Near infrared spectrum interference correction method and system for vehicle gasoline detection

Abstract

The invention relates to the technical field of gasoline detection, in particular to a near infrared spectrum interference correction method and a near infrared spectrum interference correction system for vehicle gasoline detection. During vehicle gasoline near infrared spectrum correction, near infrared spectrum data of interference components in gasoline are collected, and characteristic wave bands of the interference components are analyzed; then whether the characteristic wave bands of the target component and the interference component coincide or not is judged to determine candidate interference wave bands, different interference correction methods are determined according to the interference indexes of the corresponding candidate interference wave bands, that is, different interference correction methods are adopted for different candidate interference correction wave bands, and the pertinence and accuracy of interference correction are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of gasoline detection, and particularly to a near-infrared spectrum interference correction method and system for vehicle gasoline detection. Background Art

[0002] As an important energy product, vehicle gasoline quality detection is of great significance for ensuring engine performance and environmental protection. Methanol and ethanol, as common gasoline additives, accurate determination of their content is crucial for evaluating the quality and safety of gasoline. Near-infrared spectroscopy is a fast, non-destructive and efficient analytical technique, widely used in the field of chemical analysis. However, in the near-infrared spectrum analysis of vehicle gasoline, the spectral signals of methanol and ethanol may be interfered by other components, resulting in a decrease in the accuracy of quantitative analysis of methanol and ethanol. Therefore, developing an effective near-infrared spectrum interference correction method is of great significance for improving the accuracy of determining the content of methanol and ethanol in vehicle gasoline.

[0003] There are existing solutions for interfering with the correction of near-infrared spectra. For example, Chinese Patent Application (CN117191739A) discloses a method and system for quantitative detection of near-infrared spectra with moisture interference correction. The method includes: constructing a training set of samples, selecting a moisture correction set and a test set from the training set; using the moisture correction set to calculate the moisture correction factor by the external parameter orthogonalization algorithm; collecting the near-infrared spectra of the training set, correcting its near-infrared spectra by the moisture correction factor to obtain moisture correction spectra, extracting the characteristic spectra by the wavelength selection algorithm, performing regression modeling on the characteristic spectra and the chemical component values to be analyzed, and establishing a moisture correction quantitative detection model; extracting the characteristic spectra of the samples in the test set and inputting them into the moisture correction quantitative detection model to predict the component content. However, the above solution is only a conventional correction method using a model, resulting in low correction accuracy. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a near-infrared spectrum interference correction method and system for vehicle gasoline detection to solve the problems existing in the prior art.

[0005] According to one aspect of the present invention, there is provided a near-infrared spectrum interference correction method for vehicle gasoline detection, which is used to correct the interference of the near-infrared spectrum for detecting the content of methanol and ethanol in vehicle gasoline, and includes the following steps: S1: Obtain a vehicle gasoline sample; S2: Collect spectral data of the vehicle gasoline sample to obtain the original spectral data of the vehicle gasoline; S3: Obtain the interference information of the interfering components in the vehicle gasoline; S4: Determine the characteristic bands of the near-infrared spectra of methanol and ethanol; S5: Perform spectral map overlapping operations on the characteristic bands of the near-infrared spectrum of methanol, the characteristic bands of the near-infrared spectrum of ethanol, and the characteristic bands of interfering components to obtain candidate interference bands; S6: Evaluate the degree of interference of the candidate interference bands to obtain the interference degree evaluation result; S7: According to the interference degree evaluation result, adopt different near-infrared spectrum preprocessing methods for the spectral data of the candidate interference bands of the original spectral data of vehicle gasoline to achieve interference correction.

[0006] Preferably, in S2, a NIR-501A portable Fourier near-infrared oil analyzer is used for near-infrared spectrum acquisition, and the spectral range is 9100~4000 cm -1 , the wavenumber accuracy is 6523±1 cm -1 , the wavenumber precision is ±0.1 cm -1 , the signal-to-noise ratio is better than 10000∶1, and it is collected at 3 different orientations, and the average spectrum is taken as the original spectral data of the vehicle gasoline sample.

[0007] Preferably, S3 is specifically: S3.1: Collect near-infrared spectral data of interfering components; S3.2: Determine the characteristic bands of the near-infrared spectral data of the interfering components as interference information.

[0008] Preferably, in S6, the degree of interference is quantified by calculating the overlapping area and absorption peak intensity ratio between the characteristic bands of the target component and the potential interfering component. Among them, the interference index is defined as an index for quantifying the degree of interference, and the calculation formula of the interference index II is: ; In the formula, A target ( λ ) and A interferent ( λ ) are the absorption intensities of the target component and the interfering component at the wavelength λ respectively, overlap represents the overlapping band range of the target component and the interfering component, and targetband represents the characteristic band range of the target component.

[0009] Preferably, S7 is specifically: If the interference degree evaluation result is less than 0.2, then the first spectral correction method is adopted for the candidate interference bands; otherwise, the second spectral correction method is adopted for the candidate interference bands.

[0010] Preferably, the first spectral correction method is: performing baseline correction and smoothing processing on the corresponding candidate interference bands in the original spectral data of vehicle gasoline; the second spectral correction method is: performing baseline correction, smoothing processing, and correction using an interference correction model on the corresponding candidate interference bands in the original spectral data of vehicle gasoline.

[0011] Preferably, the interference correction model correction is a hybrid model of a convolutional neural network model and a long short-term memory network model (LSTM).

[0012] Preferably, the process of correcting using the interference correction model is specifically as follows: Sa: Construct an interference correction model; Sb: Design the loss function of the interference correction model; Sc: Input the candidate correction bands of the original spectral data of vehicle gasoline and the interference information into the interference correction model to obtain a correction result.

[0013] Preferably, the loss function is a composite loss function of root mean square error and cosine similarity damage, wherein, the formula for mean square error is: ; In the formula, N is the number of data sets, y i is the true value of the i-th sample, is the predicted value of the i-th sample; wherein, the formula for cosine similarity damage is: ; wherein, the formula for the loss function CompositeLoss is: ; In the formula, α and β are weight parameters.

[0014] According to another aspect of the present invention, there is provided a near-infrared spectral interference correction system for vehicle gasoline detection, which adopts the above-mentioned near-infrared spectral interference correction method for vehicle gasoline detection. The system includes: A sample acquisition module, used to acquire vehicle gasoline samples; An original data acquisition module, used to collect spectral data of the vehicle gasoline sample to obtain the original spectral data of vehicle gasoline; An interference information acquisition module, used to acquire the interference information of interference components in vehicle gasoline; A characteristic band determination module, used to determine the characteristic bands of the near-infrared spectra of methanol and ethanol; A candidate interference band determination module, configured to perform a spectral map overlapping operation on the characteristic bands of the near-infrared spectrum of methanol, the characteristic bands of the near-infrared spectrum of ethanol, and the characteristic bands of the interfering components to obtain candidate interference bands; An interference degree evaluation module, configured to evaluate the interference degree of the candidate interference bands to obtain an interference degree evaluation result; An interference correction module, configured to adopt different near-infrared spectrum preprocessing methods for the spectral data of the candidate interference bands of the original spectral data of vehicle gasoline according to the interference degree evaluation result to achieve interference correction.

[0015] The present invention has the following technical effects: When calibrating the near-infrared spectrum of vehicle gasoline, the present invention collects the near-infrared spectral data of the interfering components in gasoline, analyzes the characteristic bands of the interfering components, then determines the candidate interference bands by judging whether the characteristic bands of the target component and the interfering components overlap, and determines different interference correction methods according to the interference index of the corresponding candidate interference bands, that is, different interference correction methods are adopted for different candidate interference correction bands, improving the pertinence and accuracy of interference correction.

[0016] At the same time, when the present embodiment uses a deep learning model for interference correction, in the model input link, interference information is introduced as auxiliary input, enabling the model to identify and correct interference signals. For example, in a CNN-LSTM hybrid model, the spectral characteristics of the interfering components can be input into the model together with the original spectral data of vehicle gasoline. The model automatically identifies and corrects the interference signals by learning the characteristic patterns in the data, thereby achieving interference correction and improving the accuracy of interference correction. Description of the Drawings

[0017] 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. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a flowchart of a near-infrared spectrum interference correction method for vehicle gasoline detection provided by an embodiment of the present invention; Figure 2 It is a flowchart of correction using an interference correction model provided by an embodiment of the present invention. Detailed Embodiments

[0019] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope protected by the present invention.

[0020] Embodiment 1, attached Figure 1 shows a flowchart of a near-infrared spectral interference correction method for vehicle gasoline detection. As attached Figure 1 shown, the near-infrared spectral interference correction method for vehicle gasoline detection is used to correct the interference of the near-infrared spectrum for the detection of methanol and ethanol contents in vehicle gasoline, and includes the following steps: S1: Obtain a vehicle gasoline sample; Ten vehicle gasoline samples of different batches were collected from a gas station, and the samples were filtered using a 0.45 μm filter membrane to remove impurities, thereby obtaining vehicle gasoline samples.

[0021] S2: Collect spectral data of the vehicle gasoline sample to obtain the original spectral data of vehicle gasoline; Use a NIR-501A portable Fourier near-infrared oil analyzer to collect near-infrared spectra. Spectral range: 9100~4000 cm -1 , wavenumber accuracy: 6523±1 cm -1 , wavenumber precision: ±0.1 cm -1 , signal-to-noise ratio better than 10000∶1, collected at 3 different orientations, and the average spectrum was taken as the original spectral data of the vehicle gasoline sample.

[0022] S3: Obtain the interference information of the interfering components in vehicle gasoline; In fact, the interfering components in vehicle gasoline mainly include two categories: aromatics and olefins, which may interfere with the detection of methanol and ethanol contents. Therefore, in this embodiment, the spectral interference information of aromatics and olefins is mainly removed to achieve the purpose of near-infrared spectrum correction of vehicle gasoline.

[0023] Among them, the specific steps of S3 are as follows: S3.1: Collect near-infrared spectral data of the interfering components; The interfering components in vehicle gasoline mainly include two categories: aromatics and olefins. Therefore, in this embodiment, benzene is used as the interfering component of aromatics, and ethylene is used as the interfering component of olefins. Near-infrared spectral data is collected for pure benzene samples and pure ethylene samples respectively; Similarly, use a NIR-501A portable Fourier near-infrared oil analyzer to collect near-infrared spectra. Spectral range: 9100~4000 cm-1 , Wavenumber accuracy: 6523 ± 1 cm -1 , Wavenumber precision: ±0.1 cm -1 , Signal-to-noise ratio is better than 10000∶1. It is collected at 3 different orientations, and the average spectrum is taken as the near-infrared spectral data of the interfering components.

[0024] S3.2: Determine the characteristic bands of the near-infrared spectral data of the interfering components as interference information; In this step, the characteristic bands of the near-infrared spectral data of the pure aromatic hydrocarbons and pure olefins are determined by referring to the literature; and the characteristic bands of the near-infrared spectral data of the pure aromatic hydrocarbons and pure olefins are used as interference information.

[0025] The characteristic bands of the aromatic hydrocarbons (such as benzene) in the near-infrared spectrum appear at 1400 - 1500 nm and 1600 - 1700 nm, and the characteristic bands of the olefins (such as ethylene) appear at 1600 - 1700 nm and 2200 - 2300 nm.

[0026] S4: Determine the characteristic bands of the near-infrared spectra of methanol and ethanol; In this step, the near-infrared spectra of pure methanol and pure ethanol are collected respectively. The collection parameters are the same as those in the above steps, so as to obtain the near-infrared spectral data of pure methanol and pure ethanol. Then, the characteristic bands of the near-infrared spectra of methanol and ethanol are determined by referring to the literature.

[0027] S5: Perform spectral map overlapping operations on the characteristic bands of the near-infrared spectrum of methanol, the characteristic bands of the near-infrared spectrum of ethanol, and the characteristic bands of the interfering components to obtain candidate interference bands; Among them, by superimposing the spectral maps, observe the overlapping situation between the characteristic bands of the target components (methanol, ethanol) and the characteristic bands of the interfering components. For example, if the characteristic absorption peak of the aromatic hydrocarbon overlaps with the characteristic absorption peak of methanol near 1400 - 1500 nm, then interference may exist in this band. Therefore, the 1400 - 1500 nm characteristic segment is used as a candidate interference band; it should be emphasized that the candidate correction band can be one or more.

[0028] S6: Evaluate the degree of interference of the candidate interference bands to obtain the interference degree evaluation result; Among them, in this step, the degree of interference is quantified by calculating indicators such as the overlapping area and absorption peak intensity ratio between the characteristic bands of the target components and the characteristic bands of the potential interfering components. Among them, the interference index (Interference Index, II) is defined as the index for quantifying the degree of interference; Among them, the calculation formula of the interference index II is: ; In the formula, A target ( λ ) and A interferent ( λ ) are the absorption intensities of the target component and the interfering component at the wavelength λ respectively, overlap represents the overlapping band range of the target component and the interfering component, and targetband represents the characteristic band range of the target component.

[0029] S7: According to the interference degree evaluation result, different near-infrared spectral preprocessing methods are adopted for the spectral data of the candidate correction bands of the original spectral data of the vehicle gasoline to achieve interference correction.

[0030] Among them, the specific content of S7 is as follows: If the interference index of the candidate correction band is less than 0.2, the first spectral correction method is adopted for the candidate correction band; otherwise, the second spectral correction method is adopted for the candidate correction band; Among them, the first spectral correction method is: performing baseline correction and smoothing processing on the corresponding candidate correction band in the original spectral data of the vehicle gasoline; Among them, the baseline correction is to perform baseline correction on the spectral data by using the moving average method to remove the background signal, and the smoothing processing is to perform smoothing processing on the spectral data by using the moving average method or the Savitzky-Golay smoothing method to reduce the influence of noise; Among them, the second spectral correction method is: performing baseline correction, smoothing processing and correction by using an interference correction model on the corresponding candidate correction band in the original spectral data of the vehicle gasoline; In this step, this embodiment proposes a hybrid model (CNN-LSTM) of a convolutional neural network model (CNN) and a long short-term memory network model (LSTM) to construct an interference correction model. Among them, the convolutional neural network model can automatically extract local features in the spectral data, and the long short-term memory network model can capture the time series features and the interactions between components in the spectral data; through the combination of CNN and LSTM, the model can more comprehensively process the complexity of the spectral data.

[0031] As shown in the appendix Figure 2 The specific process of the correction by using the interference correction model is as follows: Sa: Construct an interference correction model; Among them, the convolutional neural network model part adopts a multi-layer convolutional layer and pooling layer structure to extract local features of spectral data; the long short-term memory network model part adopts multi-layer LSTM units to capture time series features of spectral data. Between the convolutional neural network model and the long short-term memory network model, feature fusion is performed through a fully connected layer.

[0032] Sb: Design the loss function of the interference correction model; To improve the calibration accuracy of the model, in this embodiment, a composite loss function is proposed by combining the mean square error (MSE) and the cosine similarity loss (Cosine Similarity Loss). The mean square error is used to measure the difference between the model output and the target value, and the cosine similarity loss is used to measure the directional consistency between the model output and the target value. Through the composite loss function, the model can simultaneously optimize the amplitude and shape features of spectral data.

[0033] Among them, the formula for the mean square error is: ; In the formula, N is the number of data sets, y i is the true value of the i-th sample, is the predicted value of the i-th sample; Among them, the formula for the cosine similarity loss is: ; Among them, the formula for the loss function CompositeLoss is: ; In the formula, α and β are weight parameters used to balance the importance of the two loss functions.

[0034] Sc: Input the corresponding candidate calibration bands and the interference information in the original spectral data of vehicle gasoline into the interference correction model to obtain a calibration result.

[0035] In this step, in the model input link, interference information is introduced as an auxiliary input, enabling the model to identify and correct interference signals. For example, in the CNN-LSTM hybrid model, the spectral features of interfering components can be input into the model together with the original spectral data of vehicle gasoline. The model automatically identifies and corrects interference signals by learning the feature patterns in the data, thereby achieving interference correction and improving the accuracy of interference correction. Compared with the traditional method of only inputting the spectral data to be calibrated into the model, the RMSE of the model input method in this embodiment is reduced by about 30% on the test set, the MAE is reduced by about 25%, and the R² is increased by about 5%. This shows that the model input method in this embodiment has significant advantages in the prediction accuracy and reliability of spectral data.

[0036] Example 2. The present invention further provides a near-infrared spectrum interference correction system for vehicle gasoline detection. This device adopts a near-infrared spectrum interference correction method for vehicle gasoline detection in Example 1. The system includes: A sample acquisition module for acquiring vehicle gasoline samples; An original data acquisition module for collecting spectral data of the vehicle gasoline sample to obtain the original spectral data of vehicle gasoline; An interference information acquisition module for acquiring interference information of interference components in vehicle gasoline; A characteristic band determination module for determining the characteristic bands of the near-infrared spectra of methanol and ethanol; A candidate interference band determination module for performing spectral map overlapping operations on the characteristic bands of the near-infrared spectra of methanol, the characteristic bands of the near-infrared spectra of ethanol, and the characteristic bands of interference components to obtain candidate interference bands; An interference degree evaluation module for evaluating the interference degree of the candidate interference bands to obtain an interference degree evaluation result; An interference correction module for adopting different near-infrared spectrum preprocessing methods for the spectral data of the candidate correction bands of the original spectral data of vehicle gasoline according to the interference degree evaluation result to achieve interference correction.

[0037] Example 3. The present invention further provides an electronic device, including one or more processors and a memory.

[0038] The processor can be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and can control other components in the electronic device to perform desired functions.

[0039] The memory can include one or more computer program products. The computer program products can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory can include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory can include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions can be stored on the computer-readable storage medium. The processor can run the program instructions to implement a near-infrared spectrum interference correction method for vehicle gasoline detection in any embodiment of the present application as described above and / or other desired functions. Various contents such as initial external parameters and thresholds can also be stored in the computer-readable storage medium.

[0040] In one example, the electronic device may further include: an input device and an output device, and these components are interconnected through a bus device and / or other forms of connection mechanisms (not shown). The input device may include, for example, a keyboard, a mouse, and so on. The output device may output various information to the outside, including warning prompt information, braking force, etc. The output device may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, and so on.

[0041] Of course, for simplicity, components such as buses, input / output interfaces, and so on are omitted. In addition, according to specific application scenarios, the electronic device may further include any other appropriate components.

[0042] In addition to the above methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, and when the computer program instructions are run by a processor, the processor is caused to implement the function of a near-infrared spectrum interference correction method for vehicle gasoline detection provided by any embodiment of the present application.

[0043] The computer program product may be written in any combination of one or more programming languages to write program code for performing the operations of the embodiments of the present application. The programming languages include object-oriented programming languages, such as Java, C++, etc., and also include conventional procedural programming languages, such as the "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, executed as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0044] In addition, an embodiment of the present application may also be a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are run by a processor, the processor is caused to implement a near-infrared spectrum interference correction method for vehicle gasoline detection provided by any embodiment of the present application.

[0045] The computer-readable storage medium may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may include, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor devices, devices or components, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0046] It should be noted that the terms used in the present invention are only for describing specific embodiments and do not limit the scope of the present application. As shown in the specification of the present invention, unless the context clearly indicates an exceptional situation, words such as "a", "an", "one kind" and / or "the" do not specifically refer to the singular and may also include the plural. The term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of another identical element in the process, method or device comprising the said element.

[0047] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A near infrared spectrum interference correction method for detecting gasoline for automobiles, which is used to perform interference correction on the near infrared spectrum for detecting methanol and ethanol content in gasoline for automobiles, characterized in that: include: S1: Obtain motor gasoline sample; S2: collecting spectral data of the gasoline sample to obtain original spectral data of the gasoline; S3: Obtain interference information of interfering components in motor gasoline; S4: Determine the characteristic bands of near-infrared spectra of methanol and ethanol; S5: performing a spectral overlap operation on the characteristic band of the near-infrared spectrum of methanol, the characteristic band of the near-infrared spectrum of ethanol, and the characteristic band of the interfering component to obtain a candidate interfering band; S6: Evaluate the interference level of the candidate interference band to obtain an interference level evaluation result; S7: According to the interference degree evaluation result, different near-infrared spectrum preprocessing methods are used for the spectrum data of the candidate interference bands of the original spectrum data of the motor gasoline to achieve interference correction.

2. The near infrared spectrum interference correction method for vehicle gasoline detection according to claim 1, characterized in that: In S2, a NIR-501A portable Fourier near-infrared oil analyzer is used to collect near-infrared spectra, with a spectral range of 9100 to 4000 cm -1 , wave number accuracy: 6523±1 cm -1 , wave number accuracy: ±0.1cm -1 The signal-to-noise ratio is better than 10000:

1. The data are collected at three different positions and the average spectrum is taken as the original spectrum data of the motor gasoline sample.

3. The near infrared spectrum interference correction method for vehicle gasoline detection according to claim 1 is characterized in that: The S3 is specifically: S3.1: Collect near infrared spectroscopy data for interfering components; S3.2: Determine a characteristic band of the near-infrared spectrum data of the interference component as interference information.

4. The near infrared spectrum interference correction method for vehicle gasoline detection according to claim 1 is characterized in that: In S6, the interference degree is quantified by calculating the overlapping area and the absorption peak intensity ratio of the characteristic band of the target component and the characteristic band of the potential interfering component, wherein the interference index is defined as an indicator for quantifying the interference degree, and the calculation formula of the interference index II is: ； In the formula, A target ( λ )and A interferent ( λ ) are the target component and the interfering component at wavelength λ The absorption intensity at , overlap represents the overlapping band range of the target component and the interfering component, and targetband represents the characteristic band range of the target component.

5. The near infrared spectrum interference correction method for vehicle gasoline detection according to claim 1 is characterized in that: The S7 is specifically: If the interference degree evaluation result is less than 0.2, the first spectrum correction method is adopted for the candidate interference band; otherwise, the second spectrum correction method is adopted for the candidate interference band.

6. The near infrared spectrum interference correction method for vehicle gasoline detection according to claim 5 is characterized in that: The first spectral correction method is: performing baseline correction and smoothing processing on the corresponding candidate interference bands in the original spectral data of the motor gasoline; the second spectral correction method is: performing baseline correction, smoothing processing and using an interference correction model to correct the corresponding candidate interference bands in the original spectral data of the motor gasoline.

7. The near infrared spectrum interference correction method for vehicle gasoline detection according to claim 6 is characterized in that: The interference correction model is corrected into a hybrid model of a convolutional neural network model and a long short-term memory network model.

8. The near infrared spectrum interference correction method for vehicle gasoline detection according to claim 6 is characterized in that: The specific process of using the interference correction model for correction is as follows: Sa: construct interference correction model; Sb: Design the loss function of the interference correction model; Sc: Inputting the candidate correction bands of the original spectrum data of the motor gasoline and the interference information into the interference correction model to obtain a correction result.

9. The near infrared spectrum interference correction method for vehicle gasoline detection according to claim 8, characterized in that: The loss function is a composite loss function of root mean square error and cosine similarity damage, The formula for mean square error MSE is: ； Where N is the number of data sets, y i is the true value of the i-th sample, is the predicted value of the i-th sample; Among them, the formula of cosine similarity loss is: ； Among them, the formula of the composite loss function CompositeLoss is: ； In the formula, α and β are weight parameters.

10. A near infrared spectroscopy interference correction system for vehicle gasoline detection, characterized in that: The system adopts a near-infrared spectrum interference correction method for vehicle gasoline detection according to any one of claims 1 to 9, and the system comprises: A sample acquisition module, used for acquiring a gasoline sample for a vehicle; The original data acquisition module is used to collect spectral data of the gasoline sample to obtain original spectral data of the gasoline; Interference information acquisition module, used to obtain interference information of interference components in motor gasoline; A characteristic band determination module, used for determining the characteristic bands of near-infrared spectra of methanol and ethanol; A candidate interference band determination module is used to perform a spectral overlap operation on the characteristic band of the near-infrared spectrum of methanol, the characteristic band of the near-infrared spectrum of ethanol, and the characteristic band of the interference component to obtain a candidate interference band; An interference level assessment module is used to assess the interference level of the candidate interference bands to obtain an interference level assessment result; The interference correction module is used to adopt different near-infrared spectrum preprocessing methods to the spectrum data of the candidate interference bands of the original spectrum data of the motor gasoline according to the interference degree evaluation result, so as to realize interference correction.

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