High-sensitivity photoelectric sensor based on multi-scale wave equation

Abstract

The invention discloses a high-sensitivity photoelectric sensor based on a multi-scale wave equation, which belongs to the technical field of photoelectric sensors, and comprises the following steps of: preprocessing an optical signal: sequentially carrying out filtering and noise reduction preprocessing operation on an original signal to improve the signal-to-noise ratio and observability of the signal so as to ensure that the quality of an input signal meets the algorithm requirement; the method comprises the following steps: decomposing a signal into a plurality of sub-signals with different frequencies by utilizing a multi-scale analysis method, observing and analyzing the signal on different scales, solving a wave equation, and solving the multi-scale wave equation by utilizing a numerical method according to a specific multi-scale wave equation and boundary conditions. Photoelectric signals are processed through a multi-scale wave equation algorithm, photoelectric waveform characteristic changes can be detected and analyzed on different scales, therefore, high-sensitivity response and detection on the signals are achieved, the method is particularly suitable for capturing weak signals, and the influence of environmental noise is reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of photoelectric sensors, and in particular relates to a high-sensitivity photoelectric sensor based on a multi-scale wave equation. Background Art

[0002] A photoelectric sensor is a device that converts light signals into electrical signals. Its working principle is based on the photoelectric effect. The photoelectric effect refers to the phenomenon that when light is irradiated on certain substances, the electrons of the substances absorb the energy of photons and a corresponding electrical effect occurs. According to the different photoelectric effect phenomena, the photoelectric effect is divided into three categories: external photoelectric effect, internal photoelectric effect and photovoltaic effect. Photoelectric devices include phototubes, photomultiplier tubes, photoresistors, photodiodes, phototransistors and photocells. The performance and characteristic curves of photoelectric devices are analyzed. The high-sensitivity photoelectric sensor based on the multiscale wave equation is an advanced sensing technology. It combines the theoretical model of the multiscale wave equation with the advantages of high-sensitivity photoelectric sensing technology, and can achieve high-precision measurement of the physical quantity to be measured.

[0003] Existing weak signal detection methods based on multiscale wave equations have certain shortcomings in terms of computational complexity, noise sensitivity, parameter selection, reliance on prior knowledge, and comparison with other methods. They are not suitable for capturing weak signals and are easily affected by environmental noise.

[0004] Based on this, the present invention designs a high-sensitivity photoelectric sensor based on a multi-scale wave equation to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to propose a high-sensitivity photoelectric sensor based on a multiscale wave equation in order to solve the problems that the existing weak signal detection method based on the multiscale wave equation has certain shortcomings in terms of computational complexity, noise sensitivity, parameter selection, dependence on prior knowledge and comparison with other methods, is not suitable for capturing weak signals, and is easily affected by environmental noise.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] Highly sensitive photoelectric sensors based on multiscale wave equations, including:

[0008] Optical signal preprocessing:

[0009] The original signal is filtered and denoised in turn to improve the signal-to-noise ratio and observability of the signal, so as to ensure that the quality of the input signal meets the algorithm requirements;

[0010] The signal is decomposed into multiple sub-signals of different frequencies using multi-scale analysis methods, which are used to observe and analyze the signal at different scales;

[0011] Wave equation solution:

[0012] According to the specific multi-scale wave equation and boundary conditions, the multi-scale wave equation is solved by numerical method;

[0013] By solving the multi-scale wave equation, the distribution characteristics of the signal at different positions and times are obtained;

[0014] Weak signal detection:

[0015] Observe and analyze the distribution characteristics of signals at multiple scales and identify the characteristics of weak signals;

[0016] Adaptive filtering methods are used to further extract and enhance weak signals;

[0017] According to the specific application scenarios and requirements, the detected weak signals are subsequently processed and analyzed.

[0018] As a further description of the above technical solution:

[0019] The original signal preprocessing operation comprises the following steps:

[0020] Decompose the original signal using wavelets;

[0021] Threshold processing of high-frequency coefficients of wavelet decomposition, including two technical contents of estimation and selection, where the threshold function includes soft threshold function and hard threshold function;

[0022]

[0023]

[0024] In the formula, sgn is the sign function, α is the main parameter for controlling the threshold, based on the soft threshold function and the hard threshold function, when the hard threshold function is used, the coefficient c after wavelet decomposition is i,j The absolute value of is compared with the threshold α. i,j |>α, Same as the original, otherwise the estimated wavelet coefficients When the soft threshold function is used, c i,j The absolute value of is first compared with the threshold α. i,j |>α Estimated wavelet coefficients is sgn(c i,j ) and |c i,j | and α difference, otherwise it is estimated is 0;

[0025] Threshold processing is used to preserve local edge features. On the contrary, the results obtained by soft thresholding technology are smoother and smoother. However, it is easy to cause unclear edges and distortion. A threshold function formula based on a compromise between soft and hard thresholds is:

[0026]

[0027] In the formula, α is the main parameter for controlling the threshold, β is the parameter for adjusting the threshold strength, and a and b are the parameters for controlling nonlinear attenuation, which are adjusted according to different signals;

[0028] The main parameter α that controls the threshold determines which coefficients are considered important. Usually, the value of α can be determined by observing the noise level of the signal. If the noise level is high, a larger α value is selected to remove more noise. On the contrary, if the signal is pure, a smaller α value is selected to retain more details.

[0029] Adjust the parameter β of the threshold strength. β affects the degree of influence of threshold processing on the coefficient. When β is small, the processing result is closer to the hard threshold, and when β is large, the processing result is closer to the soft threshold. Therefore, the value of β can be adjusted according to the requirements for edge clarity and smoothness;

[0030] The parameters a and b that control nonlinear attenuation jointly determine the characteristics of nonlinear attenuation. By adjusting these two parameters, the effects between hard threshold and soft threshold can be balanced. Generally speaking, a smaller a value and a larger b value will make the processing result smoother, but may cause edge blur, while a larger a value and a smaller b value will make the processing result closer to the hard threshold and retain more edge information.

[0031]

[0032] When a→1, Similar to the soft threshold number, when β→0, α→∞, a→+∞ and b=1, is equivalent to a hard threshold function, so It is a function that can change between soft and hard functions by changing a and b;

[0033] Signal wavelet reconstruction.

[0034] As a further description of the above technical solution:

[0035] Decomposing a signal into a plurality of sub-signals with different frequencies using the multi-scale analysis method comprises the following steps:

[0036] Select the number of decomposition layers. According to the characteristics of the signal and the analysis requirements, select the correct number of decomposition layers. The more decomposition layers there are, the finer the sub-signal frequency components will be.

[0037] Applying multi-scale transformation, decomposing the signal using the selected wavelet function, decomposing the signal into multiple sub-signals of different frequency scales;

[0038] Extract and analyze sub-signals, perform feature extraction and analysis on the decomposed sub-signals, and further denoise, compress, normalize and extract features from each sub-signal as needed.

[0039] As a further description of the above technical solution:

[0040] The selection of the wavelet function has a great influence on the decomposition result of the signal, and a suitable wavelet function should be selected and determined according to the characteristics of the signal and the analysis requirements.

[0041] As a further description of the above technical solution:

[0042] The calculation process of the multi-scale analysis method is relatively complicated, and a trade-off needs to be made between calculation accuracy and calculation efficiency. After the decomposition results are obtained, the results need to be verified to ensure the accuracy and stability of the decomposition.

[0043] As a further description of the above technical solution:

[0044] In solving the multiscale wave equation using the numerical method, attention is mainly paid to discretization, boundary conditions, solution algorithm and parallel computing when implementing the selected numerical method.

[0045] As a further description of the above technical solution:

[0046] The discretization discretizes continuous space and time variables into discrete points and time steps to ensure the accuracy and stability of the discretization. The boundary conditions select appropriate boundary conditions based on the physical background of the problem and ensure the correctness and consistency of the boundary conditions.

[0047] As a further description of the above technical solution:

[0048] The solution algorithm selects a suitable solution algorithm according to the selected numerical method to ensure the accuracy and efficiency of the solution algorithm.

[0049] As a further description of the above technical solution:

[0050] The parallel calculation selects a suitable solution algorithm according to the selected numerical method to ensure the accuracy and efficiency of the solution algorithm.

[0051] As a further description of the above technical solution:

[0052] In solving the multiscale wave equation using the numerical method, the numerical method can be optimized and improved according to the results of post-processing and analysis to improve the accuracy and efficiency of the solution, including adjusting the grid size, step size, boundary conditions and solution algorithm, and using more efficient algorithms and data structures.

[0053] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0054] 1. In the present invention, the multi-scale wave equation algorithm processes the photoelectric signal, which can detect and analyze the changes in the photoelectric waveform characteristics at different scales, thereby achieving high-sensitivity response and detection of the signal. This method is particularly suitable for capturing weak signals and reduces the impact of environmental noise.

[0055] 2. In the present invention, the soft-hard compromise threshold function signal preprocessing technology based on wavelet transform combines the advantages of soft threshold function and hard threshold function denoising, so that the denoised signal is smoother than the original signal, and can avoid the distortion of the original signal after denoising.

[0056] 3. In the present invention, a multi-scale analysis method is used to decompose the signal into multiple sub-signals of different frequencies, and an appropriate multi-scale analysis tool is selected as needed to implement multi-scale decomposition, so as to have a deeper understanding of the characteristics and structure of the signal and provide strong support for subsequent signal processing and analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A schematic diagram of the process of a high-sensitivity photoelectric sensor based on a multi-scale wave equation proposed by the present invention;

[0058] Figure 2 A schematic diagram of the operation flow of raw signal preprocessing in a high-sensitivity photoelectric sensor based on a multi-scale wave equation proposed in the present invention;

[0059] Figure 3 A schematic diagram of the signal decomposition process in a high-sensitivity photoelectric sensor based on a multi-scale wave equation proposed in the present invention;

[0060] Figure 4 A flowchart of a wavelet denoising algorithm for a high-sensitivity photoelectric sensor based on a multi-scale wave equation proposed by the present invention;

[0061] Figure 5 It is the soft threshold function image of the high-sensitivity photoelectric sensor based on the multi-scale wave equation proposed by the present invention;

[0062] Figure 6 This is the hard threshold function image of the high-sensitivity photoelectric sensor based on the multi-scale wave equation proposed by the present invention. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] Please refer to the attached Figure 1 -Attached Figure 6 The present invention provides a technical solution: a high-sensitivity photoelectric sensor based on a multi-scale wave equation, comprising:

[0065] Optical signal preprocessing:

[0066] The original signal is filtered and denoised in turn to improve the signal-to-noise ratio and observability of the signal, so as to ensure that the quality of the input signal meets the algorithm requirements;

[0067] The signal is decomposed into multiple sub-signals of different frequencies using multi-scale analysis methods, which are used to observe and analyze the signal at different scales;

[0068] Wave equation solution:

[0069] According to the specific multi-scale wave equation and boundary conditions, the multi-scale wave equation is solved by numerical method;

[0070] By solving the multi-scale wave equation, the distribution characteristics of the signal at different positions and times are obtained;

[0071] Weak signal detection:

[0072] Observe and analyze the distribution characteristics of signals at multiple scales and identify the characteristics of weak signals;

[0073] Adaptive filtering methods are used to further extract and enhance weak signals;

[0074] According to the specific application scenarios and requirements, the detected weak signals are subsequently processed and analyzed.

[0075] Specifically, the original signal preprocessing operation includes the following steps:

[0076] Decompose the original signal using wavelets;

[0077] Threshold processing of high-frequency coefficients of wavelet decomposition, including two technical contents of estimation and selection, where the threshold function includes soft threshold function and hard threshold function;

[0078]

[0079] In the formula, sgn is the sign function, α is the threshold, based on the soft threshold function and the hard threshold function, when the hard threshold function is used, the coefficient c after wavelet decomposition is i,j The absolute value of is compared with the threshold α. i,j |>α, Same as the original, otherwise the estimated wavelet coefficients When the soft threshold function is used, c i,j The absolute value of is first compared with the threshold α. i,j |>α Estimated wavelet coefficients is sgn(c i,j ) and |c i,j | and α difference, otherwise it is estimated is 0, the function images of hard threshold and soft threshold are as follows Figure 5 and Figure 6 :

[0080] Threshold processing is used to preserve local edge features. On the contrary, the results obtained by soft thresholding technology are smoother and smoother. However, it is easy to cause unclear edges and distortion. A threshold function formula based on a compromise between soft and hard thresholds is:

[0081]

[0082] In the formula, α is the main parameter for controlling the threshold, β is the parameter for adjusting the threshold strength, and a and b are the parameters for controlling nonlinear attenuation, which are adjusted according to different signals;

[0083] The main parameter α that controls the threshold determines which coefficients are considered important. Usually, the value of α can be determined by observing the noise level of the signal. If the noise level is high, a larger α value is selected to remove more noise. On the contrary, if the signal is pure, a smaller α value is selected to retain more details.

[0084] Adjust the parameter β of the threshold strength. β affects the degree of influence of threshold processing on the coefficient. When β is small, the processing result is closer to the hard threshold, and when β is large, the processing result is closer to the soft threshold. Therefore, the value of β can be adjusted according to the requirements for edge clarity and smoothness;

[0085] The parameters a and b that control nonlinear attenuation jointly determine the characteristics of nonlinear attenuation. By adjusting these two parameters, the effects between hard threshold and soft threshold can be balanced. Generally speaking, a smaller a value and a larger b value will make the processing result smoother, but may cause edge blur, while a larger a value and a smaller b value will make the processing result closer to the hard threshold and retain more edge information.

[0086]

[0087] When a→1, Similar to the soft threshold number, when β→0, α→∞, a→∞ and b=1, is equivalent to a hard threshold function, so It is a function that can change between soft and hard functions by changing a and b;

[0088] Signal wavelet reconstruction.

[0089] Specifically, using the multi-scale analysis method to decompose a signal into multiple sub-signals with different frequencies includes the following steps:

[0090] Select the number of decomposition layers. According to the characteristics of the signal and the analysis requirements, select the correct number of decomposition layers. The more decomposition layers there are, the finer the sub-signal frequency components will be.

[0091] Applying multi-scale transformation, decomposing the signal using the selected wavelet function, decomposing the signal into multiple sub-signals of different frequency scales;

[0092] Extract and analyze sub-signals, perform feature extraction and analysis on the decomposed sub-signals, and further denoise, compress, normalize and extract features from each sub-signal as needed.

[0093] Specifically, the selection of the wavelet function has a great influence on the decomposition result of the signal, and a suitable wavelet function should be selected and determined according to the characteristics of the signal and the analysis requirements.

[0094] Specifically, the calculation process of the multi-scale analysis method is relatively complicated, and a trade-off needs to be made between calculation accuracy and calculation efficiency. After the decomposition results are obtained, the results need to be verified to ensure the accuracy and stability of the decomposition.

[0095] Specifically, when using the numerical method to solve the multiscale wave equation, when implementing the selected numerical method, attention is mainly paid to discretization, boundary conditions, solution algorithm and parallel computing.

[0096] Specifically, the discretization discretizes continuous space and time variables into discrete points and time steps to ensure the accuracy and stability of the discretization. The boundary conditions select appropriate boundary conditions based on the physical background of the problem and ensure the correctness and consistency of the boundary conditions.

[0097] Specifically, the solution algorithm selects a suitable solution algorithm according to the selected numerical method to ensure the accuracy and efficiency of the solution algorithm.

[0098] Specifically, the parallel calculation selects a suitable solution algorithm according to the selected numerical method to ensure the accuracy and efficiency of the solution algorithm.

[0099] Specifically, when using the numerical method to solve the multiscale wave equation, the numerical method can be optimized and improved according to the results of post-processing and analysis to improve the accuracy and efficiency of the solution, including adjusting the grid size, step size, boundary conditions and solution algorithm, and using more efficient algorithms and data structures.

[0100] Working principle, when using:

[0101] Optical signal preprocessing:

[0102] The original signal is filtered and denoised in turn to improve the signal-to-noise ratio and observability of the signal, so as to ensure that the quality of the input signal meets the algorithm requirements;

[0103] The signal is decomposed into multiple sub-signals of different frequencies using multi-scale analysis methods, which are used to observe and analyze the signal at different scales;

[0104] The original signal preprocessing operation comprises the following steps:

[0105] Decompose the original signal using wavelets;

[0106] Threshold processing of high-frequency coefficients of wavelet decomposition, including two technical contents of estimation and selection, where the threshold function includes soft threshold function and hard threshold function;

[0107] Signal wavelet reconstruction;

[0108] Wave equation solution:

[0109] According to the specific multi-scale wave equation and boundary conditions, the multi-scale wave equation is solved by numerical method;

[0110] By solving the multi-scale wave equation, the distribution characteristics of the signal at different positions and times are obtained;

[0111] Decomposing a signal into a plurality of sub-signals with different frequencies using the multi-scale analysis method comprises the following steps:

[0112] Select the number of decomposition layers. According to the characteristics of the signal and the analysis requirements, select the correct number of decomposition layers. The more decomposition layers there are, the finer the sub-signal frequency components will be.

[0113] Applying multi-scale transformation, decomposing the signal using the selected wavelet function, decomposing the signal into multiple sub-signals of different frequency scales;

[0114] Extract and analyze sub-signals, perform feature extraction and analysis on the decomposed sub-signals, and further denoise, compress, normalize and extract features from each sub-signal as needed;

[0115] The selection of the wavelet function has a great influence on the decomposition result of the signal. The appropriate wavelet function should be selected and determined according to the characteristics of the signal and the analysis requirements.

[0116] The calculation process of the multi-scale analysis method is relatively complicated, and a trade-off needs to be made between calculation accuracy and calculation efficiency. After the decomposition results are obtained, the results need to be verified to ensure the accuracy and stability of the decomposition.

[0117] In solving the multiscale wave equation using the numerical method, attention is paid to discretization, boundary conditions, solution algorithm and parallel computing when implementing the selected numerical method;

[0118] The discretization discretizes continuous space and time variables into discrete points and time steps to ensure the accuracy and stability of the discretization. The boundary conditions select appropriate boundary conditions according to the physical background of the problem and ensure the correctness and consistency of the boundary conditions.

[0119] The solution algorithm selects a suitable solution algorithm according to the selected numerical method to ensure the accuracy and efficiency of the solution algorithm. The parallel calculation selects a suitable solution algorithm according to the selected numerical method to ensure the accuracy and efficiency of the solution algorithm.

[0120] Weak signal detection:

[0121] Observe and analyze the distribution characteristics of signals at multiple scales and identify the characteristics of weak signals;

[0122] Adaptive filtering methods are used to further extract and enhance weak signals;

[0123] According to the specific application scenarios and requirements, the detected weak signals are subsequently processed and analyzed.

[0124] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A highly sensitive photoelectric sensor based on a multiscale wave equation, characterized in that: include: Optical signal preprocessing: The original signal is filtered and denoised in turn to improve the signal-to-noise ratio and observability of the signal, so as to ensure that the quality of the input signal meets the algorithm requirements; The signal is decomposed into multiple sub-signals of different frequencies using multi-scale analysis methods, which are used to observe and analyze the signal at different scales; Wave equation solution: According to the specific multi-scale wave equation and boundary conditions, the multi-scale wave equation is solved by numerical method; By solving the multi-scale wave equation, the distribution characteristics of the signal at different positions and times are obtained; Weak signal detection: Observe and analyze the distribution characteristics of signals at multiple scales and identify the characteristics of weak signals; Adaptive filtering methods are used to further extract and enhance weak signals; According to the specific application scenarios and requirements, the detected weak signals are subsequently processed and analyzed.

2. The high-sensitivity photoelectric sensor based on the multiscale wave equation according to claim 1, characterized in that: The original signal preprocessing operation comprises the following steps: Decompose the original signal using wavelets; Threshold processing of high-frequency coefficients of wavelet decomposition, including two technical contents of estimation and selection, where the threshold function includes soft threshold function and hard threshold function; Signal wavelet reconstruction.

3. The high-sensitivity photoelectric sensor based on the multiscale wave equation according to claim 1, characterized in that: Decomposing a signal into a plurality of sub-signals with different frequencies using the multi-scale analysis method comprises the following steps: Select the number of decomposition layers. According to the characteristics of the signal and the analysis requirements, select the correct number of decomposition layers. The more decomposition layers there are, the finer the sub-signal frequency components will be. Applying multi-scale transformation, decomposing the signal using the selected wavelet function, decomposing the signal into multiple sub-signals of different frequency scales; Extract and analyze sub-signals, perform feature extraction and analysis on the decomposed sub-signals, and further denoise, compress, normalize and extract features from each sub-signal as needed.

4. The high-sensitivity photoelectric sensor based on the multiscale wave equation according to claim 3, characterized in that: The selection of the wavelet function has a great influence on the decomposition result of the signal, and a suitable wavelet function should be selected and determined according to the characteristics of the signal and the analysis requirements.

5. The high-sensitivity photoelectric sensor based on the multiscale wave equation according to claim 4, characterized in that: The calculation process of the multi-scale analysis method is relatively complicated, and a trade-off needs to be made between calculation accuracy and calculation efficiency. After the decomposition results are obtained, the results need to be verified to ensure the accuracy and stability of the decomposition.

6. The high-sensitivity photoelectric sensor based on the multiscale wave equation according to claim 1, characterized in that: In solving the multiscale wave equation using the numerical method, attention is mainly paid to discretization, boundary conditions, solution algorithm and parallel computing when implementing the selected numerical method.

7. The high-sensitivity photoelectric sensor based on the multiscale wave equation according to claim 6, characterized in that: The discretization discretizes continuous space and time variables into discrete points and time steps to ensure the accuracy and stability of the discretization. The boundary conditions select appropriate boundary conditions based on the physical background of the problem and ensure the correctness and consistency of the boundary conditions.

8. The high-sensitivity photoelectric sensor based on the multi-scale wave equation according to claim 7, characterized in that: The solution algorithm selects a suitable solution algorithm according to the selected numerical method to ensure the accuracy and efficiency of the solution algorithm.

9. The high-sensitivity photoelectric sensor based on the multiscale wave equation according to claim 8, characterized in that: The parallel calculation selects a suitable solution algorithm according to the selected numerical method to ensure the accuracy and efficiency of the solution algorithm.

10. The high-sensitivity photoelectric sensor based on the multi-scale wave equation according to claim 9, characterized in that: In solving the multiscale wave equation using the numerical method, the numerical method can be optimized and improved according to the results of post-processing and analysis to improve the accuracy and efficiency of the solution, including adjusting the grid size, step size, boundary conditions and solution algorithm, and using more efficient algorithms and data structures.

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