CN115882823A - Software filtering method of fA-level weak current signal, electronic device and storage medium - Google Patents
Software filtering method of fA-level weak current signal, electronic device and storage medium Download PDFInfo
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Abstract
The invention relates to a software filtering method of fA level weak current signals, electronic equipment and a storage medium, wherein the filtering method comprises the following steps: setting sampling parameters, and calculating a preset target value of the number of sampling points; starting AD data acquisition, triggering data processing software to interrupt when the number of sampling points reaches a preset target value, and performing primary filtering processing; and calling back a secondary filtering processing function, performing secondary filtering processing to obtain a filtering voltage value, wherein the primary filtering adopts an arithmetic mean filtering algorithm, and the secondary filtering adopts a recursive mean filtering algorithm. According to the invention, the two-stage software filtering algorithm of the recursive average filtering algorithm, which is improved by superposing the arithmetic average filtering algorithm, can effectively filter periodic and aperiodic interferences in weak current signals in sequence, effectively extract and filter system thermal noise in micro current signals, and retain signal dispersion to the maximum extent, so that a fitting curve after filtering is further superposed with an actual curve, and the measurement accuracy is effectively improved.
Description
Technical Field
The invention relates to the technical field of weak current signals, in particular to a software filtering method of fA-level weak current signals, electronic equipment and a storage medium.
Background
The detection of micro-current signals is widely applied to the top scientific and technological fields of instruments, sensors and the like, and fA-level current signal detection represents the top level of the current micro-current testing field. The weak current signal is easily subjected to external interference, such as mutual interference of circuits, power supply noise interference, electromagnetic interference, environment random interference and the like, a plurality of periodic or aperiodic interference signals are superimposed on the original acquisition signal, and the signal-to-noise ratio is low, so how to effectively filter the weak current signal noise is one of the difficulties in the field. In the hardware circuit design of the weak current signal detection system, partial interference can be optimally shielded by an electromagnetic shielding circuit, but the cost is generally higher and the flexibility is poorer. And power frequency interference signals with the same low-frequency characteristics as the signals, thermal noise of the system, environmental random pulse interference and the like are difficult to remove through hardware circuit design, the filtering effect is not ideal, and the filtering requirement of weak current signals cannot be met. Compared with hardware filtering, software filtering has the advantages of low cost, high flexibility and the like, and is widely applied to the field of data acquisition.
In the field of signal acquisition, traditional software filtering algorithms include an arithmetic mean filtering algorithm, an amplitude limiting filtering algorithm, a recursive mean filtering algorithm and the like. The traditional filtering algorithms cannot filter periodic noise and non-periodic noise at the same time, for example, the recursive average filtering algorithm and the arithmetic average filtering algorithm have good inhibition effect on periodic interference signals, but the response speed is low, the signal sensitivity is low, RAM is wasted, and the pulse interference cannot be effectively inhibited. The clipping filtering algorithm can effectively filter impulse interference, but has poor filtering effect on periodic interference and poor signal smoothness. Meanwhile, for weak signals, the interference of system thermal noise to the signals cannot be ignored, and the conventional software filtering method cannot effectively filter the system thermal noise submerged in the weak signals, cannot give consideration to both the dispersion and smoothness of the retained signals, and cannot meet the filtering requirement of high-accuracy weak current signals.
Disclosure of Invention
In view of the foregoing technical problems, an object of the present invention is to provide a software filtering method, an electronic device, and a storage medium for fA-level weak current signals, which effectively extract and filter system thermal noise in micro current signals, and retain signal dispersion to the greatest extent, thereby effectively improving measurement accuracy.
The technical solution for realizing the purpose of the invention is as follows: a software filtering method for fA level weak current signals comprises the following steps:
s1, setting sampling parameters, and calculating a sampling point number preset target value;
s2, starting AD data acquisition, triggering data processing software to interrupt when the number of sampling points reaches a preset target value, and performing primary filtering processing;
s3, calling back a secondary filtering processing function, performing secondary filtering processing to obtain a filtering voltage value,
the first-stage filtering adopts an arithmetic mean filtering algorithm, and the second-stage filtering adopts a recursive mean filtering algorithm.
According to one technical scheme of the invention, the method further comprises the following steps:
and S4, calculating an original weak current signal value according to the conversion resistance value.
According to an aspect of the present invention, the step S2 includes:
s21, according to the sampling sequence, sequentially storing the AD acquisition Data read in the sampling period into an original signal array AD _ buf _ Data from the first number of the array, and counting the number of sampling points;
and S22, when the number of sampling points is equal to a preset target value, triggering Data processing software to interrupt, calculating the average value of the original sampling Data in the AD _ buf _ Data [ ], and obtaining a signal value Data _ avg after primary filtering.
According to one technical solution of the present invention, in the step S3, the method specifically includes:
step S34, defining a secondary filter array AD _ buf _ normal [ ];
s35, judging whether a newly-transmitted signal value data _ avg after primary filtering is in a range of [ n-k delta, n + k delta ];
step S36, if yes, discarding the first data of the array AD _ buf _ normal [ ], sequentially moving the rest of the data forward, updating the data _ avg to the tail end of the array AD _ buf _ normal [ ], and updating the data _ avg to the array AD _ buf _ orig [ ];
step S37, if the data _ avg > n + k delta, replacing the data _ avg by n + k delta and updating the data _ avg to an array AD _ buf _ normal [ ];
step S38, if the data _ avg is less than n-k delta, replacing the data _ avg by n-k delta and updating the data _ avg to an array AD _ buf _ normal [ ];
and S39, calculating the arithmetic mean value of the AD _ buf _ normal [ ] array, and determining the arithmetic mean value as the final signal value after two-stage filtering.
According to one embodiment of the present invention, before defining the two-stage filter array in step S3, the method further includes:
s30, calling back a secondary filtering processing function, and transmitting a signal value data _ avg subjected to primary filtering into a secondary filtering processing program as a parameter;
s31, defining an AD _ buf _ orig [ ] array as a secondary filtering original data storage array;
step S32, judging whether the data of the original data storage array AD _ buf _ orig [ ] has 0;
step S33, if the data in the array AD _ buf _ orig [ ] are all not 0, calculating the mean value n and the mean square error δ of the original data array AD _ buf _ orig [ ].
According to one embodiment of the present invention, in the step S32, if there is 0 in the data in the array AD _ buf _ orig [ ], the first data of the array AD _ buf _ orig [ ] is discarded, other data are sequentially moved forward, and the sampling value data _ avg obtained by the first-stage filtering is stored at the end of the array AD _ buf _ orig [ ].
According to an aspect of the present invention, in step S3, the method further includes:
and judging whether the signal is a normal change or not according to the occurrence times that the newly-transmitted signal value data _ avg after the primary filtering is not between [ n-k delta, n + k delta ].
According to one technical scheme of the present invention, the determining whether the signal value data _ avg is a normal change of the signal according to the number of times that the newly-introduced one-stage filtered signal value data _ avg is not in the range of [ n-k δ, n + k δ ], specifically includes:
step S351, defining the data overrun number as Y;
step S352, if the data _ avg > n + k delta or the data _ avg < n-k delta, adding 1 to the count value Y;
step 353, if the data _ avg is in the range of n-k delta-n + k delta, if Y is judged to be larger than 0, the value of Y is subtracted by 1, and if not, no processing is carried out;
step S354, if the data overrun number Y is greater than 5, it is determined that the signal is changed normally and is a non-interference value, and at this time, the AD _ buf _ orig [ ] and AD _ buf _ normal [ ] arrays are cleared, the overrun count value Y is cleared, and the secondary filtering program is restarted.
According to an aspect of the present invention, there is provided an electronic apparatus including: one or more processors, one or more memories, and one or more computer programs; wherein, a processor is connected with the memory, the one or more computer programs are stored in the memory, and when the electronic device runs, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the software filtering method for the fA-class weak current signal according to any one of the above technical solutions.
According to an aspect of the present invention, there is provided a computer-readable storage medium for storing computer instructions, which when executed by a processor, implement the software filtering method for the fA-class weak current signal according to any of the above technical solutions.
According to the concept of the invention, the software filtering method, the electronic equipment and the storage medium of the fA level weak current signal are provided, the original signal effectively filters the periodic power frequency interference of a power supply in a system through a first level periodic arithmetic mean filtering algorithm, the smoothness of the signal is improved, the signal after the first level filtering passes through a second level filtering, the system thermal noise and the random pulse interference of the environment are effectively filtered, in the processing process, the dispersion degree of an original data queue is calculated firstly, the amplitude limiting is carried out on the new data entering the data queue according to the dispersion degree, the system thermal noise meeting the normal distribution rule is separated while the pulse interference is effectively shielded, the system thermal noise is effectively filtered through the arithmetic mean calculation, the abnormal value is monitored and counted, after the abnormal value counting reaches a threshold value, the signal is judged to jump, the signal change is fed back in time, the real-time change of the weak current signal can be captured in time while the smoothness of the signal is ensured, the signal fluctuation is restored to the maximum extent, the signal fluctuation is actually measured and compared with the traditional software filtering algorithm, the actually measured signal fitting degree and the actual curve is better, and the weak current signal stability and the accuracy are effectively improved after the filtering algorithm is applied.
Drawings
FIG. 1 is a flow chart schematically illustrating a software filtering method for fA level weak current signals according to an embodiment of the present invention;
fig. 2 schematically shows a flow chart of a software filtering method of the fA-class weak current signal according to another embodiment of the present invention.
Detailed Description
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 embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
The present invention is described in detail below with reference to the drawings and the specific embodiments, which are not repeated herein, but the embodiments of the present invention are not limited to the following embodiments.
As shown in fig. 1 to fig. 2, the software filtering method for the fA-level weak current signal of the present invention includes the following steps:
s1, setting sampling parameters, and calculating a preset target value of the number of sampling points;
s2, starting AD data acquisition, triggering data processing software to interrupt when the number of sampling points reaches a preset target value, and performing primary filtering processing;
s3, calling back a secondary filtering processing function, performing secondary filtering processing to obtain a filtering voltage value,
the first-stage filtering adopts an arithmetic mean filtering algorithm, and the second-stage filtering adopts a recursive mean filtering algorithm.
In the embodiment, the original signal passes through a first-stage periodic arithmetic mean filter algorithm to effectively filter periodic power frequency interference of a power supply in a system, the smoothness of the signal is improved, the signal after the first-stage filtering passes through a second-stage filtering again, the system thermal noise and the environment random pulse interference are effectively filtered, in the processing process, the dispersion of an original data queue is firstly calculated, new data entering the data queue is subjected to amplitude limiting according to the dispersion, the system thermal noise meeting a normal distribution rule is separated while the pulse interference is effectively shielded, the system thermal noise is effectively filtered through the arithmetic mean calculation, abnormal values are monitored and counted, after the abnormal value count reaches a threshold value, the signal is judged to jump, the signal change is fed back in time, the real-time change of a weak current signal can be timely captured while the smoothness of the signal is ensured, the signal fluctuation is reduced to the maximum extent, the actual measurement is compared with a traditional software filter algorithm, the contact ratio of an actually measured signal curve and an actual curve is better, and the accuracy and the stability of the weak current signal are effectively improved.
Furthermore, software threads are optimized to the maximum extent by adopting a mode of software and hardware interruption and callback function, and the response speed of filtering operation is effectively improved.
In one embodiment of the present invention, it is preferable that:
and S4, calculating an original weak current signal value according to the conversion resistance value.
In one embodiment of the present invention, preferably, in step S2, the method includes:
step S21, according to the sampling sequence, sequentially storing the AD acquisition Data read in the sampling period into an original signal array AD _ buf _ Data from the first number of the array, and counting the number of sampling points;
and S22, when the number of sampling points is equal to a preset target value, triggering Data processing software to interrupt, calculating the average value of the original sampling Data in the AD _ buf _ Data [ ], and obtaining a signal value Data _ avg after primary filtering.
In an embodiment of the present invention, preferably, in step S3, the method specifically includes:
step S34, defining a secondary filter array AD _ buf _ normal [ ];
s35, judging whether a newly-transmitted signal value data _ avg after primary filtering is in a range of [ n-k delta, n + k delta ];
step S36, if yes, discarding the first data of the array AD _ buf _ normal [ ], sequentially moving the rest of the data forward, updating the data _ avg to the tail end of the array AD _ buf _ normal [ ], and updating the data _ avg to the array AD _ buf _ orig [ ];
step S37, if the data _ avg > n + k delta, replacing the data _ avg by n + k delta and updating the data _ avg to an array AD _ buf _ normal [ ];
step S38, if the data _ avg is less than n-k delta, replacing the data _ avg with n-k delta and updating the data _ avg to an array AD _ buf _ normal [ ];
and S39, calculating the arithmetic mean value of the AD _ buf _ normal [ ] array, and determining the arithmetic mean value as the final signal value after two-stage filtering.
In this embodiment, the system thermal noise is effectively filtered out by arithmetic mean calculation. Meanwhile, the abnormal value is monitored and counted, after the abnormal value count reaches a threshold value, the signal is judged to jump, the signal change is fed back in time, the signal smoothness is guaranteed, the real-time change of the weak current signal can be captured in time, the signal fluctuation is reduced to the maximum extent, the actual measurement is carried out, the comparison with a traditional software filtering algorithm is carried out, after the filtering algorithm is applied, the contact ratio of an actually measured signal fitting curve and an actual curve is better, the accuracy and the stability of the weak current signal are effectively improved, the discrete characteristic of the signal is reserved to the maximum extent on the basis of effectively filtering system thermal noise, the contact ratio of the fitting curve after filtering and the actual curve is higher, and the measurement accuracy of the fA micro-current signal is effectively improved.
In an embodiment of the present invention, preferably, before defining the two-stage filter array, in step S3, the method further includes:
s30, calling back a secondary filtering processing function, and transmitting the signal value data _ avg subjected to primary filtering into a secondary filtering processing program as a parameter;
s31, defining an AD _ buf _ orig [ ] array as a secondary filtering original data storage array;
step S32, judging whether the data of the original data storage array AD _ buf _ orig [ ] has 0;
step S33, if the data in the array AD _ buf _ orig [ ] are all not 0, calculating the mean value n and the mean square error δ of the original data array AD _ buf _ orig [ ].
In this embodiment, by calculating the average and variance of the original data array, the ideal fluctuation range after the thermal noise and the original micro-current signal are superimposed can be determined, and the signal data _ avg after the first-stage filtering entering the AD _ buf _ orig [ ] array is limited by this range interval.
In one embodiment of the present invention, preferably, in step S32, if there is 0 in the data in the array AD _ buf _ orig [ ], the first data of the array AD _ buf _ orig [ ] is discarded, other data are sequentially moved forward, and the sampling value data _ avg obtained by the first-stage filtering is stored at the end of the array AD _ buf _ orig [ ].
In an embodiment of the present invention, in step S3, the method further includes:
and judging whether the signal is a normal change or not according to the occurrence times that the newly-transmitted signal value data _ avg after the primary filtering is not between [ n-k delta, n + k delta ].
In an embodiment of the present invention, determining whether the signal is a normal change according to the number of times that the newly-introduced one-stage filtered signal value data _ avg does not exist between [ n-k δ, n + k δ ], specifically includes:
step S351, defining the data overrun number as Y;
step S352, if the data _ avg > n + k delta or the data _ avg < n-k delta, adding 1 to the count value Y;
step S353, if the data _ avg is in the range of n-k delta-n + k delta, if Y is judged to be larger than 0, if Y is larger than 0, the value of Y is reduced by 1, and if not, no processing is carried out;
step S354, if the data overrun number Y is greater than 5, it is determined that the signal is changed normally and is a non-interference value, and at this time, the AD _ buf _ orig [ ] and AD _ buf _ normal [ ] arrays are cleared, the overrun count value Y is cleared, and the secondary filtering program is restarted.
In the embodiment, the signal is limited in this way, so that the change response of the original micro-current signal is slow, the sensitivity of the signal is reduced, whether the signal is changed normally is judged according to the occurrence frequency that the newly-transmitted signal value data _ avg after the primary filtering is not between [ n-k δ, n + k δ ], and the above problems can be effectively avoided.
According to an aspect of the present invention, there is provided an electronic apparatus including: one or more processors, one or more memories, and one or more computer programs; wherein, a processor is connected to the memory, the one or more computer programs are stored in the memory, and when the electronic device runs, the processor executes the one or more computer programs stored in the memory, so as to make the electronic device execute the software filtering method for the fA-level weak current signal according to any one of the above technical solutions.
According to an aspect of the present invention, there is provided a computer-readable storage medium for storing computer instructions, which when executed by a processor, implement the software filtering method for the fA-class weak current signal according to any of the above technical solutions.
In summary, the invention provides a software filtering method, an electronic device and a storage medium for fA level weak current signals, and provides a software filtering method, an electronic device and a storage medium for fA level weak current signals, wherein an original signal effectively filters periodic power frequency interference of a power supply in a system through a first-level periodic arithmetic mean filtering algorithm to improve smoothness of the signal, the signal after first-level filtering passes through second-level filtering to effectively filter system thermal noise and random pulse interference of the environment, in the processing process, firstly, the dispersion of an original data queue is calculated, new data entering the data queue is limited according to the dispersion, the system thermal noise meeting a normal distribution rule is separated while the pulse interference is effectively shielded, the system thermal noise is effectively filtered through arithmetic mean calculation, abnormal values are monitored and counted, after the abnormal value counting reaches a threshold value, the signal is judged to jump, signal change is fed back in time, real-time change of the current signal can be captured in time while the smoothness of the signal is ensured, the signal is restored to the maximum extent, the actual measurement is compared with a traditional filtering algorithm, the effective filtering curve of the fitting curve and the actual measurement accuracy of the weak current signal is improved.
Furthermore, it should be noted that the present invention may be provided as a method, an electronic device, and a storage medium or a computer program product. Accordingly, embodiments of 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, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media having computer-usable program code embodied in the media.
It should also be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrases "comprising one of \ 8230; \8230;" does not exclude the presence of additional like elements in a process, method, article, or terminal device that comprises the element.
Finally, it should be noted that while the above describes a preferred embodiment of the invention, it will be appreciated by those skilled in the art that, once the basic inventive concepts have been learned, numerous changes and modifications may be made without departing from the principles of the invention, which shall be deemed to be within the scope of the invention. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Claims (10)
1. A software filtering method for fA level weak current signals is characterized by comprising the following steps:
s1, setting sampling parameters, and calculating a sampling point number preset target value;
s2, starting AD data acquisition, triggering data processing software to interrupt when the number of sampling points reaches a preset target value, and performing primary filtering processing;
s3, calling back a secondary filtering processing function, performing secondary filtering processing to obtain a filtering voltage value,
the first-stage filtering adopts an arithmetic mean filtering algorithm, and the second-stage filtering adopts a recursive mean filtering algorithm.
2. The method of claim 1, further comprising:
and S4, calculating an original weak current signal value according to the conversion resistance value.
3. The method according to claim 1, wherein in step S2, comprising:
s21, according to the sampling sequence, sequentially storing the AD acquisition Data read in the sampling period into an original signal array AD _ buf _ Data from the first number of the array, and counting the number of sampling points;
and S22, when the number of sampling points is equal to a preset target value, triggering Data processing software to interrupt, calculating the average value of the original sampling Data in the AD _ buf _ Data [ ], and obtaining a signal value Data _ avg after primary filtering.
4. The method according to claim 3, wherein in step S3, specifically comprising:
step S34, defining a secondary filter array AD _ buf _ normal [ ];
s35, judging whether a newly-transmitted signal value data _ avg after primary filtering is in a range of [ n-k delta, n + k delta ];
step S36, if yes, discarding the first data of the array AD _ buf _ normal [ ], sequentially moving the rest of the data forward, updating the data _ avg to the tail end of the array AD _ buf _ normal [ ], and updating the data _ avg to the array AD _ buf _ orig [ ];
step S37, if the data _ avg > n + k delta, replacing the data _ avg by n + k delta and updating the data _ avg to an array AD _ buf _ normal [ ];
step S38, if the data _ avg is less than n-k delta, replacing the data _ avg by n-k delta and updating the data _ avg to an array AD _ buf _ normal [ ];
and S39, calculating the arithmetic mean value of the AD _ buf _ normal [ ] array, and determining the arithmetic mean value as the final signal value after two-stage filtering.
5. The method according to claim 4, wherein in step S3, before defining the two-stage filter array, the method further comprises:
s30, calling back a secondary filtering processing function, and transmitting the signal value data _ avg subjected to primary filtering into a secondary filtering processing program as a parameter;
s31, defining an AD _ buf _ orig [ ] array as a secondary filtering original data storage array;
step S32, judging whether the data of the original data storage array AD _ buf _ orig [ ] has 0;
step S33, if the data in the array AD _ buf _ orig [ ] are not 0, calculating the mean value n and the mean square error δ of the original data array AD _ buf _ orig [ ].
6. The method according to claim 5, wherein in step S32, if there is 0 in the data in the array AD _ buf _ orig [ ], the first data in the array AD _ buf _ orig [ ] is discarded, other data are sequentially shifted forward, and the sampling value data _ avg obtained by the first-stage filtering is stored at the end of the array AD _ buf _ orig [ ].
7. The method according to claim 4, wherein in the step S3, further comprising:
and judging whether the signal is in normal change or not according to the occurrence times that the newly-transmitted signal value data _ avg after the primary filtering is not between [ n-k delta, n + k delta ].
8. The method according to claim 7, wherein said determining whether there is a normal change of the signal according to the number of occurrences that the newly-incoming one-stage filtered signal value data _ avg is not between [ n-k δ, n + k δ ], specifically includes:
step S351, defining the data overrun number as Y;
step S352, if the data _ avg > n + k delta or the data _ avg < n-k delta, adding 1 to the count value Y;
step S353, if the data _ avg is in the range of n-k delta-n + k delta, if Y is judged to be larger than 0, if Y is larger than 0, the value of Y is reduced by 1, and if not, no processing is carried out;
in step S354, if the data overrun number Y is greater than 5, it is determined that the signal is changing normally and is a non-interference value, and at this time, the AD _ buf _ orig [ ] and AD _ buf _ normal [ ] arrays are cleared, the overrun count value Y is cleared, and the secondary filtering procedure is restarted.
9. An electronic device, comprising: one or more processors, one or more memories, and one or more computer programs; wherein a processor is connected to the memory, the one or more computer programs being stored in the memory, and the processor executes the one or more computer programs stored in the memory when the electronic device is running, so as to make the electronic device execute the software filtering method for the fA-class weak current signal according to any one of claims 1 to 8.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, implement the software filtering method for the fA-class weak current signal as claimed in any one of claims 1 to 8.
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