A method for determining a data discrete reference sampling interval

Abstract

The application discloses a data discrete reference sampling interval determination method, comprising: adjusting the sampling interval of a preset input signal; sampling the input signal using the adjusted sampling interval to obtain a sampling signal; performing signal reconstruction processing on the sampling signal to obtain a reconstructed signal; comparing the reconstructed signal with the input signal to determine whether the reconstructed signal meets a preset accuracy standard; according to the determination result, repeatedly performing the steps of adjusting the sampling interval, sampling again according to the adjusted sampling interval to obtain a sampling signal, reconstructing the sampling signal, and determining whether the reconstructed signal meets the preset accuracy standard until the data discrete reference sampling interval is determined; and sampling the data signal using the determined data discrete reference sampling interval, which can accurately position the sampling interval that meets the demand of information retention in the signal, thereby ensuring the reliability range and accuracy of the signal.

Description

Technical Field

[0001] This invention relates to the field of signal acquisition and processing technology, and in particular to a method for determining the data discrete reference sampling interval. Background Technology

[0002] In the field of signal acquisition and processing, the process of converting a continuous signal into a discrete signal is called the sampling process. Ideally, the sampling process can completely restore the input signal, but in actual operation, the final restoration effect of the sampled signal is often constrained by a variety of factors, among which the sampling frequency or sampling interval plays a very important role.

[0003] Taking the signal processing process in the field of resource exploration as an example, the acquisition parameters of seismic data were originally designed based on the signal-to-noise ratio, excitation wavelet frequency band, and imaging requirements. The fidelity of the signal was limited to the study of detector placement, excitation wavelet morphology, etc. However, successful exploration cases indicate that the sensitive attributes in seismic signals can more accurately describe and calibrate underground geological phenomena, and these sensitive attributes require high-precision seismic signals as support.

[0004] Seismic signals are the Earth system response to artificially generated signals, and can be described by the following expression:

[0005] f(t)=ξ(τ)*r×e -μt +n

[0006] Where f(t) is the signal received by the sensor (observation); ξ(τ) is the artificial excitation signal; r×e -μt It is the Earth system; r is the reflection sequence; e -μt It is the comprehensive response of the geological body, including spherical diffusion, and is a nonlinear attenuation term; n represents environmental noise. The received signal consists of two parts: the artificially excited signal and the response of the land system. In principle, the excited signal is more like a carrier, and studying the excited signal is about improving its ability to carry geological information; the geological information is contained in the amount of modification done to the excited signal. Therefore, the excited signal is actually a demonstration of carrier capability, while the stratigraphic response is the information of the modified signal.

[0007] Conventional seismic exploration focusing on tectonics actually focuses on the response of the reflection coefficient to the seismic excitation signal. Therefore, wave group characteristics and phase axes are used to locate the location of this response, thereby identifying the structure. This type of characteristic information is easily identified and utilized in seismic signals. Of course, this part also includes some information related to lithology and fractures, but this information is complex and is also included in the nonlinear attenuation term. Sometimes, the information in the nonlinear attenuation term may be more sensitive, such as the relationship between fracture direction and amplitude changes of seismic signals in different azimuths. Attenuation terms are also indispensable for identifying geological phenomena such as heterogeneous media and anisotropy. This comprehensive response is very complex, but it can be divided into several cases for study: First, what kind of sampling is needed to accurately represent the linear changes in the signal; what conditions are imposed on sampling to accurately represent the nonlinear changes in the signal; and finally, how can some abrupt (step) signal changes be preserved? These three types of responses basically encompass the characteristics of the amount of modification of the seismic excitation signal by the Earth system. Research shows that the sampling interval is directly related to the retention of this information in order to accurately recover it. This also indicates that for signal acquisition, the basic requirement for high-precision acquisition is that the sampling interval of the data can meet the requirement of high-precision retention of this information. Summary of the Invention

[0008] In view of the above problems, the present invention is proposed to provide a method for determining the data discrete reference sampling interval to overcome or at least partially solve the above problems.

[0009] In a first aspect, embodiments of the present invention provide a method for determining the sampling interval of a data discrete reference, comprising:

[0010] Adjust the preset sampling interval of the input signal;

[0011] The input signal is sampled using the adjusted sampling interval to obtain the sampled signal;

[0012] The sampled signal is reconstructed to obtain a reconstructed signal;

[0013] The reconstructed signal is compared with the input signal to determine whether the reconstructed signal meets the preset accuracy standard.

[0014] Based on the judgment result, the process of adjusting the sampling interval is repeated, sampling is performed again according to the adjusted sampling interval to obtain the sampling signal, the sampling signal is reconstructed, and it is judged whether the reconstructed signal meets the preset accuracy standard, until the data discrete reference sampling interval is determined.

[0015] In one embodiment, determining whether the reconstructed signal meets a preset accuracy standard includes:

[0016] Determine whether the target features of the preset input signal can be obtained from the reconstructed signal;

[0017] or;

[0018] Determine whether the error between the target features of the reconstructed signal and the target features of the input signal is within a preset error range.

[0019] In one embodiment, performing signal data reconstruction processing on the sampled signal to obtain a reconstructed signal includes:

[0020] The sampled signal is reconstructed using the following algorithm to obtain the reconstructed signal;

[0021] fr=(Ψ*F d ) -1 ;

[0022] Ψ = min|f - fr|2;

[0023] In the above formula, fr represents the reconstructed signal; F d The DCT spectrum of the sampled signal is represented by f; the input signal is represented by f; and Ψ represents the inverse transform weighting operator, where the weighting operator Ψ is the weighted value when the root mean square error of f and fr is minimized.

[0024] In one embodiment, based on the judgment result, the steps of adjusting the sampling interval, sampling again according to the adjusted sampling interval to obtain a sampled signal, reconstructing the sampled signal, and judging whether the reconstructed signal meets the preset accuracy standard are repeated until the data discrete reference sampling interval is determined, including:

[0025] If the reconstructed signal does not meet the preset accuracy standard, the sampling interval is adjusted in a decreasing manner. If the reconstructed signal corresponding to the sampling signal of the sampling interval used this time meets the accuracy standard, and the reconstructed signal corresponding to the sampling signal of the sampling interval used last time does not meet the accuracy standard, then the sampling interval used this time is determined to be the data discrete reference sampling interval.

[0026] In one embodiment, adjusting the sampling interval in a sequentially decreasing manner includes:

[0027] The sampling interval is adjusted such that the current sampling interval is 1 / n of the previous sampling interval.

[0028] In one embodiment, based on the judgment result, the steps of adjusting the sampling interval, sampling again according to the adjusted sampling interval to obtain a sampled signal, reconstructing the sampled signal, and judging whether the reconstructed signal meets the preset accuracy standard are repeated until the data discrete reference sampling interval is determined, including:

[0029] If the reconstructed signal meets the preset accuracy standard, the sampling interval is adjusted in an incremental manner. If the reconstructed signal corresponding to the sampling signal of the sampling interval used this time does not meet the accuracy standard, and the reconstructed signal corresponding to the sampling signal of the sampling interval used last time meets the accuracy standard, then the sampling interval used last time is determined to be the data discrete reference sampling interval.

[0030] In one embodiment, adjusting the sampling interval in a sequentially increasing manner includes:

[0031] The sampling interval is adjusted such that the current sampling interval is n times the previous sampling interval.

[0032] Secondly, embodiments of the present invention provide a data signal sampling method, which samples the data signal by using a data discrete reference sampling interval determined by the aforementioned data discrete reference sampling interval determination method.

[0033] Thirdly, embodiments of the present invention provide a data discrete reference sampling interval determination device, comprising:

[0034] The adjustment module is used to adjust the sampling interval of the preset input signal;

[0035] The sampling module is used to sample the input signal using the adjusted sampling interval to obtain the sampled signal;

[0036] The reconstruction module is used to perform signal reconstruction processing on the sampled signal to obtain a reconstructed signal;

[0037] The judgment module is used to compare the reconstructed signal with the input signal and determine whether the reconstructed signal meets the preset accuracy standard.

[0038] The determination module is used to repeatedly perform the steps of adjusting the sampling interval according to the judgment result, sampling again according to the adjusted sampling interval to obtain the sampling signal, reconstructing the sampling signal, and judging whether the reconstructed signal meets the preset accuracy standard, until the data discrete reference sampling interval is determined.

[0039] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for determining the data discrete reference sampling interval.

[0040] Fifthly, embodiments of the present invention provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements the aforementioned data discrete reference sampling interval determination method.

[0041] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0042] The data discrete reference sampling interval determination method provided in this invention adjusts the sampling interval of a preset input signal; samples the input signal using the adjusted sampling interval to obtain a sampled signal; performs signal reconstruction processing on the sampled signal to obtain a reconstructed signal; compares the reconstructed signal with the input signal to determine whether the reconstructed signal meets a preset accuracy standard; based on the determination result, repeats the steps of adjusting the sampling interval, sampling again using the adjusted sampling interval to obtain a sampled signal, reconstructing the sampled signal, and determining whether the reconstructed signal meets the preset accuracy standard, until the data discrete reference sampling interval is determined. By using the determined data discrete reference sampling interval to sample the data signal, the sampling interval that preserves the information in the signal to meet the requirements can be accurately located, thereby ensuring the reliability range and accuracy of the signal, improving the recognition accuracy and reliability of signal features, and effectively improving the ability of seismic exploration to identify mineral deposits in the signal processing field.

[0043] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0044] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0045] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0046] Figure 1 This is a flowchart of the method for determining the data discrete reference sampling interval in an embodiment of the present invention;

[0047] Figures 2(a) to 2(h) This is a comparison chart of errors before and after data recovery at different sampling intervals in an embodiment of the present invention;

[0048] Figure 3 This is a schematic diagram of a linear scanning signal in an embodiment of the present invention;

[0049] Figure 4(a) is a schematic diagram of the relationship between the weighting coefficient and the root mean square error for 1-millisecond sampling in an embodiment of the present invention;

[0050] Figure 4(b) is a schematic diagram of the relationship between the weighting coefficient and the root mean square error for 2-millisecond sampling in an embodiment of the present invention;

[0051] Figure 5(a) is a schematic diagram of the absolute error analysis results of 1-millisecond sampling in an embodiment of the present invention;

[0052] Figure 5(b) is a schematic diagram of the relative error analysis results of 1-millisecond sampling in an embodiment of the present invention;

[0053] Figure 5(c) is a schematic diagram of the absolute error analysis results of 2-millisecond sampling in an embodiment of the present invention;

[0054] Figure 5(d) is a schematic diagram of the relative error analysis results of 2-millisecond sampling in an embodiment of the present invention;

[0055] Figure 6 This is a structural block diagram of the data discrete reference sampling interval determination device in an embodiment of the present invention. Detailed Implementation

[0056] In this embodiment of the invention, taking signals from conventional seismic exploration as an example, seismic exploration excitation and reception receive continuous analog signals. In a digital context, these originally continuous analog signals are discretely sampled and recorded in an appropriate medium. During the digitization process, whether the information of the originally continuous signal can be completely preserved is directly related to the sampling interval of the discrete sampling. This invention clarifies the relationship between the data discretization acquisition interval and the information retention accuracy by analyzing the ability of signal characteristics to recover and retain their feature information under different sampling intervals. It also proposes a method for determining the data discretization benchmark sampling interval, which is applicable to any signal acquisition process and is not limited to the field of resource exploration.

[0057] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0058] To accurately determine the maximum sampling interval that preserves the required information in the signal, embodiments of the present invention provide a method for determining a data discrete reference sampling interval, referring to... Figure 1 As shown, the method includes the following steps:

[0059] S11. Adjust the preset sampling interval of the input signal;

[0060] S12. Sample the input signal using the adjusted sampling interval to obtain the sampled signal;

[0061] S13. Perform signal reconstruction processing on the sampled signal to obtain a reconstructed signal;

[0062] S14. Compare the reconstructed signal with the input signal to determine whether the reconstructed signal meets the preset accuracy standard;

[0063] S15. Based on the judgment result, repeat the steps of adjusting the sampling interval, sampling again according to the adjusted sampling interval, obtaining the sampling signal, reconstructing the sampling signal, and judging whether the reconstructed signal meets the preset accuracy standard, until the data discrete reference sampling interval is determined.

[0064] In this embodiment of the invention, the discrete reference sampling interval is a discrete signal sampling interval that can retain the target features or all features of the input signal. The determination condition is: when the discretely sampled signal continues to decrease the sampling interval, the target features or all features of the signal no longer change or are less than a certain preset error threshold.

[0065] In this embodiment of the invention, the preset input signal refers to a signal that verifies the ability to identify certain characteristics reflecting a target; its distribution can be one-dimensional, two-dimensional, or multi-dimensional. For example, in the field of seismic exploration, it can be a seismic signal that confirms the identification of sensitive attributes such as oil and gas or geological bodies during the interpretation process; it can also be a signal that has been verified in the laboratory to identify a certain type of feature, and this feature information can be in any direction. Alternatively, it can be an image signal in the field of image recognition, etc. This embodiment of the invention does not limit what kind of physical signal the input signal represents.

[0066] First, the sampling interval of the preset input signal is adjusted, and the input signal is sampled using the adjusted sampling interval to obtain a sampled signal. After obtaining the sampled signal, the sampled signal is reconstructed using a signal data reconstruction method to obtain a corresponding reconstructed signal. Then, it is determined whether the obtained reconstructed signal meets the preset accuracy standard. Based on the determination result, the sampling interval is adjusted for the next iteration, and the above sampling process is repeated based on the adjusted sampling interval to obtain a sampled signal, reconstruct the sampled signal, and determine whether the reconstructed signal meets the preset accuracy standard. The entire process is an iterative process until the data discrete reference sampling interval is determined.

[0067] Furthermore, in step S14 above, it is determined whether the reconstructed signal meets the preset accuracy standard in the following manner:

[0068] Determine whether the target features of the preset input signal can be obtained from the reconstructed signal;

[0069] or;

[0070] Determine whether the error between the target features of the reconstructed signal and the target features of the input signal is within a preset error range.

[0071] In this embodiment of the invention, by using the reconstructed signal for judgment, the recoverability of various information components in the signal under a certain sampling interval can be evaluated, thereby realizing the determination of the retention accuracy of various information components in the signal.

[0072] Furthermore, in step S13 above, the sampled signal can be reconstructed using, for example, the following algorithm, to obtain the reconstructed signal; other methods can also be used for data reconstruction.

[0073] fr=(Ψ*F d ) -1 ;

[0074] Ψ = min|f - fr|2;

[0075] The above algorithm is an iterative inverse transform weighted operator algorithm, which can reconstruct signals sampled at larger intervals into signals sampled at smaller intervals;

[0076] In the above formula, fr represents the reconstructed signal; F d The DCT spectrum of the sampled signal is represented by f; the input signal is represented by f; and Ψ represents the inverse transform weighting operator, where the weighting operator Ψ is the weighted value when the root mean square error of f and fr is minimized.

[0077] Further, in step S15 above, based on the judgment result, the steps of adjusting the sampling interval, sampling again according to the adjusted sampling interval to obtain the sampled signal, reconstructing the sampled signal, and judging whether the reconstructed signal meets the preset accuracy standard are repeated until the data discrete reference sampling interval is determined. This is specifically implemented in the following way:

[0078] If the reconstructed signal does not meet the preset accuracy standard, the sampling interval is adjusted in a decreasing manner. If the reconstructed signal corresponding to the sampling signal of the sampling interval used this time meets the accuracy standard, and the reconstructed signal corresponding to the sampling signal of the sampling interval used last time does not meet the accuracy standard, then the sampling interval used this time is determined to be the data discrete reference sampling interval.

[0079] For example, if the preset sampling interval for the input signal is 8 milliseconds, and the sampling interval is adjusted to 16 milliseconds, the input signal is sampled at a 16-millisecond sampling interval to obtain the corresponding sampled signal. The obtained sampled signal is then reconstructed to obtain the corresponding 8-millisecond reconstructed signal. It is then determined whether the obtained reconstructed signal meets the preset accuracy standard. If the determination result is that it does not meet the accuracy standard, the sampling interval for the input signal is reduced to 4 milliseconds, and the sampling interval is adjusted to 8 milliseconds. The input signal is then sampled at an 8-millisecond sampling interval to obtain the corresponding 8-millisecond sampled signal. The obtained 8-millisecond sampled signal is then reconstructed again to obtain the corresponding 4-millisecond reconstructed signal. It is then determined whether the obtained reconstructed signal meets the preset accuracy standard again. If the determination result this time is that it meets the accuracy standard, and combined with the previous determination result that it does not meet the accuracy standard, it can be determined that the 8-millisecond sampling interval used this time is the data discrete reference sampling interval.

[0080] In the example above, if the 4-millisecond reconstructed signal corresponding to the 8-millisecond sampled signal still does not meet the accuracy standard, the sampling interval is reduced again to 4 milliseconds, and the above steps are repeated using the 4-millisecond sampling interval. If the reconstructed signal corresponding to the sampled signal with the 4-millisecond sampling interval meets the accuracy standard, then it can be determined that the 4-millisecond sampling interval used this time is the data discrete reference sampling interval. The above process is a cyclical judgment process until a sampling interval that meets the above conditions is found.

[0081] Furthermore, in the above method, the sampling interval is adjusted in a sequentially decreasing manner, specifically implemented as follows:

[0082] The sampling interval is adjusted such that the current sampling interval is 1 / n of the previous sampling interval.

[0083] The aforementioned 1 / n can be, for example, 1 / 2, 1 / 3, 1 / 4 or other values. This embodiment of the invention does not limit which value to use.

[0084] The following example illustrates the adjustment method where n is 1 / 2:

[0085] The input signal is reconstructed and recovered by sequentially decreasing the sampling interval. For example, if the sampling interval of the input signal is 1 millisecond, the sampling interval is adjusted to 2 milliseconds. The input signal is sampled using the 2-millisecond sampling interval to obtain the corresponding sampled signal. The above reconstruction method is used to reconstruct the sampled signal obtained by the 2-millisecond sampling back to the 1-millisecond sampling signal, i.e., the reconstructed signal. If the accuracy of specific information in the reconstructed signal does not meet the accuracy standard, the sampling interval of the input signal is reduced to 0.5 milliseconds, and the sampling interval is adjusted to 1 millisecond. The sampled signal obtained by the 1-millisecond sampling is then reconstructed into the 0.5-millisecond reconstructed signal. If the accuracy of specific information in the reconstructed signal meets the accuracy standard, then the 1-millisecond sampling interval is the determined data discrete reference sampling interval. The above process can be iterated until a sampling interval that meets the conditions is determined.

[0086] Further, in step S15 above, based on the judgment result, the steps of adjusting the sampling interval, sampling again according to the adjusted sampling interval to obtain the sampled signal, reconstructing the sampled signal, and judging whether the reconstructed signal meets the preset accuracy standard are repeated until the data discrete reference sampling interval is determined. This is specifically implemented in the following way:

[0087] If the reconstructed signal meets the preset accuracy standard, the sampling interval is adjusted in an incremental manner. If the reconstructed signal corresponding to the sampling signal of the sampling interval used this time does not meet the accuracy standard, and the reconstructed signal corresponding to the sampling signal of the sampling interval used last time meets the accuracy standard, then the sampling interval used last time is determined to be the data discrete reference sampling interval.

[0088] For example, if the preset sampling interval for the input signal is 2 milliseconds, and the sampling interval is adjusted to 4 milliseconds, the input signal is sampled at a 4-millisecond sampling interval to obtain the corresponding sampled signal. The obtained sampled signal is then reconstructed to obtain the corresponding 2-millisecond reconstructed signal. It is then determined whether the obtained reconstructed signal meets the preset accuracy standard. If the determination result is that the accuracy standard is met, the sampling interval for the input signal is increased to 4 milliseconds and adjusted to 8 milliseconds. The input signal is then sampled again at an 8-millisecond sampling interval to obtain the sampled signal corresponding to the 8-millisecond sampling interval. The obtained 8-millisecond sampled signal is then reconstructed again to obtain the 4-millisecond reconstructed signal. It is then determined whether the obtained reconstructed signal meets the preset accuracy standard again. If the determination result this time is that the accuracy standard is not met, and combined with the previous determination result that the accuracy standard is met, it can be determined that the 4-millisecond sampling interval used last time is the data discrete reference sampling interval.

[0089] In the example above, if the 4-millisecond reconstructed signal corresponding to the 8-millisecond sampled signal still meets the accuracy standard, the sampling interval is increased again to 16 milliseconds, and the above steps are repeated using the 16-millisecond sampling interval. If the reconstructed signal corresponding to the sampled signal with the 16-millisecond sampling interval does not meet the accuracy standard, then it can be determined that the previously used 8-millisecond sampling interval is the data discrete reference sampling interval. The above process is a cyclical judgment process until a data sampling interval that meets the above conditions is found.

[0090] Furthermore, in the above method, the sampling interval is adjusted in an incremental manner, specifically implemented as follows:

[0091] The sampling interval is adjusted such that the current sampling interval is n times the previous sampling interval.

[0092] The n mentioned above can be 2, 3, 4, or other values. This embodiment of the invention does not limit the value used; the following example illustrates the adjustment method with n=2:

[0093] In addition to the aforementioned decreasing adjustment method, the sampling interval can also be adjusted by increasing it sequentially. This method first uses a smaller data sampling interval for sampling. If the reconstructed signal meets the preset signal accuracy standard, the data sampling interval is increased to twice the previous interval. The increased data sampling interval is used to sample the input signal and reconstruct the signal of the previous sampling interval. If the accuracy of specific information in the reconstructed signal is higher than the signal accuracy standard, the data sampling interval is increased until the reconstructed signal corresponding to the sampling signal of a certain sampling interval does not meet the signal accuracy standard, and the reconstructed signal corresponding to the sampling signal of the previous sampling interval meets the signal accuracy standard. Then, the sampling interval used last time is determined as the data discrete reference sampling interval.

[0094] This invention also provides a data signal sampling method, which samples the data signal by using the data discrete reference sampling interval determined by the aforementioned data discrete reference sampling interval determination method, so that the signal information in the data signal is retained to meet the requirements.

[0095] The data discrete reference sampling interval determination method provided in this embodiment of the invention also includes the following scheme:

[0096] When the sampling interval of the input signal is fixed and there is no possibility of decreasing or increasing the sampling interval, this method can perform reconstruction and recovery analysis on various components of the information in the signal (such as linear change information, nonlinear change information, frequency band information, and any other type of component) to determine the recoverability of various components of the information, thereby determining the retention accuracy of various components in the information and confirming the reliability of the information in the signal.

[0097] The data discrete reference sampling interval determination method provided in this invention, based on the comparison of recovery at different sampling intervals, ensures the reliability range and accuracy of signal features, thereby improving the recognition accuracy and reliability of signal features. (Refer to...) Figures 2(a) to 2(h) As shown, taking the seismic data analysis of a certain work area as an example, the well shot seismic record is recorded with a length of 6 seconds, using 1 millisecond sampling and 400 channels receiving. Figures 2(a) to 2(h) The data shows the overall absolute and relative errors of point-to-point sampling recovered to 1 millisecond sampling after 2 millisecond and 4 millisecond sampling, as well as the case of one specific error. It can be seen that although the vast majority of point-to-point errors are below -50dB, many are greater than 0dB, with the largest exceeding 100dB. On average, the relative error of recovering to 1 millisecond from 2 milliseconds is -76dB, while 4 milliseconds is only -54dB. Therefore, the smaller the sampling interval, the smaller the error and the higher the accuracy. By continuously adjusting the sampling interval and comparing it with accuracy standards, the optimal sampling interval can be determined.

[0098] To better illustrate the data discrete reference sampling interval determination method of this invention, a specific example is given below:

[0099] Reference Figure 3 As shown, Figure 3 It shows a linear scan signal sampled at 0.5 milliseconds, which contains linear frequency variation information.

[0100] Reference Figures 4(a) to 4(b) As shown, Figure 4(a) shows the relationship between the weighting coefficients and the root mean square error for 1 millisecond sampling, and Figure 4(b) shows the relationship between the weighting coefficients and the root mean square error for 2 millisecond sampling. Figures 4(a) to 4(b) The vertical axis represents the root mean square error between the recovered signal (reconstructed signal) and the input signal, and the horizontal axis represents the weighting operator index, where weighting operator = 1 + index / 100; from Figures 4(a) to 4(b) As can be seen, when the weighting operator reaches 1.41, the difference between the reconstructed signal and the original signal is minimal. This weighting value is used to reconstruct data sampled at larger intervals and to perform error analysis.

[0101] Reference Figures 5(a) to 5(d) As shown, the absolute and relative error analysis results of the reconstructed signal compared to the original signal using the above method are presented. Figure 5(a) shows the absolute error analysis results for 1 millisecond sampling, Figure 5(b) shows the relative error analysis results for 1 millisecond sampling, Figure 5(c) shows the absolute error analysis results for 2 millisecond sampling, and Figure 5(d) shows the relative error analysis results for 2 millisecond sampling. Figures 5(a) to 5(d)As can be seen, the main error originates from the high-frequency range. For linear frequency-varying information, if a 1-millisecond sampling rate is used, the maximum relative error retained in the high-frequency range for the linear change of the signal is approximately -40dB (0.5millisecond sampling), while a 2-millisecond sampling rate results in approximately -20dB (0.5millisecond sampling). If the accuracy standard for describing linear changes is specified as less than -40dB, then a 1-millisecond data sampling interval can be used as the baseline sampling interval for data discretization.

[0102] Based on the same inventive concept, this embodiment of the invention also provides a data discrete reference sampling interval determination device. Since the principle of the problem solved by this device is similar to the aforementioned data discrete reference sampling interval determination method, the implementation of this device can refer to the implementation of the aforementioned method, and the repeated parts will not be described again.

[0103] This invention provides a data discrete reference sampling interval determination device, referring to... Figure 6 As shown, it includes:

[0104] The adjustment module 61 is used to adjust the sampling interval of the preset input signal;

[0105] Sampling module 62 is used to sample the input signal using the adjusted sampling interval to obtain a sampled signal;

[0106] Reconstruction module 63 is used to perform signal reconstruction processing on the sampled signal to obtain a reconstructed signal;

[0107] The judgment module 64 is used to compare the reconstructed signal with the input signal and determine whether the reconstructed signal meets the preset accuracy standard.

[0108] The determination module 65 is used to repeatedly perform the steps of adjusting the sampling interval according to the judgment result, sampling again according to the adjusted sampling interval to obtain the sampling signal, reconstructing the sampling signal, and judging whether the reconstructed signal meets the preset accuracy standard, until the data discrete reference sampling interval is determined.

[0109] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for determining the data discrete reference sampling interval.

[0110] This invention provides a computer program product, which includes a computer program that, when executed by a processor, implements the aforementioned method for determining the data discrete reference sampling interval.

[0111] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0112] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0113] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0114] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0115] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0116] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for determining the data discrete reference sampling interval, characterized in that, include: Adjust the preset sampling interval of the input signal; The input signal is sampled using the adjusted sampling interval to obtain the sampled signal; The sampled signal is reconstructed using the following algorithm to obtain the reconstructed signal; ； ； In the above formula, This represents the reconstructed signal; The DCT spectrum of the sampled signal; This refers to the input signal; This represents the inverse transform weighted operator, and the weighted operator. Values and The weighted value when the root mean square error is minimized; The reconstructed signal is compared with the input signal to determine whether the reconstructed signal meets the preset accuracy standard. Based on the judgment result, the process of adjusting the sampling interval is repeated, sampling is performed again according to the adjusted sampling interval to obtain the sampling signal, the sampling signal is reconstructed, and it is judged whether the reconstructed signal meets the preset accuracy standard, until the data discrete reference sampling interval is determined.

2. The method as described in claim 1, characterized in that, Determining whether the reconstructed signal meets the preset accuracy standard includes: Determine whether the target features of the preset input signal can be obtained from the reconstructed signal; or; Determine whether the error between the target features of the reconstructed signal and the target features of the input signal is within a preset error range.

3. The method as described in claim 1, characterized in that, Based on the judgment result, the process of adjusting the sampling interval is repeated, sampling is performed again according to the adjusted sampling interval to obtain the sampled signal, the sampled signal is reconstructed, and it is determined whether the reconstructed signal meets the preset accuracy standard, until the data discrete reference sampling interval is determined, including: If the reconstructed signal does not meet the preset accuracy standard, the sampling interval is adjusted in a decreasing manner. If the reconstructed signal corresponding to the sampling signal of the sampling interval used this time meets the accuracy standard, and the reconstructed signal corresponding to the sampling signal of the sampling interval used last time does not meet the accuracy standard, then the sampling interval used this time is determined to be the data discrete reference sampling interval.

4. The method as described in claim 3, characterized in that, Adjusting the sampling interval in a sequentially decreasing manner includes: Based on the current sampling interval being the same as the previous sampling interval. The sampling interval is adjusted in this manner.

5. The method as described in claim 1, characterized in that, Based on the judgment result, the process of adjusting the sampling interval is repeated, sampling is performed again according to the adjusted sampling interval to obtain the sampled signal, the sampled signal is reconstructed, and it is determined whether the reconstructed signal meets the preset accuracy standard, until the data discrete reference sampling interval is determined, including: If the reconstructed signal meets the preset accuracy standard, the sampling interval is adjusted in an incremental manner. If the reconstructed signal corresponding to the sampling signal of the sampling interval used this time does not meet the accuracy standard, and the reconstructed signal corresponding to the sampling signal of the sampling interval used last time meets the accuracy standard, then the sampling interval used last time is determined to be the data discrete reference sampling interval.

6. The method as described in claim 5, characterized in that, Adjusting the sampling interval in a sequentially increasing manner includes: Based on the current sampling interval being the same as the previous sampling interval. The sampling interval is adjusted in a multiple manner.

7. A data signal sampling method, characterized in that, The data signal is sampled using the data discrete reference sampling interval determined by the data discrete reference sampling interval determination method as described in any one of claims 1 to 6.

8. A device for determining the sampling interval of a data discrete reference, characterized in that, include: The adjustment module is used to adjust the sampling interval of the preset input signal; The sampling module is used to sample the input signal using the adjusted sampling interval to obtain the sampled signal; The reconstruction module is used to perform signal reconstruction processing on the sampled signal using the following algorithm to obtain a reconstructed signal; ； ； In the above formula, This represents the reconstructed signal; The DCT spectrum of the sampled signal; This refers to the input signal; This represents the inverse transform weighted operator, and the weighted operator. Values and The weighted value when the root mean square error is minimized; The judgment module is used to compare the reconstructed signal with the input signal and determine whether the reconstructed signal meets the preset accuracy standard. The determination module is used to repeatedly perform the steps of adjusting the sampling interval according to the judgment result, sampling again according to the adjusted sampling interval to obtain the sampling signal, reconstructing the sampling signal, and judging whether the reconstructed signal meets the preset accuracy standard, until the data discrete reference sampling interval is determined.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the data discrete reference sampling interval determination method according to any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the data discrete reference sampling interval determination method according to any one of claims 1 to 6.

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