CN115329822A - Pulse identification method and device - Google Patents
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Abstract
The application discloses and provides a pulse identification method and a device, at least one signal data group to be identified is obtained from a signal set to be identified, a plurality of signal data to be identified which are continuous and change in the same direction are obtained from the signal data group to be identified, and the signal set to be identified comprises the signal data to be identified; judging whether the signal data to be identified in each signal data group to be identified continuously rises or falls; if the signal to be identified continuously rises, the signal data to be identified in the corresponding signal data group to be identified is taken as rising edge data in a pulse period; if the data of the signal to be identified in the corresponding signal data group to be identified continuously falls, the data of the signal to be identified in the corresponding signal data group to be identified is taken as falling edge data in a PULSE period, and the problems that in the prior art, the setting operation of the parameters of the matcher PULSE is complicated and the maintenance cost is high are solved.
Description
Technical Field
The invention relates to the technical field of pulse recognition, in particular to a pulse recognition method and a pulse recognition device.
Background
The existing plasma power supply system comprises a power supply, a matcher and a cavity load, wherein the power supply provides a power supply signal, the matcher acquires the power supply signal, adjusts load impedance among the power supply, the matcher and the cavity load, and forwards the power supply signal to the load.
The matcher is a passive device, and in order to enable the matcher to have the capability of detecting the rising edge and the falling edge of the PULSE, generally, when setting the PULSE parameter, the PULSE parameter needs to be acquired through external detection equipment, and then parameters such as the rising edge duration and the falling edge duration need to be actively set in the matcher.
Disclosure of Invention
Therefore, the technical problem to be solved by the present invention is to overcome the defects that in the prior art, the matcher has the capability of detecting the rising edge and the falling edge of PULSE by means of setting the parameter PULSE, the operation is complicated, and the maintenance cost is high, so as to provide a PULSE identification method and device.
To solve the above technical problem, the embodiments of the present disclosure at least provide a pulse recognition method and apparatus.
In a first aspect, an embodiment of the present disclosure provides a pulse identification method, including:
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 contains a plurality of signal data to be identified which are continuous and change in the same direction, and the signal set to be identified contains the signal data to be identified;
judging whether the signal data to be identified in each signal data group to be identified continuously rises or falls;
if the signal to be identified continuously rises, taking the signal data to be identified in the corresponding signal data group to be identified as rising edge data in a pulse period;
and if the data continuously drop, taking the data of the signal to be identified in the corresponding data group of the signal to be identified as the data of the falling edge in one pulse period.
Optionally, the obtaining at least one signal data group to be identified from the signal set to be identified includes: acquiring a high-level signal data set and a low-level signal data set from the signal set to be identified; and acquiring the signal data group to be identified from a primary selection signal data set, wherein the primary selection signal data set is a residual signal data set of the signal set to be identified except for the high-level signal data set and the low-level signal data set.
Optionally, the obtaining the signal data group to be identified from the initially selected signal set includes: 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 from the signal data to be identified in the low level signal data set; acquiring target signal data from the initially selected signal set, wherein the target signal data is signal data to be identified 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 signal data group to be identified.
Optionally, the processing the target signal data according to the sampling time information to form the at least one signal data group to be identified includes: sequencing the target signal data according to the sampling time information; and removing the signal data to be identified at the isolated time point in the target signal data to form at least one signal data group to be identified.
Optionally, the obtaining a high-level signal data set and a low-level signal data set from the signal set to be identified includes: acquiring an initial high-level signal data set and an initial low-level signal data set from the signal set to be identified; sequencing the initial high-level signal data set and the initial low-level signal data set according to time sequence according to sampling time information; acquiring supplementary signal data from the signal set to be identified, wherein the supplementary signal data are the signal data to be identified which are missing on the time line of the initial high-level signal data set and the initial low-level signal data set; and inserting the supplementary signal data into the initial high-level signal data set and the initial low-level signal data set according to sampling time.
Optionally, before the inserting the supplementary signal data into the initial high-level signal data set and the initial low-level signal data set by a sampling time, the method further comprises: 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 the filling-up signal data are all signal data to be identified in 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; and if not, inserting the supplementing signal data into the initial high-level signal data set and the initial low-level signal data set according to sampling time, re-determining the data range of the initial high-level signal data set and the initial low-level signal data set, and taking the re-determined data range as the high-level signal data set and the low-level signal data set.
Optionally, the 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 comprises: 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 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 from the second data average, the second maximum, and the second minimum.
Optionally, the obtaining a high-level signal data set and a low-level signal data set from the signal set to be identified is: and acquiring a high-level signal data set and a low-level signal data set from the signal set to be identified in a data clustering, clustering or clustering mode.
Optionally, the determining a high level threshold according to the signal data to be identified in the high level signal data set is: acquiring minimum signal data in the high-level signal data set as the high-level threshold; determining a low level threshold value according to the signal data to be identified in the low level signal data set is: and acquiring the maximum signal data in the low-level signal data set as the low-level threshold.
Optionally, before the acquiring at least one signal data group to be identified from the signal set to be identified, the method further includes: and recording the sampling time information of each signal data to be identified.
Optionally, the method further comprises: determining 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 period; and determining the duration of the falling edge according to the number of the signal data to be identified in the falling edge data in each pulse period.
Optionally, before the recording of the sampling time information of each of the signal data to be identified, the method further includes: and acquiring the signal set to be identified.
Optionally, the acquiring the set of signals to be identified includes: carrying out continuous signal data acquisition to be identified in one or more periodic waveforms; and adding the acquired signal data to be identified into the signal set to be identified.
Optionally, the method is used for a matcher.
In a second aspect, an embodiment of the present disclosure further provides a pulse recognition apparatus, including:
the device comprises a continuous signal acquisition module, a signal identification module and a signal identification module, wherein the continuous signal acquisition module is used for acquiring at least one signal data group to be identified from a signal set to be identified, the signal data group to be identified is continuous and contains a plurality of signal data to be identified with the same direction change, and the signal set to be identified contains the signal data to be identified;
the judging module is used for judging whether the signal data to be identified in each signal data group to be identified continuously rises or continuously falls;
the rising edge data determining module is used for taking the signal data to be identified in the corresponding signal data group to be identified as the rising edge data in a pulse period if the signal data to be identified continuously rises;
and the falling edge data determining module is used for taking the signal data to be identified in the corresponding signal data group to be identified as the falling edge data in a pulse period if the signal data to be identified continuously falls.
In a third aspect, an embodiment of the disclosure further provides a pulse recognition apparatus, including:
the device comprises a collector, a signal processing unit and a signal processing unit, wherein the collector is used for obtaining a signal set to be identified, and the signal set to be identified comprises signal data to be identified;
the time sequence controller is used for recording the sampling time information of each signal data to be identified;
the processor is used for acquiring at least one signal data group to be identified from a signal set to be identified according to sampling time information, judging whether the signal data to be identified in each signal data group to be identified continuously rises or continuously falls when the signal data to be identified in the signal data group to be identified is continuous and homodromous signal data; if the signal to be identified continuously rises, the signal data to be identified in the corresponding signal data group to be identified is taken as rising edge data in a pulse period; and if the signal data to be identified continuously falls, taking the signal data to be identified in the corresponding signal data group to be identified as falling edge data in a pulse period.
In a fourth aspect, an embodiment of the present disclosure further provides a computer device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating via the bus when the computer device is running, the machine-readable instructions when executed by the processor performing the steps of the first aspect described above, or any possible implementation of the first aspect.
In a fifth aspect, the disclosed embodiments of the present invention further provide a computer-readable storage medium, where a computer program is stored, and the computer program is executed by a processor to perform the steps in the first aspect or any one of the possible implementation manners of the first aspect.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
acquiring at least one signal data group to be identified from a signal set to be identified, and judging whether the signal data to be identified in each signal data group to be identified continuously rises or falls; and if the signal data to be identified continuously rises, taking the signal data to be identified in the corresponding signal data group to be identified as rising edge data in a pulse period. According to the scheme, the signal data group to be identified is acquired from the signal group to be identified, the signals are subjected to grouping calculation, the PULSE top data set, the PULSE bottom data set and the PULSE slope data set are determined according to the change condition of the signal data to be identified in each signal data group to be identified, then rising edge data or falling edge data are obtained, manual PULSE setting is not needed, PULSE identification is automatically completed, operation complexity is greatly reduced, and follow-up maintenance cost is reduced.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 illustrates a flow chart of a method of pulse identification provided by a disclosed embodiment of the invention;
FIG. 2 illustrates a flow chart of another method of pulse identification provided by the disclosed embodiment of the present invention;
FIG. 3 is a schematic diagram of a pulse recognition apparatus according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram of another pulse recognition apparatus provided in accordance with the disclosed embodiments;
fig. 5 shows a schematic structural diagram of a computer device according to an embodiment of the present disclosure.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like 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. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
Example 1
As shown in fig. 1, a flowchart of a pulse recognition method according to an embodiment of the disclosure includes:
s11: 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 contains a plurality of continuous signal data to be identified which change in the same direction, and the signal set to be identified contains the signal data to be identified;
s12: judging whether the signal data to be identified in each signal data group to be identified continuously rises or continuously falls, if so, executing S13, and if so, executing S14;
s13: taking the signal data to be identified in the corresponding signal data group to be identified as rising edge data in a pulse period;
s14: and taking the signal data to be identified in the corresponding signal data group to be identified as falling edge data in one pulse period.
It can be understood that, in the technical solution provided in this embodiment, at least one signal data group to be identified is obtained from a signal set to be identified, and whether signal data to be identified in each signal data group to be identified continuously rises or falls is determined; and if the signal data to be identified continuously rises, taking the signal data to be identified in the corresponding signal data group to be identified as rising edge data in a pulse period. According to the scheme, the signal data group to be identified is acquired from the signal group to be identified, the signals are subjected to grouping calculation, the PULSE top data set, the PULSE bottom data set and the PULSE slope data set are determined according to the change condition of the signal data to be identified in each signal data group to be identified, then rising edge data or falling edge data are obtained, manual PULSE setting is not needed, PULSE identification is automatically completed, operation complexity is greatly reduced, and follow-up maintenance cost is reduced.
Example 2
As shown in fig. 2, a flowchart of another pulse identification method according to an embodiment of the disclosure includes:
s21: and acquiring a signal set to be identified.
S22: and recording the sampling time information of each signal data to be identified.
S23: and 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 contains a plurality of signal data to be identified which are continuous and change in the same direction, and the signal set to be identified contains the signal data to be identified.
S24: and judging whether the signal data to be identified in each signal data group to be identified continuously rises or falls, if so, executing S25, and if so, executing S27.
S25: and taking the signal data to be identified in the corresponding signal data group to be identified as rising edge data in a pulse period.
S26: and determining 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 period.
S27: and taking the signal data to be identified in the corresponding signal data group to be identified as falling edge data in one pulse period.
S28: and determining the duration of the falling edge according to the number of the signal data to be identified in the falling edge data in each pulse period.
In some alternative embodiments, each set of signal data to be identified and the signal data to be identified within the set are between a low level threshold and a high level threshold.
In some alternative embodiments, not shown in the figures, S23 comprises:
s231: acquiring a high-level signal data set and a low-level signal data set from a signal set to be identified;
in some alternative 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, or clustering.
S232: and acquiring a signal data group to be identified from a primary selection signal data set, wherein the primary selection signal data set is a residual signal data set of the signal set to be identified except for the high-level signal data set and the low-level signal data set.
In some alternative embodiments, not shown in the figures, S232 may include:
s2321: determining a high level threshold value according to the signal data to be identified in the high level signal data set;
s2322: determining a low level threshold value according to signal data to be identified in the low level signal data set;
s2323: acquiring target signal data from the initially selected signal set, wherein the target signal data is signal data to be identified between a high level threshold and a low level threshold;
s2324: and processing the target signal data according to the sampling time information to form at least one signal data group to be identified.
In some alternative embodiments, not shown in the figures, S2324 may include:
A. sequencing the target signal data according to the sampling time information;
B. and removing the signal data to be identified at the isolated time point in the target signal data to form at least one signal data group to be identified.
In some alternative embodiments, not shown in the figures, S231 may include:
s231: acquiring an initial high-level signal data set and an initial low-level signal data set from a signal set to be identified;
s232: sequencing the initial high-level signal data set and the initial low-level signal data set according to the sampling time information;
s233: acquiring supplementary signal data from a signal set to be identified, wherein the supplementary signal data are missing signal data to be identified on an initial high-level signal data set and an initial low-level signal data set time line;
s234: 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;
s235: judging whether the filling-up signal data are all the signal data to be identified in the first data range or the second data range, if so, executing S236, and if not, executing S237;
s236: taking the initial high-level signal data set as a high-level signal data set, and taking the initial low-level signal data set as a low-level signal data set;
s237: inserting the filling-up 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 taking 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 a specific engineering practice, the above steps S234 to S236 are optional steps, and a person skilled in the art determines whether to implement or not according to engineering requirements.
In some alternative embodiments, not shown in the figures, the above S234 may be implemented by, but is not limited to, the following processes:
s2341: calculating a first data average value of 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;
s2342: determining a first maximum value and a first minimum value of 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;
s2343: 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;
s2344: a second data range of the initial low level signal data set is determined based on the second data average, the second maximum, and the second minimum.
In some alternative embodiments, the smallest signal data within the high level signal data set is obtained as the high level threshold; the largest signal data in the low level signal data set is obtained as the low level threshold.
In some alternative embodiments, not shown in the figures, S21 comprises:
s211: continuous signal data acquisition to be identified is carried out in one or more periodic waveforms;
s212: and adding the acquired signal data to be identified into a signal set to be identified.
In some optional embodiments, the above method may be implemented, but is not limited to, by a matcher.
In a plasma power supply system, a matcher is generally provided with 3 main elements, a time schedule controller, a collector and a processor, wherein the time schedule controller is used for timing triggering collection of the collector, the collector is used for continuously collecting power signals according to one or more periodic waveforms, and the time schedule controller sets collection time to be dense and fixed.
The processor collects the collected data provided by the collector, and divides a pulse top signal data set and a pulse bottom signal data set through similar calculation modes such as data clustering, clustering and grouping, and simultaneously finds out a lower limit of the pulse top signal data set and an upper limit of the pulse bottom signal data set, residual data except the pulse top signal data set and the pulse bottom signal data set are arranged in a collecting time sequence, and rising edge data and falling edge data are divided according to the upward continuity or downward continuity of adjacent data, and because the collecting time intervals are the same, the rising edge duration and the falling edge duration can be calculated according to the collecting number of the rising edge and the falling edge. If the acquisition intervals are different, the total duration of the rising edge and the total duration of the falling edge of the acquisition time corresponding to the acquisition point can be calculated.
It should be noted that the embodiments described in the present embodiment are only exemplary descriptions of specific implementations under the concept of the present invention, and the execution sequence of the steps in each embodiment is not limited to the embodiments provided herein, and in the specific engineering practice, the execution sequence of each step can be adjusted by those skilled in the art according to the actual situation.
It can be understood that, in the technical solution provided in this embodiment, at least one signal data group to be identified is obtained from a signal set to be identified, and whether signal data to be identified in each signal data group to be identified continuously rises or falls is determined; and if the signal data to be identified continuously rises, taking the signal data to be identified in the corresponding signal data group to be identified as rising edge data in a pulse period. According to the scheme, the signal data group to be identified is acquired from the signal group to be identified, the signals are subjected to grouping calculation, the PULSE top data set, the PULSE bottom data set and the PULSE slope data set are determined according to the change condition of the signal data to be identified in each signal data group to be identified, then rising edge data or falling edge data are obtained, manual PULSE setting is not needed, PULSE identification is automatically completed, operation complexity is greatly reduced, and follow-up maintenance cost is reduced.
Example 3
As shown in fig. 3, an embodiment of the present invention further provides a pulse recognition apparatus, including:
the continuous signal acquisition module 31 is configured to acquire at least one signal data group to be identified from a signal set to be identified, where 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 32, configured to judge whether signal data to be identified in each signal data group to be identified continuously rises or continuously falls;
a rising edge data determining module 33, configured to, if the signal data to be identified in the corresponding signal data group to be identified continuously rises, take the signal data to be identified as rising edge data in one pulse period;
and the falling edge data determining module 34 is configured to, if the signal data to be identified in the corresponding signal data group to be identified continuously falls, take the signal data to be identified in the corresponding signal data group to be the falling edge data in one pulse period.
In some alternative embodiments, as shown in phantom in fig. 3, the apparatus further comprises:
and a signal to be identified acquiring module 35, configured to acquire a signal set to be identified.
And the time recording module 36 is used for recording the sampling time information of each signal data to be identified.
And a rising edge duration determining module 37, configured to determine a rising edge duration according to the number of to-be-identified signal data in the rising and falling edge data in each pulse period.
And a falling edge duration determining module 38, configured to determine a falling edge duration according to the number of the signal data to be identified in the falling edge data in each pulse period.
In some alternative embodiments, as shown in dashed lines in fig. 3, the continuous signal acquisition module 31 includes:
the high-low data set obtaining sub-module 311 is configured to obtain a high-level signal data set and a low-level signal data set from a signal set to be identified, and specifically, may obtain the high-level signal data set and the low-level signal data set from the signal set to be identified by means of data clustering, or clustering;
the signal data set to be identified obtaining sub-module 312 is configured to obtain the signal data set to be identified from a primarily selected signal data set, where the primarily selected signal data set is a remaining signal data set of the signal set to be identified except for the high level signal data set and the low level signal data set.
In some alternative embodiments, not shown in the figures, the signal data set to be identified acquisition sub-module 312 includes:
high level threshold value determination unit 3121: for determining a high level threshold from signal data to be identified within the high level signal data set;
low level threshold value determination unit 3122: for determining a low level threshold from signal data to be identified within the low level signal data set;
to-be-recognized signal data group generation unit 3123: the method is used for acquiring target signal data from the initially selected signal set, the target signal data is signal data to be identified between a high level threshold and a low level threshold, and the target signal data is processed according to sampling time information to form at least one signal data group to be identified.
Specifically, in some optional embodiments, processing the target signal data to form at least one to-be-identified signal data group according to the sampling time information includes: sequencing the target signal data according to the sampling time information; and removing the signal data to be identified at the isolated time point in the target signal data to form at least one signal data group to be identified.
In some alternative embodiments, not shown in the drawings, the high-low data set obtaining sub-module 311 may include:
an initial data set obtaining unit 3111, configured to obtain an initial high-level signal data set and an initial low-level signal data set from a signal set to be identified;
a sorting unit 3112, configured to sort the initial high-level signal data set and the initial low-level signal data set according to time sequence according to the sampling time information;
a supplementary signal data acquiring unit 3113, configured to acquire supplementary signal data from the signal set to be identified, where the supplementary signal data is missing signal data to be identified on a time line of an initial high-level signal data set and an initial low-level signal data set;
a data padding unit 3114 for inserting padding signal data into the initial high-level signal data set and the initial low-level signal data set according to a sampling time.
In some optional embodiments, not shown in the figure, the high-low data set obtaining sub-module 311 may further include:
a range determining unit 3115 for 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;
a determining unit 3116, configured to determine whether all of the supplementary signal data are signal data to be identified in the first data range or the second data range;
a high-low level signal set confirmation unit 3117, configured to, if all of the gap signal data is signal data to be identified in the first data range or the second data range, use the initial high level signal data set as a high level signal data set, and use the initial low level signal data set as a low level signal data set; and if the filling-up signal data is not all the signal data to be identified in the first data range or the second data range, inserting the filling-up 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.
Specifically, in some alternative embodiments, not shown in the figures, the range determination module 3115 includes:
the calculating subunit is used for 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;
the extreme value confirmation subunit is used for 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;
a data range determining subunit, configured to 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; a second data range of the initial low level signal data set is determined based on the second data average, the second maximum, and the second minimum.
In some optional embodiments, the signal data group to be identified generating unit 3123 acquires the smallest signal data within the high-level signal data set as the high-level threshold; the largest signal data in the low level signal data set is obtained as the low level threshold.
In some alternative embodiments, as shown in the dotted line, the signal to be identified acquisition module 35 includes:
the data acquisition submodule 351 is used for continuously acquiring signal data to be identified in one or more periodic waveforms;
and the data set adding submodule 352 is used for adding the acquired signal data to be identified into the signal set to be identified.
In some alternative embodiments, the apparatus may be a matcher.
It can be understood that, in the technical solution provided in this embodiment, at least one signal data group to be identified is obtained from a signal set to be identified, and whether signal data to be identified in each signal data group to be identified continuously rises or continuously falls is determined; and if the signal data to be identified continuously rises, taking the signal data to be identified in the corresponding signal data group to be identified as rising edge data in a pulse period. According to the scheme, the signal data set to be recognized is obtained from the signal set to be recognized, the signal is subjected to grouping calculation, the PULSE top data set, the PULSE bottom data set and the PULSE slope data set are determined according to the change condition of the signal data to be recognized in each signal data set to be recognized, then rising edge data or falling edge data are obtained, manual PULSE setting is not needed, PULSE recognition is automatically completed, operation complexity is greatly reduced, and follow-up maintenance cost is reduced.
Example 4
As shown in fig. 4, an embodiment of the present invention further provides a pulse recognition apparatus, including:
the collector 41 is configured 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 for recording sampling timing information of each signal data to be recognized;
a processor 43, configured to obtain at least one signal data group to be identified from the signal set to be identified, where 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 determine whether the signal data to be identified in each signal data group to be identified continuously rises or falls; if the signal to be identified continuously rises, the signal data to be identified in the corresponding signal data group to be identified is taken as rising edge data in a pulse period; and if the data continuously drop, taking the signal data to be identified in the corresponding signal data group to be identified as the falling edge data in one pulse period.
It can be understood that, in the technical solution provided in this embodiment, at least one signal data group to be identified is obtained from a signal set to be identified, and whether signal data to be identified in each signal data group to be identified continuously rises or falls is determined; and if the signal data to be identified continuously rises, taking the signal data to be identified in the corresponding signal data group to be identified as rising edge data in a pulse period. According to the scheme, the signal data group to be identified is acquired from the signal group to be identified, the signals are subjected to grouping calculation, the PULSE top data set, the PULSE bottom data set and the PULSE slope data set are determined according to the change condition of the signal data to be identified in each signal data group to be identified, then rising edge data or falling edge data are obtained, manual PULSE setting is not needed, PULSE identification is automatically completed, operation complexity is greatly reduced, and follow-up maintenance cost is reduced.
Example 5
Based on the same technical concept, an embodiment of the present application further provides a computer device, which includes a memory 1 and a processor 2, as shown in fig. 5, where the memory 1 stores a computer program, and the processor 2 implements the pulse recognition method according to any one of the above descriptions when executing the computer program.
The memory 1 includes at least one type of readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, and the like. The memory 1 may in some embodiments be an internal storage unit of the OTT video traffic monitoring system, e.g. a hard disk. The memory 1 may also be an external storage device of the OTT video service monitoring system in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 1 may also include both an internal storage unit and an external storage device of the OTT video service monitoring system. The memory 1 may be used to store not only application software installed in the OTT video service monitoring system and various data, such as codes of OTT video service monitoring programs, but also temporarily store data that has been output or is to be output.
The processor 2 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor or other data Processing chip in some embodiments, and is used for executing program codes or Processing data stored in the memory 1, such as executing a pulse recognition program.
It can be understood that, in the technical solution provided in this embodiment, at least one signal data group to be identified is obtained from a signal set to be identified, and whether signal data to be identified in each signal data group to be identified continuously rises or falls is determined; and if the signal data to be identified continuously rises, taking the signal data to be identified in the corresponding signal data group to be identified as rising edge data in a pulse period. According to the scheme, the signal data group to be identified is acquired from the signal group to be identified, the signals are subjected to grouping calculation, the PULSE top data set, the PULSE bottom data set and the PULSE slope data set are determined according to the change condition of the signal data to be identified in each signal data group to be identified, then rising edge data or falling edge data are obtained, manual PULSE setting is not needed, PULSE identification is automatically completed, operation complexity is greatly reduced, and follow-up maintenance cost is reduced.
The disclosed embodiments also provide a computer-readable storage medium having a computer program stored thereon, where the computer program is executed by a processor to perform the steps of the pulse recognition method described in the above method embodiments. 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 embodiments disclosed in the present invention includes a computer readable storage medium storing a program code, where instructions included in the program code may be used to execute the steps of the pulse identification method described in the above method embodiments, which may be referred to specifically for the above method embodiments, and are not described herein again.
The embodiments disclosed herein also provide a computer program, which when executed by a processor implements any one of the methods of the preceding embodiments. The computer program product may be embodied in hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied in a computer storage medium, and in another alternative embodiment, the computer program product is embodied in a Software product, such as a Software Development Kit (SDK) or the like.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar contents in other embodiments may be referred to for the contents which are not described in detail in some embodiments.
It should be noted that the terms "first," "second," and the like in the description of the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present invention, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various 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, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (18)
1. A method of pulse recognition, comprising:
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 contains a plurality of signal data to be identified which are continuous and change in the same direction, and the signal set to be identified contains the signal data to be identified;
judging whether the signal data to be identified in each signal data group to be identified continuously rises or continuously falls;
if the signal to be identified continuously rises, the signal data to be identified in the corresponding signal data group to be identified is taken as rising edge data in a pulse period;
and if the signal data to be identified continuously falls, taking the signal data to be identified in the corresponding signal data group to be identified as falling edge data in a pulse period.
2. The pulse recognition method of claim 1, wherein the obtaining at least one signal data set to be recognized from within the signal set to be recognized comprises:
acquiring a high-level signal data set and a low-level signal data set from the signal set to be identified;
and acquiring the signal data group to be identified from a primary selection signal data set, wherein the primary selection signal data set is a residual signal data set of the signal set to be identified except for the high-level signal data set and the low-level signal data set.
3. The pulse recognition method of claim 2, wherein the obtaining the signal data set to be recognized from the initially selected 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 from the signal data to be identified in the low level signal data set;
acquiring target signal data from the initially selected signal set, wherein the target signal data is signal data to be identified 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 signal data group to be identified.
4. The pulse recognition method of claim 3, wherein the processing the target signal data according to sampling time information to form the at least one signal data group to be recognized comprises:
sequencing the target signal data according to the sampling time information;
and removing the signal data to be identified at the isolated time point in the target signal data to form at least one signal data group to be identified.
5. The pulse recognition method of claim 2, wherein obtaining a high level signal data set and a low level signal data set from within the signal set to be recognized comprises:
acquiring an initial high-level signal data set and an initial low-level signal data set from the signal set to be identified;
sequencing the initial high-level signal data set and the initial low-level signal data set according to time sequence according to sampling time information;
acquiring supplementary signal data from the signal set to be identified, wherein the supplementary signal data are the signal data to be identified which are missing on the time line of the initial high-level signal data set and the initial low-level signal data set;
and inserting the supplementary signal data into the initial high-level signal data set and the initial low-level signal data set according to sampling time.
6. The pulse recognition method of claim 5, wherein prior to said inserting said gap signal data into said initial high level signal data set and said initial low level signal data set by sample time, said obtaining a high level signal data set and a low level signal data set from said signal set to be recognized further comprises:
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 the filling-up signal data are all signal data to be identified in 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;
and if not, inserting the vacancy-filling signal data into the initial high-level signal data set and the initial low-level signal data set according to sampling time, re-determining the data range of the initial high-level signal data set and the initial low-level signal data set, and taking the re-determined data range as the high-level signal data set and the low-level signal data set.
7. The pulse recognition method of claim 6, wherein the 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 comprises:
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 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 from the second data average, the second maximum, and the second minimum.
8. The pulse recognition method of claim 2, wherein the obtaining of the high level signal data set and the low level signal data set from the signal set to be recognized is: and acquiring a high-level signal data set and a low-level signal data set from the signal set to be identified in a data clustering, clustering or clustering mode.
9. A pulse recognition method according to claim 3, wherein the determining a high level threshold from the signal data to be recognized in the high level signal data set is: acquiring minimum signal data in the high-level signal data set as the high-level threshold;
determining a low level threshold value according to the signal data to be identified in the low level signal data set is: and acquiring the largest signal data in the low-level signal data set as the low-level threshold.
10. The pulse recognition method of claim 1, wherein prior to said obtaining at least one signal data set to be recognized from within a signal set to be recognized, the method further comprises:
and recording the sampling time information of each signal data to be identified.
11. The pulse recognition method of claim 1, further comprising:
determining 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 period;
and determining the duration of the falling edge according to the number of the signal data to be identified in the falling edge data in each pulse period.
12. The pulse recognition method of claim 10, wherein prior to said recording sampling time information for each of said signal data to be recognized, said method further comprises:
and acquiring the signal set to be identified.
13. The pulse recognition method of claim 12, wherein the obtaining a set of signals to be recognized comprises:
carrying out continuous signal data acquisition to be identified in one or more periodic waveforms;
and adding the acquired signal data to be identified into the signal set to be identified.
14. A method for pulse recognition according to any one of claims 1-13, wherein the method is used in a matcher.
15. A pulse recognition apparatus, comprising:
the device comprises a continuous signal acquisition module, a signal identification module and a signal identification module, wherein the continuous signal acquisition module is used for acquiring at least one signal data group to be identified from a signal set to be identified, the signal data group to be identified is continuous and contains a plurality of signal data to be identified with the same direction change, and the signal set to be identified contains the signal data to be identified;
the judging module is used for judging whether the signal data to be identified in each signal data group to be identified continuously rises or continuously falls;
the rising edge data determining module is used for taking the signal data to be identified in the corresponding signal data group to be identified as the rising edge data in a pulse period if the signal data to be identified continuously rises;
and the falling edge data determining module is used for taking the signal data to be identified in the corresponding signal data group to be identified as the falling edge data in a pulse period if the signal data to be identified continuously falls.
16. A pulse recognition apparatus, comprising:
the device comprises a collector, a signal processing unit and a signal processing unit, wherein the collector is used for obtaining a signal set to be identified, and the signal set to be identified comprises signal data to be identified;
the time sequence controller is used for recording the sampling time information of each signal data to be identified;
the processor is used for acquiring at least one signal data group to be identified from a signal set to be identified according to sampling time information, judging whether the signal data to be identified in each signal data group to be identified continuously rises or continuously falls when the signal data to be identified in the signal data group to be identified is continuous and homodromous signal data; if the signal to be identified continuously rises, the signal data to be identified in the corresponding signal data group to be identified is taken as rising edge data in a pulse period; and if the data continuously drop, taking the data of the signal to be identified in the corresponding data group of the signal to be identified as the data of the falling edge in one pulse period.
17. A computer device, comprising: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when a computer device is running, the machine-readable instructions when executed by the processor performing the pulse recognition method of any one of claims 1 to 14.
18. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out a method for pulse recognition according to any one of claims 1 to 14.
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| CN202211249171.9A CN115329822B (en) | 2022-10-12 | 2022-10-12 | Pulse identification method and device |
| PCT/CN2023/124089 WO2024078546A1 (en) | 2022-10-12 | 2023-10-11 | Pulse recognition method and apparatus |
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| WO2024078546A1 (en) * | 2022-10-12 | 2024-04-18 | 深圳市恒运昌真空技术股份有限公司 | Pulse recognition method and apparatus |
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