A method, device, equipment and medium for decomposing components of nuclear magnetic resonance T2 spectrum

Abstract

The present application discloses a method, device, equipment and medium for decomposing components of a nuclear magnetic resonance T2 spectrum, and the method includes: obtaining nuclear magnetic resonance spectrum data of a sample; preprocessing the nuclear magnetic resonance spectrum data; converting the preprocessed nuclear magnetic resonance spectrum data into an original spectrum with relaxation time as the horizontal coordinate and signal intensity as the vertical coordinate; performing peak-finding processing on the original spectrum by a peak-finding algorithm to obtain original spectrum characteristics; modeling spectrum data based on a symmetric Gaussian function according to the original spectrum characteristics to obtain a Gaussian function model; and then determining a Gaussian curve based on the Gaussian function model; fitting and optimizing the Gaussian curve by a numerical fitting algorithm; decomposing the spectrum of the target parameter combination to obtain a decomposition result. The embodiment of the present application has a faster calculation speed and processing efficiency. The embodiment of the present application can efficiently decompose the components of the nuclear magnetic resonance spectrum. It can be widely used in the field of data processing technology.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, equipment and medium for decomposing components of a nuclear magnetic resonance T2 spectrum. Background Art

[0002] In the field of oil and gas exploration, nuclear magnetic resonance technology is widely used in rock physics and oil and gas exploration as a non-destructive and non-invasive method. Among them, nuclear magnetic resonance spectroscopy is an important analytical tool. By measuring the decay time (value) of the nuclear magnetic resonance signal, key underground reservoir information such as porosity, permeability and fluid type can be obtained. Due to the sensitivity of the spectrum to different components, it is widely used to study the distribution and dynamic changes of different phases such as water, oil and gas in the reservoir, providing strong support for oil and gas reservoir evaluation. Common methods for identifying reservoir fluids using nuclear magnetic resonance spectroscopy include:

[0003] Difference spectrum method: By obtaining the nuclear magnetic resonance spectra of the reservoir sample at two different saturations and subtracting them, a new spectrum is obtained, the so-called "difference spectrum". The information such as peak position, peak area and peak intensity in the difference spectrum can reflect the different types of fluid components in the reservoir sample, and thus can be used to distinguish different reservoir fluids.

[0004] Spectrum shifting method: By obtaining the nuclear magnetic resonance spectra of reservoir samples at different saturations and performing a translation (spectrum shifting) operation on them, a series of translated spectra are obtained. The information such as peak position, peak area and peak intensity in the shifted spectra can reflect the different types of fluid components in the reservoir samples, and thus can be used to distinguish different reservoir fluids.

[0005] However, the above-mentioned difference spectrum method and shift spectrum method are sensitive to noise and interference when identifying fluids. Due to the complex underground reservoir environment, the signal may be affected by various factors, such as rock minerals, pore structure changes, etc. These factors may introduce noise and affect the accuracy of identification. In addition, these two methods usually require more prior information, such as rock porosity, rock mineral composition, etc., to identify fluid types. In actual exploration, it may be difficult to obtain accurate prior information, especially in the case of initial exploration. And it has great limitations when facing the overlap of oil, gas and water spectra.

[0006] Application Contents

[0007] The main purpose of the embodiments of the present application is to provide a method, device, equipment and medium for decomposing nuclear magnetic resonance T2 spectrum components, which can efficiently decompose nuclear magnetic resonance T2 spectrum components.

[0008] To achieve the above purpose, one aspect of an embodiment of the present application provides a method for decomposing components of a nuclear magnetic resonance T2 spectrum, the method comprising:

[0009] Acquiring nuclear magnetic resonance T2 spectrum data of the collected sample; the nuclear magnetic resonance T2 spectrum data includes relaxation time and signal intensity;

[0010] Preprocessing of NMR T2 spectrum data;

[0011] The pre-processed nuclear magnetic resonance T2 spectrum data is converted into a T2 original spectrum with relaxation time as the horizontal axis and signal intensity as the vertical axis;

[0012] The original spectrum of T2 is processed by peak finding algorithm to obtain the original spectrum characteristics of T2;

[0013] According to the original T2 spectrum characteristics, T2 spectrum data modeling is performed based on a symmetric Gaussian function to obtain a Gaussian function model; and then a Gaussian curve is determined based on the Gaussian function model;

[0014] The Gaussian curve is fitted and optimized by a numerical fitting algorithm; wherein, in the process of fitting optimization, the relevant parameters input into the Gaussian function model are adjusted, and then the target parameter combination is determined by minimizing the fitting error and controlling the area of ​​each spectral peak;

[0015] The T2 spectrum of the target parameter combination is decomposed to obtain a decomposition result.

[0016] In some embodiments, obtaining nuclear magnetic resonance T2 spectrum data of a sample includes:

[0017] The collected sample is placed in a nuclear magnetic resonance instrument, and then the nuclear magnetic resonance T2 spectrum data is obtained by collecting a series of pulse sequences and the response signals of the pulse sequences.

[0018] In some embodiments, preprocessing the nuclear magnetic resonance T2 spectrum data includes at least one of the following:

[0019] The nuclear magnetic resonance T2 spectrum data is denoised by mathematical filtering method;

[0020] The background signal of NMR T2 spectrum data was eliminated by baseline correction;

[0021] Perform spectral line alignment on NMR T2 spectrum data.

[0022] In some embodiments, peak finding processing is performed on the T2 original spectrum using a peak finding algorithm, including:

[0023] The peak of the original T2 spectrum was found by the extreme point peak finding method.

[0024] In some embodiments, the original T2 spectrum features include the maximum amplitude, spectrum peak position and spread width of the Gaussian peak; according to the original T2 spectrum features, T2 spectrum data modeling is performed based on a symmetric Gaussian function to obtain a Gaussian function model, including:

[0025] According to the maximum amplitude, peak position and spread width of the Gaussian peak, the Gaussian function model is obtained by combining the T2 spectrum amplitude modeling corresponding to the exponential function correlation;

[0026] Among them, the expression of the Gaussian function model is:

[0027]

[0028] Where f(t) represents the T2 spectrum amplitude at time t; A represents the maximum amplitude of the Gaussian peak; exp() represents the exponential function; α represents the spectral peak position of the Gaussian peak; σ represents the spread width of the Gaussian peak.

[0029] In some embodiments, the Gaussian curve is fitted and optimized by a numerical fitting algorithm, including:

[0030] The Gaussian curve is fitted and optimized using a nonlinear least squares algorithm.

[0031] In some embodiments, when there are multiple spectrum components, each spectrum component is represented by a symmetric Gaussian function; controlling each spectrum peak area includes:

[0032] The spread width of the Gaussian peak is modified to achieve a ratio of the sum of the decomposed spectrum area to the original spectrum area within a preset range;

[0033] The sum of the decomposed spectrum areas represents the sum of the areas corresponding to each spectrum component, and the original spectrum area represents the area of ​​the original spectrum of T2.

[0034] In some embodiments, the method further comprises:

[0035] Arrange the output results based on the original T2 spectrum and decomposition results;

[0036] The output results include: the maximum intensity of each NMR signal obtained by decomposition and the numerical information of the corresponding relaxation time; the numerical area of ​​the original spectrum and the decomposed spectrum; the ratio of the sum of the decomposed spectrum area to the original spectrum area; and the generation of the NMR decomposition spectrum.

[0037] To achieve the above object, another aspect of the embodiment of the present application provides a nuclear magnetic resonance T2 spectrum component decomposition device, the device comprising:

[0038] The first module is used to obtain nuclear magnetic resonance spectrum data of the collected sample; the nuclear magnetic resonance spectrum data includes relaxation time and signal intensity;

[0039] The second module is used to pre-process the NMR spectrum data;

[0040] The third module is used to convert the pre-processed NMR spectrum data into an original spectrum with relaxation time as the horizontal axis and signal intensity as the vertical axis;

[0041] The fourth module is used to perform peak-finding processing on the original spectrum through a peak-finding algorithm to obtain the original spectrum features;

[0042] The fifth module is used to perform spectrum data modeling based on the symmetric Gaussian function according to the original spectrum features to obtain a Gaussian function model; and then determine a Gaussian curve based on the Gaussian function model;

[0043] The sixth module is used to optimize the Gaussian curve by numerical fitting algorithm; wherein, in the process of fitting optimization, relevant parameters input into the Gaussian function model are adjusted, and then the target parameter combination is determined by minimizing the fitting error and controlling the area of ​​each spectral peak;

[0044] The seventh module is used to decompose the spectrum of the target parameter combination to obtain a decomposition result.

[0045] In some embodiments, the apparatus further comprises:

[0046] The eighth module is used to organize the output results based on the T2 original spectrum and decomposition results;

[0047] The output results include: the maximum intensity of each NMR signal obtained by decomposition and the numerical information of the corresponding relaxation time; the numerical area of ​​the original spectrum and the decomposed spectrum; the ratio of the sum of the decomposed spectrum area to the original spectrum area; and the generation of the NMR decomposition spectrum.

[0048] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned method is implemented.

[0049] The embodiments of the present application include at least the following beneficial effects: The present application provides a method, device, equipment and medium for decomposing nuclear magnetic resonance T2 spectrum components, the scheme acquires nuclear magnetic resonance T2 spectrum data of a collected sample; the nuclear magnetic resonance T2 spectrum data includes relaxation time and signal intensity; the nuclear magnetic resonance T2 spectrum data is preprocessed; the preprocessed nuclear magnetic resonance T2 spectrum data is converted into a T2 original spectrum with relaxation time as the horizontal coordinate and signal intensity as the vertical coordinate; the T2 original spectrum is subjected to peak-finding processing by a peak-finding algorithm to obtain T2 original spectrum characteristics; according to the T2 original spectrum characteristics, T2 spectrum data is modeled based on a symmetric Gaussian function to obtain a Gaussian function model; and a Gaussian curve is determined based on the Gaussian function model; the Gaussian curve is fit-optimized by a numerical fitting algorithm; wherein, in the process of fitting optimization, the relevant parameters input into the Gaussian function model are adjusted, and then the target parameter combination is determined by minimizing the fitting error and controlling the area of ​​each spectrum peak; the T2 spectrum of the target parameter combination is decomposed to obtain a decomposition result. The embodiment of the present application realizes the decomposition of the nuclear magnetic resonance spectrum based on the symmetric Gaussian function model. This method can realize fast data processing and analysis through appropriate mathematical models and algorithms, improve work efficiency, and has faster calculation speed and processing efficiency. The embodiment of the present application can efficiently decompose the components of the nuclear magnetic resonance T2 spectrum. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a flow chart of a method for decomposing components of a nuclear magnetic resonance T2 spectrum provided in an embodiment of the present application;

[0051] Figure 2 It is a schematic diagram of the overall process of decomposing components of the nuclear magnetic resonance T2 spectrum provided in the embodiment of the present application;

[0052] Figure 3 It is an example diagram of the original NMR T2 spectrum of relaxation time X and signal amplitude Y provided in the embodiment of the present application;

[0053] Figure 4 This is an example diagram of a nuclear magnetic resonance T2 decomposition spectrum after decomposition using the nuclear magnetic resonance T2 spectrum component decomposition method based on a symmetric Gaussian function model provided in an embodiment of the present application;

[0054] Figure 5 It is an example diagram of relevant output information of a nuclear magnetic resonance T2 decomposition spectrum after decomposition by the nuclear magnetic resonance T2 spectrum component decomposition method based on a symmetric Gaussian function model provided in an embodiment of the present application;

[0055] Figure 6 It is a schematic structural diagram of a nuclear magnetic resonance T2 spectrum component decomposition device provided in an embodiment of the present application;

[0056] Figure 7It is a structural block diagram of a nuclear magnetic resonance T2 spectrum component decomposition device based on a symmetric Gaussian function model provided in an embodiment of the present application;

[0057] Figure 8 It is a structural block diagram of a nuclear magnetic resonance T2 spectrum component decomposition device based on a symmetric Gaussian function model provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0059] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0060] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0062] The nuclear magnetic resonance T2 spectrum component decomposition method provided in the embodiment of the present application relates to the field of data processing technology. The nuclear magnetic resonance T2 spectrum component decomposition method provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured to provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and cloud servers for basic cloud computing services such as big data and artificial intelligence platforms, and the server can also be a node server in a blockchain network; the software can be an application that implements the nuclear magnetic resonance T2 spectrum component decomposition method, etc., but is not limited to the above form.

[0063] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0064] Figure 1 is an optional flow chart of the method for decomposing components of a nuclear magnetic resonance T2 spectrum provided in an embodiment of the present application, Figure 1 The method may include but is not limited to steps S100 to S500.

[0065] S100, obtaining nuclear magnetic resonance T2 spectrum data of the collected sample;

[0066] It should be noted that the nuclear magnetic resonance T2 spectrum data includes relaxation time and signal intensity; in some embodiments, step S100 may include: placing the collected sample in a nuclear magnetic resonance instrument, and then obtaining the nuclear magnetic resonance T2 spectrum data by collecting a series of pulse sequences and response signals of the pulse sequences.

[0067] For example, in some specific embodiments, nuclear magnetic resonance (NMR) spectrum data is collected; the nuclear magnetic resonance T2 spectrum data is specifically two items, namely relaxation time x and signal intensity y. The specific collection method is: collect a sample and place it in an NMR instrument, and obtain it by collecting a series of pulse sequences and their response signals.

[0068] S200, preprocessing the nuclear magnetic resonance T2 spectrum data;

[0069] It should be noted that, in some embodiments, step S200 includes at least one of the following: denoising the nuclear magnetic resonance spectrum data by a mathematical filtering method; eliminating the background signal of the nuclear magnetic resonance spectrum data by baseline correction; and aligning the spectrum lines of the nuclear magnetic resonance spectrum data.

[0070] Exemplarily, in some specific embodiments, NMR T2 spectrum data preprocessing is performed, specifically including but not limited to removing noise by mathematical filtering methods, performing baseline correction to eliminate background signals, and performing spectral line alignment to ensure consistency between different time points.

[0071] S300, converting the pre-processed nuclear magnetic resonance T2 spectrum data into a T2 original spectrum with relaxation time as the horizontal axis and signal intensity as the vertical axis;

[0072] S400, performing peak-finding processing on the original spectrum of T2 by using a peak-finding algorithm to obtain the original spectrum characteristics of T2;

[0073] It should be noted that, in some embodiments, performing peak-finding processing on the original spectrum using a peak-finding algorithm may include: performing peak-finding processing on the original spectrum using an extreme point peak-finding method.

[0074] S500, according to the original T2 spectrum features, T2 spectrum data modeling is performed based on a symmetric Gaussian function to obtain a Gaussian function model; and then a Gaussian curve is determined based on the Gaussian function model;

[0075] It should be noted that the original T2 spectrum features include the maximum amplitude, peak position and spread width of the Gaussian peak; in some embodiments, according to the original T2 spectrum features, T2 spectrum data modeling is performed based on a symmetric Gaussian function to obtain a Gaussian function model, which may include: according to the maximum amplitude, peak position and spread width of the Gaussian peak, the T2 spectrum amplitude modeling corresponding to the exponential function association is combined to obtain a Gaussian function model; wherein the expression of the Gaussian function model is:

[0076]

[0077] Where f(t) represents the T2 spectrum amplitude at time t; A represents the maximum amplitude of the Gaussian peak; exp() represents the exponential function; α represents the spectral peak position of the Gaussian peak; σ represents the spread width of the Gaussian peak.

[0078] S600, performing fitting optimization on the Gaussian curve by using a numerical fitting algorithm; wherein, in the process of fitting optimization, adjusting relevant parameters input into the Gaussian function model, and then determining a target parameter combination by minimizing the fitting error and controlling the area of ​​each spectral peak;

[0079] It should be noted that, in some embodiments, performing fitting optimization on the Gaussian curve by using a numerical fitting algorithm may include: performing fitting optimization on the Gaussian curve by using a nonlinear least squares algorithm.

[0080] In some embodiments, when there are multiple spectral components, each spectral component is represented by a symmetric Gaussian function; controlling the area of ​​each spectral peak may include: modifying the spread width of the Gaussian peak to achieve a ratio of the sum of the decomposed spectrum area to the original spectrum area within a preset range; wherein the sum of the decomposed spectrum area represents the sum of the areas corresponding to each spectral component, and the original spectrum area represents the area of ​​the original spectrum.

[0081] For example, in some specific embodiments, when there are multiple spectral components, each component is represented by a symmetric Gaussian function. In the algorithm, the value of the spread width of the Gaussian peak is modified to ensure that the ratio of the sum of the decomposed spectrum area to the original spectrum area is approximately 1. The corresponding preset range can be adjusted according to actual needs, for example, 0.99 to 1.01 (the specific accuracy can be adjusted according to actual application, and the purpose is to make the area ratio approximately 1).

[0082] S700 , decomposing the T2 spectrum of the target parameter combination to obtain a decomposition result.

[0083] It should be noted that decomposing the T2 spectrum of the target parameter combination can decompose the T2 spectrum into contributions of various components.

[0084] For example, in some specific embodiments, the contribution of each component can be obtained by calculating the value of the corresponding Gaussian function at different T2 values.

[0085] In some embodiments, the method may further include: organizing the output results based on the T2 original spectrum and the decomposition results; wherein the output results include: the maximum intensity of each nuclear magnetic resonance signal obtained by decomposition and the numerical information of the corresponding relaxation time; the original spectrum and the decomposition spectrum numerical area; the ratio of the sum of the decomposition spectrum area to the original spectrum area; and generating a nuclear magnetic resonance decomposition spectrum.

[0086] In order to explain the principles of the technical solution of the present application in detail, the overall process of the present application is described below in combination with some specific embodiments. It is easy to understand that the following is an explanation of the technical principles of the present application and cannot be regarded as a limitation of the present application.

[0087] In some specific embodiments, Figure 2 The overall flow chart of a method for decomposing NMR spectrum components based on a symmetric Gaussian function model provided in an embodiment of the present application is as follows: Figure 2 As shown, the method includes:

[0088] Step S1, collecting nuclear magnetic resonance T2 spectrum data.

[0089] In this embodiment, the T2 spectrum data is derived from a saturated white oil rock sample, and the required nuclear magnetic resonance T2 spectrum data are two items, namely, relaxation time x and signal intensity y. The acquisition method is to place the processed rock sample in the magnetic field of the NMR instrument, adjust it to a stable state, and obtain it by acquiring a series of pulse sequences and their response signals, wherein the response signal includes the intensity values ​​of multiple time points from different nuclear magnetic resonance signals in the sample.

[0090] Step S2, preprocessing the nuclear magnetic resonance T2 spectrum data.

[0091] Optionally, noise is removed by mathematical filtering methods, baseline correction is performed to eliminate background signals, and spectral alignment is performed to ensure consistency between different time points.

[0092] In this embodiment, smoothing is performed using the sgolayfilt function of MATLAB, which is helpful for removing high-frequency noise in the T2 spectrum data, and can improve the quality of the data to a certain extent.

[0093] Step S3, converting the preprocessed nuclear magnetic resonance T2 spectrum data into a T2 original spectrum with relaxation time as the horizontal axis and signal intensity as the vertical axis.

[0094] In this embodiment, step S3 includes:

[0095] S31: Import the preprocessed NMR T2 spectrum data into the MATLAB workspace via the xlread function;

[0096] S32: Use the figure and plot functions to draw the original T2 spectrum with relaxation time as the horizontal axis and signal intensity as the vertical axis. Figure 3 Shown is the original spectrum of T2 in this embodiment.

[0097] Step S4: Use a suitable peak-finding algorithm to perform peak-finding on the obtained T2 original spectrum.

[0098] In this embodiment, the peak finding algorithm used is the extreme point peak finding method.

[0099] For specific implementation, the findpeaks function in MATLAB can be used.

[0100] Step S5, establish a model based on symmetric Gaussian function for describing T2 spectrum data, according to the original spectrum characteristics of nuclear magnetic resonance T2, these characteristics can be the position of the peak, the width of the peak, the area of ​​the peak, etc., input initial parameters and return to the symmetric Gaussian function model, these initial parameters include the center position, height, width, etc. of the symmetric Gaussian function, which need to be selected and adjusted according to the actual data. In general, the original spectrum characteristics of nuclear magnetic resonance T2 and the peak search results include the following data sub-items: 1. Time constant (T2): reflects the rate of spin relaxation inside the material molecule. 2. Signal intensity (amplitude): reflects the content of various nuclei in the sample and their spin relaxation properties. 3. Peak position: reflects the spin relaxation time of different nuclei in the sample. 4. Peak width: reflects the spin-spin interaction relaxation time of different nuclei in the sample. 5. Peak area: reflects the relationship between the number of different nuclei in the sample and the relaxation time. For the case of multiple spectrum components, each component can be represented as a symmetric Gaussian function.

[0101] In this embodiment, the Gaussian function model formula is:

[0102]

[0103] Where A represents the maximum amplitude of the Gaussian peak, α represents the spectral peak position of the Gaussian peak, σ represents the spread width of the Gaussian peak, and f(t) represents the T2 spectrum amplitude at time t.

[0104] During specific implementation, in MATLAB, the parameters in the above-mentioned Gaussian formula are respectively defined and the initial parameters are set, which are stored in the variables. In the present embodiment, there are three Gaussian peaks, so three Gaussian functions are respectively defined, and parameters are set for them respectively. At this time, it should be noted that because the curve graph finally needs to be represented by the logarithmic scale on the x-axis, it is necessary to convert the data related to the relaxation time x to logarithmic scale in the code, and log10 (x) is used in MATLAB to realize that the three Gaussian component spectra are accumulated and finally drawn into a graph.

[0105] Step S6: Use a suitable numerical fitting algorithm to optimize the Gaussian curve. The Gaussian curve here is a specific embodiment of the Gaussian function model established in step S5, and the Gaussian function model is a mathematical tool for fitting and decomposing the original T2 spectrum.

[0106] In this embodiment, the numerical fitting algorithm adopts a nonlinear least squares algorithm. In specific implementation, the built-in function polyfit of MATLAB can be used to minimize the sum of square errors between the polynomial function value and the actual data points, thereby facilitating data fitting and prediction.

[0107] Step S7, adjusting the relevant parameters input into the Gaussian function model. This process involves finding the best parameter combination by minimizing the fitting error and controlling the area of ​​each spectral peak, so that the model has the highest fit with the actual spectral data and decomposes the T2 spectrum into the contribution of each component.

[0108] In this embodiment, each Gaussian peak in the correct T2 decomposition spectrum should maintain the consistency of signal intensity with the original T2 spectrum, including the shape of the spectrum peak, including the width, shape and position of the peak. Therefore, the logarithmic trapezoidal rule is used in MATLAB to calculate the original spectrum area and the area of ​​each component spectrum. Under the premise of fitting and optimizing the Gaussian curve, the ratio of the sum of the component spectrum area to the original spectrum area is always controlled to be close to 1. Figure 4 The figure shows the nuclear magnetic resonance T2 decomposition spectrum after decomposition by the nuclear magnetic resonance T2 spectrum component decomposition method based on the symmetric Gaussian function model in this embodiment.

[0109] Step S8: Provide output.

[0110] In this embodiment, if Figure 5 As shown in the figure, the output includes: the maximum intensity of each NMR signal obtained by decomposition and the numerical information of the corresponding T2 relaxation time; the numerical area of ​​the T2 original spectrum and the T2 decomposition spectrum; the ratio of the sum of the T2 decomposition spectrum area and the T2 original spectrum area; and the generation of the NMR T2 decomposition spectrum. In order to display the analysis results more intuitively.

[0111] Specifically, the maximum intensity of each NMR signal obtained by decomposition and the numerical information of the corresponding T2 relaxation time: This information comes from the Gaussian function model fitting result established in step S5. The maximum intensity of each NMR signal and the corresponding T2 relaxation time can be obtained by fitting the height and center position of each Gaussian curve.

[0112] T2 original spectrum and T2 decomposition spectrum numerical area: The numerical area of ​​the T2 original spectrum can be directly calculated by the logarithmic trapezoidal rule. The numerical area of ​​the T2 decomposition spectrum is calculated by calculating the area of ​​each component according to the Gaussian function model fitted in step S5, and adding up the areas of all components.

[0113] The ratio of the sum of the T2 decomposition spectrum area to the T2 original spectrum area: This ratio can be obtained by dividing the sum of the numerical areas of the T2 decomposition spectrum by the numerical area of ​​the T2 original spectrum. This ratio can reflect the relationship between the proportion of each component after T2 decomposition in the sample and the original T2 spectrum.

[0114] Generate a nuclear magnetic resonance T2 decomposition spectrum: This spectrum can plot each Gaussian curve on the T2 axis according to the Gaussian function model parameters obtained in step S5 to intuitively display the T2 relaxation time and relative intensity of each component in the sample.

[0115] In the specific implementation, the plot function can be used in MATLAB to draw curves, the fprintf function can print out data, and the legend, xlabel, ylablel, title and other commands can be used to add legends, labels, figure names and other related information to the T2 decomposition spectrum.

[0116] In summary, compared with the prior art, the beneficial effects of the embodiments of the present application include at least:

[0117] The method provided by the embodiment of the present application can decompose the nuclear magnetic resonance T2 spectrum and decompose it into multiple symmetrical Gaussian function models. In this way, the different components in the sample and their related information can be described and analyzed more accurately. At the same time, by decomposing the nuclear magnetic resonance T2 spectrum, the sensitivity of the signal can be improved. For complex sample systems, traditional T2 spectrum analysis may not be able to effectively identify and analyze each component. And using the symmetrical Gaussian function model to decompose, the signals of different components can be better distinguished and the sensitivity can be improved. In addition, for complex T2 spectra, the spectrum can be decomposed into multiple peaks using a symmetrical Gaussian function model, each peak representing a component. This decomposition method can intuitively show the contribution of different components in the spectrum, making the analysis and interpretation of the spectrum clearer and more visual. Compared with traditional analysis methods, the nuclear magnetic resonance T2 spectrum decomposition method based on the symmetrical Gaussian function model has a faster calculation speed and processing efficiency. This method can achieve fast data processing and analysis through appropriate mathematical models and algorithms, and improve work efficiency.

[0118] See also Figure 6 The present application also provides a nuclear magnetic resonance T2 spectrum component decomposition device 600, which can implement the above-mentioned nuclear magnetic resonance T2 spectrum component decomposition method, and the device includes:

[0119] The first module 610 is used to obtain nuclear magnetic resonance spectrum data of the collected sample; the nuclear magnetic resonance spectrum data includes relaxation time and signal intensity;

[0120] The second module 620 is used to pre-process the nuclear magnetic resonance spectrum data;

[0121] The third module 630 is used to convert the pre-processed nuclear magnetic resonance spectrum data into an original spectrum with relaxation time as the horizontal axis and signal intensity as the vertical axis;

[0122] The fourth module 640 is used to perform peak-finding processing on the original spectrum using a peak-finding algorithm to obtain original spectrum features;

[0123] The fifth module 650 is used to perform spectrum data modeling based on the symmetric Gaussian function according to the original spectrum features to obtain a Gaussian function model; and then determine a Gaussian curve based on the Gaussian function model;

[0124] The sixth module 660 is used to optimize the Gaussian curve by using a numerical fitting algorithm; wherein, in the process of the optimization, the relevant parameters input into the Gaussian function model are adjusted, and then the target parameter combination is determined by minimizing the fitting error and controlling the area of ​​each spectral peak;

[0125] The seventh module 670 is used to decompose the spectrum of the target parameter combination to obtain a decomposition result.

[0126] In some embodiments, the apparatus may further include:

[0127] The eighth module is used to organize the output results based on the T2 original spectrum and decomposition results;

[0128] The output results include: the maximum intensity of each NMR signal obtained by decomposition and the numerical information of the corresponding relaxation time; the numerical area of ​​the original spectrum and the decomposed spectrum; the ratio of the sum of the decomposed spectrum area to the original spectrum area; and the generation of the NMR decomposition spectrum.

[0129] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0130] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned nuclear magnetic resonance T2 spectrum component decomposition method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0131] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0132] like Figure 7 As shown, the embodiment of the present application also provides a device for decomposing components of a nuclear magnetic resonance T2 spectrum based on a symmetric Gaussian function model, comprising:

[0133] at least one processor;

[0134] at least one memory for storing at least one program;

[0135] When at least one program is executed by at least one processor, the at least one processor implements the above method.

[0136] like Figure 8 As shown, the embodiment of the present application also provides a device for decomposing nuclear magnetic resonance T2 spectrum components based on a symmetric Gaussian function model, including a T2 spectrum data acquisition device and a computer device connected to the T2 spectrum data acquisition device; wherein,

[0137] T2 spectrum data acquisition equipment, used for acquiring T2 spectrum data;

[0138] Computer equipment includes:

[0139] at least one processor;

[0140] at least one memory for storing at least one program;

[0141] When at least one program is executed by at least one processor, the at least one processor implements the above method.

[0142] Specifically, the T2 spectrum data acquisition equipment includes but is not limited to nuclear magnetic resonance devices such as nuclear magnetic resonance spectrometers and nuclear magnetic resonance imaging equipment; and the computer equipment may be different types of electronic devices, including but not limited to desktop computers, laptops and other terminals.

[0143] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned nuclear magnetic resonance T2 spectrum component decomposition method is implemented.

[0144] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0145] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0146] The embodiments of the present application provide a method for decomposing nuclear magnetic resonance T2 spectrum components, a nuclear magnetic resonance T2 spectrum component decomposition device, equipment and medium, which obtain nuclear magnetic resonance T2 spectrum data of a collected sample; the nuclear magnetic resonance T2 spectrum data includes relaxation time and signal intensity; pre-processing the nuclear magnetic resonance T2 spectrum data; converting the pre-processed nuclear magnetic resonance T2 spectrum data into a T2 original spectrum with relaxation time as the horizontal axis and signal intensity as the vertical axis; performing peak-finding processing on the T2 original spectrum by a peak-finding algorithm to obtain T2 original spectrum characteristics; according to the T2 original spectrum characteristics, modeling of T2 spectrum data is performed based on a symmetric Gaussian function to obtain a Gaussian function model; then determining a Gaussian curve based on the Gaussian function model; fitting and optimizing the Gaussian curve by a numerical fitting algorithm; wherein, in the process of fitting and optimization, adjusting the relevant parameters input into the Gaussian function model, and then determining the target parameter combination by minimizing the fitting error and controlling the area of ​​each spectrum peak; decomposing the T2 spectrum of the target parameter combination to obtain a decomposition result. The embodiment of the present application realizes the decomposition of the nuclear magnetic resonance spectrum based on the symmetric Gaussian function model. This method can realize fast data processing and analysis through appropriate mathematical models and algorithms, improve work efficiency, and has faster calculation speed and processing efficiency. The embodiment of the present application can efficiently decompose the components of the nuclear magnetic resonance T2 spectrum.

[0147] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0148] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0149] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0150] Those skilled in the art will appreciate that all or some of the steps, devices, and functional modules / units in the above disclosed methods may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0151] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0152] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0153] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0154] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0155] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0156] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0157] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for decomposing components of a nuclear magnetic resonance T2 spectrum, characterized in that: The method comprises: Acquiring nuclear magnetic resonance T2 spectrum data of the collected sample; the nuclear magnetic resonance T2 spectrum data includes relaxation time and signal intensity; Preprocessing the nuclear magnetic resonance T2 spectrum data; The pre-processed nuclear magnetic resonance T2 spectrum data is converted into a T2 original spectrum with the relaxation time as the abscissa and the signal intensity as the ordinate; Performing peak-finding processing on the T2 original spectrum using a peak-finding algorithm to obtain T2 original spectrum features; The step of performing peak finding processing on the original T2 spectrum using a peak finding algorithm includes: The T2 original spectrum is subjected to peak-finding processing by using an extreme point peak-finding method; the extreme point peak-finding method is implemented by the findpeaks function in MATLAB; According to the T2 original spectrum characteristics, T2 spectrum data modeling is performed based on a symmetric Gaussian function to obtain a Gaussian function model; and then a Gaussian curve is determined based on the Gaussian function model; The Gaussian curve is fitted and optimized by a numerical fitting algorithm; wherein, in the process of the fitting optimization, the relevant parameters input into the Gaussian function model are adjusted, and then the target parameter combination is determined by minimizing the fitting error and controlling the area of ​​each spectral peak; the Gaussian curve is fitted and optimized by a numerical fitting algorithm, including: Performing fitting optimization on the Gaussian curve by using a nonlinear least squares algorithm; Wherein, when there are multiple spectrum components, each of the spectrum components is represented by a symmetric Gaussian function; and the controlling of each spectrum peak area comprises: The spread width of the Gaussian peak is modified to achieve a ratio of the sum of the decomposed spectrum area to the original spectrum area within a preset range; The sum of the decomposed spectrum areas represents the sum of the areas corresponding to each of the spectrum components, and the original spectrum area represents the area of ​​the T2 original spectrum; The T2 spectrum of the target parameter combination is decomposed to obtain a decomposition result.

2. The method according to claim 1, characterized in that: The step of acquiring the nuclear magnetic resonance T2 spectrum data of the collected sample comprises: The collected sample is placed in a nuclear magnetic resonance instrument, and the nuclear magnetic resonance T2 spectrum data is obtained by collecting a series of pulse sequences and response signals of the pulse sequences.

3. The method according to claim 1, characterized in that The preprocessing of the nuclear magnetic resonance T2 spectrum data includes at least one of the following: De-noising the nuclear magnetic resonance T2 spectrum data by a mathematical filtering method; Eliminate the background signal of the nuclear magnetic resonance T2 spectrum data by baseline correction; The nuclear magnetic resonance T2 spectrum data is aligned with the spectrum line.

4. The method according to claim 1, characterized in that: The original T2 spectrum features include the maximum amplitude, spectrum peak position and spread width of the Gaussian peak; and the T2 spectrum data modeling is performed based on the symmetric Gaussian function according to the original T2 spectrum features to obtain a Gaussian function model, including: The Gaussian function model is obtained according to the maximum amplitude of the Gaussian peak, the spectral peak position and the spread width, combined with the T2 spectrum amplitude modeling corresponding to the exponential function association; Wherein, the expression of the Gaussian function model is: Where f(t) represents the T2 spectrum amplitude at time t; A represents the maximum amplitude of the Gaussian peak; exp() represents the exponential function; α represents the spectral peak position of the Gaussian peak; σ represents the spread width of the Gaussian peak.

5. A nuclear magnetic resonance T2 spectrum component decomposition device, characterized in that: The device comprises: The first module is used to obtain nuclear magnetic resonance T2 spectrum data of the collected sample; the nuclear magnetic resonance spectrum data includes relaxation time and signal intensity; The second module is used to preprocess the nuclear magnetic resonance T2 spectrum data; The third module is used to convert the pre-processed nuclear magnetic resonance T2 spectrum data into a T2 original spectrum with the relaxation time as the horizontal coordinate and the signal intensity as the vertical coordinate; The fourth module is used to perform peak-finding processing on the T2 original spectrum by using a peak-finding algorithm to obtain T2 original spectrum features; The step of performing peak finding processing on the original T2 spectrum using a peak finding algorithm includes: The T2 original spectrum is subjected to peak-finding processing by using an extreme point peak-finding method; the extreme point peak-finding method is implemented by the findpeaks function in MATLAB; A fifth module is used to perform T2 spectrum data modeling based on a symmetric Gaussian function according to the T2 original spectrum characteristics to obtain a Gaussian function model; and then determine a Gaussian curve based on the Gaussian function model; The sixth module is used to optimize the Gaussian curve by using a numerical fitting algorithm; wherein, in the process of the optimization, the relevant parameters input into the Gaussian function model are adjusted, and then the target parameter combination is determined by minimizing the fitting error and controlling the area of ​​each spectral peak; the optimization of the Gaussian curve by using a numerical fitting algorithm includes: Performing fitting optimization on the Gaussian curve by using a nonlinear least squares algorithm; Wherein, when there are multiple spectrum components, each of the spectrum components is represented by a symmetric Gaussian function; and the controlling of each spectrum peak area comprises: The spread width of the Gaussian peak is modified to achieve a ratio of the sum of the decomposed spectrum area to the original spectrum area within a preset range; The sum of the decomposed spectrum areas represents the sum of the areas corresponding to each of the spectrum components, and the original spectrum area represents the area of ​​the T2 original spectrum; The seventh module is used to decompose the T2 spectrum of the target parameter combination to obtain a decomposition result.

6. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 4 when executing the computer program.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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