System for predicting the content of methanol and ethanol in gasoline for vehicles

Abstract

The application relates to a methanol and ethanol content prediction system for vehicle gasoline, and relates to the technical field of chemical analysis and detection. First, first chemical treatment is performed on first gasoline to improve the distinctness of components such as aromatic hydrocarbons and olefins, the proportion of associated bodies is determined according to the components other than alcohol in the first gasoline, and then the first spectral peak position offset is determined; second, second chemical treatment is performed on the first gasoline to obtain characteristic peak combinations of each alcohol component, so as to significantly improve the distinctness, and a plurality of first key wavelengths are determined based on the characteristic peak combinations; finally, a plurality of first absorbance values corresponding to the target key wavelengths are determined to finally determine the content of each target component in the first gasoline. The application combines chemometrics, near-infrared spectroscopy and artificial intelligence means, greatly improves the identification accuracy of gasoline components, and effectively corrects the nonlinear effects such as hydrogen bond association in high-concentration alcohol.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of chemical analysis and detection, and particularly relates to a methanol and ethanol content prediction system in vehicle gasoline. BACKGROUND

[0002] In the field of fuel detection, the existing technology generally uses a linear model (such as PLS) to model the spectral data of alcohol-gasoline mixtures. However, there are nonlinear interactions such as hydrogen bond association between high-concentration alcohols (such as methanol and ethanol) and gasoline components (such as aromatics and olefins), which cause complex changes in spectral characteristics. The traditional linear model cannot effectively represent such nonlinear effects, and the prediction deviation significantly increases with the increase of concentration, making it difficult to meet the demand for high-precision detection in industrial scenarios. In addition, experimental data are limited by equipment cost and environmental conditions, and it is difficult to cover the dynamic behavior under extreme conditions (such as ultrahigh temperature and ultrahigh concentration), which limits the generalization ability of the model. SUMMARY

[0003] The purpose of the present application is to provide a methanol and ethanol content prediction system in vehicle gasoline, in order to solve the technical problem of inaccurate detection caused by the difficulty in distinguishing the spectra corresponding to different components in gasoline in the prior art.

[0004] The present application provides a methanol and ethanol content prediction system in vehicle gasoline, comprising:

[0005] a first gasoline characteristic identification module, a first spectral correction compensation module, a first key wavelength identification module and a first gasoline content determination module;

[0006] The first gasoline characteristic identification module is used to identify the content of aromatics, olefins, additives and alcohols in the first gasoline;

[0007] The first spectral correction compensation module is used to obtain a first spectral peak shift amount;

[0008] The first key wavelength identification module is used to generate a plurality of first characteristic peak combinations for the first gasoline, so as to determine a plurality of first key wavelengths based on the plurality of first characteristic peak combinations;

[0009] The first gasoline content determination module is used to obtain the content of the target component in the first gasoline based on the first spectral peak shift amount and the plurality of first key wavelengths.

[0010] Preferably, the working steps of the first gasoline characteristic identification module are:

[0011] S11: performing a first chemical treatment operation on the first gasoline to obtain a first gasoline treatment liquid;

[0012] S12: obtaining first near-infrared spectrum information of the first gasoline treatment fluid, inputting the first near-infrared spectrum information into a first gasoline component analysis model to obtain first gasoline component information.

[0013] Preferably, the first chemical treatment operation comprises:

[0014] selective derivatization treatment, selective addition reaction and oxidation reaction.

[0015] Preferably, the working steps of the first spectrum correction compensation module are:

[0016] S21: constructing a first association equilibrium model, and calculating a first association body proportion based on the first gasoline component information;

[0017] S22: inputting the first association body proportion into a first spectrum peak position offset amount determination model to obtain a first spectrum peak position offset amount.

[0018] Preferably, the calculation method of the first association body proportion comprises:

[0019]

[0020] wherein, the CR-OH and CA respectively represent the concentrations of alcohol and other components in the gasoline, and the Ka is a first equilibrium constant;

[0021]

[0022] wherein, the [] represents the concentration.

[0023] Preferably, the S22 further comprises the following steps:

[0024] S221: obtaining a plurality of first experimental data under different gasoline concentrations and temperatures;

[0025] S222: obtaining a plurality of second association body proportions for the plurality of first experimental data, respectively;

[0026] S223: obtaining a first correspondence relationship between a plurality of the second association body proportions and second spectrum peak position offset amounts based on the plurality of the second association body proportions;

[0027] S224: fitting the first spectrum peak position offset amount determination model based on a plurality of the first correspondence relationships;

[0028] S225: determining the first spectrum peak position offset amount based on the first association body proportion and the first spectrum peak position offset amount determination model.

[0029] Preferably, the working steps of the first key wavelength identification module are:

[0030] S31: performing a second chemical treatment operation on the first gasoline to obtain a second gasoline treatment liquid;

[0031] S32: performing near-infrared spectrum analysis on the second gasoline treatment liquid to obtain a plurality of first characteristic peak combinations;

[0032] S33: determining a plurality of first key wavelengths based on the plurality of first characteristic peak combinations.

[0033] Preferably, the first characteristic peak combination refers to a characteristic peak combination formed by the near-infrared spectrum characteristics of methanol-methyl aldehyde or ethanol-ethanal.

[0034] Preferably, the working steps of the first gasoline content determination module include:

[0035] S41: correcting the plurality of first key wavelengths based on the first spectrum peak position offset to obtain a plurality of target key wavelengths;

[0036] S42: determining a plurality of first absorbance values corresponding to the plurality of target key wavelengths in the second near-infrared spectrum information of the second gasoline treatment liquid;

[0037] S43: determining the target component content of the first gasoline according to the plurality of first absorbance values.

[0038] Preferably, the oxidizing agent used in the second chemical treatment operation is:

[0039] Potassium permanganate or potassium dichromate.

[0040] The methanol and ethanol content prediction system for vehicle gasoline provided in the present application relates to the technical field of chemical analysis and detection. First, a first chemical treatment is performed on the first gasoline to enhance the prominence of components such as aromatic hydrocarbons and olefins, determine the association body ratio based on the components other than alcohol in the first gasoline, and further determine the first spectrum peak position offset. Second, a second chemical treatment is performed on the first gasoline to obtain characteristic peak combinations of each alcohol component, thereby significantly enhancing the prominence, and determine a plurality of first key wavelengths based on each characteristic peak combination. Finally, a plurality of first absorbance values corresponding to the plurality of target key wavelengths are determined to ultimately determine the content of each target component in the first gasoline. The present application combines chemometrics, near-infrared spectrum and artificial intelligence means to greatly improve the accuracy of gasoline component identification and effectively correct the nonlinear effects such as hydrogen bonding in high-concentration alcohol. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can be obtained from the provided drawings without creative labor.

[0042] Figure 1 is a schematic diagram of the methanol and ethanol content prediction system in the gasoline for vehicle in the present application.

[0043] Figure 2 is an example of a feature peak combination in the methanol and ethanol content prediction system in the gasoline for vehicle in the present application. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0045] The present application will be described in detail below with reference to the drawings and specific embodiments, in which the schematic embodiments and the description are only used to explain the present application, but not to limit the present application.

[0046] The methanol and ethanol content prediction system in the gasoline for vehicle in the present application will be described in detail below, and the system block diagram is specifically shown in Figure 1 .

[0047] The content prediction system mainly comprises a first gasoline characteristic identification module, a first spectrum correction compensation module, a first key wavelength identification module and a first gasoline content determination module.

[0048] The first gasoline characteristic identification module is mainly used for identifying the content of components such as aromatics, olefins and additives, and the content of alcohol (R-OH), so as to determine the characteristics of the first gasoline. It should be noted that although there is a certain deviation in identifying the content of methanol and ethanol in gasoline by near-infrared spectrum analysis, the identification of alcohol and aromatic components in gasoline can generally meet the requirement of higher accuracy, so the content of alcohol and other components can be analyzed first, so as to obtain the compensation value of near-infrared spectrum, so as to more accurately determine the content of gasoline.

[0049] The main working steps of the first gasoline characteristic identification module specifically comprise:

[0050] S11: performing a first chemical treatment operation on the first gasoline to obtain a first gasoline treatment liquid.

[0051] In this step, new functional groups are introduced or the molecular structure is changed through chemical reactions, so that the aromatic hydrocarbons, olefins and additives produce more significant absorption peaks in the near infrared spectrum, reducing spectral overlap interference.

[0052] The first chemical treatment operation specifically includes the following three parts:

[0053] 1) Chemical treatment for aromatic hydrocarbons:

[0054] Selective derivatization: using an acetal reagent (such as ethylene glycol dimethyl ether) to react with aromatic hydrocarbons to generate derivatives with unique C-O-C absorption peaks (about 8200 cm-1), which are significantly different from the C=C peaks of olefins (about 7100 cm-1).

[0055] Example reaction:

[0056] Benzene + ethylene glycol dimethyl ether → acid-catalyzed benzene acetal + H2O Benzene + ethylene glycol dimethyl ether acid catalysis

[0057] Benzene acetal + H2O

[0058] 2) Chemical treatment for olefins:

[0059] Selective addition reaction: using bromine water (Br2) to undergo addition reaction with olefins to generate brominated alkanes, whose C-Br absorption peak (about 5600 cm-1) is significantly different from the C=C peak of aromatic hydrocarbons (7100 cm-1).

[0060] Reaction conditions: carried out at low temperature (0-5°C) to avoid side reactions.

[0061] 3) Chemical treatment for additives

[0062] Oxidation of additives: oxidizing oxygen-containing additives (such as methyl tert-butyl ether MTBE) to generate ketone compounds (C=O peak about 5750 cm-1), which are separated from the absorption peaks of aromatic hydrocarbons / olefins.

[0063] Reagent selection: potassium permanganate (KMnO4) oxidizes MTBE to methyl ethyl ketone (MEK) under acidic conditions.

[0064] By performing the first chemical treatment operation including the above three steps, significant treatment can be completed for aromatic hydrocarbons, olefins and additives, so that the content of aromatic hydrocarbons, olefins and additives in the first gasoline can be more obviously and conveniently identified through near-infrared spectral data.

[0065] S12: Obtain first near-infrared spectrum information of the first gasoline treatment fluid, input the first near-infrared spectrum information into a first gasoline component analysis model, and obtain first gasoline component information.

[0066] The core of infrared spectrum technology is to capture the "absorption fingerprint" of the molecules of a substance to near-infrared light. When near-infrared light irradiates oil, the C-H, O-H, N-H and other chemical bonds in the oil will vibrate like a string and absorb light of a specific wavelength. The molecular vibration modes of different components are different, and the absorbed spectrum is also unique like a fingerprint. By analyzing these "light fingerprints", the composition and properties of the oil can be deduced.

[0067] In this step, based on the first gasoline component analysis model trained in advance, the first near-infrared spectrum information is analyzed to obtain the first gasoline component information.

[0068] The first gasoline component analysis model is specifically obtained by training in the following manner:

[0069] First, sample data is obtained. The gasoline samples in the historical gasoline sample library are selected as training data. For each historical gasoline sample, the near-infrared spectrum information thereof is taken as input data, and the gasoline sample label is taken as output data to train the convolutional neural network model, thereby obtaining the first gasoline component analysis model. The near-infrared spectrum analysis information refers to the near-infrared spectrum generated when near-infrared light irradiates the first gasoline.

[0070] After the first near-infrared spectrum information is input into the first gasoline component analysis model, the first near-infrared spectrum information is analyzed to find the gasoline sample label with the highest similarity to the first near-infrared spectrum information as the first gasoline sample label, and the first gasoline sample label is output.

[0071] For example, the first gasoline sample label can be model 1, model 2, etc., or the place of origin can be taken as the first gasoline sample label.

[0072] Further, based on the obtained first gasoline sample label, the content of components such as aromatics, olefins and additives, and the content of alcohol (R-OH) corresponding to the first gasoline sample label can be obtained through a mapping relationship. The mapping relationship is established through historical analysis results, and the content of each component can be adaptively adjusted according to the degree of similarity.

[0073] The first spectrum correction compensation module is mainly used for identifying the content of components such as aromatics, olefins and additives and the content of alcohol (R-OH) based on the content of components determined in the first gasoline characteristic identification module, so as to obtain the hydrogen bond association characteristics of the first gasoline, and obtain the spectrum peak position offset amount after analyzing the hydrogen bond association characteristics, thereby solving the technical problem of high concentration nonlinearity.

[0074] Hydrogen bond association will have a relatively direct impact on spectral characteristics. Mainly in the following two aspects:

[0075] I. Absorption peak shift and deformation:

[0076] 1. Red shift phenomenon: the free O-H stretching vibration peak (such as methanol ~ 1410 nm, ethanol ~ 1440 nm) will shift to the long wave direction after forming a hydrogen bond (methanol → 1380-1400 nm, ethanol → 1420-1430 nm).

[0077] 2. Peak shape broadening: dynamic association leads to increased asymmetry of the absorption peak, with a 20-30% increase in half-peak width.

[0078] Example: the free OH peak in pure methanol is at 1410 nm, and after adding gasoline, it may shift to 1395 nm due to association with aromatics.

[0079] II. Nonlinear absorbance response

[0080] At low concentrations (<1%), alcohol is mainly in monomer form, and the absorbance is linearly related to the concentration;

[0081] At high concentrations (>5%), the proportion of associated bodies increases, the absorbance growth slows down, and the standard curve bends (deviating from the Lambert-Beer law).

[0082] Based on the above adverse effects, the hydrogen bond association characteristics of the first gasoline will be analyzed in this step to obtain the spectrum peak position offset amount, thereby correcting the near-infrared spectrum information of the first gasoline to improve the detection accuracy of the component content in the first gasoline.

[0083] The main working steps of the first spectrum correction compensation module specifically include:

[0084] S21: Construct a first association equilibrium model, and calculate the first associated body proportion based on the first gasoline component information.

[0085] Assume that alcohol (R-OH) and gasoline components (such as aromatics A) form a hydrogen bond associated body (R-OH…A), and the equilibrium reaction is:

[0086]

[0087] Here, \cdotp indicates the association of alcohols with gasoline components.

[0088] Calculate the first equilibrium constant K a The specific calculation formula is as follows:

[0089]

[0090] Wherein, [] represents concentration, that is, [R-OH] represents the concentration of alcohol components in the first gasoline.

[0091] Input temperature T and concentration C R-OH C A The proportion f of the first associative organism is calculated using the Ka. assoc The specific calculation formula is as follows:

[0092]

[0093] S22: Input the first association ratio into the first spectral peak position offset determination model to obtain the first spectral peak position offset.

[0094] In the first spectral peak position shift determination model, the relationship between the ratio of the associative organisms and the spectral peak position shift is obtained based on the fitting of experimental data. Thus, the first spectral peak position shift can be obtained by using the first ratio of the associative organisms determined in S22.

[0095] S22 includes the following sub-steps:

[0096] S221: Obtain multiple sets of first experimental data under different gasoline concentrations and temperatures.

[0097] Specifically, it includes:

[0098] Prepare alcohol-gasoline mixtures (such as methanol / ethanol-gasoline) of different concentrations (0-50%).

[0099] The near-infrared (NIR) spectra of the samples were measured at different temperatures (20-40℃).

[0100] Each set of the first experimental data includes near-infrared spectra at a specified temperature and alcohol concentration.

[0101] S222: For multiple sets of the first experimental data, obtain multiple proportions of the second associates respectively.

[0102] In this step, the proportion of the second associate is obtained by nuclear magnetic resonance (NMR) detection.

[0103] S223: Obtain a first correspondence relationship between the second association body proportion and the second spectral peak shift based on the plurality of second association body proportions.

[0104] In this step, the characteristic absorption peak positions (such as C=O peak, O-H peak) of pure alcohol (no association) and associated state are compared under the same alcohol concentration conditions.

[0105] Calculate the second peak shift Δλ = λ assoc -λ free , where λ assoc and λ free are the peak positions of the associated state and the free state, respectively.

[0106] S224: Obtain the first spectral peak shift determination model based on the plurality of first correspondence relationships.

[0107] Since the plurality of first correspondence relationships record the correspondence relationship between the association body proportion and the spectral peak shift under different concentration and temperature conditions, the least square method can be used to obtain the change relationship between the association body proportion and the spectral peak shift.

[0108] S225: Determine the first spectral peak shift based on the first association body proportion and the first spectral peak shift determination model.

[0109] Preferably, the first spectral peak shift determination model can be in the form of a curve or a mathematical model, and the corresponding first spectral peak shift can be obtained by inputting the first association body proportion into the first spectral peak shift determination model.

[0110] The first key wavelength identification module is used for oxidizing the first gasoline, so that the methanol and ethanol in the first gasoline are both oxidized into formaldehyde and acetaldehyde, so that the near-infrared spectrum wavelength of the methanol and ethanol in the first gasoline is significantly distinguished. Since there is still a part of unreacted methanol and ethanol in the first gasoline after the peroxidation reaction, for each of the methanol and ethanol, unreacted methanol and formaldehyde after the reaction are included, and here the formaldehyde and methanol can form a group of characteristic peak combinations. Since the characteristic peak difference between formaldehyde and acetaldehyde is significantly greater than that between methanol and ethanol, the characteristic peak combination composed of formaldehyde and methanol will also produce more significant differences with the characteristic peak combination composed of acetaldehyde and ethanol, thereby facilitating accurate detection of the methanol and ethanol content in the first gasoline, and then identifying a plurality of key wavelengths.

[0111] The main working steps of the first key wavelength identification module specifically include:

[0112] S31: Perform a second chemical treatment operation on the first gasoline to obtain a second gasoline treatment fluid.

[0113] The principle of chemical treatment in this step includes: taking advantage of the chemical structure difference between methanol (CH3OH) and ethanol (C2H5OH), designing a selective reaction system to make methanol or ethanol undergo a specific chemical reaction to generate products with significant spectral differences, thereby expanding the difference in characteristic absorption peaks of the two in the near-infrared spectrum.

[0114] The specific chemical treatment steps in this step include:

[0115] Reagent selection: Selective oxidizing agents such as potassium permanganate (KMnO4) or potassium dichromate (K2Cr2O7) oxidize methanol to formaldehyde (CH2O) under acidic conditions, while ethanol is oxidized to acetaldehyde (CH3CHO).

[0116] Reaction equation:

[0117] Methanol oxidation: CH3OH + [O] → CH2O + H2O

[0118] Ethanol oxidation: C2H5OH + [O] → CH3CHO + H2O

[0119] Reaction conditions:

[0120] Temperature: 50-70°C (avoid overheating to cause side reactions).

[0121] pH value: Acidic environment (pH = 1-3, adjusted with sulfuric acid).

[0122] Reaction time: 15-30 minutes (determine the optimal reaction time through experiments to ensure complete oxidation of methanol and only partial oxidation of ethanol).

[0123] Reaction termination: Add a reducing agent such as sodium bisulfite (NaHSO3) to neutralize residual oxidizing agents and prevent further oxidation.

[0124] The spectral properties of the products after treatment are as follows:

[0125] Methanol oxidation product (formaldehyde): In the near-infrared spectrum, the C=O stretching vibration absorption peak of formaldehyde is located at about 5800 cm-1 (separated from the original -OH peak of methanol).

[0126] Ethanol oxidation product (acetaldehyde): The C=O absorption peak of acetaldehyde is located at about 5750 cm-1, which is significantly different from the original C-O-C absorption peak of ethanol (about 8200 cm-1).

[0127] Unreacted ethanol: Retains the original -OH (about 5200 cm-1) and C-O-C (about 8200 cm-1) absorption peaks.

[0128] S32: performing near-infrared spectrum analysis on the second gasoline treatment fluid to obtain a plurality of first characteristic peak combinations.

[0129] The first characteristic peak combination refers to a characteristic peak combination formed by the near-infrared spectrum characteristics of methanol-methyl aldehyde or ethanol-acetaldehyde, and each of the first characteristic peak combinations includes a plurality of characteristic peaks.

[0130] An example of introducing a new characteristic peak by chemical treatment includes:

[0131] Methanol: After oxidation, methyl aldehyde is generated, and the C=O peak (5800 cm-1) is combined with the -OH peak (5200 cm-1) of the original methanol to form a characteristic spectral combination of methanol.

[0132] Ethanol: After oxidation, acetaldehyde is generated, and the C=O peak (5750 cm-1) is combined with the C-O-C peak (8200 cm-1) of the original ethanol to form a characteristic spectral combination of ethanol.

[0133] The differentiation of different components in gasoline by characteristic peak combinations can achieve better differentiation effect, and the reasons are as follows, which can be seen from the following figures: Figure 2

[0134] Characteristic peak combination of methanol: 5200 cm-1 (-OH) + 5800 cm-1 (C=O).

[0135] Characteristic peak combination of ethanol: 5750 cm-1 (C=O) + 8200 cm-1 (C-O-C).

[0136] By comparing the intensity and position of the two combinations, methanol and ethanol can be distinguished.

[0137] S33: Based on a plurality of the first characteristic peak combinations, a plurality of first key wavelengths are determined.

[0138] There is a certain conversion relationship between the characteristic peak and the wavelength, and in this step, one or more first key wavelengths corresponding to each of the first characteristic peak combinations can be obtained by a calculation back-propagation method. Thus, a plurality of the first characteristic peak combinations can obtain a plurality of the first key wavelengths.

[0139] The first gasoline content determination module uses the spectrum peak position offset output by the first spectrum correction compensation module to correct the key wavelength output by the first key wavelength identification module, thereby obtaining a corrected target key wavelength, and finally based on the target key wavelength, the content of each component in the first gasoline can be determined.

[0140] The main working steps of the first gasoline content determination module include: ​

[0141] S41: correcting a plurality of the first critical wavelengths based on the first spectral peak shift amount to obtain a plurality of target critical wavelengths.

[0142] In this step, the above correction process can be completed based on artificial or machine learning models, thereby correcting the near-infrared spectral peak shift caused by the nonlinear concentration in gasoline.

[0143] S42: determining a plurality of first absorbance values corresponding to a plurality of the target critical wavelengths in the second near-infrared spectral information of the second gasoline treatment fluid.

[0144] In the second gasoline treatment fluid, different components correspond to different target critical wavelengths. Therefore, by determining a plurality of first absorbance values corresponding to different target critical wavelengths in the second near-infrared spectral information, the content of the corresponding target component in the first gasoline can be determined subsequently.

[0145] The calculation of the absorbance value can use the method commonly used in the art, which will not be described here.

[0146] S43: determining the target component content of the first gasoline according to a plurality of the first absorbance values.

[0147] In this step, the content of each target component in the first gasoline can be determined by the change relationship between different first absorbance values and different target component contents in gasoline.

[0148] The methanol and ethanol content prediction system for vehicle gasoline provided in the present application relates to the field of chemical analysis and detection. First, the first gasoline is subjected to first chemical treatment to enhance the prominence of components such as aromatics and olefins, determine the association body ratio based on the components other than alcohol in the first gasoline, and further determine the first spectral peak shift amount; second, the first gasoline is subjected to second chemical treatment to obtain characteristic peak combinations of each alcohol component to significantly enhance its prominence, and determine a plurality of first critical wavelengths based on each characteristic peak combination; finally, a plurality of target critical wavelengths are obtained by correcting a plurality of first critical wavelengths based on the first spectral peak shift amount, and a plurality of first absorbance values are determined to ultimately determine the content of each target component in the first gasoline. The present application combines chemometrics, near-infrared spectroscopy and artificial intelligence means to greatly improve the accuracy of gasoline component identification and effectively correct the nonlinear effects such as hydrogen bonding in high-concentration alcohol.

[0149] The above only describes the preferred embodiments of the present application, and any equivalent changes or modifications made to the structure, features and principles described in the scope of the present application are included in the scope of the present application.

Claims

1. A system for predicting the content of methanol and ethanol in gasoline for vehicles, characterized by, The method comprises the following steps of: The first gasoline characteristic identification module is used for identifying the content of aromatics, olefins, additives, and alcohols in the first gasoline. The first spectrum correction compensation module is used for obtaining a first spectrum peak position offset. The first key wavelength identification module is used for generating a plurality of first characteristic peak combinations for the first gasoline, so as to determine a plurality of first key wavelengths based on the plurality of first characteristic peak combinations. The first gasoline content determination module is used for obtaining the content of target components in the first gasoline based on the first spectrum peak position offset and the plurality of first key wavelengths. The working steps of the first gasoline characteristic identification module are as follows: S11: performing a first chemical treatment operation on the first gasoline to obtain a first gasoline treatment liquid; S12: obtaining first near-infrared spectrum information of the first gasoline treatment liquid, and inputting the first near-infrared spectrum information into a first gasoline component analysis model to obtain first gasoline component information; The working steps of the first spectrum correction compensation module are as follows: S21: constructing a first association equilibrium model, and calculating a first association body ratio based on the first gasoline component information; S22: inputting the first association body ratio into a first spectrum peak position offset determination model to obtain a first spectrum peak position offset; The S22 further comprises the following steps: S221: obtaining a plurality of first experimental data under different gasoline concentrations and temperatures; S222: obtaining a plurality of second association body ratios for the plurality of first experimental data; S223: obtaining a first correspondence relationship between a plurality of second association body ratios and second spectrum peak position offsets based on the plurality of second association body ratios; S224: fitting the first spectrum peak position offset determination model based on a plurality of the first correspondence relationships; S225: determining the first spectrum peak position offset based on the first association body ratio and the first spectrum peak position offset determination model; The working steps of the first key wavelength identification module are as follows: S31: performing a second chemical treatment operation on the first gasoline to obtain a second gasoline treatment liquid; S32: performing near-infrared spectrum analysis on the second gasoline treatment liquid to obtain a plurality of first characteristic peak combinations; S33: determining a plurality of first key wavelengths based on the plurality of first characteristic peak combinations; The working steps of the first gasoline content determination module comprise: S41: correcting a plurality of the first key wavelengths based on the first spectrum peak position offset to obtain a plurality of target key wavelengths; S42: determining a plurality of first absorbance values corresponding to the plurality of target key wavelengths in second near-infrared spectrum information of the second gasoline treatment liquid; S43: determining the content of target components in the first gasoline according to the plurality of first absorbance values.

2. The system for predicting the content of methanol and ethanol in gasoline for vehicles according to claim 1, characterized by, The first chemical treatment operation comprises: selective derivatization treatment, selective addition reaction, and oxidation reaction.

3. The system for predicting the content of methanol and ethanol in gasoline for vehicles according to claim 1, characterized by, The calculation method of the first association body ratio comprises: wherein the C R-OH , C A respectively represent the concentration of alcohols and other components in gasoline, and the K a is the first equilibrium constant; Wherein, the [] represents concentration, and the. represents the association of alcohol with gasoline component.

4. The system for predicting the content of methanol and ethanol in gasoline for vehicles according to claim 1, characterized by The first characteristic peak combination refers to the characteristic peak combination formed by the near infrared spectrum characteristics of methanol-formaldehyde or ethanol-acetaldehyde.

5. The system for predicting the content of methanol and ethanol in gasoline for vehicles according to claim 1, characterized by The oxidant used in the second chemical treatment process is: Potassium permanganate or potassium dichromate.

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