WO2024078546A1

WO2024078546A1 - Pulse recognition method and apparatus

Info

Publication number: WO2024078546A1
Application number: PCT/CN2023/124089
Authority: WO
Inventors: 林桂浩; 谢幸光; 唐亚海
Original assignee: 深圳市恒运昌真空技术股份有限公司
Priority date: 2022-10-12
Filing date: 2023-10-11
Publication date: 2024-04-18
Also published as: CN115329822A; CN115329822B

Abstract

Disclosed in the present application are a pulse recognition method and apparatus. The method comprises: acquiring, from a set of signals to be subjected to recognition, at least one group of signal data to be subjected to recognition, wherein the group of said signal data comprises a plurality of pieces of continuous signal data to be subjected to recognition that change in the same direction, and the set of said signals includes signal data to be subjected to recognition; and determining whether said signal data in each group of said signal data continuously rises or continuously falls, and if said signal data continuously rises, taking said signal data in a corresponding group of said signal data as rising-edge data within a pulse period, and if said signal data continuously falls, taking said signal data in a corresponding group of said signal data as falling-edge data within a pulse period, thereby solving the problems in the prior art of tedious setting operation and high maintenance costs of matcher PULSE parameters.

Description

Pulse identification method and device

Technical Field

The present invention relates to the technical field of pulse recognition, and in particular to a pulse recognition method and device.

Background technique

The current plasma power supply system includes a power supply, a matcher and a cavity load, wherein the power supply provides a power signal, the matcher obtains the power signal, adjusts the load impedance between the power supply, the matcher and the cavity load, and transfers the power signal to the load.

The matcher is a passive device. In order to enable the matcher to detect the rising and falling edges of the PULSE, it is usually necessary to first obtain the PULSE parameters through an external detection device when setting the PULSE parameters, and then actively set the rising edge duration, falling edge duration and other parameters to the matcher. Once the device environment changes, ages, or any setting is lost, it must be re-measured, which is very inconvenient to use and has high maintenance costs.

Summary of the invention

Therefore, the technical problem to be solved by the present invention is to overcome the defects of the prior art of setting the parameter PULSE so that the matcher has the ability to detect the rising and falling edges of PULSE, which is cumbersome to operate and has high maintenance costs, thereby providing a pulse identification method and device.

In order to solve the above technical problems, the disclosed embodiments of the present invention at least provide a pulse recognition method and device.

In a first aspect, the disclosed embodiment of the present invention provides a pulse identification method, comprising:

At least one data set of a signal to be identified is obtained from the signal set to be identified. The data group contains a plurality of signal data to be identified that are continuous and change in the same direction, and the signal set to be identified includes the signal data to be identified;

Determining whether the signal data to be identified in each of the signal data groups to be identified is continuously rising or continuously falling;

If it rises continuously, the signal data to be identified in the corresponding signal data group to be identified is used as the rising edge data in one pulse cycle;

If it drops continuously, the signal data to be identified in the corresponding signal data group to be identified is used as the falling edge data in one pulse cycle.

Optionally, obtaining at least one signal data group to be identified from the signal set to be identified includes: obtaining a high-level signal data set and a low-level signal data set from the signal set to be identified; obtaining the signal data group to be identified from a preliminary signal data set, wherein the preliminary signal data set is the remaining signal data set of the signal set to be identified excluding the high-level signal data set and the low-level signal data set.

Optionally, obtaining the signal data group to be identified from the preliminary signal set includes: determining a high-level threshold value based on the signal data to be identified in the high-level signal data set; determining a low-level threshold value based on the signal data to be identified in the low-level signal data set; obtaining target signal data from the preliminary signal set, the target signal data being the signal data to be identified between the high-level threshold value and the low-level threshold value; and processing the target signal data according to sampling time information to form the at least one signal data group to be identified.

Optionally, the processing of the target signal data according to the sampling time information to form the at least one signal data group to be identified includes: sorting the target signal data in chronological order according to the sampling time information; removing the target signal data at isolated time points in the target signal data; Signal data forms the at least one signal data group to be identified.

Optionally, obtaining a high-level signal data set and a low-level signal data set from the signal set to be identified includes: obtaining an initial high-level signal data set and an initial low-level signal data set from the signal set to be identified; sorting the initial high-level signal data set and the initial low-level signal data set in chronological order according to sampling time information; obtaining missing signal data from the signal set to be identified, the missing signal data being the signal data to be identified that is missing on the timeline of the initial high-level signal data set and the initial low-level signal data set; and inserting the missing signal data into the initial high-level signal data set and the initial low-level signal data set according to the sampling time.

Optionally, before inserting the gap-filling signal data into the initial high-level signal data set and the initial low-level signal data set according to the sampling time, the method also includes: determining a first data range of the initial high-level signal data set and a second data range of the initial low-level signal data set; judging whether all of the gap-filling signal data are signal data to be identified within the first data range or the second data range; if so, using the initial high-level signal data set as the high-level signal data set and the initial low-level signal data set as the low-level signal data set; if not, inserting the gap-filling signal data into the initial high-level signal data set and the initial low-level signal data set according to the sampling time, redetermining the data ranges of the initial high-level signal data set and the initial low-level signal data set, and using the redetermined data ranges as the high-level signal data set and the low-level signal data set.

Optionally, the determining of the first data range of the initial high-level signal data set and the second data range of the initial low-level signal data set comprises: calculating a first data average of the signal data to be identified in the initial high-level signal data set, calculating a first data average of the signal data to be identified in the initial low-level signal data set, and calculating a second data average of the signal data to be identified in the initial high-level signal data set. a second data average value of the signal data to be identified in the data set; determining a first maximum value and a first minimum value of the signal data to be identified in the initial high-level signal data set, and determining a second maximum value and a second minimum value of the signal data to be identified in the initial low-level signal data set; determining a first data range of the initial high-level signal data set according to the first data average value, the first maximum value and the first minimum value; determining a second data range of the initial low-level signal data set according to the second data average value, the second maximum value and the second minimum value.

Optionally, the obtaining of the high-level signal data set and the low-level signal data set from the signal set to be identified is: obtaining the high-level signal data set and the low-level signal data set from the signal set to be identified by data clustering, grouping or sub-grouping.

Optionally, determining the high-level threshold based on the signal data to be identified in the high-level signal data set is: obtaining the minimum signal data in the high-level signal data set as the high-level threshold; determining the low-level threshold based on the signal data to be identified in the low-level signal data set is: obtaining the maximum signal data in the low-level signal data set as the low-level threshold.

Optionally, before acquiring at least one signal data group to be identified from the signal set to be identified, the method further comprises: recording sampling time information of each signal data to be identified.

Optionally, it also includes: determining the rising edge duration according to the number of signal data to be identified in the rising and falling edge data in each pulse cycle; determining the falling edge duration according to the number of signal data to be identified in the falling edge data in each pulse cycle.

Optionally, before recording the sampling time information of each signal data to be identified, the method further includes: acquiring the signal set to be identified.

Optionally, the acquiring of the signal set to be identified includes: continuously collecting data of the signal to be identified in one or more periodic waveforms; and adding the collected data of the signal to be identified to the signal set to be identified.

Optionally, the method is used for a matcher.

In a second aspect, the disclosed embodiment of the present invention further provides a pulse identification device, comprising:

A continuous signal acquisition module, used for acquiring at least one signal data group to be identified from a signal set to be identified, wherein the signal data group to be identified includes a plurality of signal data to be identified that are continuous and change in the same direction, and the signal set to be identified includes the signal data to be identified;

A judging module, used for judging whether the signal data to be identified in each of the signal data groups to be identified is continuously rising or continuously falling;

A rising edge data determination module, used for treating the to-be-identified signal data in the corresponding to-be-identified signal data group as the rising edge data in one pulse cycle if the rising continues;

The falling edge data determination module is used to use the to-be-identified signal data in the corresponding to-be-identified signal data group as the falling edge data in one pulse cycle if the falling edge continues.

In a third aspect, the disclosed embodiment of the present invention further provides a pulse identification device, comprising:

A collector, used for acquiring a signal set to be identified, wherein the signal set to be identified includes signal data to be identified;

A timing controller, used for recording the sampling time information of each signal data to be identified;

The processor is used to obtain at least one signal data group to be identified from the signal set to be identified according to the sampling time information, wherein the signal data group to be identified contains a plurality of signal data to be identified that are continuous and change in the same direction, and to determine whether the signal data to be identified in each of the signal data groups to be identified is continuously rising or continuously falling; if it is continuously rising, the corresponding signal data to be identified is The signal data to be identified in the data group is used as the rising edge data in one pulse cycle; if it drops continuously, the signal data to be identified in the corresponding signal data group is used as the falling edge data in one pulse cycle.

In a fourth aspect, an embodiment disclosed in the present invention further provides a computer device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the above-mentioned first aspect, or any possible implementation of the first aspect are performed.

In a fifth aspect, the disclosed embodiments of the present invention further provide a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned first aspect, or any possible implementation of the first aspect are executed.

The technical solution provided by the embodiments of the present invention can have the following beneficial effects:

At least one signal data group to be identified is obtained from the signal set to be identified, and it is determined whether the signal data to be identified in each signal data group to be identified is continuously rising or continuously falling; if it is continuously rising, the signal data to be identified in the corresponding signal data group to be identified is used as the rising edge data in a pulse cycle. This solution obtains the signal data group to be identified from the signal set to be identified, calculates the signal by grouping, and determines the pulse top data set, pulse bottom data set, and pulse slope data set according to the change of the signal data to be identified in each signal data group to be identified, and then obtains the rising edge data or falling edge data. It does not require manual PULSE settings, and automatically completes the pulse identification, greatly reducing the complexity of operation and reducing subsequent maintenance costs.

It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

FIG1 shows a flow chart of a pulse identification method provided by an embodiment of the present invention;

FIG2 shows a flow chart of another pulse identification method provided by an embodiment of the present invention;

FIG3 shows a schematic diagram of the structure of a pulse identification device provided by an embodiment of the present invention;

FIG4 shows a schematic structural diagram of another pulse identification device provided by an embodiment of the present invention;

FIG5 shows a schematic diagram of the structure of a computer device provided by an embodiment disclosed in the present invention.

Detailed ways

Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

Example 1

As shown in FIG1 , a flow chart of a pulse identification method provided by an embodiment of the present invention is disclosed, and the method includes:

S11: acquiring at least one signal data group to be identified from the signal set to be identified, wherein the signal data group to be identified includes a plurality of signal data to be identified that are continuous and change in the same direction, and the signal set to be identified includes the signal data to be identified;

S12: Determine whether the signal data to be identified in each signal data group to be identified is continuously rising or continuously falling. If it is continuously rising, execute S13; if it is continuously falling, execute S14;

S13: using the signal data to be identified in the corresponding signal data group to be identified as rising edge data within a pulse cycle;

S14: Using the to-be-identified signal data in the corresponding to-be-identified signal data group as falling edge data in one pulse cycle.

It can be understood that the technical solution provided in this embodiment obtains at least one signal data group to be identified from the signal set to be identified, and determines whether the signal data to be identified in each signal data group to be identified is continuously rising or continuously falling; if it is continuously rising, the signal data to be identified in the corresponding signal data group to be identified is used as the rising edge data in a pulse cycle. This solution obtains the signal data group to be identified from the signal set to be identified, calculates the signal by grouping, and determines the pulse top data set, pulse bottom data set, and pulse slope data set according to the change of the signal data to be identified in each signal data group to be identified, and then obtains the rising edge data or the falling edge data, without manual PULSE setting, and automatically completes the pulse identification, greatly reducing the complexity of operation and reducing the subsequent maintenance cost.

Example 2

As shown in FIG2 , a flow chart of another pulse identification method provided by an embodiment of the present invention is provided, and the method includes:

S21: Obtain a signal set to be identified.

S22: Record the sampling time information of each signal data to be identified.

S23: acquiring at least one signal data group to be identified from the signal set to be identified according to the sampling time information, wherein the signal data group to be identified includes a plurality of signal data to be identified that are continuous and change in the same direction, and the signal set to be identified includes the signal data to be identified.

S24: Determine whether the signal data to be identified in each signal data group to be identified is continuously rising or continuously falling. If it is continuously rising, execute S25; if it is continuously falling, execute S27.

S25: Using the signal data to be identified in the corresponding signal data group to be identified as rising edge data in one pulse cycle.

S26: Determine the rising edge duration according to the number of signal data to be identified in the rising and falling edge data in each pulse cycle.

S27: Using the signal data to be identified in the corresponding signal data group to be identified as falling edge data in one pulse cycle.

S28: Determine the falling edge duration according to the number of signal data to be identified in the falling edge data within each pulse cycle.

In some optional embodiments, each to-be-identified signal data group and the to-be-identified signal data in the group are between a low level threshold and a high level threshold.

In some optional embodiments, not shown in the figure, S23 includes:

S231: Acquire a high-level signal data set and a low-level signal data set from the signal set to be identified;

In some optional embodiments, the high-level signal data set and the low-level signal data set may be obtained from the signal set to be identified by, but not limited to, data clustering, classification or grouping.

S232: Acquire a signal data set to be identified from the preliminary selected signal data set, wherein the preliminary selected signal data set is the remaining signal data set of the signal set to be identified except the high level signal data set and the low level signal data set. The residual signal data set.

In some optional embodiments, not shown in the figure, S232 may include:

S2321: Determine a high level threshold value according to the signal data to be identified in the high level signal data set;

S2322: Determine a low level threshold value according to the signal data to be identified in the low level signal data set;

S2323: Acquire target signal data from the preliminary signal set, where the target signal data is signal data to be identified between a high level threshold and a low level threshold;

S2324: Process the target signal data according to the sampling time information to form at least one signal data group to be identified.

In some optional embodiments, not shown in the figure, S2324 may include:

A. Sort the target signal data in chronological order according to the sampling time information;

B. Remove the signal data to be identified at isolated time points in the target signal data to form at least one signal data group to be identified.

In some optional embodiments, not shown in the figure, S231 may include:

S231: Acquire an initial high-level signal data set and an initial low-level signal data set from the signal set to be identified;

S232: sorting the initial high-level signal data set and the initial low-level signal data set in chronological order according to the sampling time information;

S233: Acquire missing signal data from the to-be-recognized signal set, where the missing signal data is the to-be-recognized signal data missing on the timeline of the initial high-level signal data set and the initial low-level signal data set;

S234: Determine a first data range of the initial high-level signal data set and a second data range of the initial low-level signal data set;

S235: Determine whether all the missing signal data are the signal data to be identified within the first data range or the second data range, if so, execute S236, if not, execute S237;

S236: using the initial high-level signal data set as the high-level signal data set, and using the initial low-level signal data set as the low-level signal data set;

S237: inserting the gap filling signal data into the initial high level signal data set and the initial low level signal data set according to the sampling time, re-determining the data range of the initial high level signal data set and the initial low level signal data set, and using the re-determined data range as the high level signal data set and the low level signal data set.

It should be noted that, in specific engineering practice, the above steps S234--S236 are optional steps, and it is up to those skilled in the art to determine whether to implement them according to engineering needs.

In some optional embodiments, not shown in the figure, the above S234 can be implemented by, but not limited to, the following process:

S2341: Calculate a first data average value of the signal data to be identified in the initial high-level signal data set, and calculate a second data average value of the signal data to be identified in the initial low-level signal data set;

S2342: Determine a first maximum value and a first minimum value of the signal data to be identified in the initial high-level signal data set, and determine a second maximum value and a second minimum value of the signal data to be identified in the initial low-level signal data set;

S2343: Determine a first data range of the initial high-level signal data set according to the first data average value, the first maximum value, and the first minimum value;

S2344: Determine a second data range of the initial low-level signal data set according to the second data average value, the second maximum value, and the second minimum value.

In some optional embodiments, the minimum signal data in the high-level signal data set is obtained as the high-level threshold; and the maximum signal data in the low-level signal data set is obtained as the low-level threshold.

In some optional embodiments, not shown in the figure, S21 includes:

S211: Continuously collecting data of the signal to be identified in one or more periodic waveforms;

S212: Add the collected data of the signal to be identified into the set of signals to be identified.

In some optional embodiments, the above method may be implemented by, but is not limited to, a matcher.

In the plasma power system, the matcher is usually equipped with three main components, a timing controller, a collector and a processor. Among them, the timing controller is used as a timing trigger for the collection of the collector, and the collector is used to continuously collect power signals for one or more periodic waveforms. The timing controller sets the collection time to be intensive and fixed.

The processor collects the data provided by the collector, and divides the data into the pulse top signal data set and the pulse bottom signal data set through data clustering, classification, and sub-grouping and other similar calculation methods, and finds the lower limit of the pulse top signal data set and the upper limit of the pulse bottom signal data set at the same time. The remaining data other than the pulse top signal data set and the pulse bottom signal data set are arranged according to the collection timing, and the rising edge data and the falling edge data are divided according to the upward continuity or downward continuity of the adjacent data. Because the collection time interval is the same, the rising edge duration and the falling edge duration can be calculated based on the number of collections of the rising edge and the falling edge. Even if the collection intervals are different, the total duration of the rising edge and the falling edge can be calculated based on the collection time corresponding to the collection point.

It should be noted that the embodiments recorded in this specific implementation method are only illustrative descriptions of the specific implementation methods under the concept of the present invention. The execution order of the steps in each embodiment is not limited to the embodiments provided in this document. In specific engineering practices, the execution order of each step can be adjusted by technical personnel in this field according to actual conditions.

Example 3

As shown in FIG3 , an embodiment of the present invention further provides a pulse recognition device, comprising:

The continuous signal acquisition module 31 is used to acquire at least one signal data group to be identified from the signal set to be identified, wherein the signal data group to be identified is a plurality of signal data to be identified that are continuous and change in the same direction, and the signal set to be identified includes the signal data to be identified;

A judging module 32, used for judging whether the signal data to be identified in each signal data group to be identified is continuously rising or continuously falling;

A rising edge data determination module 33 is used to use the to-be-identified signal data in the corresponding to-be-identified signal data group as the rising edge data in one pulse cycle if the rising continues;

The falling edge data determination module 34 is used to use the signal data to be identified in the corresponding signal data group to be identified as the falling edge data in one pulse period if the falling edge continues.

In some optional embodiments, as shown in the dotted line portion in FIG3 , the device further includes:

The to-be-identified signal acquisition module 35 is used to acquire a to-be-identified signal set.

The time recording module 36 is used to record the sampling time information of each signal data to be identified.

The rising edge duration determination module 37 is used to determine the rising edge duration according to the number of signal data to be identified in the rising and falling edge data in each pulse cycle.

The falling edge duration determination module 38 is used to determine the falling edge duration according to the number of signal data to be identified in the falling edge data within each pulse period.

In some optional embodiments, as shown in the dotted line portion in FIG. 3 , the continuous signal acquisition module 31 includes:

The high-low data set acquisition submodule 311 is used to acquire a high-level signal data set and a low-level signal data set from the signal set to be identified. Specifically, the high-level signal data set and the low-level signal data set can be acquired from the signal set to be identified by data clustering, clustering or grouping.

The to-be-identified signal data set acquisition submodule 312 is used to acquire the to-be-identified signal data set from the preliminary selected signal data set, wherein the preliminary selected signal data set is the remaining signal data set of the to-be-identified signal set excluding the high-level signal data set and the low-level signal data set.

In some optional embodiments, not shown in the figure, the to-be-identified signal data group acquisition submodule 312 includes:

A high level threshold determination unit 3121 is used to determine a high level threshold according to the signal data to be identified in the high level signal data set;

A low level threshold determination unit 3122 is used to determine a low level threshold according to the signal data to be identified in the low level signal data set;

The signal data group generating unit 3123 to be identified is used to obtain target signal data from the preliminary signal set, where the target signal data is the signal data to be identified between the high level threshold and the low level threshold, and processes the target signal data according to the sampling time information to form at least one signal data group to be identified.

Specifically, in some optional embodiments, processing the target signal data according to the sampling time information to form at least one signal data group to be identified includes: sorting the target signal data in chronological order according to the sampling time information; removing the signal data to be identified at isolated time points in the target signal data to form at least one signal data group to be identified.

In some optional embodiments, not shown in the figure, the high and low data set acquisition submodule 311 may include:

An initial data set acquisition unit 3111 is used to acquire an initial high-level signal data set and an initial low-level signal data set from the signal set to be identified;

A sorting unit 3112, used to sort the initial high-level signal data set and the initial low-level signal data set in chronological order according to the sampling time information;

The gap filling data acquisition unit 3113 is used to acquire gap filling signal data from the signal set to be identified, where the gap filling signal data is the signal data to be identified that is missing on the time line of the initial high-level signal data set and the initial low-level signal data set;

The data gap filling unit 3114 is used to insert the gap filling signal data into the initial high level signal data set and the initial low level signal data set according to the sampling time.

In some optional embodiments, not shown in the figure, the high and low data set acquisition submodule 311 may also include:

A range determination unit 3115, configured to determine a first data range of an initial high-level signal data set and a second data range of an initial low-level signal data set;

A judging unit 3116, configured to judge whether all the missing signal data are signal data to be identified within the first data range or the second data range;

The high and low level signal set confirmation unit 3117 is used to confirm that if the missing signal data is all the first data If the signal data to be identified are within the first data range or the second data range, the initial high-level signal data set is used as the high-level signal data set, and the initial low-level signal data set is used as the low-level signal data set; if the missing signal data are not all the signal data to be identified within the first data range or the second data range, the missing signal data are inserted into the initial high-level signal data set and the initial low-level signal data set according to the sampling time, the data ranges of the initial high-level signal data set and the initial low-level signal data set are re-determined, and the re-determined data ranges are used as the high-level signal data set and the low-level signal data set.

Specifically, in some optional embodiments, not shown in the figure, the range determination module 3115 includes:

A calculation subunit, used to calculate a first data average value of the signal data to be identified in the initial high-level signal data set, and calculate a second data average value of the signal data to be identified in the initial low-level signal data set;

An extreme value confirmation subunit, used to determine a first maximum value and a first minimum value of the signal data to be identified in the initial high-level signal data set, and to determine a second maximum value and a second minimum value of the signal data to be identified in the initial low-level signal data set;

The data range determination subunit is used to determine the first data range of the initial high-level signal data set according to the first data average, the first maximum value and the first minimum value; and to determine the second data range of the initial low-level signal data set according to the second data average, the second maximum value and the second minimum value.

In some optional embodiments, the to-be-identified signal data set generating unit 3123 obtains the minimum signal data in the high-level signal data set as the high-level threshold; and obtains the maximum signal data in the low-level signal data set as the low-level threshold.

In some optional embodiments, as shown by the dotted line in the figure, the to-be-identified signal acquisition module 35 includes:

The data acquisition submodule 351 is used to continuously acquire data of the signal to be identified in one or more periodic waveforms;

The data set adding submodule 352 is used to add the collected signal data to be identified into the signal set to be identified.

In some optional embodiments, the device may be a matcher.

Example 4

As shown in FIG4 , an embodiment of the present invention further provides a pulse identification device, including:

A collector 41 is used to obtain a signal set to be identified, where the signal set to be identified includes signal data to be identified;

A timing controller 42, used to record the sampling time information of each signal data to be identified;

The processor 43 is used to obtain at least one signal data group to be identified from the signal set to be identified, wherein the signal data group to be identified is a plurality of signal data to be identified that are continuous and change in the same direction, and to determine whether the signal data to be identified in each signal data group to be identified is continuously rising or continuously falling. if it rises continuously, the corresponding signal data to be identified in the signal data group is used as the rising edge data within a pulse cycle; if it falls continuously, the corresponding signal data to be identified in the signal data group is used as the falling edge data within a pulse cycle.

Example 5

Based on the same technical concept, an embodiment of the present application further provides a computer device, including a memory 1 and a processor 2, as shown in FIG5 , wherein the memory 1 stores a computer program, and the processor 2 implements any of the pulse recognition methods described above when executing the computer program.

The memory 1 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 1 may be an internal storage unit of the OTT video service monitoring system, such as a hard disk. In other embodiments, the memory 1 may also be an external storage device of the OTT video service monitoring system, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash memory card, or a plurality of other storage devices. (Flash Card), etc. Furthermore, the memory 1 may include both an internal storage unit of the OTT video service monitoring system and an external storage device. The memory 1 may be used not only to store application software and various data installed in the OTT video service monitoring system, such as the code of the OTT video service monitoring program, but also to temporarily store data that has been output or is to be output.

In some embodiments, the processor 2 can be a central processing unit (CPU), a controller, a microcontroller, a microprocessor or other data processing chip, which is used to run the program code stored in the memory 1 or process data, such as executing a pulse recognition program.

The disclosed embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the pulse identification method described in the above method embodiment are executed. The storage medium may be a volatile or non-volatile computer-readable storage medium.

The computer program product of the pulse identification method provided in the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute The steps of performing the pulse identification method described in the above method embodiment can be specifically referred to the above method embodiment, which will not be repeated here.

The disclosed embodiment of the present invention also provides a computer program, which implements any one of the methods of the aforementioned embodiments when executed by a processor. The computer program product can be implemented specifically by hardware, software or a combination thereof. In an optional embodiment, the computer program product is specifically embodied as a computer storage medium. In another optional embodiment, the computer program product is specifically embodied as a software product, such as a software development kit (Software Development Kit, SDK), etc.

It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

It should be understood that various parts of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, As in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, a dedicated integrated circuit having a suitable combinational logic gate circuit, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

Although the embodiments of the present invention have been shown and described above, it will be appreciated that the embodiments described above are The embodiments are exemplary and should not be construed as limiting the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

A pulse identification method, characterized in that the method is performed by a matcher and is used for power signal pulse identification of a plasma power system, the method comprising:

Acquire at least one signal data group to be identified from a signal set to be identified, wherein the signal data group to be identified includes a plurality of signal data to be identified that are continuous and change in the same direction, and the signal set to be identified includes the signal data to be identified;

Determining whether the signal data to be identified in each of the signal data groups to be identified is continuously rising or continuously falling;

If it rises continuously, the signal data to be identified in the corresponding signal data group to be identified is used as the rising edge data in one pulse cycle;

If it is continuously decreasing, the corresponding signal data to be identified in the signal data group to be identified is used as the falling edge data in one pulse cycle;

The step of acquiring at least one data group of signals to be identified from the set of signals to be identified comprises:

Acquire a high-level signal data set and a low-level signal data set from the signal set to be identified;

Acquire the to-be-identified signal data set from a preliminary selected signal data set, wherein the preliminary selected signal data set is a remaining signal data set of the to-be-identified signal set excluding the high-level signal data set and the low-level signal data set;

The step of obtaining the high-level signal data set and the low-level signal data set from the signal set to be identified comprises: obtaining the high-level signal data set and the low-level signal data set from the signal set to be identified by data clustering, grouping or grouping;

The step of obtaining the to-be-identified signal data set from the preliminary signal set comprises: determining a high level threshold value according to the to-be-identified signal data in the high level signal data set; determining a high level threshold value according to the to-be-identified signal data in the low level signal data set; Determine a low level threshold for the signal data to be identified in the signal data set; obtain target signal data from the preliminary signal set, the target signal data being the signal data to be identified between the high level threshold and the low level threshold; process the target signal data according to the sampling time information to form the at least one signal data set to be identified;

Before inserting the gap-filling signal data into the initial high-level signal data set and the initial low-level signal data set according to the sampling time, the obtaining of the high-level signal data set and the low-level signal data set from the signal set to be identified further includes: determining a first data range of the initial high-level signal data set and a second data range of the initial low-level signal data set; judging whether all the gap-filling signal data are the signal data to be identified within the first data range or the second data range; if so, taking the initial high-level signal data set as the high-level signal data set and taking the initial low-level signal data set as the low-level signal data set; if not, inserting the gap-filling signal data into the initial high-level signal data set and the initial low-level signal data set according to the sampling time, re-determining the data ranges of the initial high-level signal data set and the initial low-level signal data set, and taking the re-determined data ranges as the high-level signal data set and the low-level signal data set;

Determining the first data range of the initial high-level signal data set and the second data range of the initial low-level signal data set includes: calculating a first data average value of the signal data to be identified in the initial high-level signal data set, and calculating a second data average value of the signal data to be identified in the initial low-level signal data set; determining a first maximum value and a first minimum value of the signal data to be identified in the initial high-level signal data set, and determining a second maximum value and a second minimum value of the signal data to be identified in the initial low-level signal data set; determining the initial high-level signal data set according to the first data average value, the first maximum value and the first minimum value. a first data range of the signal data set; and determining a second data range of the initial low-level signal data set according to the second data average, the second maximum value and the second minimum value.
The pulse identification method according to claim 1, characterized in that the step of processing the target signal data according to the sampling time information to form the at least one signal data group to be identified comprises:

Sorting the target signal data in chronological order according to the sampling time information;

The to-be-identified signal data at isolated time points in the target signal data are removed to form the at least one to-be-identified signal data group.
The pulse identification method according to claim 2, characterized in that obtaining a high-level signal data set and a low-level signal data set from the signal set to be identified comprises:

Acquire an initial high-level signal data set and an initial low-level signal data set from the signal set to be identified;

According to the sampling time information, the initial high-level signal data set and the initial low-level signal data set are respectively sorted in chronological order;

Acquire missing signal data from the to-be-identified signal set, wherein the missing signal data is the to-be-identified signal data missing on the timeline of the initial high-level signal data set and the initial low-level signal data set;

The gap-filling signal data is inserted into the initial high-level signal data set and the initial low-level signal data set according to the sampling time.
The pulse identification method according to claim 3 is characterized in that the determining of the high-level threshold value according to the signal data to be identified in the high-level signal data set comprises: obtaining the minimum signal data in the high-level signal data set as the high-level threshold value;

The determining of the low level threshold value according to the signal data to be identified in the low level signal data set comprises: obtaining the maximum signal data in the low level signal data set as the low level threshold value.
The pulse identification method according to claim 1, characterized in that before acquiring at least one data group of a signal to be identified from the signal set to be identified, the method further comprises:

The sampling time information of each signal data to be identified is recorded.
The pulse identification method according to claim 1, further comprising:

Determine the rising edge duration according to the number of the signal data to be identified in the rising and falling edge data in each pulse cycle;

The falling edge duration is determined according to the quantity of the signal data to be identified in the falling edge data within each pulse cycle.
The pulse identification method according to claim 6, characterized in that before recording the sampling time information of each signal data to be identified, the method further comprises:

The signal set to be identified is obtained.
The pulse identification method according to claim 7, characterized in that the step of obtaining the signal set to be identified comprises:

Continuously collecting data of the signal to be identified in one or more periodic waveforms;

The collected data of the signal to be identified are added to the signal set to be identified.
A pulse identification device, characterized in that the device is arranged in a matcher and is used for power signal pulse identification of a plasma power system, and the device comprises:

The continuous signal acquisition module is used to acquire at least one signal data group to be identified from the signal set to be identified, wherein the signal data group to be identified is a plurality of signals to be identified that are continuous and change in the same direction. signal data, the signal set to be identified includes the signal data to be identified;

A judging module, used for judging whether the signal data to be identified in each of the signal data groups to be identified is continuously rising or continuously falling;

A rising edge data determination module, used for treating the to-be-identified signal data in the corresponding to-be-identified signal data group as the rising edge data in one pulse cycle if the rising continues;

A falling edge data determination module, used for treating the signal data to be identified in the corresponding signal data group to be identified as falling edge data in one pulse cycle if the falling edge continues;

The continuous signal acquisition module includes: a high-low data set acquisition submodule, which is used to acquire a high-level signal data set and a low-level signal data set from the signal set to be identified. Specifically, the high-level signal data set and the low-level signal data set can be acquired from the signal set to be identified by data clustering, grouping or grouping; a signal data group acquisition submodule to be identified, which is used to acquire a signal data group to be identified from a preliminary signal data set, wherein the preliminary signal data set is the remaining signal data set of the signal set to be identified except the high-level signal data set and the low-level signal data set; the high-low data set acquisition submodule acquires the high-level signal data set and the low-level signal data set from the signal set to be identified by data clustering, grouping or grouping;

The to-be-identified signal data group acquisition submodule comprises: a high-level threshold determination unit: used to determine a high-level threshold according to the to-be-identified signal data in the high-level signal data set; a low-level threshold determination unit: used to determine a low-level threshold according to the to-be-identified signal data in the low-level signal data set; a to-be-identified signal data group generation unit: used to obtain target signal data from the preliminary signal set, the target signal data being the to-be-identified signal data between the high-level threshold and the low-level threshold, and processing the target signal data according to the sampling time information to form at least one to-be-identified signal data group;

The high and low data set acquisition submodule also includes: a range determination unit for determining the initial high a first data range of a level signal data set and a second data range of an initial low-level signal data set; a judging unit, used to judge whether the missing signal data are all signal data to be identified within the first data range or the second data range; a high and low level signal set confirmation unit, used to, if the missing signal data are all signal data to be identified within the first data range or the second data range, use the initial high level signal data set as the high level signal data set and the initial low level signal data set as the low level signal data set; if the missing signal data are not all signal data to be identified within the first data range or the second data range, insert the missing signal data into the initial high level signal data set and the initial low level signal data set according to the sampling time, redefine the data range of the initial high level signal data set and the initial low level signal data set, and use the redetermined data range as the high level signal data set and the low level signal data set;

The range determination module includes: a calculation subunit, which is used to calculate the first data average value of the signal data to be identified in the initial high-level signal data set, and calculate the second data average value of the signal data to be identified in the initial low-level signal data set; an extreme value confirmation subunit, which is used to determine the first maximum value and the first minimum value of the signal data to be identified in the initial high-level signal data set, and determine the second maximum value and the second minimum value of the signal data to be identified in the initial low-level signal data set; a data range determination subunit, which is used to determine the first data range of the initial high-level signal data set according to the first data average value, the first maximum value and the first minimum value; and determine the second data range of the initial low-level signal data set according to the second data average value, the second maximum value and the second minimum value.
A pulse identification device, characterized in that the device is arranged in a matcher and is used for power signal pulse identification of a plasma power system, and the device comprises:

A collector, used for acquiring a signal set to be identified, wherein the signal set to be identified includes signal data to be identified;

A timing controller, used for recording the sampling time information of each signal data to be identified;

A processor, for acquiring at least one signal data group to be identified from a signal set to be identified according to sampling time information, wherein the signal data group to be identified contains a plurality of signal data to be identified that are continuous and change in the same direction, and for determining whether the signal data to be identified in each signal data group to be identified is continuously rising or continuously falling; if it is continuously rising, the signal data to be identified in the corresponding signal data group to be identified is used as rising edge data in a pulse cycle; if it is continuously falling, the signal data to be identified in the corresponding signal data group to be identified is used as falling edge data in a pulse cycle, wherein acquiring at least one signal data group to be identified from the signal set to be identified comprises: acquiring a high-level signal data set and a low-level signal data set from the signal set to be identified; acquiring the signal data group to be identified from a preliminary signal data set, wherein the preliminary signal data set is the remaining signal data set of the signal set to be identified except the high-level signal data set and the low-level signal data set; The method of obtaining a high-level signal data set and a low-level signal data set from the signal set to be identified comprises: obtaining a high-level signal data set and a low-level signal data set from the signal set to be identified by data clustering, clustering or grouping; the method of obtaining the signal data set to be identified from the preliminary signal set comprises: determining a high-level threshold value according to the signal data to be identified in the high-level signal data set; determining a low-level threshold value according to the signal data to be identified in the low-level signal data set; obtaining target signal data from the preliminary signal set, wherein the target signal data is the signal data to be identified between the high-level threshold value and the low-level threshold value; processing the target signal data according to sampling time information to form the at least one signal data set to be identified; and before inserting the missing signal data into the initial high-level signal data set and the initial low-level signal data set according to the sampling time, the method of obtaining the high-level signal data set and the low-level signal data set from the signal set to be identified further comprises: determining the The method comprises the steps of: determining a first data range of the initial high-level signal data set and a second data range of the initial low-level signal data set; determining whether all the missing signal data are signal data to be identified within the first data range or the second data range; if so, using the initial high-level signal data set as the high-level signal data set, and using the initial low-level signal data set as the low-level signal data set; if not, inserting the missing signal data into the initial high-level signal data set and the initial low-level signal data set according to the sampling time, re-determining the data range of the initial high-level signal data set and the initial low-level signal data set, and using the re-determined data range as the high-level signal data set and the low-level signal data set; determining the initial high-level signal data set The first data range of the set and the second data range of the initial low-level signal data set include: calculating the first data average value of the signal data to be identified in the initial high-level signal data set, and calculating the second data average value of the signal data to be identified in the initial low-level signal data set; determining the first maximum value and the first minimum value of the signal data to be identified in the initial high-level signal data set, and determining the second maximum value and the second minimum value of the signal data to be identified in the initial low-level signal data set; determining the first data range of the initial high-level signal data set according to the first data average value, the first maximum value and the first minimum value; determining the second data range of the initial low-level signal data set according to the second data average value, the second maximum value and the second minimum value.
A computer device, characterized in that it comprises: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the pulse identification method according to any one of claims 1 to 8 is executed.
A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the pulse identification method as claimed in any one of claims 1 to 8 is executed.