A stress curve denoising method, device, electronic device and storage medium

Abstract

The present invention relates to a stress curve denoising method, device, electronic device and storage medium, and belongs to the technical field of geological exploration. The method comprises obtaining a sampling point time series and a stress value series on a stress curve; then sparsely resampling the sampling point time series and the stress value series based on a nonlinear method to obtain a sparsely resampled time point series and a sparsely resampled stress value series; and denoising the sparsely resampled time point series and the sparsely resampled stress value series to obtain a denoised sampling time point series and a denoised stress value series. A denoised stress curve can then be obtained based on the denoised sampling time point series and the denoised stress value series. After sparsely resampling using the nonlinear method, denoising is performed, thereby retaining effective information in the stress curve and improving the accuracy of the denoised stress curve.

Description

Technical Field

[0001] The present invention relates to the field of geological exploration technology, and in particular to a stress curve denoising method, device, electronic equipment and storage medium. Background Art

[0002] Seismic waves experience velocity dispersion and energy attenuation during their propagation in the strata, which is related to the structure of the strata and rocks, and the properties of the fluids. Oil and gas exploration, as well as coalfield exploration, rely primarily on seismic exploration to detect details of underground geological structures and fluid distribution, and then formulate development plans. The velocity of seismic waves is the main parameter, and its attenuation information is also gradually gaining attention. Seismic rock physics is a bridge that converts field observation data into rock physical parameters, and seismic digital rock physics is an important research area. Existing seismic digital rock physics methods and technologies use elastic parameter calculation methods mainly including finite element statics simulation, transmission wave simulation, dynamic stress-strain simulation, quasi-static stress-strain simulation, and damped wave equation simulation methods.

[0003] Calculating the dispersion and attenuation curves of digital cores based on dynamic stress-strain simulations is an important research topic in the field of digital rock physics. However, high-frequency noise exists in the stress curves of dynamic stress-strain simulations. Since the full-band information of the stress curve is valid, bandpass filtering technology is difficult to use as an effective means of suppressing noise.

[0004] Therefore, it is urgent to propose a stress curve denoising method, device, electronic device and storage medium to solve the technical problem in the existing technology that the full-band information of the stress curve is valid information and the stress curve cannot be denoised by bandpass filtering technology. Summary of the Invention

[0005] In view of this, it is necessary to provide a stress curve denoising method, device, electronic device and storage medium to solve the technical problem in the prior art that the full-band information of the stress curve is valid information and the stress curve cannot be denoised by bandpass filtering technology.

[0006] In order to solve the above problems, the present invention provides a stress curve denoising method, comprising:

[0007] Obtain the sampling point time series and stress value series on the stress curve;

[0008] Performing sparse resampling on the sampling point time series and the stress value series based on a nonlinear method to obtain a sparse resampled time point series and a sparse resampled stress value series;

[0009] The sparse resampled time point sequence and the sparse resampled stress value sequence are processed to obtain a denoised sampling time point sequence and a denoised stress value sequence, and a denoised stress curve is obtained according to the denoised sampling time point sequence and the denoised stress value sequence.

[0010] In a possible implementation, performing sparse resampling on the sampling point time series and the stress value series based on a nonlinear method to obtain a sparse resampled time point series and a sparse resampled stress value series includes:

[0011] Performing sparse resampling on the sampling point time series based on a nonlinear method to obtain a sparse resampled time point series;

[0012] A sparse resampled stress value sequence is obtained according to the sampling point time sequence, the stress value sequence and the sparse resampled time point sequence.

[0013] In a possible implementation, performing sparse resampling on the sampling point time series based on a nonlinear method to obtain a sparse resampled time point series includes:

[0014] Performing sparse resampling on the sampling point time series based on a nonlinear method to obtain an initial time point series;

[0015] The data in the initial time point sequence is screened to obtain a sparse resampled time point sequence.

[0016] In a possible implementation, screening the data in the initial time point sequence to obtain a sparse resampled time point sequence includes:

[0017] Filtering the data in the initial time point sequence according to a first preset condition to obtain a filtered time point sequence; the first preset condition is used to judge each time point in the initial time point sequence;

[0018] The supplementary resampling time points in the screening time point sequence are determined according to a second preset condition to obtain a sparse resampling time point sequence; the second preset condition is used to determine the maximum value in the screening time point sequence.

[0019] In a possible implementation, obtaining a sparse resampled stress value sequence according to the sampling point time series, the stress value sequence, and the sparse resampled time point sequence includes:

[0020] Determining, according to the size of each resampling time point in the sparse resampling time point sequence, a first sampling time point and a second sampling time point corresponding to each resampling time point in the sampling point time series;

[0021] determining, according to the stress value sequence, a first curve value corresponding to the first sampling time point and a second curve value corresponding to the second sampling time point;

[0022] Obtaining a resampling curve value corresponding to each resampling time point according to the first sampling time point, the second sampling time point, the first curve value, and the second curve value corresponding to each resampling time point;

[0023] A sparse resampled stress value sequence is obtained according to all resampled curve values ​​in the sparse resampled time point sequence.

[0024] In a possible implementation, the processing of the sparse resampled time point sequence and the sparse resampled stress value sequence to obtain a denoised sampling time point sequence and a denoised stress value sequence includes:

[0025] Obtaining a sparse resampling curve according to the sparse resampling time point sequence and the sparse resampling stress value sequence;

[0026] Performing denoising processing on the sparse resampled curve to obtain a denoised stress curve, and obtaining a processing sampling time point sequence and a processing stress value sequence of the denoised stress curve;

[0027] The processed stress value sequence is encrypted sampled according to the sampling point time sequence and the processed sampling time point sequence to obtain a denoised sampling time point sequence and a denoised stress value sequence.

[0028] In a possible implementation, performing encrypted sampling on the processed stress value sequence according to the sampling point time sequence and the processed sampling time point sequence to obtain a denoised sampling time point sequence and a denoised stress value sequence includes:

[0029] Determining, according to the size of each sampling time point in the sampling point time series, a first processing sampling time point and a second processing sampling time point corresponding to each sampling time point in the processing sampling time point series;

[0030] determining, according to the processing stress value sequence, a first processing curve value corresponding to the first processing sampling time point and a second processing curve value corresponding to the second processing sampling time point;

[0031] Obtaining an encrypted sampling curve value corresponding to each sampling time point according to the first processing sampling time point, the second processing sampling time point, the first processing curve value, and the second processing curve value corresponding to each sampling time point;

[0032] A denoised stress value sequence is obtained according to all encrypted sampling curve values ​​in the sampling point time series, and the sampling point time series is determined as a denoised sampling time point sequence.

[0033] On the other hand, the present invention also provides a stress curve denoising device, comprising:

[0034] Sequence acquisition module, used to obtain the sampling point time series and stress value series on the stress curve;

[0035] A sparse resampling module, configured to perform sparse resampling on the sampling point time series and the stress value series based on a nonlinear method to obtain a sparse resampled time point series and a sparse resampled stress value series;

[0036] A curve denoising module is used to process the sparse resampled time point sequence and the sparse resampled stress value sequence to obtain a denoised sampling time point sequence and a denoised stress value sequence, and to obtain a denoised stress curve based on the denoised sampling time point sequence and the denoised stress value sequence.

[0037] On the other hand, an embodiment of the present invention discloses an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the various steps of the above-mentioned stress curve denoising method embodiment.

[0038] On the other hand, an embodiment of the present invention discloses a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the computer program implements the various steps of the above-mentioned stress curve denoising method embodiment.

[0039] The beneficial effects of the present invention are as follows: a sampling point time series and a stress value series on a stress curve are obtained; then, the sampling point time series and the stress value series can be sparsely resampled based on a nonlinear method to obtain a sparsely resampled time point series and a sparsely resampled stress value series; and the sparsely resampled time point series and the sparsely resampled stress value series are denoised to obtain a denoised sampling time point series and a denoised stress value series, and then a denoised stress curve can be obtained based on the denoised sampling time point series and the denoised stress value series. After sparse resampling by a nonlinear method and then denoising, effective information in the stress curve can be retained, thereby improving the accuracy of the denoised stress curve. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A schematic flow chart of an embodiment of a stress curve denoising method provided by the present invention;

[0041] Figure 2A schematic diagram of coordinates of an embodiment of a typical stress curve provided by the present invention;

[0042] Figure 3 A schematic flow chart of an embodiment of the sparse resampled stress value sequence provided by the present invention;

[0043] Figure 4 For the present invention Figure 1 A schematic flow chart of an embodiment of step S103;

[0044] Figure 5 A coordinate diagram of an embodiment of a stress curve after denoising provided by the present invention;

[0045] Figure 6 A schematic structural diagram of an embodiment of a stress curve denoising device provided by the present invention;

[0046] Figure 7 This is a schematic structural diagram of an embodiment of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0047] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0048] like Figure 1 As shown, a specific embodiment of the present invention discloses a stress curve denoising method, comprising:

[0049] S101, obtaining a sampling point time series and a stress value series on a stress curve;

[0050] S102, performing sparse resampling on the sampling point time series and the stress value series based on a nonlinear method to obtain a sparse resampled time point series and a sparse resampled stress value series;

[0051] S103 , processing the sparse resampled time point sequence and the sparse resampled stress value sequence to obtain a denoised sampling time point sequence and a denoised stress value sequence, and obtaining a denoised stress curve according to the denoised sampling time point sequence and the denoised stress value sequence.

[0052] It should be understood that the calculation of dispersion and attenuation curves of digital cores based on dynamic stress-strain simulation of digital cores is an important research topic in the current field of digital rock physics. However, there is high-frequency noise in the stress curve of dynamic stress-strain simulation. Figure 2 As shown, Figure 2 It is a typical stress curve, which can be represented by two corresponding series: sampling point time series , stress value series .exist Figure 2 In time In 10 2 to 10 5 High-frequency jitter within the range is a manifestation of high-frequency noise.

[0053] In a specific embodiment of the present invention, the information of the full frequency band of the stress curve is valid information. In order to retain the valid information, the time series of the sampling points on the stress curve can be obtained first. and stress value series , then the sampling point time series can be analyzed based on nonlinear methods and stress value series Sparse resampling is performed to obtain a sparse resampled time point sequence and a sparse resampled stress value sequence, which can then be processed, for example, by denoising methods, to obtain a denoised sampling time point sequence and a denoised stress value sequence, which can then be plotted to obtain a denoised stress curve.

[0054] Compared with the prior art, the present embodiment provides a method for obtaining a sampling point time series and a stress value series on a stress curve; then, the sampling point time series and the stress value series can be sparsely resampled based on a nonlinear method to obtain a sparse resampled time point series and a sparse resampled stress value series; and the sparse resampled time point series and the sparse resampled stress value series are denoised to obtain a denoised sampling time point series and a denoised stress value series, and then a denoised stress curve can be obtained based on the denoised sampling time point series and the denoised stress value series. After sparse resampling using a nonlinear method, denoising is performed, which can retain effective information in the stress curve and improve the accuracy of the denoised stress curve.

[0055] In some embodiments of the present invention, step S102 includes:

[0056] Based on the nonlinear method, the sampling point time series is sparsely resampled to obtain a sparse resampled time point series;

[0057] According to the sampling point time series, the stress value series and the sparse resampling time point series, a sparse resampling stress value series is obtained.

[0058] In a specific embodiment of the present invention, a sparse resampling of the sampling point time series can be performed using a nonlinear method to obtain a sparse resampled time point sequence. The specific process of the specific nonlinear method can be set according to actual conditions and is not limited by the present embodiment. Then, the sampling point time series, the stress value sequence, and the sparse resampled time point sequence can be calculated for each resampled time point to obtain a sparse resampled stress value sequence corresponding to the sparse resampled time point sequence. The specific calculation process can be set according to actual conditions and is not limited by the present embodiment.

[0059] In some embodiments of the present invention, sparse resampling is performed on a sampling point time series based on a nonlinear method to obtain a sparse resampled time point series, including:

[0060] Based on the nonlinear method, the sampling point time series is sparsely resampled to obtain the initial time point series;

[0061] The data in the initial time point series are filtered to obtain a sparse resampled time point series.

[0062] In a specific embodiment of the present invention, a sparse resampling of the sampling point time series is performed based on a nonlinear method, and the resampling time point formula is obtained as shown in formula (1):

[0063] (1)

[0064] Where, is the resampling time point; is the number of time points of the original stress curve, that is, the number of data points of the original stress curve; It is a resampling adjustment coefficient greater than 1, which determines the coefficient degree of the resampling time point. The final value needs to be determined by trial and error, and generally should not be greater than 1.5. The specific embodiments of the present invention are not limited here.

[0065] The sampling point time series is calculated by formula (1) By calculating the time points in , we can get the initial time point sequence, and then we can filter the data in the initial time point sequence to get the sparse resampled time point sequence.

[0066] In some embodiments of the present invention, filtering the data in the initial time point sequence to obtain a sparse resampled time point sequence includes:

[0067] The data in the initial time point sequence is filtered according to a first preset condition to obtain a filtered time point sequence; the first preset condition is used to judge each time point in the initial time point sequence;

[0068] The supplementary resampling time points in the screening time point sequence are determined according to a second preset condition to obtain a sparse resampling time point sequence; the second preset condition is used to determine the maximum value in the screening time point sequence.

[0069] In a specific embodiment of the present invention, after the resampling time point corresponding to each time point is obtained by formula (1), the resampling time point can be judged according to the first preset condition. The first preset condition is used to judge each time point in the initial time point sequence. Specifically: if the calculated resampling time point The value is much greater than , then the value is greater than of Delete, you can get the remaining filter time point sequence after deletion. Then you can determine the filter time point sequence for processing based on the second preset condition. The second preset condition is used to judge the maximum value in the filter time point sequence. Specifically: In, if The largest The value is less than , then a resampling time point needs to be added In this way, a sparse resampled time point sequence is formed , The sequence defines the resampling time points, which are different from the original time series The range is the same, but the number of time points is reduced.

[0070] In some embodiments of the present invention, Figure 3 As shown in Figure 2, according to the sampling point time series, stress value series and sparse resampling time point series, the sparse resampling stress value series is obtained, including:

[0071] S301, determining a first sampling time point and a second sampling time point corresponding to each resampling time point in the sampling point time series according to the size of each resampling time point in the sparse resampling time point series;

[0072] S302, determining a first curve value corresponding to a first sampling time point and a second curve value corresponding to a second sampling time point according to a stress value sequence;

[0073] S303, obtaining a resampling curve value corresponding to each resampling time point according to the first sampling time point, the second sampling time point, the first curve value, and the second curve value corresponding to each resampling time point;

[0074] S304 , obtaining a sparse resampled stress value sequence according to all resampled curve values ​​in the sparse resampled time point sequence.

[0075] In a specific embodiment of the present invention, when resampling, the sparse resampling time point sequence is traversed. , for each resampling time point , find out the time series of sampling points that are less than and greater than , and with The two most recent time points are the first sampling time points. and the second sampling time point , in the stress value sequence, find the first sampling time point The corresponding first curve value and the second sampling time point The corresponding second curve value , the linear interpolation method shown in formula (2) is used to calculate the resampling curve value at the resampling time point , as shown in formula (2):

[0076] (2)

[0077] like Just with someone If they overlap, Direct value The stress curve formed by sparse resampling can be represented by two sequences: sparse resampling time point sequence , sparse resampled stress value series Sparse resampling reduces stress relaxation curves originally containing millions to tens of millions of time points to thousands of time points.

[0078] In some embodiments of the present invention, Figure 4 As shown, step S103 includes:

[0079] S401, obtaining a sparse resampling curve according to a sparse resampling time point sequence and a sparse resampling stress value sequence;

[0080] S402, performing denoising processing on the sparse resampled curve to obtain a denoised stress curve, and obtaining a sequence of processed sampling time points and a sequence of processed stress values ​​of the denoised stress curve;

[0081] S403 , performing encrypted sampling on the processed stress value sequence according to the sampling point time sequence and the processed sampling time point sequence to obtain a denoised sampling time point sequence and a denoised stress value sequence.

[0082] In a specific embodiment of the present invention, the sparsely resampled stress is stored in the computer storage space in the form of a one-dimensional array and copied into two copies. The first array is used to draw a curve on the computer screen based on the sparsely resampled time point sequence and the sparsely resampled stress value sequence to obtain a sparsely resampled curve. The second array is used to manually adjust the stress to achieve curve smoothing and denoising. Manual denoising is mainly performed on the oscillation part of the curve. Figure 2 The peak point of the oscillation curve and the data points nearby are adjusted downward (the value is reduced). Figure 2 The valley point of the oscillation curve and the data points near it are adjusted upward (increase the value). The manually adjusted array is plotted as a curve and compared with the curve before denoising. If the denoised curve can represent the overall trend of the undenoised curve, the denoising is complete. Manually adjusting the data size in the array is inefficient. You can use Python and other languages ​​to write code, use graphical interaction and mouse dragging to adjust the data, and obtain the denoised stress curve. Then, you can get the sequence of processed sampling time points on the denoised stress curve. and processing stress value series .

[0083] In some embodiments of the present invention, step S403 includes:

[0084] Determine, according to the size of each sampling time point in the sampling point time series, a first processing sampling time point and a second processing sampling time point corresponding to each sampling time point in the processing sampling time point series;

[0085] Determining, according to the processing stress value sequence, a first processing curve value corresponding to a first processing sampling time point and a second processing curve value corresponding to a second processing sampling time point;

[0086] Obtaining an encrypted sampling curve value corresponding to each sampling time point according to the first processing sampling time point, the second processing sampling time point, the first processing curve value, and the second processing curve value corresponding to each sampling time point;

[0087] According to all the encrypted sampling curve values ​​in the sampling point time series, a denoised stress value sequence is obtained, and the sampling point time series is determined as a denoised sampling time point sequence.

[0088] In a specific embodiment of the present invention, the processing sampling time point sequence on the denoised stress curve can also be and processing stress value series Perform encrypted sampling, the encrypted sampling method is similar to formula (2). Traverse the sampling point time series , for each sampling time point , when processing the sampling time sequence Find the values ​​less than and greater than, and The last two time points, namely the first processing sampling time point and the second processing sampling time point Then, the first processing curve value corresponding to the first processing sampling time point can be determined according to the processing stress value sequence. The second processing curve value corresponding to the second processing sampling time point , the stress value of the encrypted sampling is calculated using the linear interpolation method of formula (3) , as shown in formula (3):

[0089] (3)

[0090] like Just with someone If they overlap, Direct value .like Figure 5 As shown, Figure 5 yes Figure 2 The result of denoising the stress curve can be represented by two corresponding sequences: , stress value series .exist Figure 5 In time High frequency jitter in the 102 to 105 range has been virtually eliminated.

[0091] In order to better implement the stress curve denoising method in the embodiment of the present invention, based on the stress curve denoising method, the embodiment of the present invention also provides a stress curve denoising device, such as Figure 6 As shown, the stress curve denoising device 600 includes:

[0092] A sequence acquisition module 601 is used to acquire a time series of sampling points and a stress value series on a stress curve;

[0093] A sparse resampling module 602 is configured to perform sparse resampling on the sampling point time series and the stress value series based on a nonlinear method to obtain a sparse resampled time point series and a sparse resampled stress value series;

[0094] The curve denoising module 603 is used to process the sparse resampled time point sequence and the sparse resampled stress value sequence to obtain a denoised sampling time point sequence and a denoised stress value sequence, and obtain a denoised stress curve based on the denoised sampling time point sequence and the denoised stress value sequence.

[0095] The stress curve denoising device 600 provided in the above embodiment can implement the technical solution described in the above stress curve denoising method embodiment. The specific implementation principles of the above modules or units can be found in the corresponding content in the above stress curve denoising method embodiment, which will not be repeated here.

[0096] like Figure 7 As shown, the present invention also provides an electronic device 700. The electronic device 700 includes a processor 701, a memory 702 and a display 703. Figure 7 Only some of the components of the electronic device 700 are shown, but it should be understood that it is not required to implement all of the shown components, and more or fewer components may be implemented instead.

[0097] In some embodiments, the memory 702 may be an internal storage unit of the electronic device 700, such as a hard disk or memory of the electronic device 700. In other embodiments, the memory 702 may also be an external storage device of the electronic device 700, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 700.

[0098] Furthermore, the memory 702 may include both an internal storage unit of the electronic device 700 and an external storage device. The memory 702 is used to store application software installed in the electronic device 700 and various data.

[0099] In some embodiments, the processor 701 may be a central processing unit (CPU), a microprocessor, or other data processing chip, configured to execute program codes stored in the memory 702 or process data, such as the stress curve denoising method of the present invention.

[0100] In some embodiments, display 703 can be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 703 is used to display information about electronic device 700 and to display a visual user interface. Components 701-703 of electronic device 700 communicate with each other via a system bus.

[0101] In some embodiments of the present invention, when the processor 701 executes the stress curve denoising program in the memory 702, the following steps may be implemented:

[0102] Obtain the sampling point time series and stress value series on the stress curve;

[0103] Based on the nonlinear method, the sampling point time series and stress value series are sparsely resampled to obtain the sparse resampled time point series and sparse resampled stress value series;

[0104] The sparse resampled time point sequence and the sparse resampled stress value sequence are processed to obtain a denoised sampling time point sequence and a denoised stress value sequence, and a denoised stress curve is obtained according to the denoised sampling time point sequence and the denoised stress value sequence.

[0105] It should be understood that, when the processor 701 executes the stress curve denoising program in the memory 702 , in addition to the above functions, it can also implement other functions. For details, please refer to the description of the corresponding method embodiment above.

[0106] Furthermore, the embodiments of the present invention do not specifically limit the type of electronic device 700 mentioned. The electronic device 700 may be a portable electronic device such as a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, or laptop computer. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The portable electronic devices mentioned above may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 700 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0107] Accordingly, an embodiment of the present application also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, the stress curve denoising method steps or functions provided in the above-mentioned method embodiments can be implemented.

[0108] Those skilled in the art will appreciate that all or part of the process flow of the above-described method embodiment can be implemented by instructing related hardware (such as a processor, controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0109] The above is a detailed introduction to the stress curve denoising method, device, equipment and storage medium provided by the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method and core ideas of the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A stress curve denoising method, characterized in that: include: Obtain the sampling point time series and stress value series on the stress curve; Performing sparse resampling on the sampling point time series and the stress value series based on a nonlinear method to obtain a sparse resampled time point series and a sparse resampled stress value series; Processing the sparse resampled time point sequence and the sparse resampled stress value sequence to obtain a denoised sampling time point sequence and a denoised stress value sequence, and obtaining a denoised stress curve based on the denoised sampling time point sequence and the denoised stress value sequence; The sparse resampling of the sampling point time series and the stress value series based on a nonlinear method to obtain a sparse resampled time point series and a sparse resampled stress value series includes: Performing sparse resampling on the sampling point time series based on a nonlinear method to obtain a sparse resampled time point series; Obtaining a sparse resampled stress value sequence according to the sampling point time series, the stress value sequence, and the sparse resampled time point sequence; The step of performing sparse resampling on the sampling point time series based on a nonlinear method to obtain a sparse resampled time point series includes: Performing sparse resampling on the sampling point time series based on a nonlinear method to obtain an initial time point series; Screening the data in the initial time point sequence to obtain a sparse resampled time point sequence; The filtering of the data in the initial time point sequence to obtain a sparse resampled time point sequence includes: Filtering the data in the initial time point sequence according to a first preset condition to obtain a filtered time point sequence; the first preset condition is used to judge each time point in the initial time point sequence; Determining the supplementary resampling time points in the screening time point sequence according to a second preset condition to obtain a sparse resampling time point sequence; wherein the second preset condition is used to determine the maximum value in the screening time point sequence; The step of obtaining a sparse resampled stress value sequence according to the sampling point time sequence, the stress value sequence, and the sparse resampled time point sequence includes: Determining, according to the size of each resampling time point in the sparse resampling time point sequence, a first sampling time point and a second sampling time point corresponding to each resampling time point in the sampling point time series; determining, according to the stress value sequence, a first curve value corresponding to the first sampling time point and a second curve value corresponding to the second sampling time point; Obtaining a resampling curve value corresponding to each resampling time point according to the first sampling time point, the second sampling time point, the first curve value, and the second curve value corresponding to each resampling time point; A sparse resampled stress value sequence is obtained according to all resampled curve values ​​in the sparse resampled time point sequence.

2. The stress curve denoising method according to claim 1, characterized in that: The processing of the sparse resampled time point sequence and the sparse resampled stress value sequence to obtain a denoised sampling time point sequence and a denoised stress value sequence includes: Obtaining a sparse resampling curve according to the sparse resampling time point sequence and the sparse resampling stress value sequence; Performing denoising processing on the sparse resampled curve to obtain a denoised stress curve, and obtaining a processing sampling time point sequence and a processing stress value sequence of the denoised stress curve; The processed stress value sequence is encrypted sampled according to the sampling point time sequence and the processed sampling time point sequence to obtain a denoised sampling time point sequence and a denoised stress value sequence.

3. The stress curve denoising method according to claim 2, characterized in that: The encrypted sampling of the processed stress value sequence according to the sampling point time sequence and the processed sampling time point sequence to obtain a denoised sampling time point sequence and a denoised stress value sequence includes: Determining, according to the size of each sampling time point in the sampling point time series, a first processing sampling time point and a second processing sampling time point corresponding to each sampling time point in the processing sampling time point series; determining, according to the processing stress value sequence, a first processing curve value corresponding to the first processing sampling time point and a second processing curve value corresponding to the second processing sampling time point; Obtaining an encrypted sampling curve value corresponding to each sampling time point according to the first processing sampling time point, the second processing sampling time point, the first processing curve value, and the second processing curve value corresponding to each sampling time point; A denoised stress value sequence is obtained according to all encrypted sampling curve values ​​in the sampling point time series, and the sampling point time series is determined as a denoised sampling time point sequence.

4. A stress curve denoising device, characterized in that: include: Sequence acquisition module, used to obtain the sampling point time series and stress value series on the stress curve; A sparse resampling module, configured to perform sparse resampling on the sampling point time series and the stress value series based on a nonlinear method to obtain a sparse resampled time point series and a sparse resampled stress value series; a curve denoising module, configured to process the sparsely resampled time point sequence and the sparsely resampled stress value sequence to obtain a denoised sampling time point sequence and a denoised stress value sequence, and obtain a denoised stress curve based on the denoised sampling time point sequence and the denoised stress value sequence; The sparse resampling module is further configured to perform sparse resampling on the sampling point time series based on a nonlinear method to obtain an initial time point sequence; and filter the data in the initial time point sequence according to a first preset condition to obtain a filtered time point sequence; The first preset condition is used to judge each time point in the initial time point sequence; Determining the resampling time points to be supplemented in the screening time point sequence according to a second preset condition to obtain a sparse resampling time point sequence; The second preset condition is used to determine the maximum value in the screening time point sequence; Determine, based on the size of each resampling time point in the sparse resampling time point sequence, a first sampling time point and a second sampling time point corresponding to each resampling time point in the sampling point time series; determine, based on the stress value sequence, a first curve value corresponding to the first sampling time point and a second curve value corresponding to the second sampling time point; and obtain, based on the first sampling time point, the second sampling time point, the first curve value, and the second curve value corresponding to each resampling time point, a resampling curve value corresponding to each resampling time point; A sparse resampled stress value sequence is obtained according to all resampled curve values ​​in the sparse resampled time point sequence.

5. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the steps of the stress curve denoising method according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the stress curve denoising method according to any one of claims 1 to 3 are implemented.

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