Lubricating oil identification method, device and equipment based on infrared spectrum and medium

Abstract

The invention discloses a lubricating oil identification method, device and equipment based on an infrared spectrum and a medium, and relates to the technical field of lubricating oil identification, the method comprises the following steps: establishing a lubricating oil product characteristic spectrum library; the lubricating oil product characteristic spectrum library comprises a plurality of standard infrared spectrums of different brands of lubricating oil; after the to-be-identified lubricating oil sample is radiated by the generated target interference light, an interference light signal penetrating through the to-be-identified lubricating oil sample is collected; converting the interference light signal into an electric signal; generating an infrared spectrogram corresponding to the to-be-identified lubricating oil sample based on the electric signal; and comparing the infrared spectrogram corresponding to the to-be-identified lubricating oil sample with the standard infrared spectrum in the lubricating oil product characteristic spectrum library to obtain an authenticity identification result of the to-be-identified lubricating oil sample.

Description

Technical Field

[0001] The present application relates to the technical field of lubricating oil identification, and in particular to a lubricating oil identification method, device, equipment and medium based on infrared spectroscopy. Background Art

[0002] In view of the current situation of uneven quality of lubricating oil products and numerous counterfeit and shoddy products, it is difficult to effectively identify these counterfeit and shoddy products through technical means in market supervision, and it is impossible to effectively prevent and control the hidden risks of product quality and safety caused by them. At present, some manufacturers only identify based on information such as outer packaging and batch code, but more and more illegal elements rely on collecting outer packaging barrels of lubricating oil to evade the identification of counterfeit products, which makes market supervision very difficult. Therefore, a lubricating oil identification method with high identification accuracy is urgently needed. Summary of the invention

[0003] The purpose of this application is to provide a lubricant identification method, device, equipment and medium based on infrared spectroscopy, which can improve the accuracy of lubricant identification.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a lubricant identification method based on infrared spectroscopy, comprising:

[0006] Establishing a lubricant product characteristic spectrum library; the lubricant product characteristic spectrum library includes standard infrared spectra of several different brands of lubricants;

[0007] After irradiating the lubricating oil sample to be identified with the generated target interference light, collecting the interference light signal transmitted through the lubricating oil sample to be identified;

[0008] Converting the interference light signal into an electrical signal;

[0009] generating an infrared spectrum corresponding to the lubricating oil sample to be identified based on the electrical signal;

[0010] The infrared spectrum corresponding to the lubricant oil sample to be identified is compared with the standard infrared spectrum in the lubricant oil product characteristic spectrum library to obtain the authenticity identification result of the lubricant oil sample to be identified.

[0011] Optionally, a lubricant product characteristic spectrum library is established, specifically including:

[0012] For each brand, collecting the optical signal of the lubricating oil sample of the brand after being irradiated by the target interference light;

[0013] Converting the optical signal of the lubricating oil sample of the brand after being radiated by the target interference light into an electrical signal;

[0014] A standard infrared spectrum corresponding to the brand of lubricant oil sample is generated based on the electrical signal corresponding to the brand of lubricant oil sample; and the standard infrared spectra corresponding to all the brand of lubricant oil samples constitute a lubricant oil product characteristic spectrum library.

[0015] Optionally, the process of generating the target interference light includes:

[0016] A first interference light generating module is used to generate target interference light; the first interference light generating module includes a first light source, a beam splitter, a moving mirror and a fixed mirror; the first light source is used to generate a first original interference light; the beam splitter is used to split the first original interference light to obtain a first split light and a second split light; the first split light reaches the moving mirror through transmission, and the second split light reaches the fixed mirror through reflection; the first split light and the second split light passing through the moving mirror and the fixed mirror are reunited in the beam splitter to form target interference light.

[0017] Optionally, the process of generating the target interference light includes:

[0018] Generate target interference light using a second interference light generating module; the second interference light generating module includes a second light source and a grating;

[0019] The second light source is used to generate a second original interference light;

[0020] The grating is used to decompose the second original interference light into a plurality of target interference lights with different wavelengths.

[0021] Optionally, generating an infrared spectrum corresponding to the lubricating oil sample to be identified based on the electrical signal specifically includes:

[0022] The electrical signal is subjected to Fourier transform processing to generate an infrared spectrum corresponding to the lubricating oil sample to be identified.

[0023] Optionally, comparing the infrared spectrum corresponding to the lubricant sample to be identified with a standard infrared spectrum in a lubricant product characteristic spectrum library to obtain an authenticity identification result of the lubricant sample to be identified, specifically includes:

[0024] Determine the parameters to be identified of the lubricating oil sample to be identified; the parameters to be identified include the position and intensity of the absorption peak of the infrared spectrum corresponding to the lubricating oil sample to be identified;

[0025] Determining standard parameters of each standard infrared spectrum in the lubricating oil product characteristic spectrum library; the standard parameters include the position and intensity of the absorption peak of the standard infrared spectrum;

[0026] The parameters to be identified are compared with the standard parameters of each standard infrared spectrum to obtain the authenticity identification result of the lubricating oil sample to be identified.

[0027] Optionally, the target interference light includes at least one of near infrared light, mid infrared light and far infrared light.

[0028] In a second aspect, the present application provides a lubricating oil identification device based on infrared spectroscopy, comprising:

[0029] A lubricating oil product characteristic spectrum library establishment module is used to establish a lubricating oil product characteristic spectrum library; the lubricating oil product characteristic spectrum library includes standard infrared spectra of several different brands of lubricating oils;

[0030] An optical signal collection module, used for collecting the interference light signal transmitted through the lubricating oil sample to be identified after irradiating the generated target interference light to the lubricating oil sample to be identified;

[0031] A photoelectric conversion module, used for converting the interference light signal into an electrical signal;

[0032] An infrared spectrum generation module, used for generating an infrared spectrum corresponding to the lubricating oil sample to be identified based on the electrical signal;

[0033] The authenticity identification module is used to compare the infrared spectrum corresponding to the lubricant sample to be identified with the standard infrared spectrum in the lubricant product characteristic spectrum library to obtain the authenticity identification result of the lubricant sample to be identified.

[0034] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned lubricant identification method based on infrared spectroscopy.

[0035] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned lubricant identification method based on infrared spectroscopy.

[0036] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0037] The present application provides a lubricant identification method, device, equipment and medium based on infrared spectroscopy. By establishing a lubricant product characteristic spectrum library containing infrared spectra of lubricants of different brands, after obtaining the infrared spectrum corresponding to the lubricant sample to be identified, the infrared spectrum corresponding to the lubricant sample to be identified is compared with each standard infrared spectrum in the lubricant product characteristic spectrum library to obtain the authenticity identification result of the lubricant sample to be identified. The molecular big data method is used to characterize the characteristic differences of products of different brands, and counterfeit and shoddy products are identified and authenticated and effectively supervised from the perspective of chemical composition, thereby improving the accuracy of lubricant identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 This is an application environment diagram of a lubricating oil identification method based on infrared spectroscopy in an embodiment of the present application;

[0040] Figure 2 A schematic diagram of a flow chart of a lubricating oil identification method based on infrared spectroscopy provided in one embodiment of the present application;

[0041] Figure 3 A schematic diagram of functional modules of a lubricating oil identification device based on infrared spectroscopy provided in one embodiment of the present application;

[0042] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0044] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0045] The lubricant identification method based on infrared spectroscopy provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send a lubricant identification request to be processed to the server 104. After the server 104 receives the lubricant identification request, for the lubricant identification request, the server 104 generates an infrared spectrum corresponding to the lubricant sample to be identified based on the electrical signal, and compares the infrared spectrum corresponding to the lubricant sample to be identified with the standard infrared spectrum in the lubricant product characteristic spectrum library to obtain the authenticity identification result of the lubricant sample to be identified. The server 104 can feedback the authenticity identification result of the lubricant sample to be identified to the terminal 102. In addition, in some embodiments, the lubricant identification method based on infrared spectroscopy can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly perform lubricant identification on the lubricant sample to be identified, or the server 104 can obtain a lubricant identification request from the data storage system and perform lubricant identification on the lubricant identification request.

[0046] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0047] In an exemplary embodiment, Figure 2 As shown, a lubricant identification method based on infrared spectroscopy is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 201 to 205. Among them:

[0048] Step 201, establishing a lubricant product characteristic spectrum library; the lubricant product characteristic spectrum library includes standard infrared spectra of several different brands of lubricants.

[0049] Step 202 : after irradiating the lubricating oil sample to be identified with the generated target interference light, collecting the interference light signal transmitted through the lubricating oil sample to be identified.

[0050] Step 203: convert the interference light signal into an electrical signal.

[0051] Step 204: Generate an infrared spectrum corresponding to the lubricating oil sample to be identified based on the electrical signal.

[0052] Step 205 , comparing the infrared spectrum corresponding to the lubricant sample to be identified with the standard infrared spectrum in the lubricant product characteristic spectrum library to obtain the authenticity identification result of the lubricant sample to be identified.

[0053] By implementing the above-mentioned steps 201 to 205, a lubricant product characteristic spectrum library including infrared spectra of lubricants of different brands is established. After obtaining the infrared spectrum corresponding to the lubricant sample to be identified, the infrared spectrum corresponding to the lubricant sample to be identified is compared with each standard infrared spectrum in the lubricant product characteristic spectrum library to obtain the authenticity identification result of the lubricant sample to be identified. The molecular big data method is used to characterize the characteristic differences of products of different brands, and counterfeit and shoddy products are identified and authenticated and effectively supervised from the perspective of chemical composition, thereby improving the accuracy of lubricant identification.

[0054] In another exemplary embodiment of the present application, step 201 may include the following steps 301 to 303 .

[0055] Step 301: For each brand, collect the optical signal of the lubricant sample of the brand after being irradiated by the target interference light, and use the intensity change to calculate the absorption characteristics of the sample, and reflect the energy absorbed by the lubricant sample through the intensity change.

[0056] Step 302: converting the optical signal of the lubricating oil sample of the brand after being radiated by the target interference light into an electrical signal.

[0057] Step 303: Generate a standard infrared spectrum corresponding to the brand of lubricant oil sample based on the electrical signal corresponding to the brand of lubricant oil sample; all standard infrared spectra corresponding to the brand of lubricant oil samples constitute a lubricant oil product characteristic spectrum library.

[0058] The wavelength range of the infrared spectrum generated in this application is 400 to 7800 nm.

[0059] Lubricant samples of different brands are collected from source manufacturers and other formal channels, and a lubricant product characteristic spectrum library is established through infrared spectral modeling.

[0060] Taking near-infrared light as an example, when near-infrared light of different frequencies is used to irradiate lubricating oil samples, the absorption of near-infrared light by lubricating oil samples will show selectivity. After the near-infrared light passes through the sample, the near-infrared light in some bands will weaken, so that the near-infrared spectrum after transmission can reflect the internal composition and property information of the substance. Infrared spectrum can reflect the internal chemical properties of the substance. There are many group structure information in the vibration frequency region. The chemical bonds contained in the substance, such as CH, 0-H, NH and SH, will produce absorption. For example, secondary and tertiary frequency will be generated in the 780-860nm and 900-1180nm bands. Near-infrared spectrum can characterize the frequency and sum absorption of hydrogen-containing groups such as CH, NH and OH in lubricating oil sample substances, and the absorption response bands of different functional groups in the near-infrared spectrum are also different, as shown in Table 1. Through the established lubricating oil product characteristic spectral library, the molecular big data method is used to characterize the differences in the characteristics of different brands of products, and to identify and identify counterfeit and inferior products from the perspective of chemical composition.

[0061] Table 1 Characteristic response bands of different functional groups in the near-infrared spectral region (nm)

[0062]

[0063]

[0064] In another exemplary embodiment of the present application, the process of obtaining the infrared spectrum corresponding to the lubricating oil sample to be identified may include the following two steps:

[0065] The first method uses the Fourier working principle to obtain the infrared spectrum corresponding to the lubricant sample to be identified: the generation process of the target interference light is: the target interference light is generated by the first interference light generation module; the first interference light generation module includes a first light source, a beam splitter, a moving mirror and a fixed mirror; the first light source is used to generate the first original interference light; the beam splitter is used to split the first original interference light to obtain the first split light and the second split light; the first split light reaches the moving mirror through transmission, and the second split light reaches the fixed mirror through reflection; the first split light and the second split light that pass through the moving mirror and the fixed mirror reunite in the beam splitter to form the target interference light. After the target interference light passes through the lubricant sample, it is received by the detector and converted into an electrical signal. The computer performs Fourier transform processing on the electrical signal to obtain the infrared spectrum of the lubricant sample. That is, the Fourier transform processes the electrical signal to generate a spectrum, and the authenticity of the lubricant is identified by matching the absorption peaks.

[0066] Then step 204 may include: performing Fourier transform processing on the electrical signal to generate an infrared spectrum corresponding to the lubricating oil sample to be identified.

[0067] Based on molecular vibration spectroscopy. When a molecule vibrates around its atomic nucleus, it absorbs a certain amount of energy in different frequency regions of the infrared spectrum. Since the vibration frequencies of different chemical bonds are unique, the chemical bonds in the molecule and their combinations can be determined by measuring the absorption of infrared light by lubricating oil samples.

[0068] The infrared light emitted by the light source is divided into two beams after passing through the beam splitter. One beam reaches the moving mirror through transmission, and the other reaches the fixed mirror through reflection. The two beams of light reunite at the beam splitter to form interference light. After the interference light passes through the lubricating oil sample, the detector receives the interference light transmitted through the sample and converts it into an electrical signal. By analyzing the difference in light intensity between the transmitted light and the incident light, the infrared light energy absorbed by the lubricating oil sample is calculated. The computer performs Fourier transform processing on these electrical signals to obtain the infrared spectrum of the lubricating oil sample.

[0069] Light source: Multiple light sources are provided to measure spectra in different ranges, and commonly used light sources include tungsten filament lamps (near infrared light), silicon carbon rods (mid-infrared light), high-pressure mercury lamps, and thorium oxide lamps (far infrared light). The target interference light in step 202 includes at least one of near infrared light, mid-infrared light, and far infrared light. The light source covers near infrared, mid-infrared, and far infrared light, ensuring that the vibration frequencies of different chemical bonds can be detected.

[0070] The infrared spectrum corresponding to the lubricating oil sample to be identified can be extracted through a Michelson interferometer.

[0071] Instrument structure:

[0072] Beam splitter: It is the key component of the Michelson interferometer. Its function is to split the incident light beam into two parts: reflected and transmitted, and then recombine them.

[0073] Interferometer: It is mainly composed of a beam splitter, a moving mirror and a fixed mirror, and is used to generate interference light.

[0074] Sample cell: The place where the lubricating oil sample is placed. The lubricating oil sample absorbs infrared light in the sample cell.

[0075] Detector: Commonly used detectors include triglycan titanium sulfate (TGS), barium strontium niobate, mercury cadmium telluride, indium antimonide, etc., which are used to convert interference light signals into electrical signals.

[0076] Computer data processing system: used to control the operation of the instrument, collect and process data, and display infrared spectra.

[0077] The second method uses the working principle of grating infrared spectroscopy to obtain the infrared spectrum corresponding to the lubricant sample to be identified: the process of generating the target interference light includes: using the second interference light generation module to generate the target interference light; the second interference light generation module includes a second light source and a grating; the second light source is used to generate a second original interference light; the grating is used to decompose the second original interference light into several target interference lights of different wavelengths.

[0078] Based on the spectral principle of grating and the characteristics of infrared spectrum. Grating is an optical element with a series of equal width and equal distance grooves. When a beam of composite light is incident on the grating, the light will be decomposed into spectra of different wavelengths through multi-slit diffraction and interference. The grating equation describes this process, as shown below:

[0079]

[0080] Where I is the angle of incidence, θ is the diffraction angle, m is the spectral order, d is the grating constant, and λ is the wavelength.

[0081] Infrared spectrometers use the absorption, emission or scattering phenomena generated when infrared radiation interacts with matter to analyze the chemical composition and structure of matter. The structure of an infrared spectrometer includes a light source, an interferometer, a sample chamber and a detector. The infrared light emitted by the light source is modulated by the interferometer and irradiated onto the lubricating oil sample. The sample absorbs infrared radiation of different wavelengths differently, and this absorption information is recorded by the detector to form an infrared spectrum. The grating infrared spectrometer combines the accuracy of grating spectrometry with the chemical analysis function of infrared spectroscopy. When a beam of composite infrared light is incident on the grating, the grating decomposes it into spectra of different wavelengths. These spectra are then detected by the infrared detector, which converts the optical signal into an electrical signal, and finally obtains the infrared spectrum of the lubricating oil sample. By analyzing the position and intensity of the absorption peaks in the spectrum, the chemical bonds and functional groups contained in the lubricating oil sample can be determined, thereby analyzing the composition and structure of the material.

[0082] In another exemplary embodiment of the present application, the above step 205 may include the following steps 401 to 403.

[0083] Step 401: determining the parameters to be identified of the lubricating oil sample to be identified; the parameters to be identified include the position and intensity of the absorption peak of the infrared spectrum corresponding to the lubricating oil sample to be identified;

[0084] Step 402: determining standard parameters of each standard infrared spectrum in the lubricating oil product characteristic spectrum library; the standard parameters include the position and intensity of the absorption peak of the standard infrared spectrum;

[0085] Step 403: Compare the parameters to be identified with the standard parameters of each standard infrared spectrum to obtain the authenticity identification result of the lubricating oil sample to be identified.

[0086] In terms of infrared characteristic spectral library, the infrared spectrum data after preprocessing is used as a calibration set and combined with mathematical methods to establish an analysis model, and the accuracy and stability of the prediction model are evaluated by appropriate indicators. Preprocessing can be smoothing and denoising to eliminate interference factors such as noise and baseline drift, and improve the quality of spectral data.

[0087] Spectral modeling methods include discriminant analysis, multivariate linear regression, partial least squares regression, etc. The present application can also use discriminant analysis, multivariate linear regression, partial least squares regression and other methods to establish a prediction model using a sample set, wherein the sample set includes infrared spectra of several pretreated lubricant samples. In the process of model analysis, appropriate indicators are selected to judge its quality. Model assessment indicators can include correlation coefficient (R), corrected root mean square error (RMSEC) and predicted root mean square error (RMSEP). The larger the R value (the closer to 1), the smaller the RMSEC and RMSEP values, indicating that the model has a strong prediction ability; otherwise, it indicates that the prediction ability is low. By collecting lubricant samples of different brands, a lubricant product characteristic spectral library is established according to the characteristic functional groups in lubricant samples of different brands, which is used for authenticity identification of lubricant products. After the prediction model is established, the infrared spectrum corresponding to the lubricant sample to be identified is input into the prediction model to obtain the authenticity identification result of the lubricant sample to be identified.

[0088] After infrared spectroscopy modeling, differential analysis is performed to determine the different substances between samples; differential substances and groups are screened, and a sample classification prediction module is established to conduct qualitative analysis of substances at characteristic positions and infrared models of characteristic functional groups; a characteristic spectral library of lubricant products of different categories and brands is established, and then counterfeit lubricant products are identified.

[0089] This application will obtain the big data fingerprint information of lubricating oil products at the molecular level (i.e., the infrared spectrum corresponding to the lubricating oil sample), which can be used to identify counterfeit and inferior lubricating oil products and provide data support for the formulation of relevant standards, thereby reducing the occurrence of harm caused by counterfeit and inferior products, creating a healthy consumption environment, and providing technical support for protecting the legitimate rights and interests of consumers and enhancing the early warning and response capabilities for sudden quality and safety incidents. The relevant research results can also guide lubricating oil manufacturers to recognize the hazards and difference information of counterfeit and inferior lubricating oils, provide technical support for combating counterfeit and inferior products, recover losses for regular manufacturers, and provide consumers with a healthy market order, which will produce good social benefits.

[0090] The present application also provides an application scenario, which applies the above-mentioned lubricant identification method based on infrared spectroscopy. Specifically: the lubricant identification method based on infrared spectroscopy provided in this embodiment can be applied in the automotive lubricant identification scenario. The automotive lubricant identification scenario includes a request generation link and a lubricant identification link; the lubricant identification request enters the lubricant identification link from the request generation link, and the corresponding authenticity identification result is obtained through human-computer collaboration. The lubricant identification method based on infrared spectroscopy provided in this embodiment belongs to the lubricant identification link. Specifically, in the lubricant identification link process for the lubricant to be identified, an infrared spectrum corresponding to the lubricant sample to be identified can be generated based on the electrical signal, and the infrared spectrum corresponding to the lubricant sample to be identified is compared with the standard infrared spectrum in the lubricant product characteristic spectrum library to obtain the authenticity identification result of the lubricant sample to be identified.

[0091] Based on the same inventive concept, the embodiment of the present application also provides a lubricant identification device based on infrared spectroscopy for implementing the above-mentioned lubricant identification method based on infrared spectroscopy. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations of one or more lubricant identification device embodiments based on infrared spectroscopy provided below can refer to the limitations of the lubricant identification method based on infrared spectroscopy above, and will not be repeated here.

[0092] In an exemplary embodiment, Figure 3 As shown, a lubricating oil identification device based on infrared spectroscopy is provided, comprising:

[0093] The lubricating oil product characteristic spectrum library establishment module T1 is used to establish the lubricating oil product characteristic spectrum library; the lubricating oil product characteristic spectrum library includes standard infrared spectra of several different brands of lubricating oils;

[0094] The optical signal acquisition module T2 is used to collect the interference light signal transmitted through the lubricating oil sample to be identified after irradiating the generated target interference light to the lubricating oil sample to be identified;

[0095] The photoelectric conversion module T3 is used to convert the interference light signal into an electrical signal;

[0096] An infrared spectrum generation module T4, used for generating an infrared spectrum corresponding to the lubricating oil sample to be identified based on the electrical signal;

[0097] The authenticity identification module T5 is used to compare the infrared spectrum corresponding to the lubricant sample to be identified with the standard infrared spectrum in the lubricant product characteristic spectrum library to obtain the authenticity identification result of the lubricant sample to be identified.

[0098] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store lubricant identification related data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a lubricant identification method based on infrared spectroscopy is implemented.

[0099] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0100] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0101] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0102] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0103] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0104] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0105] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0106] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A lubricating oil identification method based on infrared spectroscopy, characterized in that: The lubricating oil identification method based on infrared spectroscopy comprises: Establishing a lubricant product characteristic spectrum library; the lubricant product characteristic spectrum library includes standard infrared spectra of several different brands of lubricants; After irradiating the lubricating oil sample to be identified with the generated target interference light, collecting the interference light signal transmitted through the lubricating oil sample to be identified; Converting the interference light signal into an electrical signal; generating an infrared spectrum corresponding to the lubricating oil sample to be identified based on the electrical signal; The infrared spectrum corresponding to the lubricant oil sample to be identified is compared with the standard infrared spectrum in the lubricant oil product characteristic spectrum library to obtain the authenticity identification result of the lubricant oil sample to be identified.

2. The lubricating oil identification method based on infrared spectroscopy according to claim 1 is characterized in that: Establish a characteristic spectrum library of lubricant products, including: For each brand, collecting the optical signal of the lubricating oil sample of the brand after being irradiated by the target interference light; Converting the optical signal of the lubricating oil sample of the brand after being radiated by the target interference light into an electrical signal; A standard infrared spectrum corresponding to the brand of lubricant oil sample is generated based on the electrical signal corresponding to the brand of lubricant oil sample; and the standard infrared spectra corresponding to all the brand of lubricant oil samples constitute a lubricant oil product characteristic spectrum library.

3. The lubricating oil identification method based on infrared spectroscopy according to claim 1 is characterized in that: The process of generating target interference light includes: A first interference light generating module is used to generate target interference light; the first interference light generating module includes a first light source, a beam splitter, a moving mirror and a fixed mirror; the first light source is used to generate a first original interference light; the beam splitter is used to split the first original interference light to obtain a first split light and a second split light; the first split light reaches the moving mirror through transmission, and the second split light reaches the fixed mirror through reflection; the first split light and the second split light passing through the moving mirror and the fixed mirror are reunited in the beam splitter to form target interference light.

4. The lubricating oil identification method based on infrared spectroscopy according to claim 1 is characterized in that: The process of generating target interference light includes: Generate target interference light using a second interference light generating module; the second interference light generating module includes a second light source and a grating; The second light source is used to generate a second original interference light; The grating is used to decompose the second original interference light into a plurality of target interference lights with different wavelengths.

5. The lubricating oil identification method based on infrared spectroscopy according to claim 1 is characterized in that: Generating an infrared spectrum corresponding to the lubricating oil sample to be identified based on the electrical signal specifically includes: The electrical signal is subjected to Fourier transform processing to generate an infrared spectrum corresponding to the lubricating oil sample to be identified.

6. The lubricating oil identification method based on infrared spectroscopy according to claim 1 is characterized in that: The infrared spectrum corresponding to the lubricant sample to be identified is compared with the standard infrared spectrum in the lubricant product characteristic spectrum library to obtain the authenticity identification result of the lubricant sample to be identified, which specifically includes: Determine the parameters to be identified of the lubricating oil sample to be identified; the parameters to be identified include the position and intensity of the absorption peak of the infrared spectrum corresponding to the lubricating oil sample to be identified; Determining standard parameters of each standard infrared spectrum in the lubricating oil product characteristic spectrum library; the standard parameters include the position and intensity of the absorption peak of the standard infrared spectrum; The parameters to be identified are compared with the standard parameters of each standard infrared spectrum to obtain the authenticity identification result of the lubricating oil sample to be identified.

7. The lubricating oil identification method based on infrared spectroscopy according to claim 1 is characterized in that: The target interference light includes at least one of near infrared light, mid infrared light and far infrared light.

8. A lubricating oil identification device based on infrared spectroscopy, characterized in that: The lubricating oil identification device based on infrared spectroscopy comprises: A lubricating oil product characteristic spectrum library establishment module is used to establish a lubricating oil product characteristic spectrum library; the lubricating oil product characteristic spectrum library includes standard infrared spectra of several different brands of lubricating oils; An optical signal collection module, used for collecting the interference light signal transmitted through the lubricating oil sample to be identified after irradiating the generated target interference light to the lubricating oil sample to be identified; A photoelectric conversion module, used for converting the interference light signal into an electrical signal; An infrared spectrum generation module, used for generating an infrared spectrum corresponding to the lubricating oil sample to be identified based on the electrical signal; The authenticity identification module is used to compare the infrared spectrum corresponding to the lubricant sample to be identified with the standard infrared spectrum in the lubricant product characteristic spectrum library to obtain the authenticity identification result of the lubricant sample to be identified.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the lubricant identification method based on infrared spectroscopy as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the infrared spectroscopy-based lubricant identification method described in any one of claims 1 to 7 is implemented.

Images