CN114942065B - Weighing signal noise reduction method and device, electronic equipment and computer storage medium - Google Patents
Weighing signal noise reduction method and device, electronic equipment and computer storage medium Download PDFInfo
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Abstract
The invention relates to a weighing signal noise reduction method, a weighing signal noise reduction device, electronic equipment and a computer storage medium, wherein the weighing signal noise reduction method comprises the following steps: acquiring an original weighing signal, and carrying out standardized processing on the original weighing signal to obtain a standard weighing signal; performing wavelet decomposition operation on the standard weighing signal by using a preset wavelet basis function to obtain a high-frequency component decomposition coefficient and a low-frequency component decomposition coefficient; aiming at each high-frequency component decomposition coefficient, carrying out coefficient adjustment on each high-frequency component decomposition coefficient by utilizing a preset adjustable flexible threshold function to obtain an adjusted high-frequency component decomposition coefficient; and carrying out wavelet reconstruction operation on the adjusted high-frequency component decomposition coefficient and the low-frequency component decomposition coefficient, and outputting a noise reduction weighing signal. The invention can effectively remove noise in the output signal of the weighing sensor in the quality characteristic parameter measuring equipment, reduce random error of the weighing result and improve the measuring accuracy of the equipment.
Description
Technical Field
The present invention relates to the field of signal noise reduction technologies, and in particular, to a weighing signal noise reduction method, a weighing signal noise reduction device, an electronic device, and a computer storage medium.
Background
The mass characteristic parameters of the object play an important role in many aspects, and because the errors of the structure and the manufacturing process of the object are difficult to obtain from a theoretical model, the mass characteristic parameters are basically obtained by mass characteristic measuring equipment through specific experimental data.
Many scholars at home and abroad have made certain progress in the measuring method and the device structure method, but most of the signal processing modes of the weighing sensor in the currently used device still adopt older filtering methods such as average filtering, the methods can effectively remove linear and stable noise contained in the weighing signal, but along with the development of society, various noise sources appear, the weighing signal contains more and more nonlinear and non-stable noise, and the noise causes the output result of the quality characteristic parameter measuring device to contain larger random errors. Therefore, how to accurately reduce the nonlinear and non-stationary noise is a problem to be solved.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, apparatus, electronic device and computer storage medium for noise reduction of weighing signals for accurate noise reduction of nonlinear and non-stationary noise.
In order to solve the above problems, in a first aspect, the present invention provides a weighing signal noise reduction method, including:
acquiring an original weighing signal, and carrying out standardized processing on the original weighing signal to obtain a standard weighing signal;
performing wavelet decomposition operation on the standard weighing signal by using a preset wavelet basis function to obtain a high-frequency component decomposition coefficient and a low-frequency component decomposition coefficient;
aiming at each high-frequency component decomposition coefficient, carrying out coefficient adjustment on each high-frequency component decomposition coefficient by utilizing a preset adjustable flexible threshold function to obtain an adjusted high-frequency component decomposition coefficient;
and carrying out wavelet reconstruction operation on the adjusted high-frequency component decomposition coefficient and the low-frequency component decomposition coefficient, and outputting a noise reduction weighing signal.
Optionally, the performing a wavelet decomposition operation on the standard weighing signal by using a preset wavelet basis function includes:
configuring the number of decomposition layers for carrying out wavelet decomposition on the standard weighing signal based on a preset energy increment rule;
and determining a threshold value corresponding to the decomposition layer number.
Optionally, the configuring the number of decomposition layers for performing wavelet decomposition on the standard weighing signal based on a preset energy increment rule includes:
determination of the first
Layer energy->
：/>
Wherein->
For the high frequency component decomposition coefficients, < >>
Is->
Layer (a)
High-frequency component decomposition coefficients, < >>
，/>
The number of high-frequency component coefficients is the number of the current decomposition layer number;
determination of the first
Layer energy increment->
:/>
Wherein->
Is->
Layer energy;
if said first
Layer energy increment->
According with the preset energy increment principle, determining the value of the decomposition layer of the standard weighing signal as +.>
。
Optionally, the determining the threshold corresponding to the decomposition layer number includes:
the threshold value of the current decomposition layer number is determined according to the number of the high-frequency component decomposition coefficients of the current decomposition layer number and the noise standard deviation, and is as follows:
wherein->
Is the number of decomposition coefficients of the high frequency component of the current number of decomposition layers,/->
Is the standard deviation of noise>
The number of layers is currently decomposed.
Optionally, the preset adjustable flexibility threshold function includes:
wherein, the liquid crystal display device comprises a liquid crystal display device,
for the adjusted high-frequency component decomposition coefficient, < ->
For post-regulation->
Layer->
High-frequency component decomposition coefficients, < >>
As a sign function +.>
For the speed coefficient +.>
Is an adjustable coefficient>
Is a tunable power number.
Optionally, the adjustable coefficient
Comprises:
determining adjustable coefficients based on smoothness of noise reduction waveforms
Is a value range of (a);
constructing a parameter selection index according to the change rate of a signal evaluation index, wherein the signal evaluation index comprises a signal-to-noise ratio, a mean square error and smoothness;
selecting an index from the adjustable coefficients based on the parameters
Determining an adjustable coefficient in the range of values of (2)>
Is a value of (2).
In a second aspect, the present invention also provides a weighing signal noise reduction device, including:
the signal acquisition module is used for acquiring an original weighing signal and carrying out standardized processing on the original weighing signal to obtain a standard weighing signal;
the signal decomposition module is used for carrying out wavelet decomposition operation on the standard weighing signal by utilizing a preset wavelet basis function to obtain a high-frequency component decomposition coefficient and a low-frequency component decomposition coefficient;
the coefficient adjusting module is used for adjusting the coefficient of each high-frequency component decomposition coefficient by utilizing a preset adjustable flexible threshold function according to each high-frequency component decomposition coefficient to obtain an adjusted high-frequency component decomposition coefficient;
and the signal reconstruction module is used for carrying out wavelet reconstruction operation on the adjusted high-frequency component decomposition coefficient and the low-frequency component decomposition coefficient and outputting a noise reduction weighing signal.
In a third aspect, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the steps in the weighing signal noise reduction method described above when executing the computer program.
In a fourth aspect, the present invention also provides a computer storage medium storing a computer program which, when executed by a processor, implements the steps of the weighing signal noise reduction method described above.
The beneficial effects of adopting the embodiment are as follows:
according to the invention, the original weighing signal is subjected to standardized processing, so that the subsequent decomposition processing of the signal is facilitated, and the wavelet decomposition operation is performed on the standard weighing signal by utilizing the preset wavelet basis function, so that the signal-to-noise ratio of the weighing signal after noise reduction is improved; through utilizing the flexible threshold value function of prearranged adjustable to carry out coefficient adjustment to each high frequency component decomposition coefficient, then reconstruct the decomposition coefficient, output the weighing signal of making an uproar that falls, noise in the weighing sensor output signal in can effectively getting rid of quality characteristic parameter measuring equipment, reduce the random error of weighing result, improved the accuracy of equipment measurement.
In addition, the adjustable flexible threshold function provided by the invention can overcome the defects of the traditional threshold function, and can achieve the preset noise reduction effect by adjusting the adjustable parameters.
Drawings
FIG. 1 is a flow chart of an embodiment of a method for reducing noise of weighing signals according to the present invention;
FIG. 2 is a comparison diagram of the weighing signal before and after noise reduction according to an embodiment of the present invention;
FIG. 3 is a graph showing the high frequency component decomposition coefficients of the improved threshold function versus the conventional threshold function according to an embodiment of the present invention;
FIGS. 4 (a), 4 (b) and 4 (c) are respectively different adjustable power numbers of the weighing signal according to an embodiment of the invention
Under the condition, SNR, MSE and R of the weighing signal after noise reduction and adjustable coefficient +.>
Is a relationship diagram of (1);
FIG. 5 is a relative error plot of quality results obtained before and after denoising weighing signals according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of an embodiment of a weighing signal device according to the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to the present invention.
Detailed Description
The following detailed description of preferred embodiments of the invention is made in connection with the accompanying drawings, which form a part hereof, and together with the description of the embodiments of the invention, are used to explain the principles of the invention and are not intended to limit the scope of the invention.
In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise. Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The invention provides a weighing signal noise reduction method, a weighing signal noise reduction device, electronic equipment and a computer storage medium, and the weighing signal noise reduction method, the weighing signal noise reduction device, the electronic equipment and the computer storage medium are respectively described below.
Referring to fig. 1, fig. 1 is a flowchart of an embodiment of a method for noise reduction of a weighing signal according to the present invention, and a method for noise reduction of a weighing signal according to an embodiment of the present invention is disclosed, including:
step S101: acquiring an original weighing signal, and carrying out standardized processing on the original weighing signal to obtain a standard weighing signal;
firstly, it should be noted that, according to the present invention, the original weighing signal of the object is obtained from the weighing sensor, specifically, the mass of the measured object may be measured by using a three-point or four-point weighing method, for example, the measuring of the mass of the measured object by using the three-point weighing method includes: three resistance strain type pressure sensors are arranged below a horizontal weighing platform, can convert sensed pressure into voltage signals, have linear relation in the measuring range of the sensors, and obtain corresponding digital signals after amplification and AD conversion.
Further, 9 groups of original signals of the three groups of weighing sensors are obtained when the weighing platform is empty, standard objects are placed and measured objects are placed respectively
And is introduced into MATLAB.
It will be appreciated that, after the original weighing signal is obtained, the original weighing signal may be subjected to a dispersion normalization process to obtain a standard weighing signal for the purpose of facilitating the processing of the signal.
Step S102: performing wavelet decomposition operation on the standard weighing signal by using a preset wavelet basis function to obtain a high-frequency component decomposition coefficient and a low-frequency component decomposition coefficient;
it will be appreciated that the wavelet decomposition operation of the present invention is performed with respect to 9 sets of original signals, one of which will now be described as an example.
First, the wavelet transform is a processing method for processing nonlinear and nonstationary signals, which belongs to a time domain processing method. The method decomposes the original signal into superposition of a plurality of coefficients of a base function, wherein the useful signal corresponds to the coefficients with large amplitude, the useful signal corresponds to the coefficients with small amplitude, and the useful signal is contained in high-frequency components, so that a threshold value can be set for the expansion coefficient of the high-frequency components of the signal, the coefficients smaller than the threshold value are removed, and the coefficients larger than the threshold value are reserved. If quantization of the coefficients can be well completed, noise contained in the signal can be effectively removed.
It will be appreciated that wavelet basis functions include the "harr" series, "db" series, "coif" series and "sym" series. The "harr" series of functions is the earliest used orthogonal wavelet basis function with tight support in wavelet analysis; the db' series of functions is also a tightly supported orthonormal wavelet, mostly asymmetric; the coif series function is a wavelet function constructed by db, and has better symmetry; the sym "series of functions is a function modified by db and is approximately symmetrical.
The original signal is processed by adopting the function, so that the most suitable wavelet base is selected by the maturity evaluation index SNR (signal to noise ratio) of the noise-reduced signal. Referring to table 1, table 1 is the SNR of the noise reduction signal corresponding to the different series of optimal wavelet bases.
TABLE 1 SNR of noise reduction signals under different wavelet bases
As can be seen from table 1, the SNR of the "sym6" series is maximum, and thus the preset wavelet basis function is selected as the sym6 series function.
In one embodiment of the present invention, performing a wavelet decomposition operation on a standard weighing signal using a preset wavelet basis function includes:
configuring the number of decomposition layers for wavelet decomposition of the standard weighing signal based on a preset energy increment rule;
wherein the preset energy incrementThe rule includes the energy increment minimum principle, and the first is determined
Layer energy->
：
Wherein->
For the high frequency component decomposition coefficients, < >>
Is the firstpLayer (a)qThe decomposition coefficients of the high-frequency components,
，/>
the number of high-frequency component coefficients is the number of the current decomposition layer number;
determination of the first
Layer energy increment->
:/>
Wherein->
Is->
Layer energy;
referring to table 2, table 2 is the energy increment for each layer.
TABLE 2.1-9 layer energy increment
Since the preset energy increment principle is the energy increment minimum principle, it can be seen from Table 2 that the energy increment of the 7 th layer is minimum, so that the value of the decomposition layer is calculated
And 7, selecting.
In addition, the threshold value of the current decomposition layer number can be determined according to the number of the high-frequency component decomposition coefficients of the current decomposition layer number and the noise standard deviation:
wherein->
Is the number of decomposition coefficients of the high frequency component of the current number of decomposition layers,/->
Is the standard deviation of noise, if the noise is unknown, +.>
，/>
Is a median function.
Step S103: aiming at each high-frequency component decomposition coefficient, carrying out coefficient adjustment on each high-frequency component decomposition coefficient by utilizing a preset adjustable flexible threshold function to obtain an adjusted high-frequency component decomposition coefficient;
it can be understood that in the wavelet denoising process, it is critical to select a suitable threshold function to complete quantization of the high-frequency component coefficients. The traditional commonly used threshold functions are hard and soft threshold functions. However, the hard threshold function is discontinuous at the segmentation points, resulting in the possibility that the processed signal may excessively oscillate; the coefficient obtained by the soft threshold function has a fixed deviation from a true value when the coefficient is larger than the threshold value, so that signal distortion is caused, and the two traditional threshold functions are directly set to zero when the coefficient is smaller than the threshold value, so that a small part of useful information can be discarded.
Therefore, the embodiment of the invention adopts the adjustable flexible threshold function to avoid the risk of the traditional threshold function. Specifically, the preset adjustable flexibility threshold function includes:
wherein, the liquid crystal display device comprises a liquid crystal display device,
for the adjusted high-frequency component decomposition coefficient, < ->
For post-regulation->
Layer->
High-frequency component decomposition coefficients, < >>
As a sign function +.>
For the speed coefficient +.>
Is an adjustable coefficient>
For adjustable power, speed coefficient +.>
Typically 1, adjustable power->
Generally 1, 2 and 3 are taken.
It can be understood that the adjustable flexible threshold function is an odd function, and the processing of the coefficients is only related to the magnitudes of the coefficients and the signs of the coefficients; the adjustable flexible threshold function is continuous in real number, so that the defect that the hard threshold function has a break point is overcome, and the oscillation of noise reduction signals is reduced; can be used forThe adjustable flexible threshold function decomposes coefficients with high frequency components when the threshold is larger than the threshold
Is added, the adjusted high frequency component decomposition coefficient +.>
Can quickly approach to +.>
The disadvantage of the inherent bias of the soft threshold function is overcome.
In one embodiment of the invention, the coefficients are adjustable
Comprises:
determining adjustable coefficients based on smoothness of noise reduction waveforms
Is a value range of (a);
it will be appreciated that the noise-reduced signal must maintain a certain degree of smoothness, i.e. the smoothness of the noise-reduced waveform obtained with the adjustable flexible threshold function must be less than the hard threshold function. Thus at adjustable power
When the adjustable coefficient
。
Constructing a parameter selection index according to the change rate of the signal evaluation index, wherein the signal evaluation index comprises a signal-to-noise ratio, a mean square error and smoothness;
the value of the adjustable coefficient is determined from the range of values of the adjustable coefficient based on the parameter selection indicator.
Wherein, the signal evaluation index
The construction process of (2) is as follows: firstly, normalizing three evaluation indexes to obtain
、/>
And->
The normalization formula may include the discrete normalization formula described above; secondly, the change of three evaluation indexes is calculated>
、/>
、/>
Wherein the formula of the variation of the index is
，/>
Representing any one of the evaluation indexes, and finally constructing parameter selection indexes
。
Firstly, taking signal evaluation index
About 1 +.>
If the value of (2)>
And->
Does not meet the requirement, then +.1 is reduced at a rate of 0.1>
Until the requirements are met. Therefore, in the adjustable power->
When (I)>
，/>
。
Referring to fig. 2, fig. 2 is a comparison diagram of a weighing signal before and after noise reduction according to an embodiment of the invention. It will be appreciated that FIG. 2 is a velocity coefficient
Adjustable power->
，/>
，/>
And (5) comparing the waveforms before and after noise reduction.
Referring to Table 3, table 3 shows an adjustable threshold function
，/>
，/>
，/>
At this time, different threshold functions correspond to SNR, MSE, and R.
TABLE 3 SNR, MSE and R for different threshold functions
From table 3, it can be seen that the adjustable flexible threshold function has the maximum SNR (signal to noise ratio), the minimum MSE (mean square error) and moderate R (smoothness), and overcomes the defects of low precision of the soft threshold function and large oscillation of the hard threshold function.
For better understanding of the invention, in the process of theoretical analysis of the adjustable flexible threshold function, the threshold value is calculated
Since the adjustable flexibility threshold function is an odd function, and is set to 0.5, in order to facilitate clear observation, only the image of the first quadrant is drawn in the embodiment of the invention.
Referring to fig. 3, fig. 3 is a graph showing a comparison of high frequency component decomposition coefficients of an improved threshold function and a conventional threshold function according to an embodiment of the present invention. And through the change of the speed coefficient, the decomposition coefficients of the high-frequency components after different adjustment are displayed.
As can be seen from fig. 3, the adjustable flexibility threshold function is continuous in real numbers, and values close to 0 but not 0 retain some useful information when less than the threshold value of 0.5; above threshold 0.5, between the soft and hard threshold functions, and may be trended towards the hard threshold function at a faster speed node by increasing the speed coefficient
The value of (2) may also increase its approach speed.
When less than threshold 0.5, change
And->
With reference to fig. 4 (a), 4 (b) and 4 (c), the values of fig. 4 (a), 4 (b) and 4 (c) are respectively a different adjustable power +_in ∈of one embodiment of the present invention>
Under the condition, SNR, MSE and R of the weighing signal after noise reduction and adjustable coefficient +.>
Is a graph of the relationship of (1). It can be seen from FIG. 4 (a), FIG. 4 (b) and FIG. 4 (c) that when +.>
Unchanged, in->
Within a certain range, SNR (signal to noise ratio) is dependent on +.>
Increasing with increasing MSE (mean square error)>
The increase and decrease of (a), namely the signal after noise reduction is better; when->
At a certain value, the SNR (signal to noise ratio) reaches a maximum and the MSE (mean square error) reaches a minimum. R (smoothness) is as follows->
Is always increased and is +.>
Smaller times smaller. When (when)
When the k is unchanged, the SNR becomes larger, the MSE becomes smaller, and R becomes larger.
Comparing the abscissa and the graph trend of fig. 4 (a), 4 (b) and 4 (c), it can be found that
Is to->
Is an enlargement of (a). Therefore, the parameters can be adjusted to achieve the preset noise reduction effect.
Step S104: and carrying out wavelet reconstruction operation on the adjusted high-frequency component decomposition coefficient and the low-frequency component decomposition coefficient, and outputting a noise reduction weighing signal.
It can be understood that the adjusted high-frequency component decomposition coefficient and the adjusted low-frequency component decomposition coefficient are subjected to wavelet reconstruction operation, so that a noise-reduced weighing signal can be output, wherein the wavelet reconstruction is the inverse operation of wavelet decomposition, and the invention is not described in detail.
According to the invention, the original weighing signal is subjected to standardized processing, so that the subsequent decomposition processing of the signal is facilitated, and the wavelet decomposition operation is performed on the standard weighing signal by utilizing the preset wavelet basis function, so that the signal-to-noise ratio of the weighing signal after noise reduction is improved; through utilizing the flexible threshold value function of prearranged adjustable to carry out coefficient adjustment to each high frequency component decomposition coefficient, then reconstruct the decomposition coefficient, output the weighing signal of making an uproar that falls, noise in the weighing sensor output signal in can effectively getting rid of quality characteristic parameter measuring equipment, reduce the random error of weighing result, improved the accuracy of equipment measurement.
In addition, the adjustable flexible threshold function provided by the invention can overcome the defects of the traditional threshold function, and can achieve the preset noise reduction effect by adjusting the adjustable parameters.
In one embodiment of the present invention, the method further comprises:
and obtaining a quality result according to the weighing signal, and comparing the relative errors of the quality result before and after signal denoising, wherein the relative errors comprise standard deviation, maximum relative error and relative error standard deviation.
Specifically, firstly, determining the corresponding digital signals when the weighing platform is empty, measuring standard objects and measuring measured objects
And->
，/>
，
The method comprises the steps of carrying out a first treatment on the surface of the Then according to->
And the mass of standard objects->
Finding the sensor slope +.>
，
The method comprises the steps of carrying out a first treatment on the surface of the Finally, the mass of the detected object is calculated>
，/>
。
The relative error for each sampling point can be found using the average value as the reference mass. Referring to table 4, table 4 is a comparison table of mass relative error before and after noise reduction of the weighing signal.
Table 4 comparison table of relative error between the mass of weighing signal before and after noise reduction
As can be seen from table 4, the reference mass is unchanged before and after the noise reduction of the weighing signal, i.e. the measurement result obtained by the noise-reduced weighing signal is not distorted; the standard deviation of the mass value is reduced by 32%; the maximum relative error is reduced by 19.68%; the average relative error is reduced by 34.04%; the standard deviation of the relative error is reduced by 29.8%; therefore, the adjustable flexible wavelet threshold function noise reduction effectively removes noise mixed in weighing signals, and random errors in measurement results are reduced.
Referring to fig. 5, fig. 5 is a relative error diagram of quality results obtained before and after denoising a weighing signal according to an embodiment of the present invention. As can be seen from fig. 5, the relative error of the weighing signal after noise reduction is reduced, so that noise mixed in the weighing signal is effectively removed by noise reduction of the adjustable flexible wavelet threshold function, and random error in the measurement result is reduced.
In order to better implement the weighing signal method according to the embodiment of the present invention, referring to fig. 6 correspondingly, fig. 6 is a schematic structural diagram of an embodiment of a weighing signal noise reduction device according to the present invention, where the embodiment of the present invention provides a weighing signal noise reduction device 600, including:
the signal acquisition module 601 is configured to acquire an original weighing signal, and perform standardization processing on the original weighing signal to obtain a standard weighing signal;
the signal decomposition module 602 is configured to perform wavelet decomposition operation on the standard weighing signal by using a preset wavelet basis function to obtain a high-frequency component decomposition coefficient and a low-frequency component decomposition coefficient;
the coefficient adjustment module 603 is configured to perform coefficient adjustment on each high-frequency component decomposition coefficient by using a preset adjustable flexible threshold function according to each high-frequency component decomposition coefficient, so as to obtain an adjusted high-frequency component decomposition coefficient;
the signal reconstruction module 604 is configured to perform wavelet reconstruction operation on the adjusted high-frequency component decomposition coefficient and the adjusted low-frequency component decomposition coefficient, and output a noise reduction weighing signal.
What needs to be explained here is: the apparatus 600 provided in the foregoing embodiments may implement the technical solutions described in the foregoing method embodiments, and the specific implementation principles of the foregoing modules or units may be referred to the corresponding content in the foregoing method embodiments, which is not repeated herein.
Based on the weighing signal method, the embodiment of the invention further provides an electronic device, which comprises: a processor and a memory, and a computer program stored in the memory and executable on the processor; the steps in the weighing signal method of the embodiments described above are implemented by the processor when executing a computer program.
A schematic structural diagram of an electronic device 700 suitable for use in implementing embodiments of the present invention is shown in fig. 7. The electronic device in the embodiment of the present invention may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a car-mounted terminal (e.g., car navigation terminal), etc., and a stationary terminal such as a digital TV, a desktop computer, etc. The electronic device shown in fig. 7 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the invention.
An electronic device includes: a memory and a processor, where the processor may be referred to as a processing device 701 hereinafter, the memory may include at least one of a Read Only Memory (ROM) 702, a Random Access Memory (RAM) 703, and a storage device 708 hereinafter, as specifically shown below:
as shown in fig. 7, the electronic device 700 may include a processing means (e.g., a central processor, a graphics processor, etc.) 701, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 702 or a program loaded from a storage means 708 into a Random Access Memory (RAM) 703. In the RAM703, various programs and data required for the operation of the electronic device 700 are also stored. The processing device 701, the ROM702, and the RAM703 are connected to each other through a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
In general, the following devices may be connected to the I/O interface 705: input devices 706 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 707 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 708 including, for example, magnetic tape, hard disk, etc.; and a communication device 709. The communication means 709 may allow the electronic device 700 to communicate wirelessly or by wire with other devices to exchange data. While fig. 7 shows an electronic device 700 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.
In particular, according to embodiments of the present invention, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via communication device 709, or installed from storage 708, or installed from ROM 702. When being executed by the processing means 701, performs the above-described functions defined in the method of the embodiment of the present invention.
Based on the weighing signal method, the embodiment of the invention further provides a corresponding computer readable storage medium, wherein the computer readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to realize the steps in the weighing signal method of each embodiment.
Those skilled in the art will appreciate that all or part of the flow of the methods of the embodiments described above may be accomplished by way of a computer program to instruct associated hardware, where the program may be stored on a computer readable storage medium. Wherein the computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory, etc.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention.
Claims (8)
1. A method for reducing noise of a weighing signal, comprising:
acquiring an original weighing signal, and carrying out standardized processing on the original weighing signal to obtain a standard weighing signal;
performing wavelet decomposition operation on the standard weighing signal by using a preset wavelet basis function to obtain a high-frequency component decomposition coefficient and a low-frequency component decomposition coefficient;
for each high-frequency component decomposition coefficient, performing coefficient adjustment on each high-frequency component decomposition coefficient by using a preset adjustable flexible threshold function to obtain an adjusted high-frequency component decomposition coefficient, wherein the preset adjustable flexible threshold function comprises:
and carrying out wavelet reconstruction operation on the adjusted high-frequency component decomposition coefficient and the low-frequency component decomposition coefficient, and outputting a noise reduction weighing signal.
2. The method of claim 1, wherein the performing a wavelet decomposition operation on the standard weighing signal using a preset wavelet basis function comprises:
configuring the number of decomposition layers for carrying out wavelet decomposition on the standard weighing signal based on a preset energy increment rule;
and determining a threshold value corresponding to the decomposition layer number.
3. The method of claim 2, wherein the configuring the number of decomposition layers for wavelet decomposing the standard weighing signal based on a preset energy increment rule comprises:
determination of the first
Layer energy->
：/>
Wherein->
For the high frequency component decomposition coefficients, < >>
Is->
Layer->
High-frequency component decomposition coefficients, < >>
，/>
The number of high-frequency component coefficients is the number of the current decomposition layer number;
determination of the first
Layer energy increment->
:/>
Wherein->
Is->
Layer energy;
if said first
Layer energy increment->
According with the preset energy increment principle, determining the value of the decomposition layer of the standard weighing signal as +.>
。
4. The method of claim 2, wherein the determining a threshold corresponding to the number of decomposition layers comprises:
the threshold value of the current decomposition layer number is determined according to the number of the high-frequency component decomposition coefficients of the current decomposition layer number and the noise standard deviation, and is as follows:
wherein->
Is the number of decomposition coefficients of the high frequency component of the current number of decomposition layers,/->
Is the standard deviation of noise>
The number of layers is currently decomposed.
5. The method of claim 1, wherein the adjustable coefficients
Comprises:
determining adjustable coefficients based on smoothness of noise reduction waveforms
Is a value range of (a);
constructing a parameter selection index according to the change rate of a signal evaluation index, wherein the signal evaluation index comprises a signal-to-noise ratio, a mean square error and smoothness;
selecting an index from the adjustable coefficients based on the parameters
Determining an adjustable coefficient in the range of values of (2)>
Is a value of (2).
6. A weighing signal noise reduction device, comprising:
the signal acquisition module is used for acquiring an original weighing signal and carrying out standardized processing on the original weighing signal to obtain a standard weighing signal;
the signal decomposition module is used for carrying out wavelet decomposition operation on the standard weighing signal by utilizing a preset wavelet basis function to obtain a high-frequency component decomposition coefficient and a low-frequency component decomposition coefficient;
the coefficient adjusting module is configured to perform coefficient adjustment on each high-frequency component decomposition coefficient by using a preset adjustable flexible threshold function according to each high-frequency component decomposition coefficient, so as to obtain an adjusted high-frequency component decomposition coefficient, where the preset adjustable flexible threshold function includes:
and the signal reconstruction module is used for carrying out wavelet reconstruction operation on the adjusted high-frequency component decomposition coefficient and the low-frequency component decomposition coefficient and outputting a noise reduction weighing signal.
7. An electronic device comprising a memory and a processor, wherein the memory is configured to store a program; the processor, coupled to the memory, is configured to execute the program stored in the memory to implement the steps of the weighing signal noise reduction method according to any one of the preceding claims 1 to 5.
8. A computer readable storage medium storing a computer readable program or instructions which, when executed by a processor, is capable of carrying out the steps of the weighing signal noise reduction method according to any one of the preceding claims 1 to 5.
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