CN115660957A

CN115660957A - Resampling method, device, equipment and medium for waveform data

Info

Publication number: CN115660957A
Application number: CN202211400705.3A
Authority: CN
Inventors: 季尔优; 周磊
Original assignee: Acela Micro Co ltd
Current assignee: Acela Micro Co ltd
Priority date: 2022-11-09
Filing date: 2022-11-09
Publication date: 2023-01-31

Abstract

The invention discloses a method, a device, equipment and a medium for resampling waveform data. A method of resampling waveform data, comprising: acquiring target waveform sampling data of a target channel; initializing jumping point detection data by using target waveform sampling data, and identifying jumping sampling points in the jumping point detection data; obtaining a current interpolation algorithm in the interpolation algorithm set, and performing interpolation processing between each jumping sampling point and the previous adjacent sampling point of the jumping sampling points by using the current interpolation algorithm; after the interpolation data is used for updating the jumping point detection data, returning to execute the operation of identifying jumping sampling points in the jumping point detection data until the use of an interpolation algorithm for preset times is completed; and according to the interpolation position recorded before the interpolation processing, resampling the waveform acquisition data of the channel to be processed. The technical scheme of the embodiment of the invention can greatly improve the sampling precision of waveform data and meet the occasions with higher data precision requirement.

Description

Resampling method, device, equipment and medium for waveform data

Technical Field

The present invention relates to the field of communications technologies, and in particular, to a method, an apparatus, a device, and a medium for resampling waveform data.

Background

Resampling is a common means for switching sampling rate, and has a great number of applications in the field of communications.

Due to the fact that collection errors exist in the waveform data collection process, error accumulation is caused in the follow-up data processing process, and the output waveform data and the original collected waveform data have large differences. For example, a standard sine wave data is collected and output as a triangular wave data.

However, the interpolation method adopted by the existing resampling method cannot meet the situation with higher precision requirement.

Disclosure of Invention

The invention provides a method, a device, equipment and a medium for resampling waveform data, which can greatly improve the sampling precision of the waveform data and meet the occasions with higher data precision requirements.

According to an aspect of the present invention, there is provided a method of resampling waveform data, comprising:

acquiring target waveform sampling data of a target channel from waveform acquisition data acquired by a plurality of channels;

initializing jumping point detection data by using target waveform sampling data, and identifying jumping sampling points in the jumping point detection data;

obtaining a current interpolation algorithm in the interpolation algorithm set, and performing interpolation processing between each jumping sampling point and the previous adjacent sampling point of the jumping sampling points by using the current interpolation algorithm;

after updating the jumping point detection data by using the interpolation data, returning to execute the operation of identifying jumping sampling points in the jumping point detection data until the use of an interpolation algorithm of preset times is completed;

and according to the interpolation position recorded by each interpolation algorithm before interpolation processing, the interpolation algorithm concentrates, and the waveform acquisition data of the channel to be processed is resampled.

In various embodiments, the channel to be processed includes the target channel, or does not include the target channel. When multi-channel resampling processing is carried out, and interpolation processing is carried out on the jumping point detection data associated with the target channel, interpolation positions recorded by the used interpolation algorithms before interpolation processing are used as trigger signals of other multi-channel channels, so that other multi-channel channels can acquire waveform data. It should be noted that the interpolation position recorded by each interpolation algorithm before the interpolation processing may also be used as a trigger signal of the target channel, which is specifically determined according to actual needs and is not limited.

According to another aspect of the present invention, there is provided a resampling apparatus for waveform data, comprising:

the sampling data acquisition module is used for acquiring target waveform sampling data of a target channel from the waveform acquisition data acquired by the plurality of channels;

the jump sampling point identification module is used for initializing jump point detection data by using the target waveform sampling data and identifying jump sampling points in the jump point detection data;

the interpolation processing module is used for acquiring a current interpolation algorithm in the interpolation algorithm set and performing interpolation processing between each jumping sampling point and the previous adjacent sampling point of the jumping sampling point by using the current interpolation algorithm;

the iteration execution module is used for returning to execute the operation of identifying the jumping sampling point in the jumping point detection data after updating the jumping point detection data by using the interpolation data until the use of the interpolation algorithm of the preset times is finished;

and the resampling processing module is used for resampling the waveform acquisition data of the channel to be processed according to the interpolation position recorded by each interpolation algorithm before interpolation processing in the interpolation algorithm set.

According to another aspect of the present invention, there is provided an electronic apparatus including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,

the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform a method of resampling waveform data according to any of the embodiments of the invention.

According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the method for resampling waveform data according to any embodiment of the present invention when executed.

According to the technical scheme of the embodiment of the invention, target waveform sampling data of a target channel is obtained from waveform acquisition data acquired by a plurality of channels, then target waveform sampling data is used for initializing jumping point detection data, jumping sampling points are identified in the jumping point detection data, so that a current interpolation algorithm is intensively obtained in the interpolation algorithm, interpolation processing is carried out between each jumping sampling point and the previous adjacent sampling point of the jumping sampling point by using the current interpolation algorithm, after the jumping point detection data is further updated by using the interpolation data, the operation of identifying the jumping sampling points in the jumping point detection data is returned to be executed until the use of the interpolation algorithm of preset times is completed, and thus the waveform acquisition data of the channel to be processed are resampled according to the interpolation positions recorded by each interpolation algorithm in the interpolation algorithm before the interpolation processing. According to the scheme, interpolation processing can be performed for preset times through a plurality of interpolation algorithms in which the interpolation algorithms are concentrated, namely, high-precision interpolation can be performed on data more flexibly, so that the interpolation positions recorded by the interpolation algorithms before the interpolation processing are used according to the target channel, and the channel to be processed is triggered to be resampled, the problem that the situation with higher precision requirement cannot be met due to the interpolation mode adopted by the existing resampling method is solved, the sampling precision of waveform data can be greatly improved, and the situation with higher data precision requirement is met.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a flowchart of a method for resampling waveform data according to an embodiment of the present invention;

fig. 2 is a flowchart of a waveform data resampling method according to a second embodiment of the present invention;

fig. 3 is a schematic diagram illustrating a principle of searching for a negative number in original data to jump to a positive number according to a third embodiment of the present invention;

fig. 4 is a schematic diagram of a Sinc interpolation principle provided by the third embodiment of the present invention;

fig. 5 is a schematic diagram of a zero-crossing point after Sinc interpolation is searched according to a third embodiment of the present invention;

fig. 6 is a schematic diagram illustrating an implementation principle of linear interpolation according to a third embodiment of the present invention;

FIG. 7 is a schematic flow chart illustrating the determination of output data after linear interpolation according to a third embodiment of the present invention;

fig. 8 is a schematic flow chart illustrating an implementation of channel triggered resampling according to a third embodiment of the present invention;

fig. 9 is a schematic structural diagram of a waveform data resampling apparatus according to a fourth embodiment of the present invention;

FIG. 10 shows a schematic diagram of an electronic device that may be used to implement an embodiment of the invention.

Detailed Description

In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It is noted that the terms "initial", "target", and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example one

Fig. 1 is a flowchart of a waveform data resampling method according to an embodiment of the present invention, where the present embodiment is applicable to a case of waveform high-precision recovery, the method may be performed by a waveform data resampling apparatus, the waveform data resampling apparatus may be implemented in a form of hardware and/or software, and the waveform data resampling apparatus may be configured in an electronic device. As shown in fig. 1, the method includes:

and S110, acquiring target waveform sampling data of a target channel from the waveform acquisition data acquired by the plurality of channels.

The waveform acquisition data may be a result of data acquisition of waveform data transmitted in the channel. The target channel may be a selected one of a plurality of channels for collecting data from the transmit waveform. The target waveform sample data may be waveform data collected from a target channel in the waveform collection data.

In the embodiment of the present invention, one channel may be selected from the multiple channels as a target channel, and target waveform sampling data of the target channel may be screened out from waveform acquisition data acquired by the multiple channels.

And S120, initializing the jumping point detection data by using the target waveform sampling data, and identifying jumping sampling points in the jumping point detection data.

The jumping point detection data may be data that needs to be subjected to data jumping determination. Optionally, the data transition types may include zero-crossing transition (for example, positive number transition to negative number, or negative number transition to positive number), and threshold transition (one of two adjacent data is greater than a set threshold, and the other data is smaller than the set threshold, where the set threshold is not 0). The transition sample point may be a sample point at which a data transition occurs in the trip point detection data. For example, the latter data b of two adjacent data a, b where data transition occurs may be taken as a transition sampling point.

In the embodiment of the invention, before the first interpolation processing is carried out, the target waveform sampling data is required to be used as the jumping point detection data, so that the data jumping type is selected, and the jumping sampling point in the jumping point detection data is identified based on the selected data jumping type.

S130, obtaining a current interpolation algorithm in the interpolation algorithm set, and performing interpolation processing between each jumping sampling point and the previous adjacent sampling point of the jumping sampling points by using the current interpolation algorithm.

Wherein the set of interpolation algorithms may include at least one interpolation algorithm. The current interpolation algorithm may be a set of interpolation algorithms for performing the interpolation algorithm employed for the current interpolation process. The previous adjacent sample point may be the previous waveform acquisition data adjacent to the jump sample point.

In the embodiment of the invention, the current interpolation algorithm can be obtained from the interpolation algorithm set, the previous adjacent waveform acquisition data of each hopping sampling point is determined, and the previous adjacent waveform acquisition data of each hopping sampling point is used as the previous adjacent sampling point of the corresponding hopping sampling point, so that the current interpolation algorithm is utilized to carry out interpolation processing between each hopping sampling point and the previous adjacent sampling point of the hopping sampling point.

And S140, after updating the jumping point detection data by using the interpolation data, returning to execute the operation of identifying the jumping sampling point in the jumping point detection data until the use of the interpolation algorithm of the preset times is completed.

Wherein the preset number of times may be a total number of times of interpolation processing set in advance.

In the embodiment of the present invention, interpolation data generated when interpolation processing is performed on each jumping sampling point and a previous adjacent sampling point of the jumping sampling points based on a current interpolation algorithm may be used as interpolation data matched with the current interpolation algorithm, and an interpolation result composed of the interpolation data matched with the current interpolation algorithm, the jumping sampling point matched with the interpolation data, and a previous adjacent sampling point of the jumping sampling point matched with the interpolation data may be used as new jumping point detection data, and an operation of identifying the jumping sampling point in the jumping point detection data is returned, that is, the jumping sampling point is identified in the updated jumping point detection data until use of the interpolation algorithm of a preset number of times is completed.

For example, assuming that a jumping sampling point b exists in the jumping point detection data, a previous adjacent sampling point of the jumping sampling point b is a, interpolation processing is performed between a and b based on a current interpolation algorithm, if the interpolation data is a, a jumping sampling point matched with the interpolation data a is a jumping sampling point b, a previous adjacent sampling point of the jumping sampling point matched with the interpolation data a is a jumping sampling point a, and a, a and b are used as interpolation results.

Optionally, the interpolation algorithm set may include a plurality of interpolation algorithms, but when the interpolation algorithms of the preset number of times are used, all the interpolation algorithms in the interpolation algorithm set may not be used. For example, there are five interpolation algorithms in the interpolation algorithm set, and the preset number of times may be two, or only two algorithms in the interpolation algorithm set are included to correspond to two interpolation processes.

Optionally, the use of the interpolation algorithm for the preset times may be completed according to the use sequence of the interpolation algorithm. When the preset times is at least three times, interpolation algorithms selected by adjacent two times of interpolation processing are different, and interpolation algorithms corresponding to non-adjacent interpolation processing can be the same. For example, when the use of the cubic interpolation algorithm is completed, the same interpolation algorithm may be selected for the first time and the third time, and the different interpolation algorithms selected for the second time are different from the interpolation algorithms selected for the first time and the third time, and at this time, only two interpolation algorithms may be in the interpolation algorithm set.

And S150, resampling the waveform acquisition data of the channel to be processed according to the interpolation position recorded by each interpolation algorithm before interpolation processing in the interpolation algorithm set.

Wherein the interpolation position may be an addition position of the interpolation data. The channel to be processed may be a channel transmitting waveform acquisition data.

In the embodiment of the invention, before interpolation processing is carried out between each jumping sampling point and the previous adjacent sampling point of the jumping sampling point by using the current interpolation algorithm, the position of the jumping sampling point in the corresponding jumping point detection data can be recorded, so that synchronous resampling processing can be carried out on the waveform acquisition data of the channel to be processed according to the interpolation position recorded by each interpolation algorithm before the interpolation processing.

Example two

Fig. 2 is a flowchart of a waveform data resampling method according to the second embodiment of the present invention, and this embodiment gives, based on the above embodiment, a relevant operation before performing interpolation processing between each jumping sample point and a previous adjacent sample point of the jumping sample point by using a current interpolation algorithm, and the specific process is as follows: acquiring the ordinal number of each jumping sampling point in the jumping point detection data; and recording the interpolation position matched with the current interpolation algorithm according to the ordinal number of each jumping sampling point in the jumping point detection data. As shown in fig. 2, the method includes:

s210, acquiring target waveform sampling data of a target channel from the waveform acquisition data acquired by the plurality of channels.

S220, initializing jumping point detection data by using the target waveform sampling data, and identifying jumping sampling points in the jumping point detection data.

In an alternative embodiment of the present invention, identifying the jumping sample points in the jumping point detection data may include: setting initial target data to be compared; dividing the jumping point detection data into a first jumping point detection data group and a second jumping point detection data group; determining first detection data of the current same data position in a first detection data group of the trip point and second detection data of the trip point; and if the first detection data of the jumping point is smaller than the initial target data to be compared and the second detection data of the jumping point is larger than the initial target data to be compared, or the first detection data of the jumping point is larger than the initial target data to be compared and the second detection data of the jumping point is smaller than the initial target data to be compared, taking the second detection data of the jumping point as a jumping sampling point.

The initial target data to be compared may be data for determining a jump sampling point. The trip point first detection data group may be a data set made up of data from the first data to the second last data (second to last data) in the trip point detection data. The trip point second detection data group may be a data group constituted by data between the second leading data (next data adjacent to the first data) to the last data in the trip point detection data. The first detection data of the jump point has the same data position with the second detection data of the jump point, but the first detection data of the jump point belongs to the first detection data group of the jump point, and the second detection data of the jump point belongs to the second detection data group of the jump point.

In the embodiment of the present invention, data to be compared of an initial target may be preset, and a data set formed by data from first data to second last data in the trip point detection data is used as a first detection data group of the trip point, and a data set formed by data from second first data to last data in the trip point detection data is used as a second detection data group of the trip point, and the first detection data of the trip point and the second detection data of the trip point at the same current data position in the first detection data group and the second detection data of the trip point are further determined respectively, and the first detection data of the trip point and the second detection data of the trip point are compared with the data to be compared of the initial target, and if the first detection data of the trip point is smaller than the data to be compared of the initial target and the second detection data of the trip point is larger than the data to be compared of the initial target, or if the first detection data of the trip point is larger than the data to be compared of the initial target and the second detection data of the trip point is smaller than the data to be compared of the initial target, the first detection data of the trip point is used as a second detection sample point.

It should be noted that the initial target data to be compared set after initializing the trip point detection data using the target waveform sampling data and after each update of the trip point detection data may be the same or different.

In an optional embodiment of the present invention, dividing the trip point detection data into a trip point first detection data group and a trip point second detection data group may include: acquiring the position of first data, the position of last data, the position of second first data and the position of second last data of the jumping point detection data; screening a first detection data group of the trip point from the trip point detection data according to the position of the first data of the trip point detection data and the position of the second last data; and screening a second detection data group of the jumping points from the jumping point detection data according to the position of the first data of the jumping point detection data and the position of the last data of the jumping point detection data.

In the embodiment of the present invention, a position of first data, a position of last data, a position of second first data, and a position of second last data of the trip point detection data may be obtained first, and then a data set formed by data from the first data to the second last data in the trip point detection data is selected from the trip point detection data according to the position of the first data and the position of the second last data of the trip point detection data, and a data set formed by data from the first data to the second last data in the trip point detection data is used as a first detection data group of the trip point, and further a data set formed by data from the second first data to the last data in the trip point detection data is selected from the trip point detection data according to the position of the second first data and the position of the last data in the trip point detection data, and a data set formed by data from the second first data to the last data in the trip point detection data is used as a second detection data group.

And S230, acquiring ordinal numbers of each jumping sampling point in the jumping point detection data.

In the embodiment of the invention, before interpolation processing is carried out between each jumping sampling point and the previous adjacent sampling point of the jumping sampling points by using a current interpolation algorithm, the ordinal number of the detection data of each jumping sampling point at the corresponding jumping point is determined.

And S240, recording the interpolation position matched with the current interpolation algorithm according to the ordinal number of each jumping sampling point in the jumping point detection data.

In the embodiment of the invention, the position of each jumping sampling point in the corresponding jumping point detection data can be determined according to the ordinal number of each jumping sampling point in the corresponding jumping point detection data, and the position of each jumping sampling point in the corresponding jumping point detection data is used as the interpolation position matched with the current interpolation algorithm and is recorded.

And S250, acquiring a current interpolation algorithm in the interpolation algorithm set, and performing interpolation processing between each jumping sampling point and the previous adjacent sampling point of the jumping sampling points by using the current interpolation algorithm.

And S260, after updating the jumping point detection data by using the interpolation data, returning to execute the operation of identifying the jumping sampling point in the jumping point detection data until the use of the interpolation algorithm of preset times is completed.

In an optional embodiment of the present invention, after completing the use of the interpolation algorithm for the preset number of times, the method may further include: acquiring a last interpolation processing result matched with a last interpolation algorithm; acquiring data to be compared of a last target; determining a jump sampling point to be processed and a previous adjacent sampling point of the jump sampling point to be processed according to a last interpolation processing result and last target data to be compared; and determining a target sampling point according to a first difference value between the jump sampling point to be processed and the data to be compared of the last target and a second difference value between a previous adjacent sampling point of the jump sampling point to be processed and the data to be compared of the last target, and recording the position of the target sampling point.

The last interpolation algorithm may be an interpolation algorithm used in the last interpolation processing. The last interpolation processing result may be composed of interpolation data at the time of last interpolation processing, a jump sampling point matched with the interpolation data of last interpolation processing, and a previous adjacent sampling point of the jump sampling point. The last target data to be compared can be used for determining the jumping sampling points in the last interpolation processing result. The jump sampling point to be processed can be a jump sampling point obtained by performing data jump identification on the last interpolation processing result. The first difference may be a difference between the transition sampling point to be processed and the last target data to be compared. The second difference may be a difference between a previous adjacent sampling point of the to-be-processed jump sampling point and the last target to-be-compared data. The target sampling point may be a to-be-processed jump sampling point and a sampling point adjacent to the to-be-processed jump sampling point in the past, where an absolute value of a difference between the to-be-processed jump sampling point and the last target to-be-compared data is smaller. The target sampling point position may be a position of the target sampling point at the last interpolation processing result.

In the embodiment of the invention, interpolation data matched with a last interpolation algorithm can be obtained firstly, then interpolation data during interpolation processing of the last interpolation algorithm, a jump sampling point matched with the interpolation data and a previous adjacent sampling point of the jump sampling point are obtained, a last interpolation processing result is generated, and then data to be compared of a last target is obtained, so that jump point detection data is updated according to the last interpolation processing result, the jump sampling point of the updated jump point detection data is identified based on the data to be compared of the last target, a jump sampling point to be processed is obtained, a first difference value between the data to be compared of the jump sampling point to be processed and the last target is calculated, a second difference value between the previous adjacent sampling point of the jump sampling point to be processed and the data to be compared of the last target is obtained, the absolute value of the first difference value is compared with the absolute value of the second difference value, the sampling point corresponding to a smaller absolute value is taken as a target sampling point, the position of the target sampling point in the last interpolation processing result is determined, and the position of the target sampling point is obtained and recorded.

And S270, resampling the waveform acquisition data of the channel to be processed according to the interpolation position recorded by each interpolation algorithm before interpolation processing in the interpolation algorithm set.

In an optional embodiment of the present invention, the resampling processing, according to interpolation positions recorded by the interpolation algorithms in the interpolation algorithm set before the interpolation processing, on the waveform acquisition data of the channel to be processed may include: carrying out interpolation processing on the waveform acquisition data of the channel to be processed according to the interpolation processing sequence of each interpolation algorithm used in the interpolation algorithm set and the interpolation position recorded by each interpolation algorithm before the interpolation processing; and acquiring a last interpolation processing result matched with the waveform acquisition data of the channel to be processed, and determining resampling output waveform data according to the last interpolation processing result matched with the waveform acquisition data of the channel to be processed and the target sampling point position.

The resampled output waveform data may be resampled waveform data determined according to a last interpolation processing result matched with the waveform acquisition data of the channel to be processed and a target sampling point position.

In the embodiment of the present invention, before resampling processing is performed on waveform acquisition data of a channel to be processed, interpolation processing may be performed on the waveform acquisition data of the channel to be processed according to an interpolation processing sequence of each interpolation algorithm used in an interpolation algorithm set and an interpolation position recorded by each interpolation algorithm before interpolation processing, so as to obtain a last interpolation processing result matched with the waveform acquisition data of the channel to be processed, and further determine a target sampling point position matched with the last interpolation processing result matched with the waveform acquisition data of the channel to be processed, so that the channel to be processed may determine resampling output waveform data according to the last interpolation processing result of the channel and the target sampling point position matched with the last interpolation processing result of the channel, and perform output of the resampling output waveform data.

In an alternative embodiment of the present invention, the set of interpolation algorithms may include at least two of a shannon Sinc interpolation algorithm, a linear interpolation algorithm, a lagrange interpolation algorithm, a nearest neighbor interpolation algorithm, and a conformal piecewise cubic interpolation algorithm.

According to the technical scheme of the embodiment of the invention, target waveform sampling data of a target channel is obtained from waveform acquisition data acquired by a plurality of channels, then target waveform sampling data is used for initializing jump point detection data, jump sampling points are identified in the jump point detection data, the ordinal number of each jump sampling point in the jump point detection data is obtained, an interpolation position matched with a current interpolation algorithm is recorded according to the ordinal number of each jump sampling point in the jump point detection data, the current interpolation algorithm is further obtained in a centralized mode, interpolation processing is carried out between each jump sampling point and the previous adjacent sampling point of each jump sampling point by using the current interpolation algorithm, and therefore after the jump point detection data is updated by using the interpolation data, the operation of identifying the jump sampling points in the jump point detection data is returned to be executed until the use of the interpolation algorithm with the preset times is completed, and then the interpolation positions recorded before the interpolation processing is carried out according to the interpolation algorithm, and the waveform acquisition data of the processing channels are resampled. According to the scheme, interpolation processing can be performed for preset times through a plurality of interpolation algorithms in which the interpolation algorithms are concentrated, namely, high-precision interpolation can be performed on data more flexibly, so that the interpolation positions recorded by the interpolation algorithms before the interpolation processing are used according to the target channel, and the channel to be processed is triggered to be resampled, the problem that the situation with higher precision requirement cannot be met due to the interpolation mode adopted by the existing resampling method is solved, the sampling precision of waveform data can be greatly improved, and the situation with higher data precision requirement is met.

EXAMPLE III

The embodiment of the present invention provides a specific optional implementation manner of waveform data resampling, and the specific implementation manner can be referred to the following. The technical terms that are the same as or corresponding to the above embodiments are not repeated herein.

The embodiment provides an interpolation mode combining a Sinc interpolation algorithm and a linear interpolation algorithm, and the resampling precision can be effectively improved. In practice, waveform data resampling can be realized by setting the Sinc interpolation multiple to be N times, linear interpolation to be M times, any one triggering mode and any combination of multiple interpolation modes.

In this embodiment, the waveform data resampling principle is explained by taking an example that the Sinc interpolation multiple is 8 times, the linear interpolation is 16 times, and the negative number jump is a positive number to trigger:

step 1, searching data of which the negative number jumps into a positive number in original data (target waveform sampling data).

After the original data are obtained, the original data are used as initial jumping point detection data, and then jumping sampling points which jump from negative numbers to positive numbers in the initial jumping point detection data are determined, and the specific process is as follows: extracting first-order to last-order data in initial jumping point detection data, storing the first-order to last-order data in a first jumping point detection data group (marked as data 0), extracting second-order to last-order data in the initial jumping point detection data, storing the second-order to second jumping point detection data group (marked as data 1), wherein the lengths of the two data groups are the same, judging data at the same position in the two data groups, if the data at the same data position data0 is less than 0 and the data at the data position data1 is more than 0, meeting the condition of jumping from a negative number to a positive number, taking out zero-crossing point data (namely a jumping sampling point and a previous adjacent sampling point of the jumping sampling point) meeting the condition, preparing for the interpolation of subsequent Sinc, wherein a filter is required to be designed by the Sinc interpolation method, if the Sinc interpolation multiple is N, N-1 points are needed to be inserted between zero-crossing data of which the negative number jump is changed into a positive number, in order to simulate the N-1 points, zero-crossing data needing interpolation processing and (N-2)/2 data before and after the zero-crossing data are calculated with a filter by N data in total, then the data before and after the zero-crossing data are taken out to be spliced into an array of N data, if the data before the zero-crossing data and/or the data after the zero-crossing data are not enough (N-2)/2, the data before the zero-crossing data are filled with the first data of data0 (filled before the first data of data 0) until the data satisfy (N-2)/2, and/or the data after the zero-crossing data are filled with the last data of data1 (filled after the last data of data 1), the principle that the number of data is equal to (N-2)/2, and then the data and zero-crossing point data are combined into N data, and the negative number in the original data is searched for and is changed into a positive number is shown in fig. 3. Optionally, in the embodiment of the present invention, the number of data to be operated with the filter is not limited, that is, the number of data to be operated with the filter may not be related to the Sinc interpolation multiple, and M data and (M-2)/2 data before and after a zero-crossing point that need to be interpolated may be operated with the filter (M is not equal to N), and the greater the number of data to be operated with the filter, the better the data fitting effect is.

Step 2, sinc interpolation processing

In the implementation process of the Sinc interpolation, according to the principle of the Sinc interpolation implementation, firstly, a proper filter is designed, and operation is performed according to the designed filter coefficient. Taking an N-fold Sinc interpolation multiple as an example, in an actual situation, N-1 data points need to be inserted between two zero-crossing points, and each time an inserted data point is simulated, coefficients of N filters are needed, so in order to realize the N-fold Sinc interpolation, at least (N-1) × N coefficients of the filters are needed, and an implementation principle of the Sinc interpolation can be specifically shown in fig. 4.

Step 3, searching the zero crossing point after Sinc interpolation

Since the subsequent linear interpolation is performed on the data after the Sinc interpolation, the zero crossing point in the data after the Sinc interpolation needs to be searched, and since the Sinc interpolation is to insert data into the zero crossing point, the zero crossing point needs to be searched again in the original zero crossing point data and the inserted data when the zero crossing point is searched, the principle of searching the zero crossing point in the data after the Sinc interpolation is similar to that of searching the zero crossing point in the original data, and the principle is shown in fig. 5.

Step 4, realizing linear interpolation

The linear interpolation is realized in an equal division manner, for the data of the zero-crossing point obtained after the Sinc interpolation, a positive number is used to subtract a negative number to obtain a difference value between the two, then the interpolation processing is carried out according to M times of equal division, the data of the Nth linear interpolation is the difference value between the two divided by M, and then multiplied by N, and the data before the zero-crossing is added, and the specific process is shown in fig. 6.

Step 5, searching the data with the nearest distance to 0 after linear interpolation

In order to increase the precision of triggering, it is necessary to search for a zero crossing point in the data after linear interpolation, and search for data closer to 0 in the two data, output the data at this time, and record the data position at this time, as shown in fig. 7.

Step 6, channel triggering resampling

In the process of multichannel triggering, the multi-channel waveform acquisition data is interpolated at the same time, and since the position information of the zero-crossing point is retained when the zero-crossing point is found, for other channels (to-be-processed channels) except the triggering channel (target channel), the function of resampling can be realized only by taking out the data according to the position information, and the realization process is shown in fig. 8.

Example four

Fig. 9 is a schematic structural diagram of a waveform data resampling apparatus according to a fourth embodiment of the present invention. As shown in fig. 9, the apparatus includes: a sample data acquisition module 310, a skip sample point identification module 320, an interpolation processing module 330, an iteration execution module 340, and a resampling processing module 350, wherein,

a sampling data obtaining module 310, configured to obtain target waveform sampling data of a target channel from waveform acquisition data acquired by multiple channels;

a jumping sampling point identifying module 320 for initializing jumping point detection data using the target waveform sampling data and identifying a jumping sampling point in the jumping point detection data;

the interpolation processing module 330 is configured to obtain a current interpolation algorithm in the interpolation algorithm set, and perform interpolation processing between each jumping sampling point and a previous adjacent sampling point of the jumping sampling point by using the current interpolation algorithm;

the iteration execution module 340 is configured to, after updating the trip point detection data by using the interpolation data, return to executing the operation of identifying the trip sampling point in the trip point detection data until the use of the interpolation algorithm for the preset number of times is completed;

and the resampling processing module 350 is configured to perform resampling processing on the waveform acquisition data of the channel to be processed according to the interpolation position recorded by each interpolation algorithm before interpolation processing in the interpolation algorithm set.

According to the technical scheme of the embodiment of the invention, target waveform sampling data of a target channel is obtained from waveform acquisition data acquired by a plurality of channels, then the target waveform sampling data is used for initializing jumping point detection data, jumping sampling points are identified in the jumping point detection data, so that a current interpolation algorithm is obtained in an interpolation algorithm set, the current interpolation algorithm is used for carrying out interpolation processing between each jumping sampling point and the previous adjacent sampling point of the jumping sampling point, the jumping sampling points are identified in the jumping point detection data after the jumping point detection data are further updated by the interpolation data, the operation of identifying the jumping sampling points in the jumping point detection data is returned to be executed until the use of the interpolation algorithm with preset times is completed, and therefore, the waveform acquisition data of the processing channel are subjected to resampling processing according to the interpolation positions recorded by each interpolation algorithm before interpolation processing in the interpolation algorithm set. According to the scheme, interpolation processing can be performed for preset times through a plurality of interpolation algorithms in which the interpolation algorithms are concentrated, namely, high-precision interpolation can be performed on data more flexibly, so that the interpolation positions recorded by the interpolation algorithms before the interpolation processing are used according to the target channel, and the channel to be processed is triggered to be resampled, the problem that the situation with higher precision requirement cannot be met due to the interpolation mode adopted by the existing resampling method is solved, the sampling precision of waveform data can be greatly improved, and the situation with higher data precision requirement is met.

Optionally, the skip sampling point identifying module 320 includes a data setting unit, a data grouping unit, a detected data determining unit, and a skip sampling point identifying unit, where the data setting unit is configured to set initial target data to be compared; a data grouping unit configured to divide the trip point detection data into a trip point first detection data group and a trip point second detection data group; a detection data determining unit configured to determine first detection data of a trip point and second detection data of a trip point at a current same data position in the first detection data group of the trip point and the second detection data group of the trip point; and the jumping sampling point identification unit is used for taking the second detection data of the jumping point as the jumping sampling point if the first detection data of the jumping point is smaller than the data to be compared of the initial target and the second detection data of the jumping point is larger than the data to be compared of the initial target, or the first detection data of the jumping point is larger than the data to be compared of the initial target and the second detection data of the jumping point is smaller than the data to be compared of the initial target.

Optionally, the detection data determining unit is configured to obtain a position of a first data of the trip point detection data, a position of a last data of the trip point detection data, a position of a second first data of the trip point detection data, and a position of a second last data of the trip point detection data; screening a first detection data group of the trip point from the trip point detection data according to the position of the first data of the trip point detection data and the position of the second last data; and screening a second detection data group of the jump point from the jump point detection data according to the position of the first data of the jump point detection data and the position of the last data of the jump point detection data.

Optionally, the waveform data resampling apparatus further includes an interpolation position recording module, configured to obtain an ordinal number of each jumping sampling point in the jumping point detection data; and recording the interpolation position matched with the current interpolation algorithm according to the ordinal number of each jumping sampling point in the jumping point detection data.

Optionally, the waveform data resampling apparatus further includes a target sampling point position determining module, configured to obtain a last interpolation processing result matched with a last interpolation algorithm; acquiring data to be compared of a last target; determining a jump sampling point to be processed and a previous adjacent sampling point of the jump sampling point to be processed according to the last interpolation processing result and the last target data to be compared; and determining target sampling points according to a first difference value between the to-be-processed jump sampling point and the last target to-be-compared data and a second difference value between a previous adjacent sampling point of the to-be-processed jump sampling point and the last target to-be-compared data, and recording the positions of the target sampling points.

Optionally, the resampling processing module 350 is configured to perform interpolation processing on the waveform acquisition data of the channel to be processed according to an interpolation processing sequence of each interpolation algorithm used in the interpolation algorithm set and an interpolation position recorded before the interpolation processing by each interpolation algorithm; and obtaining a last interpolation processing result matched with the waveform acquisition data of the channel to be processed, and determining resampling output waveform data according to the last interpolation processing result matched with the waveform acquisition data of the channel to be processed and the position of the target sampling point.

Optionally, the set of interpolation algorithms may include at least two of a shannon Sinc interpolation algorithm, a linear interpolation algorithm, a lagrange interpolation algorithm, a nearest neighbor interpolation algorithm, and a conformal piecewise cubic interpolation algorithm.

The resampling device for waveform data provided by the embodiment of the invention can execute the resampling method for waveform data provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.

EXAMPLE five

FIG. 10 shows a schematic diagram of an electronic device that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 10, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as a resampling method of the waveform data.

In some embodiments, the method of resampling waveform data may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the above described method of resampling waveform data may be performed. Alternatively, in other embodiments, the processor 11 may be configured by any other suitable means (e.g., by means of firmware) to perform a resampling method of the waveform data.

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.

The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for resampling waveform data, comprising:

initializing jumping point detection data by using the target waveform sampling data, and identifying jumping sampling points in the jumping point detection data;

obtaining a current interpolation algorithm in the interpolation algorithm set, and performing interpolation processing between each jumping sampling point and the previous adjacent sampling point of the jumping sampling point by using the current interpolation algorithm;

after updating the jumping point detection data by using interpolation data, returning to execute the operation of identifying jumping sampling points in the jumping point detection data until the use of an interpolation algorithm of preset times is completed;

2. The method of claim 1, wherein identifying a jumping sample point in the jumping point detection data comprises:

setting initial target data to be compared;

dividing the jumping point detection data into a first jumping point detection data group and a second jumping point detection data group;

determining the first detection data of the current same data position in the first detection data group of the trip point and the second detection data of the trip point;

and if the first detection data of the jumping point is smaller than the data to be compared with the initial target and the second detection data of the jumping point is larger than the data to be compared with the initial target, or the first detection data of the jumping point is larger than the data to be compared with the initial target and the second detection data of the jumping point is smaller than the data to be compared with the initial target, the second detection data of the jumping point is used as the jumping sampling point.

3. The method of claim 2, wherein dividing the trip point detection data into a trip point first detection data group and a trip point second detection data group comprises:

acquiring the position of the first data, the position of the last data, the position of the second first data and the position of the second last data of the jumping point detection data;

screening a first detection data group of the trip point from the trip point detection data according to the position of the first data of the trip point detection data and the position of the second last data;

and screening a second detection data group of the jump point from the jump point detection data according to the position of the first data of the jump point detection data and the position of the last data of the jump point detection data.

4. The method of claim 1, prior to said interpolating between each of said saltating sample and a sample immediately preceding said saltating sample using said current interpolation algorithm, comprising:

acquiring the ordinal number of each jumping sampling point in the jumping point detection data;

and recording the interpolation position matched with the current interpolation algorithm according to the ordinal number of each jumping sampling point in the jumping point detection data.

5. The method of claim 1, further comprising, after completing the use of the interpolation algorithm a preset number of times:

obtaining a last interpolation processing result matched with a last interpolation algorithm;

acquiring data to be compared of a last target;

determining a jump sampling point to be processed and a previous adjacent sampling point of the jump sampling point to be processed according to the last interpolation processing result and the last target data to be compared;

and determining a target sampling point according to a first difference value between the jump sampling point to be processed and the data to be compared of the last target and a second difference value between a previous adjacent sampling point of the jump sampling point to be processed and the data to be compared of the last target, and recording the position of the target sampling point.

6. The method according to claim 5, wherein the resampling processing is performed on the waveform acquisition data of the channel to be processed according to the interpolation position recorded by each interpolation algorithm in the set of interpolation algorithms before the interpolation processing, and includes:

carrying out interpolation processing on the waveform acquisition data of the channel to be processed according to the interpolation processing sequence of each interpolation algorithm used in the interpolation algorithm set and the interpolation position recorded by each interpolation algorithm before the interpolation processing;

and obtaining a last interpolation processing result matched with the waveform acquisition data of the channel to be processed, and determining resampling output waveform data according to the last interpolation processing result matched with the waveform acquisition data of the channel to be processed and the position of the target sampling point.

7. The method of claim 1, wherein the set of interpolation algorithms comprises at least two of a shannon Sinc interpolation algorithm, a linear interpolation algorithm, a lagrange interpolation algorithm, a nearest neighbor interpolation algorithm, and a conformal piecewise cubic interpolation algorithm.

8. An apparatus for resampling waveform data, comprising:

the jumping sampling point identification module is used for initializing jumping point detection data by using the target waveform sampling data and identifying jumping sampling points in the jumping point detection data;

the iteration execution module is used for returning to execute the operation of identifying the jumping sampling point in the jumping point detection data after updating the jumping point detection data by using interpolation data until the use of an interpolation algorithm with preset times is finished;

9. An electronic device, characterized in that the electronic device comprises:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of resampling waveform data according to any of claims 1-7.

10. A computer-readable storage medium storing computer instructions for causing a processor to implement the method of resampling waveform data according to any one of claims 1 to 7 when executed.