Infrared spectrum detection method and device for rubber, electronic equipment and storage medium

Abstract

The invention discloses a rubber infrared spectrum detection method and device, electronic equipment and a storage medium, the method and the device are applied to the electronic equipment, and specifically, aperiodic modeling is performed based on a chemical structural formula of a to-be-detected rubber part to obtain a trial model; acquiring dot matrix data of peak intensity and peak position of the trial model; performing simplification processing and data arrangement on the dot matrix data to sequentially obtain simplified dot matrix data and a table view dot matrix; drawing based on the table viewpoint matrix to obtain a total spectrum; performing peak marking processing on the total spectrum to obtain a plurality of peak positions; correcting each peak position to obtain a plurality of corrected peak positions; and carrying out replacement processing on the corresponding peak positions in the total spectrum by utilizing the corrected peak positions to obtain an infrared spectrogram. According to the scheme, accurate and rapid infrared detection on the rubber failure part can be realized under the condition that the rubber failure part does not need to be damaged, and an infrared spectrum is relatively clean by virtue of an optimized treatment means, so that weak change of the content of functional groups can be distinguished.

Description

Technical Field

[0001] This application relates to the field of rubber failure detection technology, and more specifically, to an infrared spectroscopy detection method, apparatus, electronic device, and storage medium for rubber. Background Technology

[0002] As a primary object of non-metallic failure analysis, rubber comes in a wide variety of types and functions. It plays a crucial role in aircraft seals, flexible conduits for electrical wires, and even elastic bearings in helicopters. For example, nitrile rubber films are sensitive components used in aircraft engine control systems. When analyzing rubber failures, infrared spectroscopy is needed to make a preliminary judgment on the rubber material and composition. Furthermore, comparing changes in functional groups is an important basis for confirming rubber aging.

[0003] Currently, when monitoring faulty components, total reflectance (ATR) is generally used to detect the infrared spectrum of the component's surface. Although this method is relatively convenient and quick to obtain results, the oily environment or carbon black filler that the faulty component has experienced can reduce the spectral resolution, making it difficult for analysts to distinguish subtle changes in functional group content. While extraction and pyrolysis methods yield clean spectra, these methods result in material loss from the faulty component and cannot achieve accurate and rapid detection. Summary of the Invention

[0004] In view of this, this application provides an infrared spectral detection method, apparatus, electronic device, and storage medium for rubber, which can obtain clean infrared spectra through non-destructive means, thereby helping analysts to achieve accurate, rapid comparison and in-depth analysis of rubber failure parts.

[0005] To achieve the above objectives, the following solution is proposed:

[0006] An infrared spectroscopy detection method for rubber, applied to electronic devices, the infrared spectroscopy detection method comprising the following steps:

[0007] A non-periodic model is performed based on the chemical structure of the rubber component to be tested, resulting in a trial model.

[0008] Obtain the Hessian matrix and corresponding peak intensity and peak position lattice data of the trial model;

[0009] The dot matrix data is simplified to obtain simplified dot matrix data;

[0010] The simplified dot matrix data is processed to obtain a table dot matrix;

[0011] Based on the aforementioned viewpoint matrix, a graph is drawn to obtain the total spectrum;

[0012] Peak labeling is performed on the total spectrum to obtain multiple peak positions;

[0013] Each peak position is corrected using an empirical formula to obtain multiple corrected peak positions;

[0014] The corresponding peak position in the total spectrum is replaced using the corrected peak position to obtain the corrected infrared spectrum.

[0015] Optionally, the step of performing aperiodic modeling based on the chemical structural formula of the rubber component to be tested to obtain a trial model includes the following steps:

[0016] Based on the chemical structural formula, the repeating units of each functional group were identified, and the repeating units were modeled to obtain the repeating unit model.

[0017] The trial model is obtained by performing non-periodic modeling based on the recurring unit model.

[0018] Optionally, the step of simplifying the dot matrix data to obtain simplified dot matrix data includes the following steps:

[0019] The vibrational mode signals of all chain-end atoms in the lattice data are removed;

[0020] The simplified dot matrix data is obtained by extracting the dot matrix data within the required range after the elimination process.

[0021] Optionally, the step of processing the simplified dot matrix data to obtain a table view matrix includes the following steps:

[0022] The simplified dot matrix data can be retrieved within a specific range;

[0023] The simplified dot matrix data retrieved is merged and labeled using signal strength difference as the criterion to obtain a table dot matrix.

[0024] Optionally, the step of performing peak labeling on the total spectrum to obtain multiple peak positions includes the following steps:

[0025] Peak labeling is performed on the total spectrum to obtain the multiple peak positions;

[0026] Gaussian fitting is performed on the total spectrum to output a simplified lattice and the peak widths corresponding to the simplified lattice.

[0027] Signals with peak widths exceeding preset values ​​will be discarded.

[0028] Optionally, the step of correcting each peak position using an empirical formula to obtain multiple corrected peak positions includes the following steps:

[0029] The peak positions corresponding to the simplified lattice are corrected using the empirical formula to obtain the corrected peak positions.

[0030] Optionally, the empirical formula is:

[0031] F ajust =45.5 + 0.963 * F cal

[0032] Among them, F cal To correspond to the peak position of the simplified lattice, F adjust This refers to the corrected peak position.

[0033] An infrared spectroscopy detection device for rubber, used in electronic devices, the infrared spectroscopy detection device comprising:

[0034] The modeling processing module is configured to perform non-periodic modeling based on the chemical structure of the rubber component to be tested, and obtain a trial model.

[0035] The data acquisition module is configured to acquire the Hessian matrix and corresponding peak intensity and peak position lattice data of the trial model;

[0036] A simplification processing module is configured to simplify the dot matrix data to obtain simplified dot matrix data.

[0037] The data processing module is configured to process the simplified dot matrix data to obtain a table view matrix;

[0038] The mapping processing module is configured to perform mapping based on the table viewpoint matrix to obtain the total spectrum;

[0039] The peak marking module is configured to perform peak marking on the total spectrum to obtain multiple peak positions.

[0040] The correction processing module is configured to correct each of the peak positions using an empirical formula to obtain multiple corrected peak positions.

[0041] The spectral output module is configured to replace the corresponding peak position in the total spectrum with the corrected peak position to obtain the corrected infrared spectrum.

[0042] An electronic device includes at least one processor and a memory connected to the processor, wherein:

[0043] The memory is used to store computer programs or instructions;

[0044] The processor is used to execute the computer program or instructions to enable the electronic device to implement the infrared spectroscopy detection method as described above.

[0045] A computer-readable storage medium is applied to an electronic device, the storage medium carrying one or more computer programs that can be executed by the electronic device to enable the electronic device to perform the infrared spectroscopy detection method as described above.

[0046] As can be seen from the above technical solution, this application discloses an infrared spectral detection method, device, electronic device, and storage medium for rubber. This method and device are applied to electronic devices, specifically involving: performing a non-periodic modeling based on the chemical structural formula of the rubber component to be detected to obtain a trial model; acquiring the Hessian matrix of the trial model and the corresponding peak intensity and position lattice data; simplifying the lattice data to obtain simplified lattice data; organizing the simplified lattice data to obtain a table view array; plotting based on the table view array to obtain the total spectrum; peak marking processing of the total spectrum to obtain multiple peak positions; correcting each peak position using empirical formulas to obtain multiple corrected peak positions; and replacing the corresponding peak positions in the total spectrum with the corrected peak positions to obtain a corrected infrared spectrum. This solution achieves accurate and rapid infrared detection of failed rubber components without causing damage. Furthermore, because of the optimized processing methods, the obtained infrared spectrum is relatively clean, enabling analysts to easily identify subtle changes in functional group content. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart of an infrared spectroscopy detection method for rubber according to an embodiment of this application;

[0049] Figure 2a This is a schematic diagram of acrylonitrile monomer according to an embodiment of this application;

[0050] Figure 2b This is a schematic diagram of the butadiene monomer according to an embodiment of this application;

[0051] Figure 2c A schematic diagram of the trial calculation model for setting acrylonitrile at nodes 4# and 7# in an embodiment of this application;

[0052] Figure 3 This is a schematic diagram of the total spectrum based on the table viewpoint array according to an embodiment of this application;

[0053] Figure 4This is a simplified schematic diagram of a dot matrix according to an embodiment of this application;

[0054] Figure 5 This is a schematic diagram comparing the infrared spectrum of an embodiment of this application with the experimental spectrum;

[0055] Figure 6 This is a block diagram of an infrared spectroscopy detection device for rubber according to an embodiment of this application;

[0056] Figure 7 This is a block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0057] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0058] This application describes the infrared spectroscopy detection method in detail using nitrile rubber as an example, and specific embodiments are as follows.

[0059] Figure 1 This is a flowchart of an infrared spectroscopy detection method for rubber according to an embodiment of this application.

[0060] like Figure 1 As shown, the infrared spectroscopy detection method for rubber provided in this embodiment is applied to electronic equipment to achieve accurate and rapid infrared detection of failed rubber parts through non-destructive means, thereby obtaining a clean infrared spectrum. This helps analysts easily identify subtle changes in functional group content. The electronic equipment can be understood as a computer, server, or cloud platform with data computing and information processing capabilities. The infrared spectroscopy detection method specifically includes the following steps:

[0061] S1. Perform non-periodic modeling based on the chemical structure of the rubber component to be tested.

[0062] A trial model of the rubber component was obtained through aperiodic modeling. The specific process is as follows:

[0063] First, based on the chemical structural formula of the rubber component to be tested, the repetitive units of each functional group are identified, and the repetitive units are modeled to obtain the repetitive unit model. Taking nitrile rubber as an example, the repetitive units include acrylonitrile units and butadiene units, such as acrylonitrile monomers... Figure 2a As shown, butadiene monomers are as follows Figure 2b As shown.

[0064] Aperiodic modeling was performed based on the described iterative unit model, with a chain length of 10, resulting in a trial model. Since nitrile rubber is not an end-group compound, nodes 3#-8# in the scaled-down model can all be replaced with acrylonitrile. Trial models for acrylonitrile were set at nodes 4# and 7# as follows... Figure 2c As shown.

[0065] In addition, during the modeling process in this application, a full-process trial calculation is performed using a trial model to select a basis set with guaranteed accuracy and lower time cost.

[0066] For the Materials Studio computing platform, there are the following two computing base sets:

[0067] (1) CASTEP:

[0068] A plane-wave basis set offers high accuracy for calculating periodic structures, with system energy expressed in eV by default. However, for aperiodic structures such as polymers, periodicity must be assumed for calculation. CASTEP typically increases the cutoff energy to enhance the plane-wave basis set describing the wavefunction expansion, improving accuracy but also increasing computational cost. The K-point is a parameter used in periodic structure calculations; for polymers, choosing a relatively small K-point reduces computational error. Both parameters need to be determined through convergence testing before geometric optimization.

[0069] (2) Dmol3:

[0070] A localized basis set, with system energy expressed in Ha by default, is characterized by its fast computation speed, capable of calculating both periodic and aperiodic structures without requiring cutoff energy-related convergence tests. However, it offers relatively few computable properties. To ensure comparability with CASTEP calculations, we also assumed a periodic structure, optimized the unit cell, and performed vibrational property calculations under the same accuracy requirements.

[0071] The calculation times obtained by the trial model at the same precision are shown in the table below. After conversion, the system energy calculated by CASTEP is significantly higher, which may be due to the difficulty in expressing van der Waals forces in smaller periodic structures. The Dmol3 calculation result is lower than the CASTEP calculation result, indicating that the system obtained by Dmol3 is more stable.

[0072] Calculation results and time for CASTEP and Dmol3

[0073]

[0074] The total computation time shows that the small molecule model using Dmol3 is faster, and its computational efficiency and accuracy for frequencies are significantly higher than CASTEP, making it more suitable for debugging infrared spectroscopy simulations. Furthermore, it is anticipated that calculations using long-chain polymer models will incur higher computational costs. Therefore, the Dmol3 basis set will be used for subsequent simulations.

[0075] S2. Obtain the Hessian matrix and corresponding peak intensity and peak position lattice data of the trial model.

[0076] That is, after the trial model is constructed, the Hessian matrix and the corresponding peak intensity and peak position lattice data are obtained from the model. Since this application uses nitrile rubber as an example, the trial model is a nitrile rubber model.

[0077] S3. Simplify the dot matrix data to obtain simplified dot matrix data.

[0078] Specifically, a visualization tool was used to examine the vibrational modes corresponding to the peak positions in the model, thereby eliminating various vibrational mode signals from the chain-end atoms. The above trial model shows that CH and CH3- vibrational signals exist at the ends. The results of the visualization vibrational mode analysis are shown in the table below:

[0079]

[0080] In addition, after removing vibrational mode signals from all chain-end atoms in the lattice data, the lattice data within the required range is truncated to obtain simplified lattice data. Typically, a range of 400-4000 cm⁻¹ is used. -1 .

[0081] S4. The simplified dot matrix data is processed to obtain the table dot matrix.

[0082] The simplified dot matrix data was processed, and signals with an intensity difference of 10:1 or greater within 50 cm⁻¹ were removed; a search was then performed within a specific range (usually 5 cm). -1 And 50cm -1 The data is merged based on the difference in signal strength, typically with a signal-to-noise ratio of 10:1 during noise reduction and a strength ratio of 1.5:1 during merging. The merged data is then labeled to obtain the processed table view matrix.

[0083] S5. Based on the table viewpoint matrix, a graph is drawn to obtain the total spectrum.

[0084] Input the table viewpoint matrix into a charting tool for plotting, such as using Excel, Origin, or formula-based charting. Figure 3 As shown, the formula used for formula-based drawing is as follows:

[0085]

[0086] Where A is the peak intensity, μ is the peak position, and FWHM is the peak width. To be realistic, the peak width is usually chosen to be 15cm. -1 .

[0087] S6. Perform peak labeling on the total spectrum to obtain multiple peak positions.

[0088] Specifically, firstly, peak labeling is performed on the total spectrum to obtain multiple peak positions. Then, Gaussian fitting is applied to the total spectrum to output a simplified lattice and the corresponding peak widths. Finally, to prevent distortion caused by overfitting, peak widths exceeding 50cm are excluded. -1 After signal removal, the simplified dot matrix is ​​as follows: Figure 4 As shown.

[0089] S7. Each peak position is corrected using empirical formulas to obtain multiple corrected peak positions.

[0090] The peak positions within the simplified lattice are corrected using empirical formulas, such as those applied throughout the entire lattice or at 2000 cm⁻¹. -1 The linear relationship is defined by the boundary at this point, and the difference between the corrected peak position and the standard sample spectrum is generally less than 20 cm⁻¹. -1 This indicates that the correction is effective and can be used for practical analysis, as shown in the table below:

[0091]

[0092] F exp For the peak position corresponding to the standard sample test spectrum, F cal To correct the previous peak position, F adjust This is the corrected peak position.

[0093] The empirical formula used in this application is:

[0094] F ajust =45.5 + 0.963 * F cal

[0095] Among them, F cal To correspond to the peak position of the simplified lattice, F adjust This refers to the corrected peak position.

[0096] S8. Replace the corresponding peaks in the total spectrum with the corrected peak positions.

[0097] The corrected infrared spectrum was obtained through replacement processing. Figure 5 As shown, the corrected infrared spectrum has a strong correlation with the experimental spectrum, proving that the infrared spectrum obtained in this application has strong auxiliary analytical value.

[0098] As can be seen from the above technical solution, this embodiment provides an infrared spectral detection method for rubber. This method is applied to electronic devices. Specifically, it involves performing a non-periodic modeling based on the chemical structure of the rubber component to be detected to obtain a trial model; acquiring the Hessian matrix of the trial model and the corresponding peak intensity and position lattice data; simplifying the lattice data to obtain simplified lattice data; organizing the simplified lattice data to obtain a table view lattice; plotting based on the table view lattice to obtain the total spectrum; peak marking processing of the total spectrum to obtain multiple peak positions; correcting each peak position using empirical formulas to obtain multiple corrected peak positions; and replacing the corresponding peak positions in the total spectrum with the corrected peak positions to obtain the corrected infrared spectrum. This solution achieves accurate and rapid infrared detection of the rubber component without damaging it. Furthermore, because of the optimized processing methods, the obtained infrared spectrum is relatively clean, which helps analysts easily identify subtle changes in functional group content.

[0099] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0100] Although the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous.

[0101] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0102] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer.

[0103] Figure 6 This is a block diagram of an infrared spectroscopy detection device for rubber according to an embodiment of this application.

[0104] like Figure 6 As shown, the infrared spectroscopy detection device for rubber provided in this embodiment is applied to electronic equipment to achieve accurate and rapid infrared detection of failed rubber parts through non-destructive means, thereby obtaining a clean infrared spectrum and helping analysts easily identify subtle changes in functional group content. This electronic equipment can be understood as a computer, server, or cloud platform with data computing and information processing capabilities. Specifically, the infrared spectroscopy detection device includes a modeling processing module 10, a data acquisition module 20, a simplification processing module 30, a data processing module 40, a plotting processing module 50, a peak marking processing module 60, a correction processing module 70, and a spectral output module 80.

[0105] The modeling processing module is used for non-periodic modeling based on the chemical structure of the rubber component to be tested.

[0106] A trial model of the rubber component was obtained through aperiodic modeling. The specific process is as follows:

[0107] First, based on the chemical structural formula of the rubber component to be tested, the repetitive units of each functional group are identified, and the repetitive units are modeled to obtain the repetitive unit model. Taking nitrile rubber as an example, the repetitive units include acrylonitrile units and butadiene units, such as acrylonitrile monomers... Figure 2a As shown, butadiene monomers are as follows Figure 2b As shown.

[0108] Aperiodic modeling was performed based on the described iterative unit model, with a chain length of 10, resulting in a trial model. Since nitrile rubber is not an end-group compound, nodes 3#-8# in the scaled-down model can all be replaced with acrylonitrile. Trial models for acrylonitrile were set at nodes 4# and 7# as follows... Figure 2c As shown.

[0109] In addition, during the modeling process in this application, a full-process trial calculation is performed using a trial model to select a basis set with guaranteed accuracy and lower time cost.

[0110] For the Materials Studio computing platform, there are the following two computing base sets:

[0111] (1) CASTEP:

[0112] A plane-wave basis set offers high accuracy for calculating periodic structures, with system energy expressed in eV by default. However, for aperiodic structures such as polymers, periodicity must be assumed for calculation. CASTEP typically increases the cutoff energy to enhance the plane-wave basis set describing the wavefunction expansion, improving accuracy but also increasing computational cost. The K-point is a parameter used in periodic structure calculations; for polymers, choosing a relatively small K-point reduces computational error. Both parameters need to be determined through convergence testing before geometric optimization.

[0113] (2) Dmol3:

[0114] A localized basis set, with system energy expressed in Ha by default, is characterized by its fast computation speed, capable of calculating both periodic and aperiodic structures without requiring cutoff energy-related convergence tests. However, it offers relatively few computable properties. To ensure comparability with CASTEP calculations, we also assumed a periodic structure, optimized the unit cell, and performed vibrational property calculations under the same accuracy requirements.

[0115] The calculation times obtained by the trial model at the same precision are shown in the table below. After conversion, the system energy calculated by CASTEP is significantly higher, which may be due to the difficulty in expressing van der Waals forces in smaller periodic structures. The Dmol3 calculation result is lower than the CASTEP calculation result, indicating that the system obtained by Dmol3 is more stable.

[0116] Calculation results and time for CASTEP and Dmol3

[0117]

[0118]

[0119] The total computation time shows that the small molecule model using Dmol3 is faster, and its computational efficiency and accuracy for frequencies are significantly higher than CASTEP, making it more suitable for debugging infrared spectroscopy simulations. Furthermore, it is anticipated that calculations using long-chain polymer models will incur higher computational costs. Therefore, the Dmol3 basis set will be used for subsequent simulations.

[0120] The data acquisition module is used to acquire the Hessian matrix and the corresponding peak intensity and peak position lattice data of the trial model.

[0121] That is, after the trial model is constructed, the Hessian matrix and the corresponding peak intensity and peak position lattice data are obtained from the model. Since this application uses nitrile rubber as an example, the trial model is a nitrile rubber model.

[0122] The simplification processing module is used to simplify the dot matrix data to obtain simplified dot matrix data.

[0123] Specifically, a visualization tool was used to examine the vibrational modes corresponding to the peak positions in the model, thereby eliminating various vibrational mode signals from the chain-end atoms. The above trial model shows that CH and CH3- vibrational signals exist at the ends. The results of the visualization vibrational mode analysis are shown in the table below:

[0124]

[0125]

[0126] In addition, after removing vibrational mode signals from all chain-end atoms in the lattice data, the lattice data within the required range is truncated to obtain simplified lattice data. Typically, a range of 400-4000 cm⁻¹ is used. -1 .

[0127] The data processing module is used to process simplified dot matrix data to obtain tabular dot matrix data.

[0128] The simplified dot matrix data was processed, and signals with an intensity difference of 10:1 or greater within 50 cm⁻¹ were removed; a search was then performed within a specific range (usually 5 cm). -1 And 50cm -1 The data is merged based on the difference in signal strength, typically with a signal-to-noise ratio of 10:1 during noise reduction and a strength ratio of 1.5:1 during merging. The merged data is then labeled to obtain the processed table view matrix.

[0129] The mapping module is used to create maps based on the table viewpoint matrix to obtain the total spectrum.

[0130] Input the table viewpoint matrix into a charting tool for plotting, such as using Excel, Origin, or formula-based charting. Figure 3 As shown, the formula used for formula-based drawing is as follows:

[0131]

[0132] Where A is the peak intensity, μ is the peak position, and FWHM is the peak width. To be realistic, the peak width is usually chosen to be 15cm. -1 .

[0133] The peak marking module is used to mark the peaks in the total spectrum to obtain multiple peak positions.

[0134] Specifically, firstly, peak labeling is performed on the total spectrum to obtain multiple peak positions. Then, Gaussian fitting is applied to the total spectrum to output a simplified lattice and the corresponding peak widths. Finally, to prevent distortion caused by overfitting, peak widths exceeding 50cm are excluded. -1 After signal removal, the simplified dot matrix is ​​as follows: Figure 4 As shown.

[0135] The correction processing module is used to correct each peak position using empirical formulas, resulting in multiple corrected peak positions.

[0136] The peak positions within the simplified lattice are corrected using empirical formulas, such as those applied throughout the entire lattice or at 2000 cm⁻¹. -1 The linear relationship is defined by the boundary at this point, and the difference between the corrected peak position and the standard sample spectrum is generally less than 20 cm⁻¹. -1 This indicates that the correction is effective and can be used for practical analysis, as shown in the table below:

[0137]

[0138]

[0139] F exp For the peak position corresponding to the standard sample test spectrum, F cal To correct the previous peak position, F adjust This is the corrected peak position.

[0140] The empirical formula used in this application is:

[0141] F ajust =45.5 + 0.963 * F cal

[0142] Among them, F cal To correspond to the peak position of the simplified lattice, F adjust This refers to the corrected peak position.

[0143] The spectral output module is used to replace the corresponding peak positions in the total spectrum using the corrected peak positions.

[0144] The corrected infrared spectrum was obtained through replacement processing. Figure 5 As shown, the corrected infrared spectrum has a strong correlation with the experimental spectrum, proving that the infrared spectrum obtained in this application has strong auxiliary analytical value.

[0145] As can be seen from the above technical solution, this embodiment provides an infrared spectral detection device for rubber. This device is applied to electronic devices. Specifically, it involves performing a non-periodic modeling based on the chemical structure of the rubber component to be detected to obtain a trial model; acquiring the Hessian matrix of the trial model and the corresponding peak intensity and position lattice data; simplifying the lattice data to obtain simplified lattice data; organizing the simplified lattice data to obtain a table view lattice; plotting based on the table view lattice to obtain the total spectrum; peak marking processing of the total spectrum to obtain multiple peak positions; correcting each peak position using empirical formulas to obtain multiple corrected peak positions; and replacing the corresponding peak positions in the total spectrum with the corrected peak positions to obtain the corrected infrared spectrum. This solution achieves accurate and rapid infrared detection of rubber failure components without damaging them. Furthermore, because of the optimized processing methods, the obtained infrared spectrum is relatively clean, which helps analysts easily identify subtle changes in functional group content.

[0146] The units described in the embodiments of this disclosure can be implemented in software or in hardware. The name of a unit does not necessarily limit the unit itself; for example, the first acquisition unit can also be described as "a unit that acquires at least two Internet Protocol addresses".

[0147] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0148] Figure 7 This is a block diagram of an electronic device according to an embodiment of this application.

[0149] refer to Figure 7The diagram illustrates a structural schematic suitable for implementing the electronic device in the embodiments of this disclosure. The terminal device in the embodiments of this disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. This electronic device is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this disclosure.

[0150] The electronic device may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from an input device 706 into a random access memory (RAM) 703. The RAM also stores various programs and data required for the operation of the electronic device. The processing unit, ROM, and RAM are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0151] Typically, the following devices can be connected to the I / O interface: input devices including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 707 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 708 including, for example, magnetic tapes, hard disks, etc.; and communication devices 709. Communication device 709 allows the electronic device to communicate wirelessly or wiredly with other devices to exchange data. Although electronic devices with various devices are shown in the figures, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0152] This application also provides an embodiment of a computer-readable storage medium.

[0153] The aforementioned computer-readable storage medium is applied to an electronic device and carries one or more computer programs. When these programs are executed by the electronic device, the device performs a non-periodic modeling based on the chemical structure of the rubber component to be detected, obtaining a trial model. It then acquires the Hessian matrix of the trial model and the corresponding peak intensity and position lattice data. The lattice data is simplified to obtain simplified lattice data. The simplified lattice data is then organized to obtain a table view lattice. Based on the table view lattice, a graph is plotted to obtain the total spectrum. The total spectrum is then peak-labeled to obtain multiple peak positions. Each peak position is corrected using empirical formulas to obtain multiple corrected peak positions. The corrected peak positions are used to replace the corresponding peak positions in the total spectrum to obtain a corrected infrared spectrum. This method enables accurate and rapid infrared detection of failed rubber components without causing damage. Furthermore, because the optimized processing methods result in a cleaner infrared spectrum, it helps analysts easily identify subtle changes in functional group content.

[0154] It should be noted that the computer-readable medium described above in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0155] In this disclosure, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0156] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0157] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0158] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0159] The technical solution provided by the present invention has been described in detail above. Specific examples have been used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. An infrared spectroscopy detection method for rubber, applied to electronic devices, characterized in that, The infrared spectroscopy detection method includes the following steps: A non-periodic model is performed based on the chemical structure of the rubber component to be tested, resulting in a trial model. Obtain the Hessian matrix and corresponding peak intensity and peak position lattice data of the trial model; The dot matrix data is simplified to obtain simplified dot matrix data; The simplified dot matrix data is processed to obtain a table dot matrix; Based on the aforementioned viewpoint matrix, a graph is drawn to obtain the total spectrum; Peak labeling is performed on the total spectrum to obtain multiple peak positions; Each peak position is corrected using an empirical formula to obtain multiple corrected peak positions; The corresponding peak position in the total spectrum is replaced using the corrected peak position to obtain the corrected infrared spectrum.

2. The infrared spectroscopy detection method as described in claim 1, characterized in that, The non-periodic modeling based on the chemical structure of the rubber component to be tested, to obtain the trial model, includes the following steps: Based on the chemical structural formula, the repeating units of each functional group were identified, and the repeating units were modeled to obtain the repeating unit model. The trial model is obtained by performing non-periodic modeling based on the recurring unit model.

3. The infrared spectroscopy detection method as described in claim 1, characterized in that, The step of simplifying the dot matrix data to obtain simplified dot matrix data includes the following steps: The vibrational mode signals of all chain-end atoms in the lattice data are removed; The simplified dot matrix data is obtained by extracting the dot matrix data within the required range after the elimination process.

4. The infrared spectroscopy detection method as described in claim 1, characterized in that, The step of processing the simplified dot matrix data to obtain a table view matrix includes the following steps: The simplified dot matrix data can be retrieved within a specific range; The simplified dot matrix data retrieved is merged and labeled using signal strength difference as the criterion to obtain a table dot matrix.

5. The infrared spectroscopy detection method as described in claim 1, characterized in that, The process of peak labeling the total spectrum to obtain multiple peak positions includes the following steps: Peak labeling is performed on the total spectrum to obtain the multiple peak positions; Gaussian fitting is performed on the total spectrum to output a simplified lattice and the peak widths corresponding to the simplified lattice. Signals with peak widths exceeding preset values ​​will be discarded.

6. The infrared spectroscopy detection method as described in claim 4, characterized in that, The step of correcting each peak position using an empirical formula to obtain multiple corrected peak positions includes the following steps: The peak positions corresponding to the simplified lattice are corrected using the empirical formula to obtain the corrected peak positions.

7. The infrared spectroscopy detection method as described in claim 6, characterized in that, The empirical formula is: F ajust =45.5+0.963*F cal Among them, F cal To correspond to the peak position of the simplified lattice, F adjust This refers to the corrected peak position.

8. An infrared spectroscopy detection device for rubber, used in electronic equipment, characterized in that, The infrared spectroscopy detection device includes: The modeling processing module is configured to perform non-periodic modeling based on the chemical structure of the rubber component to be tested, and obtain a trial model. The data acquisition module is configured to acquire the Hessian matrix and corresponding peak intensity and peak position lattice data of the trial model; A simplification processing module is configured to simplify the dot matrix data to obtain simplified dot matrix data. The data processing module is configured to process the simplified dot matrix data to obtain a table view matrix; The mapping processing module is configured to perform mapping based on the table viewpoint matrix to obtain the total spectrum; The peak marking module is configured to perform peak marking on the total spectrum to obtain multiple peak positions. The correction processing module is configured to correct each of the peak positions using an empirical formula to obtain multiple corrected peak positions. The spectral output module is configured to replace the corresponding peak position in the total spectrum with the corrected peak position to obtain the corrected infrared spectrum.

9. An electronic device, characterized in that, The electronic device includes at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs or instructions; The processor is used to execute the computer program or instructions to enable the electronic device to implement the infrared spectroscopy detection method as described in any one of claims 1 to 7.

10. A computer-readable storage medium for use in electronic devices, characterized in that, The storage medium carries one or more computer programs that can be executed by the electronic device, thereby enabling the electronic device to implement the infrared spectroscopy detection method as described in any one of claims 1 to 7.

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