model, and an acetone-copper atom complex model in the Gauss View 5.0 software. The acetone-gold atom complex model is a model of a complex formed by an acetone molecule and a single gold atom; the acetone-silver atom complex model is a model of a complex formed by an acetone molecule and a single silver atom; the acetone-copper atom complex model is a model of a complex formed by an acetone molecule and a single copper atom.

[0104] S32. Optimize the acetone-gold atom complex model, acetone-silver atom complex model, and acetone-copper atom complex model according to Gaussian software in the optimal simulation combination, and perform simulation calculations on the optimized acetone-gold atom complex model, acetone-silver atom complex model, and acetone-copper atom complex model to obtain the simulated Raman spectra of acetone-gold atoms, acetone-silver atoms, and acetone-copper atoms;

[0105] After establishing the acetone-gold atom complex model, acetone-silver atom complex model, and acetone-copper atom complex model, the acetone molecule detection device in transformer oil optimizes the acetone-gold atom complex model, acetone-silver atom complex model, and acetone-copper atom complex model according to Gaussian software in the optimal simulation combination, and performs simulation calculations on the optimized acetone-gold atom complex model, acetone-silver atom complex model, and acetone-copper atom complex model to obtain the simulated Raman spectra of acetone-gold atoms, acetone-silver atoms, and acetone-copper atoms. Among them, the simulated Raman spectrum of acetone-gold atoms; the simulated Raman spectrum of acetone-silver atoms is as shown in the simulated Raman spectrum of acetone single-metal atoms corresponding to the optimal noble metal Figure 12 shown; the simulated Raman spectrum of acetone-copper atoms is as shown in the simulated Raman spectrum of acetone single-metal atoms corresponding to the optimal noble metal Figure 13 shown.

[0106] In this embodiment, optimizing the acetone-gold atom complex model, acetone-silver atom complex model, and acetone-copper atom complex model according to Gaussian software in the optimal simulation combination may include:

[0107] According to the Gaussian09W Gaussian software, the acetone molecules in the acetone-gold atom complex model, acetone-silver atom complex model, and acetone-copper atom complex model are optimized respectively in the optimal simulation combination; according to the Gaussian09W Gaussian software, the gold atoms in the acetone-gold atom complex model, silver atoms in the acetone-silver atom complex model, and copper atoms in the acetone-copper atom complex model are optimized in the density functional theory and Lanl2dz basis set in the optimal simulation combination. Determine the acetone atom coordinates of C, H, and O atoms in each acetone-silver atom complex model; according to the Gauss 09W Gaussian software and the acetone atom coordinates, calculate and monitor the acetone molecules in each acetone-silver atom complex model in the optimal simulation combination to complete the optimization of the acetone molecules in the acetone-silver atom complex model; determine the coordinates of the silver atoms in each acetone-silver atom complex model, and according to the Gauss 09W Gaussian software and the coordinates of the silver atoms, calculate and monitor the silver atoms in each acetone-silver atom complex model in the density functional theory and Lanl2dz basis set in the optimal simulation combination to complete the optimization of the silver atoms in the acetone-silver atom complex model. Among them, in order to calculate bond lengths, bond angles, and vibration frequencies more accurately, for C, H, and O atoms, the 6-311+g(3d) basis set is selected, which has a moderate computational cost while ensuring accuracy and is suitable for small and medium-sized molecular systems; for gold atoms, silver atoms, and copper atoms, the LANL2DZ basis set is selected. Gold atoms, silver atoms, and copper atoms have large nuclei, requiring the basis set to be large enough and contain enough high-order Gaussian functions. The Lanl2dz basis set is large enough to meet the requirements of the optimization simulation calculations for gold atoms, silver atoms, and copper atoms.

[0108] S33. Compare and analyze the simulated Raman spectra of acetone-gold atoms, acetone-silver atoms, and acetone-copper atoms with the optimal simulated Raman spectrum respectively to determine the optimal noble metal;

[0109] After the acetone molecule detection device in transformer oil obtains the simulated Raman spectra of acetone-gold atoms, acetone-silver atoms, and acetone-copper atoms, compare and analyze the simulated Raman spectra of acetone-gold atoms, acetone-silver atoms, and acetone-copper atoms with the optimal simulated Raman spectrum respectively to determine the optimal noble metal.

[0110] In this embodiment, comparing and analyzing the simulated Raman spectra of gold atoms in acetone, silver atoms in acetone, and copper atoms in acetone with the optimal simulated Raman spectrum respectively to determine the optimal noble metal may include: reading the characteristic peak values and the characteristic peak intensities corresponding to the characteristic peaks from the simulated Raman spectra of gold atoms in acetone, silver atoms in acetone, copper atoms in acetone, and the optimal simulated Raman spectrum respectively to form a first data set, a second data set, a third data set, and a fourth data set; comparing the characteristic peak intensities corresponding to each characteristic peak in the fourth data set with the characteristic peak intensities corresponding to each characteristic peak in the first data set, the second data set, and the third data set respectively, and screening out the data set in which the characteristic peak intensities corresponding to each characteristic peak in the first data set, the second data set, and the third data set are all greater than those corresponding to each characteristic peak in the fourth data set and the intensity weighted average value obtained by weighted averaging the characteristic peak intensities corresponding to each characteristic peak in the first data set, the second data set, and the third data set with a preset weight is the largest, and taking the noble metal corresponding to the screened data set as the optimal noble metal. For example, as Figure 11 shown, the first data set is: (798, 0.585), (1092, 0.067), (1246, 3.597), (1393, 0.521), (1484, 1371), and (1750, 0.631). As Figure 12 shown, the second data set is: (788, 0.483), (1092, 0.261), (1244, 1.702), (1396, 0.136), (1484, 1.324), and (1764, 2.271). As Figure 13 shown, the third data set is: (798, 7.049), (1085, 25.844), (1253, 0.194), (1393, 12.638), (1477, 5.989), and (1708, 138.661). As Figure 3 、 Figure 11 、 Figure 12 or 13 shown, the fourth data set is: (784, 1.062), (1092, 0.066), (1293, 0.259), (1393, 0.081), (1484, 0.820), and (1785, 0.659). Among them, the preset weight can be the same for the 6 characteristic peaks, or the weights of the 6 characteristic peaks can be different. As Figures 11 to 13As shown in the figure, it is analyzed that gold, silver, and copper noble metal atoms all have an enhancement effect on the Raman simulation of acetone molecules. However, the Raman peaks enhanced by each atom are different, and the enhancement effects are also different. Among them, the enhancement effect of copper atoms is more significant than that of gold and silver atoms. By analyzing the Raman simulation spectra of acetone-gold atom, acetone-silver atom, and acetone-copper atom, copper is determined to be the optimal noble metal.

[0111] In this application, models of gold, silver, and copper noble metal atoms combined with acetone are constructed, and appropriate basis sets are selected to perform simulation calculations on the three models. By comparing and analyzing the three simulation results, it is found that gold, silver, and copper noble metal atoms all have an enhancement effect on the Raman simulation of acetone molecules. However, the Raman peaks enhanced by each atom are different, and the enhancement effects are also different. Among them, the enhancement effect of copper atoms is more significant than that of gold and silver atoms.

[0112] S34. Based on the optimal noble metal, an acetone-bimetallic complex model and an acetone-trimetallic complex model are established, and simulation calculations are performed on the acetone-bimetallic complex model and the acetone-trimetallic complex model in the optimal simulation combination according to Gaussian software to obtain simulated Raman spectra.

[0113] After obtaining the optimized acetone molecule model, the transformer oil acetone molecule detection device establishes an acetone-bimetallic complex model and an acetone-trimetallic complex model based on the optimal noble metal, and performs simulation calculations on the acetone-bimetallic complex model and the acetone-trimetallic complex model in the optimal simulation combination according to Gaussian software to obtain simulated Raman spectra. Among them, in order to calculate bond lengths, bond angles, and vibration frequencies more accurately, the 6-311+g(3d) basis set is selected for C, H, and O atoms. While ensuring accuracy, the computational cost is moderate and it is suitable for small and medium-sized molecular systems; for copper as the optimal noble metal atom, the LANL2DZ basis set is selected. The optimal noble metal atom has a large nucleus, requiring the basis set to be large enough and contain enough high-order Gaussian functions. The Lanl2dz basis set is large in scale and meets the requirements for the simulation calculation of silver atoms.

[0114] In this embodiment, an acetone bimetallic complex model and an acetone trimetallic complex model are established based on the optimal noble metal, and simulation calculations are performed on the acetone bimetallic complex model and the acetone trimetallic complex model in the optimal simulation combination according to Gaussian software to obtain a simulated Raman spectrum diagram, which may include: establishing an acetone bimetallic complex model and an acetone trimetallic complex model of acetone with two and three optimal noble metal atoms; optimizing the acetone molecules in the acetone bimetallic complex model and the acetone trimetallic complex model respectively in the optimal simulation combination according to Gaussian09W Gaussian software; optimizing the optimal noble metal atoms in the acetone bimetallic complex model and the acetone trimetallic complex model in the density functional theory and Lanl2dz basis set in the optimal simulation combination according to Gaussian09W Gaussian software; performing simulation calculations on the optimized acetone bimetallic complex model and acetone trimetallic complex model to obtain an acetone bimetallic simulated Raman spectrum diagram and an acetone trimetallic simulated Raman spectrum diagram, and combining the acetone monometallic atom simulated Raman spectrum diagram corresponding to the optimal noble metal, the acetone bimetallic simulated Raman spectrum diagram, and the acetone trimetallic simulated Raman spectrum diagram to obtain a simulated Raman spectrum diagram. Among them, the acetone bimetallic complex model is a complex model formed by acetone and two optimal noble metal atoms, and the acetone trimetallic complex model is a complex model formed by acetone and three optimal noble metal atoms. The simulated Raman spectrum diagram is as Figure 14 shown.

[0115] After obtaining that the optimal noble metal is copper, an acetone bimetallic complex model and an acetone trimetallic complex model can be established according to the optimal noble metal with reference to S34 of the third embodiment, and simulation calculations are performed on the acetone bimetallic complex model and the acetone trimetallic complex model in the optimal simulation combination according to Gaussian software to obtain a simulated Raman spectrum diagram; establish an acetone di-gold-atom complex model and an acetone tri-gold-atom complex model, and perform simulation calculations on the acetone di-gold-atom complex model and the acetone tri-gold-atom complex model in the optimal simulation combination according to Gaussian software to obtain a simulated acetone gold-atom Raman spectrum diagram as Figure 15 shown; establish an acetone di-silver-atom complex model and an acetone tri-silver-atom complex model, and perform simulation calculations on the acetone di-silver-atom complex model and the acetone tri-silver-atom complex model in the optimal simulation combination according to Gaussian software to obtain a simulated acetone silver-atom Raman spectrum diagram as Figure 16 shown. Compare and analyze the simulated Raman spectrum diagram, the simulated acetone gold-atom Raman spectrum diagram, and the simulated acetone silver-atom Raman spectrum diagram, and it is found that the increase in the number of metal atoms will significantly enhance both the Raman activity and the intensity of the Raman peak.

[0116] In this application, models of two and three copper atoms combined with acetone molecules are respectively constructed, and both the Raman activity and the intensity of Raman peaks are significantly enhanced. The defect of weak Raman signals and difficult accurate detection in traditional methods is effectively solved through specific metal design.

[0117] S40. Obtain the actual Raman spectrogram of the mixture to be detected by Raman spectroscopy; the mixture to be detected is a mixture obtained by mixing the pretreated transformer oil with the optimal noble metal nanoparticles.

[0118] After determining the optimal noble metal, the acetone molecule detection device for transformer oil obtains the actual Raman spectrogram of the mixture to be detected by Raman spectroscopy. The mixture to be detected is a mixture obtained by mixing the pretreated transformer oil with the optimal noble metal nanoparticles. The pretreatment of the transformer oil includes: extracting the transformer oil to be detected and taking its aqueous phase to obtain the pretreated transformer oil. The mixture to be detected is a mixture obtained by mixing the pretreated transformer oil with copper nanoparticles.

[0119] In a possible implementation, an operator takes out the transformer oil from the transformer to be detected, extracts the transformer oil to be detected and takes its aqueous phase to obtain the pretreated transformer oil; mixes the pretreated transformer oil with the optimal noble metal nanoparticles to obtain a mixture;

[0120] S50. Match the actual Raman spectrogram with the simulated Raman spectrogram to determine whether there are acetone molecules in the transformer oil.

[0121] After obtaining the simulated Raman spectrogram, the acetone molecule detection device for transformer oil matches the actual Raman spectrogram with the simulated Raman spectrogram to determine whether there are acetone molecules in the transformer oil.

[0122] Matching the actual Raman spectrogram with the simulated Raman spectrogram to determine whether there are acetone molecules in the transformer oil may include: matching the actual Raman spectrogram with the simulated Raman spectrogram; when the similarity between the actual Raman spectrogram and the simulated Raman spectrogram is higher than or equal to a preset threshold, it is determined that there are acetone molecules in the transformer oil; when the similarity between the actual Raman spectrogram and the simulated Raman spectrogram is lower than the preset threshold, it is determined that there are no acetone molecules in the transformer oil. Among them, the preset threshold can be 70%, 75%, 80%, 85%, 90% or 95%.

[0123] In summary, by establishing an acetone molecular model and multiple acetone single-precious-metal atom complex models, the optimal precious metal is determined and the simulated Raman spectrum is obtained; the actual Raman spectrum is matched with the simulated Raman spectrum to determine whether acetone molecules exist in transformer oil; the sensitivity of detecting acetone molecules in transformer oil is improved, and the accuracy of detecting acetone molecules in transformer oil is improved. The surface-enhanced laser Raman spectroscopy technology can directly perform non-contact rapid detection on transformer oil samples, meet the requirements of on-line monitoring, overcome the problems of insufficient sensitivity and poor accuracy of traditional detection methods, and the laser Raman spectroscopy method has good detection stability.

[0124] For the same as above, please refer to Figure 17 , Figure 17 This application embodiment provides a structural schematic diagram of a device for detecting acetone molecules in transformer oil. As Figure 17 shown, the device includes:

[0125] A model establishment module 1701, configured to establish an acetone molecular model and obtain a standard Raman spectrum of acetone molecules;

[0126] An optimal combination determination module 1702, configured to perform simulation calculations on the acetone molecular model according to Gaussian software, compare the results of the simulation calculations with the standard Raman spectrum, and determine the optimal simulated Raman spectrum and the optimal simulation combination; the optimal simulation combination is a combination formed by density functional theory and split valence basis set;

[0127] A simulated spectrum acquisition module 1703, configured to establish multiple acetone single-precious-metal atom complex models, and determine the optimal precious metal and obtain a simulated Raman spectrum according to Gaussian software, the optimal simulated Raman spectrum, the optimal simulation combination, and the multiple acetone single-precious-metal atom complex models; the simulated Raman spectrum is obtained by performing simulation calculations on a complex model formed by acetone and at least one optimal precious metal;

[0128] An actual spectrum acquisition module 1704, configured to obtain an actual Raman spectrum of a Raman spectrum detection of a mixture to be detected; the mixture to be detected is a mixture obtained by mixing pretreated transformer oil with optimal precious metal nanoparticles;

[0129] A result determination module 1705, configured to match the actual Raman spectrum with the simulated Raman spectrum to determine whether acetone molecules exist in transformer oil.

[0130] This application embodiment also provides a terminal device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, and when the processor executes the computer program, the steps in the embodiment of the method for detecting acetone molecules in transformer oil are implemented.

[0131] The embodiment of the present application further provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute some or all of the steps of any one of the acetone molecule detection methods in the above method embodiments.

[0132] The embodiment of the present application further provides a computer program product, and the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program enables a computer to execute some or all of the steps of any one of the acetone molecule detection methods in the above method embodiments.

[0133] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above method embodiments of the present application, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable storage medium can at least include: any entity or device that can carry the computer program code to the device / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable storage medium may not be an electrical carrier signal and a telecommunication signal.

[0134] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0135] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0136] In the embodiments provided in the present application, it should be understood that the disclosed device / terminal device and method can be implemented in other ways. For example, the device / terminal device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in electrical, mechanical or other forms.

[0137] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Claims

1. A method for detecting acetone molecules in transformer oil, characterized in that Including: Establish an acetone molecular model and obtain the standard Raman spectrum of the acetone molecule; Perform simulation calculations on the acetone molecular model according to Gaussian software, and compare the results of the simulation calculations with the standard Raman spectrum to determine the optimal simulated Raman spectrum and the optimal simulation combination; the optimal simulation combination is the combination formed by the density functional theory and the split valence basis set; Establish multiple acetone single-precious-metal atom complex models, and determine the optimal precious metal and obtain the simulated Raman spectrum according to Gaussian software, the optimal simulated Raman spectrum, the optimal simulation combination, and the multiple acetone single-precious-metal atom complex models; the simulated Raman spectrum is obtained by performing simulation calculations on the complex model formed by acetone and at least one optimal precious metal; Obtain the actual Raman spectrum of the mixture to be detected by Raman spectroscopy; the mixture to be detected is a mixture obtained by mixing the pretreated transformer oil with the optimal precious metal nanoparticles; Match the actual Raman spectrum with the simulated Raman spectrum to determine whether acetone molecules exist in the transformer oil.

2. The method for detecting acetone molecules in transformer oil according to claim 1, wherein The step of establishing multiple acetone single-precious-metal atom complex models and determining the optimal precious metal and obtaining the simulated Raman spectrum according to Gaussian software, the optimal simulated Raman spectrum, the optimal simulation combination, and the multiple acetone single-precious-metal atom complex models includes: Establish an acetone-gold atom complex model, an acetone-silver atom complex model, and an acetone-copper atom complex model; Optimize the acetone-gold atom complex model, the acetone-silver atom complex model, and the acetone-copper atom complex model in the optimal simulation combination according to Gaussian software, and perform simulation calculations on the optimized acetone-gold atom complex model, the acetone-silver atom complex model, and the acetone-copper atom complex model to obtain the simulated Raman spectrum of the acetone-gold atom, the simulated Raman spectrum of the acetone-silver atom, and the simulated Raman spectrum of the acetone-copper atom; Compare and analyze the simulated Raman spectrum of the acetone-gold atom, the simulated Raman spectrum of the acetone-silver atom, and the simulated Raman spectrum of the acetone-copper atom with the optimal simulated Raman spectrum respectively to determine the optimal precious metal; Establish an acetone two-metal complex model and an acetone three-metal complex model according to the optimal precious metal, and perform simulation calculations on the acetone two-metal complex model and the acetone three-metal complex model in the optimal simulation combination according to Gaussian software to obtain the simulated Raman spectrum.

3. The method for detecting acetone molecules in transformer oil according to claim 2, wherein The step of optimizing the acetone-gold atom complex model, the acetone-silver atom complex model, and the acetone-copper atom complex model in the optimal simulation combination according to Gaussian software includes: Optimize the acetone molecules in the acetone-gold atom complex model, the acetone-silver atom complex model, and the acetone-copper atom complex model respectively in the optimal simulation combination according to Gaussian09W Gaussian software; Optimize the gold atom in the acetone-gold atom complex model, the silver atom in the acetone-silver atom complex model, and the copper atom in the acetone-copper atom complex model in the density functional theory and the Lanl2dz basis set in the optimal simulation combination according to Gaussian09W Gaussian software.

4. The method for detecting acetone molecules in transformer oil according to claim 2, wherein Comparing and analyzing the simulated Raman spectra of gold acetone atoms, silver acetone atoms, and copper acetone atoms with the optimal simulated Raman spectrum respectively to determine the optimal noble metal, including: Reading the characteristic peak values and the characteristic peak intensities corresponding to the characteristic peaks from the simulated Raman spectra of gold acetone atoms, silver acetone atoms, copper acetone atoms, and the optimal simulated Raman spectrum respectively to form a first data set, a second data set, a third data set, and a fourth data set; Comparing the characteristic peak intensity corresponding to each characteristic peak in the fourth data set with the characteristic peak intensities corresponding to each characteristic peak in the first data set, the second data set, and the third data set respectively, and screening out the data set in which the characteristic peak intensity corresponding to each characteristic peak in the first data set, the second data set, and the third data set is greater than that corresponding to each characteristic peak in the fourth data set and the intensity weighted average value obtained by weighted averaging the characteristic peak intensities corresponding to each characteristic peak in the first data set, the second data set, and the third data set with a preset weight is the largest, and taking the noble metal corresponding to the screened data set as the optimal noble metal.

5. The method for detecting acetone molecules in transformer oil according to claim 2, characterized in that Establishing an acetone bimetallic complex model and an acetone trimetallic complex model based on the optimal noble metal, and performing simulation calculations on the acetone bimetallic complex model and the acetone trimetallic complex model in the optimal simulation combination according to Gaussian software to obtain simulated Raman spectra, including: Establishing an acetone bimetallic complex model and an acetone trimetallic complex model of acetone with two and three optimal noble metal atoms respectively; Optimizing the acetone molecules in the acetone bimetallic complex model and the acetone trimetallic complex model respectively in the optimal simulation combination according to Gaussian09W Gaussian software; Optimizing the optimal noble metal atoms in the acetone bimetallic complex model and the acetone trimetallic complex model in the density functional theory and the Lanl2dz basis set in the optimal simulation combination according to Gaussian09W Gaussian software; Performing simulation calculations on the optimized acetone bimetallic complex model and acetone trimetallic complex model to obtain an acetone bimetallic simulated Raman spectrum and an acetone trimetallic simulated Raman spectrum, and combining the acetone monometallic atom simulated Raman spectrum, the acetone bimetallic simulated Raman spectrum, and the acetone trimetallic simulated Raman spectrum corresponding to the optimal noble metal to obtain a simulated Raman spectrum.

6. The method for detecting acetone molecules in transformer oil according to any one of claims 1 to 5, characterized in that, Performing simulation calculations on the acetone molecular model according to Gaussian software, and comparing the results of the simulation calculations with the standard Raman spectrum to determine the optimal simulated Raman spectrum and the optimal simulation combination, including: Performing Raman spectrum data simulation calculations on the acetone molecular model in combinations formed by various density functional theories and various split valence bond basis sets through Gaussian software to obtain a set of simulated Raman spectra; Comparing each simulated Raman spectrum in the set of simulated Raman spectra with the standard Raman spectrum respectively to obtain the optimal simulated Raman spectrum; Taking the combination formed by the density functional theory and the split valence bond basis set corresponding to the optimal simulated Raman spectrum as the optimal simulation combination.

7. The method for detecting acetone molecules in transformer oil according to claim 6, characterized in that, Comparing each simulated Raman spectrum in the set of simulated Raman spectra with the standard Raman spectrum respectively to obtain the optimal simulated Raman spectrum, including: Obtaining the highest simulated Raman characteristic peak value of each simulated Raman spectrum from each simulated Raman spectrum; Obtaining the highest standard Raman characteristic peak value from the standard Raman spectrum; Taking the absolute value of the difference obtained by subtracting the highest standard Raman value from the highest simulated Raman characteristic peak value in each simulated Raman spectrum to obtain the frequency shift amount; Taking the simulated Raman spectrum with the smallest frequency shift amount and the highest similarity to the standard Raman spectrum as the optimal simulated Raman spectrum; Or, the density functional theory includes: BPV86 functional, HCTH functional, PBEPBE functional, BY3LP functional, and CAM - B3LYP functional, and the split valence basis set includes 3 - 21G basis set, 6 - 31G basis set, 6 - 311G basis set, 6 - 311G+ basis set; Simulating and calculating the Raman spectrum data of the acetone molecular model in combinations formed by various density functional theories and various split valence basis sets through Gaussian software to obtain a set of simulated Raman spectra, including: Performing simulation calculations on the acetone molecular model through the BPV86 functional in Gaussian09W software in combinations of 3 - 21G basis set, 6 - 31G basis set, 6 - 311G basis set, and 6 - 311G+ basis set to obtain the first set of simulated Raman spectra; Performing simulation calculations on the acetone molecular model through the HCTH functional in Gaussian09W software in combinations of 3 - 21G basis set, 6 - 31G basis set, 6 - 311G basis set, and 6 - 311G+ basis set to obtain the second set of simulated Raman spectra; Performing simulation calculations on the acetone molecular model through the PBEPBE functional in Gaussian09W software in combinations of 3 - 21G basis set, 6 - 31G basis set, 6 - 311G basis set, and 6 - 311G+ basis set to obtain the third set of simulated Raman spectra; Performing simulation calculations on the acetone molecular model through the BY3LP functional in Gaussian09W software in combinations of 3 - 21G basis set, 6 - 31G basis set, 6 - 311G basis set, and 6 - 311G+ basis set to obtain the fourth set of simulated Raman spectra; Performing simulation calculations on the acetone molecular model through the CAM - B3LYP functional in Gaussian09W software in combinations of 3 - 21G basis set, 6 - 31G basis set, 6 - 311G basis set, and 6 - 311G+ basis set to obtain the fifth set of simulated Raman spectra; Merging the first set of simulated Raman spectra, the second set of simulated Raman spectra, the third set of simulated Raman spectra, the fourth set of simulated Raman spectra, and the fifth set of simulated Raman spectra to obtain a set of simulated Raman spectra.

8. A detection device for acetone molecules in transformer oil, characterized in that, Including: A model establishment module for establishing an acetone molecular model and obtaining the standard Raman spectrum of the acetone molecule; An optimal combination determination module, configured to perform simulation calculations on an acetone molecular model according to Gaussian software, and compare the results of the simulation calculations with a standard Raman spectrogram to determine an optimal simulated Raman spectrogram and an optimal simulation combination; the optimal simulation combination is a combination formed by density functional theory and split valence basis set; A simulated spectrum acquisition module, configured to establish multiple acetone single noble metal atom complex models, and determine an optimal noble metal and obtain a simulated Raman spectrogram according to Gaussian software, the optimal simulated Raman spectrogram, the optimal simulation combination, and the multiple acetone single noble metal atom complex models; the simulated Raman spectrogram is obtained by performing simulation calculations on a complex model formed by acetone and at least one optimal noble metal; An actual spectrum acquisition module, configured to acquire an actual Raman spectrogram of a Raman spectrum detection of a mixture to be detected; the mixture to be detected is a mixture obtained by mixing pretreated transformer oil with optimal noble metal nanoparticles; A result determination module, configured to match the actual Raman spectrum with the simulated Raman spectrum to determine whether acetone molecules exist in the transformer oil.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for detecting acetone molecules in transformer oil according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for detecting acetone molecules in transformer oil according to any one of claims 1 to 7.

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