CN113610942A

CN113610942A - Pulse waveform segmentation method, system, equipment and storage medium

Info

Publication number: CN113610942A
Application number: CN202110655807.9A
Authority: CN
Inventors: 张软玉; 冯灏; 曾阳阳; 付佳
Original assignee: Maple Valley Chengdu Technology Co ltd
Current assignee: Maple Valley Chengdu Technology Co ltd
Priority date: 2021-06-11
Filing date: 2021-06-11
Publication date: 2021-11-05
Anticipated expiration: 2041-06-11
Also published as: CN113610942B

Abstract

The invention discloses a pulse waveform segmentation method, a system, equipment and a storage medium, wherein the method comprises the following steps of 1, converting an analog pulse signal output by a sensor into a digital pulse signal; step 2, converting the digital pulse signals into two-dimensional waveform images in the space domain; step 3, performing skeleton extraction on the two-dimensional waveform image in the airspace to obtain an isometric airspace waveform skeleton; step 4, dividing the equal-length airspace waveform skeleton into N sections of curves; and 5, mapping the segmented waveform back to a time domain, and performing waveform segmentation precision control to obtain a waveform time domain segmentation result meeting the precision requirement. According to the invention, after the same digital signal is subjected to space domain and frequency domain analysis according to the specificity of the physical signal output by the sensor, the digital signal is divided in the time domain according to the shape and frequency characteristics, and different filtering strategies can be adopted for different parts subsequently, so that a finer and more effective processing effect is achieved.

Description

Pulse waveform segmentation method, system, equipment and storage medium

Technical Field

The invention belongs to the technical field of digital signal analysis and processing, and particularly relates to a pulse waveform segmentation method, a pulse waveform segmentation system, pulse waveform segmentation equipment and a pulse waveform segmentation storage medium based on time domain, frequency domain and space domain joint analysis.

Background

In the era of internet and internet of things, a large number of sensors with different forms and functions are always distributed at a network terminal, different sensors output pulse signals carrying different physical information, the signals are often analog signals, in the network layout, the near end of the sensor is closely followed by a waveform digitization device, the analog signals output by the sensors are translated into digital signals through analog/digital conversion as soon as possible, and the digital signals have the following commonalities:

1) the digital signal can be represented by a series of (time, amplitude) data sequences which can be converted into two-dimensional space waveforms through time domain/space domain conversion; the pulse data sequence is subjected to Fourier transform, and can become a series of (frequency, intensity) frequency spectrum data. When these conversions are performed, the shape and frequency characteristics of a digital signal can be clearly displayed, and the shapes of different portions of the waveform correspond to the spectral characteristics thereof.

2) Noise and interference are mixed in signals output by the waveform digitization device, and various digital filters are adopted subsequently to reduce the noise to the minimum while preserving original physical information as much as possible. However, in the process of processing the digital signal by the conventional filter, while the superimposed noise is eliminated, the shape of the signal and the effective information carried by the signal may be changed, which results in poor processing effect and affects data reliability and data processing precision.

3) The shapes of different types of physical waveforms output by the same sensor are different, and the shapes of the same type of physical signals output by the same sensor are highly similar. The same filtering strategy is adopted for signals with different waveform shapes, and the processing precision and reliability are also influenced.

Disclosure of Invention

In order to solve the problems, the invention provides a pulse waveform segmentation method, which is characterized in that after the same digital signal is subjected to space domain and frequency domain analysis according to the specificity of a physical signal output by a sensor, the digital signal is segmented in a time domain according to the shape and frequency characteristics of the digital signal, and different filtering strategies can be adopted for different parts subsequently, so that a finer and more effective processing effect is achieved.

The invention is realized by the following technical scheme:

a pulse waveform segmentation method, comprising:

step 1, converting an analog pulse signal output by a sensor into a digital pulse signal;

step 2, converting the digital pulse signals into two-dimensional waveform images in the space domain;

step 3, performing skeleton extraction on the two-dimensional waveform image in the airspace to obtain an isometric airspace waveform skeleton;

step 4, dividing the equal-length airspace waveform skeleton into N sections of curves;

and 5, mapping the segmented waveform back to a time domain, and performing waveform segmentation precision control to obtain a waveform time domain segmentation result meeting the precision requirement.

The present invention can implement the same waveform segmentation scheme for determining certain types of physical signals output by the sensor, and once determined, the scheme can be directly used for processing the same type of signals output by the sensor. Different processing strategies are adopted for different parts of the divided signals, so that the processing precision of high-frequency parts can be improved, and the data volume of low-frequency parts can be reduced.

Preferably, the step 5 of performing waveform division precision control according to the present invention specifically includes:

step 5.1, performing Fourier transform on the time domain data of each section of curve in sequence to obtain frequency domain data of each section of curve;

step 5.2, analyzing the frequency spectrum of each section of waveform, comparing the frequency spectrum with the frequency spectra of the left and right adjacent sections of waveform, and judging whether the confidence of the segmentation points arranged at the two sides of the section of waveform meets the preset requirement or not;

and 5.3, if the preset requirements are met, the segmentation point set by the segment of the waveform is considered to meet the preset requirements, otherwise, the step 4 is returned to determine the segmentation point again until the preset requirements are met.

Preferably, the step 5.2 of determining whether the confidence σ of the waveform segmentation point meets the preset requirement specifically includes:

step 5.21, finding out the peak value f of each section of frequency spectrum in N sections of waveform frameworks_i，i＝2，…N；

Step 5.22, find out the full width at half maximum fw of each section of frequency spectrum_iI 2, … N, i.e.

(iii) width of the processing peak;

step 5.23, calculating the peak distance delta f between two adjacent sections of waveforms_iI.e. Δ f_i＝f_i-f_i-1The initial value of i is 2;

step 5.24, if Δ f_i＞fw_iIf σ is equal to 1, that is, the ith segmentation point meets the requirement, i is equal to i +1, and the step 5.23 is returned until i is greater than N; if Δ f_i＜fw_iIf σ is equal to 0, the procedure returns to step 4 to re-determine the ith division point.

Preferably, step 4 of the present invention specifically includes:

step 4.1, obtaining an extreme point with a slope of 0 in the waveform framework;

step 4.2, searching points which are smaller than the preset precision value and closest to the extreme point in the numerical value of the second derivative around the extreme point as segmentation points;

step 4.3, respectively performing curve fitting on the left and right of the segmentation points;

and 4.4, re-acquiring other extreme points with the slope of 0 in the waveform skeleton, returning to execute the step 4.2-the step 4.3, and dividing the waveform skeleton into N sections of curves.

Preferably, step 2 of the present invention adopts a bwmorph algorithm to convert the digital signal obtained in step 1 into a two-dimensional binary waveform image in the spatial domain.

Preferably, in step 3 of the method, a shrink algorithm is adopted to perform skeleton extraction on the two-dimensional waveform image in the spatial domain.

In a second aspect, the present invention provides a pulse waveform segmentation system, which includes a segmentation module and a control module;

the segmentation module comprises a digitization unit, a shape extraction unit, a skeleton extraction unit, an image identification unit and a mapping unit;

the digital unit is used for converting an analog pulse signal output by the sensor into a digital pulse signal;

the shape extraction unit changes the digital pulse signal into a two-dimensional waveform image in a space domain;

the framework extraction unit carries out framework extraction on the two-dimensional waveform image in the airspace to obtain an isometric airspace waveform framework;

the image recognition unit divides the waveform skeleton into N sections;

the mapping unit maps the segmented waveform back to a time domain, and waveform segmentation precision control is carried out through the control module, so that the system outputs a waveform time domain segmentation result meeting the precision requirement.

Preferably, the control module of the invention comprises a frequency domain unit, a frequency domain data extraction unit and a confidence judgment unit;

the frequency domain unit sequentially performs Fourier transform on each segment of time domain data to obtain frequency domain data of each segment of curve;

the frequency domain data extraction unit obtains the frequency spectrum data of each section from the frequency domain data of each section of curve;

and the confidence coefficient judging unit judges the confidence coefficient of the waveform segmentation point according to each section of frequency spectrum data, if the preset requirement is met, the waveform segmentation point meets the requirement, and otherwise, a control signal is output to control the image recognition unit to re-determine the segmentation point.

In a third aspect, the invention proposes a computer device comprising a memory storing a computer program and a processor implementing the steps of the method of the invention when executing the computer program.

In a fourth aspect, the invention proposes a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method according to the invention.

The invention has the following advantages and beneficial effects:

according to the invention, according to the characteristics of waveform shape and frequency of a digital pulse signal, one pulse is divided into a plurality of sections, and a signal processing scheme is formulated in a segmented manner, so that different signal processing strategies can be adopted for different parts of the same signal subsequently, the signal processing precision of a high-frequency part is improved, the data volume of a low-frequency part can be effectively reduced, and the fidelity compression ratio is improved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:

FIG. 1 is a schematic flow chart of the method of the present invention.

FIG. 2 is a schematic diagram of the confidence determination of the waveform segmentation point according to the present invention.

FIG. 3 is a schematic diagram of a computer device according to the present invention.

Fig. 4 is a schematic diagram of the system structure of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.

Example 1

The present embodiment provides a pulse waveform segmentation method, as shown in fig. 1, the method of the present embodiment mainly includes two parts: and performing a waveform segmentation step and a waveform segmentation precision control step according to the shape characteristics of the digital pulse signal.

The step of waveform segmentation according to the shape specification of the digitized pulse signal specifically comprises the following steps:

step 1, converting an analog pulse signal output by a sensor into a digital pulse signal.

In this embodiment, the physical signal detected by the sensor is output in the form of analog amplitude pulses, which are then processed by a waveform digitizer followed by the sensor_sInto a series of digitised (time, amplitude) sequences, i.e. numbersThe signal is quantized. Expressed as:

V(t)＝(v₁，v₂，…v_i，…v_n) (1)

in the formula, v_iDenotes the ith digital pulse amplitude sequence, where i is 1, 2, … n.

Step 2, converting the digital signal obtained in the step 1 into a two-dimensional binary waveform image in the space domain by using a bwmorphh algorithm, wherein the two-dimensional binary waveform image is represented as follows:

wherein BW [ b ], []An operator representing a time domain sequence to a spatial domain binary image; (x)_i，y_j) A value at the ith row and jth column position of the spatial domain image, specifically 0 or 1, where i is 1, 2, … n; j is 1, 2, … n.

Step 3, performing skeleton extraction on the two-dimensional binary waveform image M (x, y) in the airspace by using a shrink algorithm to obtain an isometric airspace waveform skeleton sequence M (x ', y'):

wherein Srk [ alpha ], [ beta ], [ alpha ], [ beta]An operator for performing skeleton extraction on the existing image; (x'_i，y′_j) A value indicating a position of an ith row and a jth column in the waveform skeleton image, specifically 0 or 1, where i is 1, 2, … n; j is 1, 2, … n.

And 4, dividing the skeleton sequence into N sections.

The embodiment utilizes a maximum likelihood algorithm to fit the isometric airspace waveform framework sequence M (x ', y') point by point into a plurality of primary curves or quadratic curves. The method comprises the following specific steps:

(1) obtaining an extreme point with a slope of 0 in the skeleton image, and searching a point which is smaller than a preset precision value delta 0 and closest to the extreme point as a segmentation point P around the extreme point, wherein the value of the second derivative is_i；

(2) At the division point P_iRespectively performing curve fitting on the left and the right;

(3) then, the steps (1) to (2) are adopted, and the framework which represents the shape of the signal is fitted to [ M ] by using N sections of curves₁，M₂，…M_N]。

Step 5, mixing [ M ]₁，M₂，…M_N]And performing inverse transformation to the time domain to obtain N segments of time domain signal sequences, which are expressed as follows:

[Y₁，Y₂，…Y_N]＝BW^-1[M₁，M₂，…M_N] (4)

in the formula (BW)^-1[]Representing spatial/temporal transform operators.

After each part in the segmented waveform skeleton is mapped back to a time domain, waveform segmentation precision control is carried out, and the specific process mainly comprises the following steps:

step 6, for [ Y₁，Y₂，…Y_N]Performing discrete fourier transform segment by segment to obtain a frequency spectrum sequence of the waveform mapped by the skeleton in the time domain, which can be expressed as:

in the formula (I), the compound is shown in the specification,

representing a fourier transform operator.

Step 7, finding the peak value corresponding to the peak value of each section of signal spectrum, and expressing as:

(f₁，f₂，…，f_N)＝Max1[F₁，F₂，…F_N] (6)

in the formula, Max1[ ] represents a coordinate position operator corresponding to the search function value.

Step 8, calculating the full width at half maximum (fw) of each section of spectrum peak₁，fw₂，…，fw_N) Expressed as:

(fw₁，fw₂，…，fw_N)＝FHFW[F₁，F₂，…F_N] (7)

in the formula, FFW [ ] represents a calculated peak full width at half maximum operator.

And 9, judging the confidence of each segmentation point. The method comprises the following specific steps:

(1) let i 2: and N, step 1, entering a loop.

(2) Calculating the peak position distance delta f of two adjacent sections of waveform frequency spectrums_i：

Δf_i＝f_i-f_i-1

(3) Comparing the peak position distance of two adjacent waveform spectra with the full width at half maximum of the spectrum peak of the current division point, if delta f_i＞fw_iIf σ is 1; i is i + 1; confirming the time coordinate t of the ith division point_iEntering the next loop (returning to execute step 9 (2)) until i > N; otherwise, if σ is 0, the loop is skipped, the process returns to the step (1) in the step 4, the fitted segmentation point is moved leftward by a preset step size as a new segmentation point, and in order to take efficiency and precision into consideration, the preset step size in this embodiment is preferably 2 points, and the steps 4 to 9 are performed again. As shown in fig. 2.

Step 10, obtaining the preset precision delta₀And the confidence coefficient is 1.

If the time domain amplitude sequence of a time domain pulse signal is expressed as:

[(0，v₀)，(f_s，v₁)，…，(nf_s，v_n)]the pulse signal is divided into N segments, denoted as [ Y ]₁，Y₂，…Y_N]Wherein Y is_i-1And Y_iThe abscissa of the dividing point therebetween is t_iThen the whole waveform is divided into N segments by N-1 division points, and the positions of the division points are expressed as: t ═ T (T)₁，…，t_N-1)。

The embodiment also provides a computer device for executing the method of the embodiment.

As shown in fig. 3 in particular, the computer device includes a processor, an internal memory, and a system bus; various device components including internal memory and processors are connected to the system bus. A processor is hardware used to execute computer program instructions through basic arithmetic and logical operations in a computer system. An internal memory is a physical device used to temporarily or permanently store computing programs or data (e.g., program state information). The system bus may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus. The processor and the internal memory may be in data communication via a system bus. Including read-only memory (ROM) or flash memory (not shown), and Random Access Memory (RAM), which typically refers to main memory loaded with an operating system and computer programs.

Computer devices typically include an external storage device. The external storage device may be selected from a variety of computer readable media, which refers to any available media that can be accessed by the computer device, including both removable and non-removable media. For example, computer-readable media includes, but is not limited to, flash memory (micro SD cards), CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer device.

A computer device may be logically connected in a network environment to one or more network terminals. The network terminal may be a personal computer, a server, a router, a smart phone, a tablet, or other common network node. The computer apparatus is connected to the network terminal through a network interface (local area network LAN interface). A Local Area Network (LAN) refers to a computer network formed by interconnecting within a limited area, such as a home, a school, a computer lab, or an office building using a network medium. WiFi and twisted pair wiring ethernet are the two most commonly used technologies to build local area networks.

It should be noted that other computer systems including more or less subsystems than computer devices can also be suitable for use with the invention.

As described above in detail, the computer apparatus adapted to the present embodiment can perform the specified operation of the pulse waveform division method. The computer device performs these operations in the form of software instructions executed by a processor in a computer-readable medium. These software instructions may be read into memory from a storage device or from another device via a local area network interface. The software instructions stored in the memory cause the processor to perform the method of processing group membership information described above. Furthermore, the present invention can be implemented by hardware circuits or by a combination of hardware circuits and software instructions. Thus, implementation of the present embodiments is not limited to any specific combination of hardware circuitry and software.

Example 2

The embodiment provides a pulse waveform segmentation system, and specifically as shown in fig. 4, the system includes a segmentation module and a control module;

the segmentation module of the embodiment comprises a digitization unit, a shape extraction unit, a skeleton extraction unit, an image identification unit and a mapping unit;

the digital unit is used for converting an analog pulse signal output by the sensor into a digital pulse signal; the digitizing unit of this embodiment is implemented by using the algorithm in step 1 of embodiment 1, and is not described herein again.

The shape extraction unit changes the digital pulse signal into a two-dimensional waveform image in a space domain; the shape extraction unit of this embodiment is implemented by using the algorithm of step 2 in embodiment 1, and details are not described here.

The framework extraction unit carries out framework extraction on the two-dimensional waveform image in the airspace to obtain an isometric airspace waveform framework; the skeleton extraction unit of this embodiment is implemented by using the algorithm in step 3 of embodiment 1, and details are not described here.

The image recognition unit divides the waveform skeleton into N sections; the image recognition unit of this embodiment is implemented by using the algorithm in step 4 of embodiment 1, and details are not described here.

The mapping unit maps the segmented waveform back to a time domain, and waveform segmentation precision control is carried out through the control module, so that the system outputs a waveform time domain segmentation result meeting the precision requirement. The mapping unit of this embodiment is implemented by using the algorithm in step 5 of embodiment 1, and details are not described here.

The control module of the embodiment comprises a frequency domain unit, a frequency domain data extraction unit and a confidence judgment unit;

the frequency domain unit performs Fourier transform on each segment of time domain data in sequence to obtain frequency domain data of each segment of curve; the frequency domain unit of this embodiment is implemented by using the algorithm in step 6 of embodiment 1, and details are not described here.

The frequency domain data extraction unit obtains the frequency spectrum data of each section from the frequency domain data of each section of curve; the frequency domain data extraction unit of this embodiment is implemented by using the algorithm of steps 7-8 in embodiment 1, which is not described herein again.

And the confidence coefficient judging unit judges the confidence coefficient of the waveform segmentation point according to each section of spectral data, if the preset requirement is met, the waveform segmentation point meets the requirement, and otherwise, a control signal is output to control the image recognition unit to re-determine the segmentation point. The confidence determining unit in this embodiment is implemented by using the algorithm in step 9 in embodiment 1, and details are not described here.

The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims

1. A method for dividing a pulse waveform, comprising:

2. The method according to claim 1, wherein the performing of waveform division precision control in step 5 specifically comprises:

3. The method as claimed in claim 2, wherein the step 5.2 of determining whether the confidence σ of the waveform segmentation point satisfies a preset requirement specifically includes:

(iii) width of the processing peak;

4. The method according to any one of claims 1 to 3, wherein the step 4 specifically comprises:

5. The method for segmenting the pulse waveform according to claim 1, wherein the step 2 adopts a bwmorphh algorithm to convert the digital signal obtained in the step 1 into a two-dimensional binary waveform image in the space domain.

6. The method for segmenting the pulse waveform according to claim 1, wherein in the step 3, a skeleton extraction is performed on the two-dimensional waveform image in the spatial domain by using a shrnk algorithm.

7. A pulse waveform segmentation system is characterized by comprising a segmentation module and a control module;

the image recognition unit divides the waveform skeleton into N sections;

8. The pulse waveform segmentation system according to claim 7, wherein the control module includes a frequency domain unit, a frequency domain data extraction unit, and a confidence level determination unit;

9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor realizes the steps of the method according to any of claims 1-6 when executing the computer program.

10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.