Denoising method and device for stress curve, electronic equipment and storage medium

Abstract

The invention relates to a de-noising method and device for a stress curve, electronic equipment and a storage medium, and belongs to the technical field of geological prospecting, and the method comprises the steps: obtaining a sampling point time sequence and a stress value sequence on the stress curve; then sparse resampling is carried out on the sampling point time sequence and the stress value sequence based on a nonlinear method, and a sparse resampling time point sequence and a sparse resampling stress value sequence are obtained; a sparse resampling time point sequence and a sparse resampling stress value sequence are obtained, denoising processing is performed on the sparse resampling time point sequence and the sparse resampling stress value sequence, a denoising sampling time point sequence and a denoising stress value sequence are obtained, and then a denoising stress curve can be obtained according to the denoising sampling time point sequence and the denoising stress value sequence. And then de-noising processing is carried out, so that effective information in the stress curve can be reserved, and the precision of the de-noised stress curve is improved.

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 propagation in the strata, which is related to the structure of the strata and rocks, and the properties of the fluid. Oil and gas exploration, as well as coalfield exploration, rely mainly on seismic exploration to detect the 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 aspect. The elastic parameter calculation methods used in existing seismic digital rock physics methods and technologies mainly include finite element statics simulation, transmission wave simulation, dynamic stress-strain simulation, quasi-static stress-strain simulation, and damped wave equation simulation methods.

[0003] In the prior art, calculating the 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, and the full-band information of the stress curve is valid information. Therefore, bandpass filtering technology is difficult to be used as an effective means to suppress noise.

[0004] Therefore, it is urgent to propose a stress curve denoising method, device, electronic device and storage medium to solve the technical problem that the full-band information of the stress curve in the prior art 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 problem, the present invention provides a stress curve denoising method, comprising: Obtain the sampling point time series and stress value series on the stress curve; Based on a nonlinear method, sparse resampling is performed on the sampling point time series and the stress value series to obtain a sparse resampled time point series and a sparse resampled stress value series; 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.

[0007] In a possible implementation, 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; A sparse resampled stress value sequence is obtained according to the sampling point time series, the stress value sequence and the sparse resampled time point sequence.

[0008] In a possible implementation, the performing sparse resampling on the sampling point time series based on a nonlinear method to obtain a sparse resampled time point series includes: Based on a nonlinear method, sparse resampling is performed on the sampling point time series to obtain an initial time point series; The data in the initial time point sequence is screened to obtain a sparse resampled time point sequence.

[0009] In a possible implementation, the filtering of the data in the initial time point sequence to obtain a sparse resampled time point sequence includes: The data in the initial time point sequence is screened according to a first preset condition to obtain a screened time point sequence; the first preset condition is used to judge each time point in the initial time point sequence; The resampling time points supplemented 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 judge the maximum value in the screening time point sequence.

[0010] 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: Determine, 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; Determine, 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.

[0011] 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: Obtaining a sparse resampling curve according to the sparse resampling time point sequence and the sparse resampling stress value sequence; De-noising the sparse resampled curve to obtain a de-noised stress curve, and obtain a processing sampling time point sequence and a processing stress value sequence of the de-noised stress curve; The processed stress value sequence is encrypted and 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.

[0012] In a possible implementation, 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: 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; 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.

[0013] On the other hand, the present invention also provides a stress curve denoising device, comprising: A sequence acquisition module is used to obtain the sampling point time series and stress value series on the stress curve; A sparse resampling module, used for 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; The 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 obtain a denoised stress curve according to the denoised sampling time point sequence and the denoised stress value sequence.

[0014] 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 when the computer program is executed by the processor, each step of the above-mentioned stress curve denoising method embodiment is implemented.

[0015] On the other hand, an embodiment of the present invention discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the various steps of the above-mentioned stress curve denoising method embodiment are implemented.

[0016] 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 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 by a nonlinear method, denoising is performed, so that effective information in the stress curve can be retained and the accuracy of the denoised stress curve is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic flow chart of an embodiment of a stress curve denoising method provided by the present invention; Figure 2 A schematic diagram of coordinates of an embodiment of a typical stress curve provided by the present invention; Figure 3 A schematic diagram of a flow chart of an embodiment of a sparse resampled stress value sequence provided by the present invention; Figure 4 For the present invention Figure 1 A schematic flow chart of an embodiment of step S103; Figure 5 A coordinate schematic diagram of an embodiment of a stress curve after denoising provided by the present invention; Figure 6A schematic structural diagram of an embodiment of a stress curve denoising device provided by the present invention; Figure 7 A schematic structural diagram of an embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0018] The preferred embodiments of the present invention are 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, but are not used to limit the scope of the present invention.

[0019] like Figure 1 As shown, a specific embodiment of the present invention discloses a stress curve denoising method, comprising: S101, obtaining a sampling point time series and a stress value series on a stress curve; 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; 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.

[0020] 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 point In 10 2 Up to 10 5 High-frequency jitter within the range is a manifestation of high-frequency noise.

[0021] 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, and then the sparse resampled time point sequence and the sparse resampled stress value sequence can be processed, for example, denoising processing and other methods can be used to obtain a denoised sampling time point sequence and a denoised stress value sequence, and then the denoised sampling time point sequence and the denoised stress value sequence can be plotted to obtain a denoised stress curve.

[0022] 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 by a nonlinear method, denoising is performed, so that effective information in the stress curve can be retained and the accuracy of the denoised stress curve is improved.

[0023] In some embodiments of the present invention, step S102 includes: Based on the nonlinear method, the sampling point time series is sparsely resampled to obtain a sparse resampled time point series; According to the sampling point time series, the stress value series and the sparse resampled time point series, a sparse resampled stress value series is obtained.

[0024] In a specific embodiment of the present invention, the sampling point time series can be sparsely resampled by 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 the embodiment of the present invention is not limited here. 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 the embodiment of the present invention is not limited here.

[0025] In some embodiments of the present invention, sparse resampling is performed on the sampling point time series based on a nonlinear method to obtain a sparse resampled time point series, including: Based on the nonlinear method, the sampling point time series is sparsely resampled to obtain the initial time point series; The data in the initial time point sequence is filtered to obtain a sparse resampled time point sequence.

[0026] 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 a resampling time point formula is obtained as shown in formula (1): (1) In the formula, 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; 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, and the specific embodiments of the present invention are not limited here.

[0027] The sampling point time series is calculated by formula (1): By calculating the time points in the , the initial time point sequence can be obtained, and then the data in the initial time point sequence can be filtered to obtain a sparse resampled time point sequence.

[0028] In some embodiments of the present invention, the data in the initial time point sequence is screened to obtain a sparse resampled time point sequence, including: The data in the initial time point sequence is screened according to a first preset condition to obtain a screened time point sequence; the first preset condition is used to judge each time point in the initial time point sequence; The resampling time points supplemented in the screening time point sequence are determined according to the second preset condition to obtain a sparse resampling time point sequence; the second preset condition is used to judge the maximum value in the screening time point sequence.

[0029] 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 larger than , then the value is greater than of After the deletion, the remaining screening time point sequence can be obtained. Then the screening time point sequence can be processed according to the second preset condition. The second preset condition is used to judge the maximum value in the screening time point sequence. Specifically: In, if The largest The value is less than , then you need to add a resampling time point 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.

[0030] In some embodiments of the present invention, Figure 3 As shown, 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: 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 sequence; 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; 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; S304 , obtaining a sparse resampled stress value sequence according to all resampled curve values ​​in the sparse resampled time point sequence.

[0031] 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 , find the first sampling time point in the stress value sequence 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): (2) like Just with someone If 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 that originally have millions to tens of millions of time points in length to thousands of time points.

[0032] In some embodiments of the present invention, Figure 4 As shown, step S103 includes: S401, obtaining a sparse resampling curve according to a sparse resampling time point sequence and a sparse resampling stress value sequence; S402, 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; S403 , encrypting and sampling 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.

[0033] 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 is copied into two copies. The first array is used to draw a curve on the computer screen according to the sparsely resampled time point sequence and the sparsely resampled stress value sequence to obtain a sparsely resampled curve, and the second array is used to manually adjust the stress to achieve smoothing and denoising of the curve. Manual denoising is mainly used in the oscillation part of the curve. Figure 2 The peak point of the oscillation curve and the data points near it 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 completed. 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 of stress value sequences .

[0034] In some embodiments of the present invention, step S403 includes: 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; Determine, 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; 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, the encrypted sampling curve value corresponding to each sampling time point is obtained; 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.

[0035] In a specific embodiment of the present invention, the processing sampling time point sequence on the denoised stress curve can also be and processing of stress value sequences Perform encrypted sampling. The encrypted sampling method is similar to formula (2). Traverse the sampling point time series , for each sampling time point , in processing the sampling time sequence Find the values ​​less than and greater than, and The last two time points, respectively, 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): (3) like Just with someone If 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 point High frequency jitter in the 102 to 105 range has been virtually eliminated.

[0036] 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: A sequence acquisition module 601 is used to acquire a sampling point time series and a stress value series on a stress curve; A sparse resampling module 602 is used 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; 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 according to the denoised sampling time point sequence and the denoised stress value sequence.

[0037] 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 refer to the corresponding contents in the above stress curve denoising method embodiment, which will not be repeated here.

[0038] 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 components of the electronic device 700 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0039] 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.

[0040] 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.

[0041] In some embodiments, the processor 701 may be a central processing unit (CPU), a microprocessor or other data processing chip, and is used to run program codes stored in the memory 702 or process data, such as the stress curve denoising method of the present invention.

[0042] In some embodiments, the display 703 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 703 is used to display information of the electronic device 700 and to display a visual user interface. The components 701-703 of the electronic device 700 communicate with each other via a system bus.

[0043] In some embodiments of the present invention, when the processor 701 executes the denoising program of the stress curve in the memory 702, the following steps may be implemented: Obtain the sampling point time series and stress value series on the stress curve; Based on the nonlinear method, the sampling point time series and the stress value series are sparsely resampled to obtain the sparse resampled time point series and the sparse resampled stress value series; 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.

[0044] It should be understood that: when the processor 701 executes the denoising program of the stress curve in the memory 702, in addition to the above functions, other functions may also be realized, and the details may refer to the description of the corresponding method embodiment above.

[0045] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 700 mentioned, and the electronic device 700 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable electronic devices 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 a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0046] 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 denoising method steps or functions of the stress curve provided in the above-mentioned method embodiments can be implemented.

[0047] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0048] 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 in this article 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 of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on 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; Based on a nonlinear method, sparse resampling is performed on the sampling point time series and the stress value series to obtain a sparse resampled time point series and a sparse resampled stress value series; 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.

2. The stress curve denoising method according to claim 1, characterized in that: The method of 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: Performing sparse resampling on the sampling point time series based on a nonlinear method to obtain a sparse resampled time point series; A sparse resampled stress value sequence is obtained according to the sampling point time series, the stress value sequence and the sparse resampled time point sequence.

3. The stress curve denoising method according to claim 2, characterized in that: The method of performing sparse resampling on the sampling point time series based on a nonlinear method to obtain a sparse resampled time point series includes: Based on a nonlinear method, sparse resampling is performed on the sampling point time series to obtain an initial time point series; The data in the initial time point sequence is screened to obtain a sparse resampled time point sequence.

4. The stress curve denoising method according to claim 3, characterized in that: The step of screening the data in the initial time point sequence to obtain a sparse resampled time point sequence includes: The data in the initial time point sequence is screened according to a first preset condition to obtain a screened time point sequence; the first preset condition is used to judge each time point in the initial time point sequence; The resampling time points supplemented 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 judge the maximum value in the screening time point sequence.

5. The stress curve denoising method according to claim 4, characterized in that: 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 comprises: Determine, 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; Determine, 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.

6. The stress curve denoising method according to claim 1, characterized in that: The step of 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 includes: Obtaining a sparse resampling curve according to the sparse resampling time point sequence and the sparse resampling stress value sequence; De-noising the sparse resampled curve to obtain a de-noised stress curve, and obtain a processing sampling time point sequence and a processing stress value sequence of the de-noised stress curve; The processed stress value sequence is encrypted and 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.

7. The stress curve denoising method according to claim 6, 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: 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; 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.

8. A stress curve denoising device, characterized in that: include: A sequence acquisition module is used to obtain the sampling point time series and stress value series on the stress curve; A sparse resampling module, used for 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; The 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 obtain a denoised stress curve according to the denoised sampling time point sequence and the denoised stress value sequence.

9. 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 7 are implemented.

10. 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 7 are implemented.

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