CN113030558B - Method, device and equipment for identifying data abnormity and readable storage medium - Google Patents
Method, device and equipment for identifying data abnormity and readable storage medium Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a readable storage medium for identifying data abnormity, wherein the method comprises the following steps: acquiring sampling data, wherein the sampling data comprises signal data and time data corresponding to the signal data; carrying out differential operation on the sampling data to obtain first derivative data; determining a rising period, a steady-state period and a falling period of the signal data according to the first derivative data; judging whether the signal data is abnormal or not in the rising period, the steady-state period and the falling period of the signal data according to the first-order derivative data; and if abnormal signal data exist in the rising time period, the steady-state time period or the falling time period, determining that the sampled data have abnormality. By implementing the invention, whether the sampling data is normal or not can be automatically, standardize, quickly and accurately identified.
Description
Technical Field
The present application relates to the field of signal detection technologies, and in particular, to a method, an apparatus, a device, and a readable storage medium for identifying data anomalies.
Background
When an oscilloscope is used for measuring signals of electronic components, the existing method in the industry basically adjusts sampling waveforms manually according to personal practical experience of testers, and judges whether the sampling data are normal or not by observing the sampling waveforms by naked eyes. However, by adopting the method, misjudgment is easy to occur due to eye fatigue of testers, meanwhile, for primary testers, related experience is lacked, the probability of misjudgment is higher, and the product quality is seriously influenced after later-stage product deployment.
Disclosure of Invention
The embodiment of the invention provides a method, a device and equipment for identifying data abnormity and a readable storage medium, which are used for solving the technical problem that misjudgment is easy to occur when the method in the prior art is adopted to judge whether sampling data of an oscilloscope is normal.
In order to solve the above problem, in a first aspect, an embodiment of the present invention provides a method for identifying data anomalies, including: acquiring sampling data, wherein the sampling data comprises signal data and time data corresponding to the signal data; carrying out differential operation on the sampling data to obtain first derivative data; determining a rising period, a steady-state period and a falling period of the signal data according to the first derivative data; for the rising period of the signal data, if a first derivative data smaller than a first preset threshold exists in the rising period, determining that abnormal signal data exists in the rising period of the signal data; for the steady-state time period of the signal data, if a first derivative data which is larger than a first preset threshold value or smaller than the first preset threshold value exists in the steady-state time period, determining that abnormal signal data exists in the steady-state time period of the signal data; for the falling time period of the signal data, if a first derivative data larger than a first preset threshold exists in the falling time period, determining that abnormal signal data exists in the falling time period of the signal data; and if abnormal signal data exist in the rising time period, the steady-state time period or the falling time period, determining that the sampled data have abnormality.
Optionally, before acquiring the sample data, the method for identifying data abnormality further includes: acquiring sampling parameters of an oscilloscope; and sending the oscilloscope sampling parameters to the oscilloscope so that the oscilloscope performs sampling according to the sampling parameters.
Optionally, after acquiring the sample data and before performing a differential operation on the sample data, the method for identifying data anomalies further includes: and performing down-sampling processing on the sampled data.
Optionally, the differentiating the sampling data to obtain a first derivative data includes: and carrying out differential operation on the sampling data by adopting a second-order central algorithm to obtain first-order derivative data.
Optionally, after performing a differential operation on the sampled data to obtain first derivative data, before determining a rising period, a steady-state period, and a falling period of the signal data according to the first derivative data, the method for identifying data anomalies further includes: setting the value of the first derivative data to zero if the value of the first derivative data is within a first preset threshold range; and if the value of the first derivative data is not within the first preset threshold range, keeping the value of the first derivative data unchanged.
Optionally, determining a rising period, a steady-state period, and a falling period of the signal data according to the first derivative data includes: if the signal data reaches a second preset threshold and the first-order derivative data corresponding to the signal data is greater than the first preset threshold, determining the starting time when the first-order derivative data is greater than the first preset threshold as the starting time of the rising period of the signal data; if the signal data reaches a third preset threshold and first derivative data corresponding to the signal data is within a first preset threshold range, determining the starting time of the first derivative data within the first preset threshold range as the ending time of the rising period of the signal data and the starting time of the steady-state period of the signal data; determining a rising period of the signal data according to a starting time of the rising period of the signal data and an ending time of the rising period of the signal data; if the signal data is in the second preset threshold range and the first-order derivative data corresponding to the signal data is smaller than the first preset threshold, determining the starting time when the first-order derivative data is smaller than the first preset threshold as the starting time of the signal data falling period and the ending time of the signal data steady-state period; determining the steady-state time period of the signal data according to the starting time of the steady-state time period of the signal data and the starting time of the steady-state time period of the signal data; if the signal data reaches the second preset threshold and the first derivative data corresponding to the signal data is within the first preset threshold range, determining the moment of the first derivative data within the first preset threshold range as the end moment of the signal data falling period; the falling period of the signal data is determined according to the start time of the falling period of the signal data and the end time of the falling period of the signal data.
In a second aspect, an embodiment of the present invention provides an apparatus for identifying data anomalies, including: the device comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring sampling data, and the sampling data comprises signal data and time data corresponding to the signal data; the computing unit is used for carrying out differential operation on the sampling data to obtain first derivative data; a first determining unit for determining a rising period, a steady-state period, and a falling period of the signal data from the first-order derivative data; a second determining unit, configured to determine, for a rising period of the signal data, that abnormal signal data exists within the rising period of the signal data if a first derivative data smaller than a first preset threshold exists within the rising period; for the steady-state time period of the signal data, if a first derivative data which is larger than a first preset threshold value or smaller than the first preset threshold value exists in the steady-state time period, determining that abnormal signal data exists in the steady-state time period of the signal data; for the falling time period of the signal data, if a first derivative data larger than a first preset threshold exists in the falling time period, determining that abnormal signal data exists in the falling time period of the signal data; and the third determining unit is used for determining that the sampling data has abnormity if the abnormal signal data exists in the rising time period, the steady-state time period or the falling time period.
Optionally, before the first obtaining unit, the apparatus for identifying data abnormality further includes: the second acquisition unit is used for acquiring sampling parameters of the oscilloscope; and the sending unit is used for sending the sampling parameters of the oscilloscope to the oscilloscope so as to enable the oscilloscope to sample according to the sampling parameters.
In a third aspect, an embodiment of the present invention provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform a method of identifying data anomalies as described in the first aspect or any implementation of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where computer instructions are stored, and the computer instructions are configured to cause a computer to execute the method for identifying data anomalies according to the first aspect or any implementation manner of the first aspect.
According to the method, the device, the equipment and the readable storage medium for identifying the data abnormality, provided by the embodiment of the invention, because the first derivative data of the sampling data is larger than the first preset threshold value in the rising period of the signal data, the first derivative data of the sampling data is within the first preset threshold value in the steady-state period of the signal data, and the first derivative data of the sampling data is smaller than the first preset threshold value in the falling period of the signal data, the sampling data comprises the signal data and the time data corresponding to the signal data by acquiring the sampling data; carrying out differential operation on the sampling data to obtain first derivative data; determining a rising period, a steady-state period and a falling period of the signal data according to the first derivative data; for the rising period of the signal data, if a first derivative data smaller than a first preset threshold exists in the rising period, determining that abnormal signal data exists in the rising period of the signal data; for the steady-state time period of the signal data, if a first derivative data which is larger than a first preset threshold value or smaller than the first preset threshold value exists in the steady-state time period, determining that abnormal signal data exists in the steady-state time period of the signal data; for the falling time period of the signal data, if a first derivative data larger than a first preset threshold exists in the falling time period, determining that abnormal signal data exists in the falling time period of the signal data; if abnormal signal data exist in the rising time period, the steady-state time period or the falling time period, determining that the sampled data are abnormal; therefore, whether the sampling data is normal or not can be automatically, standardize, quickly and accurately identified.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
FIG. 1 is a flowchart illustrating a method for identifying data anomalies according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an apparatus for identifying data anomalies according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a method for identifying data abnormity, which can be applied to a LabVIEW test platform, and as shown in figure 1, the method for identifying the data abnormity comprises the following steps:
s101, acquiring sampling data, wherein the sampling data comprises signal data and time data corresponding to the signal data; specifically, the LabVIEW test platform can be connected with the oscilloscope in an authentication mode, and sampling data of the oscilloscope are obtained from the oscilloscope. The sampling data of the oscilloscope comprises signal data and time data corresponding to the signal data. The signal data may specifically be voltage data.
S102, carrying out differential operation on the sampling data to obtain first-order derivative data; specifically, after the sampling data is acquired, the sampling data may be subjected to differential operation according to a first derivative operation formula to obtain first derivative data.
S103, determining a rising time period, a steady-state time period and a falling time period of the signal data according to the first derivative data; specifically, by analyzing the sampling data in combination with the first derivative data, the rising period, the steady-state period, and the falling period of the signal data can be determined quickly.
S104, for the rising period of the signal data, if a first derivative data smaller than a first preset threshold exists in the rising period, determining that abnormal signal data exists in the rising period of the signal data; for the steady-state time period of the signal data, if a first derivative data which is larger than a first preset threshold value or smaller than the first preset threshold value exists in the steady-state time period, determining that abnormal signal data exists in the steady-state time period of the signal data; for the falling time period of the signal data, if a first derivative data larger than a first preset threshold exists in the falling time period, determining that abnormal signal data exists in the falling time period of the signal data;
specifically, for normal signal data, in a rising period, first derivative data of the sampling data is larger than zero and larger than a first preset threshold, and when the sampling data in the rising period is abnormal, 1. when the signal data has a back hook, first derivative data smaller than zero exists; 2. when the signal data has a step, a first derivative data within a first preset threshold value range exists, so that for the rising period of the signal data, if a first derivative data smaller than the first preset threshold value exists within the rising period, it is determined that abnormal signal data exists within the rising period of the signal data. For normal signal data, in a steady-state period, first derivative data of the sampling data is within a first preset threshold range, and if the first derivative data is larger than the first preset threshold or smaller than the first preset threshold, abnormal signal data is determined to exist in the steady-state period of the signal data; for normal signal data, in a descending period, first derivative data of the sampling data is smaller than a first preset threshold and smaller than zero, and if the first derivative data larger than the first preset threshold exists in the descending period, abnormal signal data exists in the descending period of the signal data.
And S105, if abnormal signal data exist in the rising time period, the steady-state time period or the falling time period, determining that the sampled data are abnormal. Specifically, as long as abnormal signal data exists in any one of the rising period, the steady-state period, and the falling period, it is determined that the sampled data is abnormal, and the test result of the signal data can be displayed on the display screen of the LabVIEW test platform, and the test result can be displayed as FAIL. In the case where it is determined that the sample data is not abnormal, the test result may be displayed as PASS. The sampled data and corresponding test results may then be recorded to a designated database for accumulating a large amount of data for subsequent optimization of algorithms for identifying data anomalies.
In the method for identifying data abnormality provided by the embodiment of the present invention, because the first derivative data of the sampled data is greater than the first preset threshold value in the rising period of the signal data, the first derivative data of the sampled data is within the range of the first preset threshold value in the steady-state period of the signal data, and the first derivative data of the sampled data is smaller than the first preset threshold value in the falling period of the signal data, the sampled data includes the signal data and the time data corresponding to the signal data by acquiring the sampled data; carrying out differential operation on the sampling data to obtain first derivative data; determining a rising period, a steady-state period and a falling period of the signal data according to the first derivative data; for the rising period of the signal data, if a first derivative data smaller than a first preset threshold exists in the rising period, determining that abnormal signal data exists in the rising period of the signal data; for the steady-state time period of the signal data, if a first derivative data which is larger than a first preset threshold value or smaller than the first preset threshold value exists in the steady-state time period, determining that abnormal signal data exists in the steady-state time period of the signal data; for the falling time period of the signal data, if a first derivative data larger than a first preset threshold exists in the falling time period, determining that abnormal signal data exists in the falling time period of the signal data; if abnormal signal data exist in the rising time period, the steady-state time period or the falling time period, determining that the sampled data are abnormal; therefore, whether the sampling data is normal or not can be automatically, standardize, quickly and accurately identified.
In an optional embodiment, before acquiring the sample data in step S101, the method for identifying data anomalies further includes: acquiring sampling parameters of an oscilloscope; and sending the oscilloscope sampling parameters to the oscilloscope so that the oscilloscope performs sampling according to the sampling parameters.
Specifically, after the LabVIEW test platform is connected with the oscilloscope in an authentication manner, sampling parameters of the oscilloscope can be set on the LabVIEW test platform, and the LabVIEW test platform can send the sampling parameters of the oscilloscope to the oscilloscope. The sampling parameters comprise a test item, X-axis scales, Y-axis scales and the like. And then the LabVIEW test platform sends a test starting command to the oscilloscope, the oscilloscope starts sampling according to the sampling parameters, after waiting for the preset time, the LabVIEW test platform sends a sampling suspending command to the oscilloscope, and the oscilloscope stops sampling. And the LabVIEW test platform reads the sampling data from the oscilloscope by acquiring the sampling data command.
In the embodiment of the invention, before the sampling data is acquired, the sampling parameters of the oscilloscope are acquired, and the sampling parameters of the oscilloscope are sent to the oscilloscope, so that the oscilloscope adopts the sampling parameters according to the sampling parameters, and the sampling parameters do not need to be manually set on the oscilloscope one by one, so that the total test time of the sampling data can be shortened.
In an optional embodiment, after the step S101 of acquiring the sample data, before the step S102 of performing a differential operation on the sample data, the method for identifying data anomalies further includes: and performing down-sampling processing on the sampled data.
In the embodiment of the invention, the quantity of the sampling data in the subsequent differential operation can be reduced by performing down-sampling processing on the sampling data, so that the operation time of the data abnormality identification algorithm can be reduced, and whether the sampling data is normal or not can be quickly judged.
In an alternative embodiment, in step S102, performing a differential operation on the sampled data to obtain first derivative data includes: and carrying out differential operation on the sampling data by adopting a second-order central algorithm to obtain first-order derivative data.
Specifically, the calculation formula for calculating the first derivative data by the second-order center algorithm is as follows:
wherein, in the step (A),i=0, 1, 2 … … n-1, n being the sampling depth;
is the initial condition, i.e. the first sample element;
is the final condition, i.e. the last sample element;
is the firstiThe number of +1 sampling elements is,
is the firstiThe number of +2 sampling elements is,
is that
The sampling time and
the time difference of the sampling instants.
In the embodiment of the invention, the second-order center algorithm is adopted to carry out differential operation on the sampling data, and the second-order center algorithm is converged more quickly, so that the operation time of the first derivative data can be reduced when the first derivative data is calculated.
In an alternative embodiment, after performing a differential operation on the sampled data to obtain first-order derivative data in step S102, in step S103, before determining a rising period, a steady-state period, and a falling period of the signal data according to the first-order derivative data, the method for identifying data anomalies further includes: setting the value of the first derivative data to zero if the value of the first derivative data is within a first preset threshold range; and if the value of the first derivative data is not within the first preset threshold range, keeping the value of the first derivative data unchanged.
Specifically, a fluctuation threshold of the first derivative data, i.e., a first preset threshold may be set. Then comparing the first derivative data with a first preset threshold value, and if the value of the first derivative data is within the range of the first preset threshold value, indicating that the signal data has small change, at the moment, setting the value of the first derivative data to zero; if the value of the first derivative data is not within the first preset threshold range, which indicates that the signal data has large variation, the value of the first derivative data can be kept unchanged.
In this embodiment of the present invention, by setting a fluctuation threshold to the first-order derivative data, the rising period, the steady-state period, and the falling period of the signal data in the sample data can be quickly analyzed according to the first-order derivative data after the fluctuation threshold is processed.
In an alternative embodiment, step S103 determines a rising period, a steady-state period, and a falling period of the signal data according to the first derivative data, and specifically includes: if the signal data reaches a second preset threshold and the first-order derivative data corresponding to the signal data is greater than the first preset threshold, determining the starting time when the first-order derivative data is greater than the first preset threshold as the starting time of the rising period of the signal data; if the signal data reaches a third preset threshold and first derivative data corresponding to the signal data is within a first preset threshold range, determining the starting time of the first derivative data within the first preset threshold range as the ending time of the rising period of the signal data and the starting time of the steady-state period of the signal data; determining a rising period of the signal data according to a starting time of the rising period of the signal data and an ending time of the rising period of the signal data; if the signal data is in the second preset threshold range and the first-order derivative data corresponding to the signal data is smaller than the first preset threshold, determining the starting time when the first-order derivative data is smaller than the first preset threshold as the starting time of the signal data falling period and the ending time of the signal data steady-state period; determining the steady-state time period of the signal data according to the starting time of the steady-state time period of the signal data and the starting time of the steady-state time period of the signal data; if the signal data reaches the second preset threshold and the first derivative data corresponding to the signal data is within the first preset threshold range, determining the moment of the first derivative data within the first preset threshold range as the end moment of the signal data falling period; the falling period of the signal data is determined according to the start time of the falling period of the signal data and the end time of the falling period of the signal data.
Specifically, the second preset threshold may be a low level voltage threshold of the voltage signal of the oscilloscope, and the third preset threshold may be a high level voltage threshold of the voltage signal. In the signal data rising period, because the signal data reaches the second preset threshold at the starting time of the signal data rising period, the first derivative data is greater than the fluctuation threshold of the first derivative data, the signal data reaches the third threshold at the ending time of the signal data rising period, and the first derivative data is within the fluctuation threshold range of the first derivative data, if the signal data reaches the second preset threshold, the starting time of the first derivative data greater than the first preset threshold is the starting time of the signal data rising period; if the signal data of the oscilloscope signal reaches the third preset threshold, the starting time of the first derivative data in the first preset threshold range is the ending time of the rising period of the signal data and the starting time of the steady-state period of the signal data. In the signal data falling period, because the signal data is in the second threshold range at the beginning of the signal data falling period, the first derivative data is smaller than the fluctuation threshold of the first derivative data, and the signal data reaches the second preset threshold at the ending of the signal data falling period, and the first derivative data is in the first preset threshold range, if the signal data is in the second preset threshold range, the beginning of the first derivative data smaller than the first preset threshold is the beginning of the signal data falling period and the ending of the signal data steady-state period; and if the signal data reaches the second preset threshold, the moment of the first derivative data in the range of the first preset threshold is the end moment of the signal data falling period. By this method, the rising period, the steady-state period, and the falling period of the signal data can be determined quickly.
An embodiment of the present invention further provides a device for identifying data anomalies, as shown in fig. 2, including:
a first obtaining unit 201, configured to obtain sampling data, where the sampling data includes signal data and time data corresponding to the signal data; the detailed description of the specific implementation manner is given in step S101 of the above method embodiment, and is not repeated herein.
The calculating unit 202 is configured to perform differential operation on the sampling data to obtain first derivative data; the detailed description of the specific implementation manner is given in step S102 of the above method embodiment, and is not repeated herein.
A first determining unit 203 for determining a rising period, a steady-state period, and a falling period of the signal data according to the first-order derivative data; the detailed description of the specific implementation manner is given in step S103 of the above method embodiment, and is not repeated herein.
A second determining unit 204, configured to determine, for a rising period of the signal data, that abnormal signal data exists in the rising period of the signal data if a first derivative data smaller than a first preset threshold exists in the rising period; for the steady-state time period of the signal data, if a first derivative data which is larger than a first preset threshold value or smaller than the first preset threshold value exists in the steady-state time period, determining that abnormal signal data exists in the steady-state time period of the signal data; for the falling time period of the signal data, if a first derivative data larger than a first preset threshold exists in the falling time period, determining that abnormal signal data exists in the falling time period of the signal data; the detailed description of the specific implementation manner is given in step S104 of the above method embodiment, and is not repeated herein.
A third determining unit 205, configured to determine that the sampled data is abnormal if abnormal signal data exists in the rising period, the steady-state period, or the falling period. The detailed description of the specific implementation manner is given in step S105 of the above method embodiment, and is not repeated herein.
According to the device for identifying data abnormality provided by the embodiment of the invention, because the first derivative data of the sampling data is larger than the first preset threshold value in the rising period of the signal data, the first derivative data of the sampling data is within the range of the first preset threshold value in the steady-state period of the signal data, and the first derivative data of the sampling data is smaller than the first preset threshold value in the falling period of the signal data, the sampling data is obtained and comprises the signal data and the time data corresponding to the signal data; carrying out differential operation on the sampling data to obtain first derivative data; determining a rising period, a steady-state period and a falling period of the signal data according to the first derivative data; for the rising period of the signal data, if a first derivative data smaller than a first preset threshold exists in the rising period, determining that abnormal signal data exists in the rising period of the signal data; for the steady-state time period of the signal data, if a first derivative data which is larger than a first preset threshold value or smaller than the first preset threshold value exists in the steady-state time period, determining that abnormal signal data exists in the steady-state time period of the signal data; for the falling time period of the signal data, if a first derivative data larger than a first preset threshold exists in the falling time period, determining that abnormal signal data exists in the falling time period of the signal data; if abnormal signal data exist in the rising time period, the steady-state time period or the falling time period, determining that the sampled data are abnormal; therefore, whether the sampling data is normal or not can be automatically, standardize, quickly and accurately identified.
In an optional embodiment, before the first obtaining unit 201, the apparatus for identifying data abnormality further includes: the second acquisition unit is used for acquiring sampling parameters of the oscilloscope; and the sending unit is used for sending the sampling parameters of the oscilloscope to the oscilloscope so as to enable the oscilloscope to sample according to the sampling parameters.
Specifically, after the LabVIEW test platform is connected with the oscilloscope in an authentication manner, sampling parameters of the oscilloscope can be set on the LabVIEW test platform, and the LabVIEW test platform can send the sampling parameters of the oscilloscope to the oscilloscope. The sampling parameters comprise a test item, X-axis scales, Y-axis scales and the like. And then the LabVIEW test platform sends a test starting command to the oscilloscope, the oscilloscope starts sampling according to the sampling parameters, after waiting for the preset time, the LabVIEW test platform sends a sampling suspending command to the oscilloscope, and the oscilloscope stops sampling. And the LabVIEW test platform reads the sampling data from the oscilloscope by acquiring the sampling data command.
In the embodiment of the invention, before the first acquiring unit, the second acquiring unit is arranged to acquire the sampling parameters of the oscilloscope, and the sending unit is arranged to send the sampling parameters of the oscilloscope to the oscilloscope, so that the oscilloscope adopts the sampling parameters according to the sampling parameters, and the sampling parameters do not need to be manually set on the oscilloscope one by one, so that the total test time of the sampling data can be shortened.
Based on the same inventive concept as the method for identifying data anomalies in the foregoing embodiment, the present invention further provides an electronic device, as shown in fig. 3, including: a processor 31 and a memory 32, wherein the processor 31 and the memory 32 may be connected by a bus or other means, and the connection by the bus is illustrated in fig. 3 as an example.
The processor 31 may be a Central Processing Unit (CPU). The Processor 31 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof.
The memory 32, which is a non-transitory computer readable storage medium, may be used for storing non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the method for identifying data anomalies in embodiments of the present invention. The processor 31 executes various functional applications and data processing of the processor by executing non-transitory software programs, instructions and modules stored in the memory 32, namely, implements the method for identifying data abnormality in the above method embodiment.
The memory 32 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 31, and the like. Further, the memory 32 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 32 may optionally include memory located remotely from the processor 31, and these remote memories may be connected to the processor 31 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
One or more of the modules described above are stored in the memory 32 and, when executed by the processor 31, perform the method of identifying data anomalies as in the embodiment shown in FIG. 1.
The details of the electronic device may be understood with reference to the corresponding related description and effects in the embodiment shown in fig. 1, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable information processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable information processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable information processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable information processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method of identifying data anomalies, comprising:
acquiring sampling data, wherein the sampling data comprises signal data and time data corresponding to the signal data;
carrying out differential operation on the sampling data to obtain first derivative data;
determining a rising time period, a steady-state time period and a falling time period of the signal data according to the first derivative data;
for a rising period of signal data, if a derivative data smaller than a first preset threshold exists in the rising period, determining that abnormal signal data exists in the rising period of the signal data; for a steady-state period of signal data, if a first derivative data which is greater than a first preset threshold or smaller than the first preset threshold exists in the steady-state period, determining that abnormal signal data exists in the steady-state period of the signal data; for a falling period of signal data, if a first derivative data larger than a first preset threshold exists in the falling period, determining that abnormal signal data exists in the falling period of the signal data;
and if abnormal signal data exist in the rising time period, the steady-state time period or the falling time period, determining that the sampled data are abnormal.
2. The method of identifying data anomalies according to claim 1, further comprising, prior to said acquiring sample data:
acquiring sampling parameters of an oscilloscope;
and sending the oscilloscope sampling parameters to an oscilloscope so that the oscilloscope performs sampling according to the sampling parameters.
3. The method of identifying data anomalies according to claim 1, further comprising, after said obtaining sample data and prior to said differentiating said sample data:
and performing down-sampling processing on the sampling data.
4. The method of claim 1, wherein the differentiating the sampled data to obtain first derivative data comprises:
and carrying out differential operation on the sampling data by adopting a second-order central algorithm to obtain first-order derivative data.
5. The method of claim 1, wherein after the differentiating the sampled data to obtain first derivative data, before the determining the rising period, the steady-state period, and the falling period of the signal data according to the first derivative data, further comprises:
if the value of the first derivative data is within a first preset threshold range, setting the value of the first derivative data to zero;
and if the value of the first derivative data is not in the first preset threshold range, keeping the value of the first derivative data unchanged.
6. The method of identifying data anomalies according to claim 1, wherein said determining a rise period, a steady state period, and a fall period of signal data from said first derivative data comprises:
if the signal data reaches a second preset threshold value and first-order derivative data corresponding to the signal data is larger than a first preset threshold value, determining the starting moment when the first-order derivative data is larger than the first preset threshold value as the starting moment of the rising period of the signal data; if the signal data reaches a third preset threshold and first derivative data corresponding to the signal data is within a first preset threshold range, determining the starting time of the first derivative data within the first preset threshold range as the ending time of the rising period of the signal data and the starting time of the steady-state period of the signal data; determining a rising period of the signal data according to a starting time of the rising period of the signal data and an ending time of the rising period of the signal data;
if the signal data is within a second preset threshold range and first-order derivative data corresponding to the signal data is smaller than a first preset threshold, determining the starting time when the first-order derivative data is smaller than the first preset threshold as the starting time of a signal data descending period and the ending time of a signal data steady-state period; determining the steady-state time period of the signal data according to the starting time of the steady-state time period of the signal data and the starting time of the steady-state time period of the signal data;
if the signal data reaches a second preset threshold and first derivative data corresponding to the signal data is within a first preset threshold range, determining the moment of the first derivative data within the first preset threshold range as the end moment of a signal data falling period; and determining the falling time period of the signal data according to the starting time of the falling time period of the signal data and the ending time of the falling time period of the signal data.
7. An apparatus for identifying data anomalies, comprising:
the device comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring sampling data, and the sampling data comprises signal data and time data corresponding to the signal data;
the calculating unit is used for carrying out differential operation on the sampling data to obtain first derivative data;
a first determination unit configured to determine a rising period, a steady-state period, and a falling period of the signal data according to the first derivative data;
a second determining unit, configured to determine, for a rising period of signal data, that abnormal signal data exists within the rising period of the signal data if a first derivative data smaller than a first preset threshold exists within the rising period; for a steady-state period of signal data, if a first derivative data which is greater than a first preset threshold or smaller than the first preset threshold exists in the steady-state period, determining that abnormal signal data exists in the steady-state period of the signal data; for a falling period of signal data, if a first derivative data larger than a first preset threshold exists in the falling period, determining that abnormal signal data exists in the falling period of the signal data;
and the third determining unit is used for determining that the sampling data has abnormity if abnormal signal data exists in the rising time period, the steady-state time period or the falling time period.
8. The apparatus for identifying data anomalies according to claim 7, characterized in that before the first acquisition unit, it further includes:
the second acquisition unit is used for acquiring sampling parameters of the oscilloscope;
and the sending unit is used for sending the oscilloscope sampling parameters to the oscilloscope so as to enable the oscilloscope to sample according to the sampling parameters.
9. An electronic device, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform a method of identifying data anomalies as claimed in any one of claims 1 to 6.
10. A computer-readable storage medium storing computer instructions for causing a computer to perform the method for identifying data anomalies according to any one of claims 1-6.
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