Optimal filtering error repairing method and system for complex environment disturbance

Abstract

The invention discloses a superior filtering error repairing method and system aiming at complex environment disturbance. The method comprises the following steps: acquiring original sensor signal data information; sending the original sensor signal data to a preset singular spectrum analysis module to obtain a reconstructed signal; sending the reconstructed signal to a preset continuous wavelet transform module to obtain a noise reduction signal; obtaining the temperature value of the current sensor, and determining the zero offset variation of the current sensor according to the temperature value of the current sensor; multiplying the reconstructed signal by a corresponding weight coefficient, multiplying the noise reduction signal by a corresponding weight coefficient, multiplying the zero offset variation of the sensor by a corresponding weight coefficient, and adding the products to obtain a revised output signal; according to the invention, through SSA-CWT time-frequency domain cooperative processing, dynamic parameter optimization and multi-domain fusion compensation, the data precision of the sensor under vibration, wind vibration and temperature interference is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of signal data processing, and more specifically, to a superior filtering error repair method and system for complex environmental disturbances. Background Art

[0002] In modern industry and scientific research, sensors, as the core devices for data acquisition, are widely used in fields such as navigation, attitude measurement, and environmental monitoring. However, in actual applications, sensors are often affected by complex environmental disturbances, such as vibration, wind vibration, temperature changes, etc. These disturbances will cause a large amount of noise and deviation in the sensor output signal, seriously reducing the accuracy and reliability of the data. Summary of the Invention

[0003] In order to solve at least one of the above technical problems, the present invention provides a superior filtering error repair method and system for complex environmental disturbances, which can improve the data accuracy of sensors under vibration, wind vibration, and temperature interference.

[0004] The first aspect of the present invention provides a superior filtering error repair method for complex environmental disturbances, including:

[0005] Obtain the original sensor signal data information;

[0006] Send the original sensor signal data to a preset singular spectrum analysis module to obtain a reconstructed signal;

[0007] Send the reconstructed signal to a preset continuous wavelet transform module to obtain a noise-reduced signal;

[0008] Obtain the temperature value of the current sensor, and determine the zero-bias change amount of the current sensor according to the temperature value of the current sensor;

[0009] Multiply the reconstructed signal by a corresponding weight coefficient, multiply the noise-reduced signal by a corresponding weight coefficient, multiply the sensor zero-bias change amount by a corresponding weight coefficient, and add the products to obtain a revised output signal;

[0010] Send the revised output signal to a preset management terminal for display.

[0011] In this solution, the step of sending the original sensor signal data to a preset singular spectrum analysis module to obtain a reconstructed signal specifically includes:

[0012] Set the original sensor signal as x(t) and the length as N;

[0013] Based on a preset initial window length L, construct an L*A Hankel trajectory matrix P, where A = N - L + 1;

[0014] Perform singular value decomposition on the trajectory matrix P to obtain the singular value spectrum σ i , the corresponding left singular vector U i and the right singular vector V i , where i = 1, 2, …, d, and d = min(L, A);

[0015] Set the reconstructed signal as x SSA (t), and its formula is where r represents the number of principal components to be retained, which is determined by the singular value energy ratio threshold.

[0016] In this solution, it also includes:

[0017] Divide the original sensor signal into several segments to obtain signal segments;

[0018] Conduct comparative analysis between the signal segments to obtain the local variance of the signal segments;

[0019] If the local variance of the signal segment is within the preset local variance range, the current initial window length is normal;

[0020] If the local variance of the signal segment is less than the minimum value in the preset local variance range, subtract the local variance of the signal segment from the minimum value in the preset local variance range to obtain the first difference of the local variance;

[0021] Determine the window length increment value of the current signal segment according to the first difference of the local variance;

[0022] Add the window length increment value of the current signal segment to the initial window length to obtain the window length revision value of the current signal segment;

[0023] If the local variance of the signal segment is greater than the maximum value in the preset local variance range, subtract the maximum value in the preset local variance range from the local variance of the signal segment to obtain the second difference of the local variance;

[0024] Determine the window length reduction value of the current signal segment according to the second difference of the local variance;

[0025] Subtract the window length reduction value from the initial window length of the current signal segment to obtain the window length revision value of the current signal segment.

[0026] In this solution, the step of sending the reconstructed signal to a preset continuous wavelet transform module to obtain a denoised signal specifically includes:

[0027] Perform continuous wavelet transform on the reconstructed signal to obtain the wavelet coefficients W x =(a, b), where a represents the scale parameter, which is inversely proportional to the frequency; b represents the translation parameter, indicating the position of the wavelet window on the time axis;

[0028] Divide the wavelet coefficients into frequency bands according to the preset interference characteristics to obtain the vibration interference frequency band and the wind vibration interference frequency band; and perform hard threshold processing on the vibration interference frequency band and soft threshold processing on the wind vibration interference frequency band to obtain the wavelet coefficients after threshold processing;

[0029] Perform inverse transformation on the wavelet coefficients after threshold processing to obtain the noise-reduced signal.

[0030] In this solution, the step of determining the zero-bias change amount of the current sensor according to the temperature value of the current sensor specifically includes:

[0031] Set the temperature value of the current sensor as T current ;

[0032] Set the zero-bias value at the current temperature as B(T current ), and its formula is where a k represents the polynomial coefficient and n represents the polynomial order;

[0033] Subtract the zero-bias value at the preset calibration temperature from the zero-bias value at the current temperature to obtain the zero-bias change amount of the current sensor.

[0034] This solution further includes:

[0035] Under static conditions, obtain the output values of the sensor at different time nodes;

[0036] Calculate the average value of the output values of the sensor at different time nodes to obtain the average output value, and set the average output value as the static zero bias of the corresponding sensor;

[0037] Obtain the output values of the corresponding sensor at different temperatures;

[0038] Subtract the static zero bias from the output values of the corresponding sensor at different temperatures to obtain the zero-bias values B(T) at different temperatures;

[0039] The formula for fitting the temperature T and the zero bias B(T) using the polynomial model is: Based on the least squares method, obtain the polynomial coefficient a k .

[0040] This solution further includes:

[0041] Based on a preset first time period, obtain the output value of the corresponding sensor under static conditions;

[0042] Calculate the difference between the output value of the corresponding sensor under static conditions and the static zero bias of the corresponding sensor to obtain the first value;

[0043] If the first value is greater than or equal to a preset first threshold, the static zero-offset adjustment information is triggered, and the static zero-offset is recalculated according to the static zero-offset adjustment information.

[0044] In a second aspect of the present invention, a superior filtering error repair system for complex environment disturbances is provided, including a memory and a processor. A program of a superior filtering error repair method for complex environment disturbances is stored in the memory. When the program of the superior filtering error repair method for complex environment disturbances is executed by the processor, the following steps are implemented:

[0045] Obtain the original sensor signal data information;

[0046] Send the original sensor signal data to a preset singular spectrum analysis module to obtain a reconstructed signal;

[0047] Send the reconstructed signal to a preset continuous wavelet transform module to obtain a noise-reduced signal;

[0048] Obtain the temperature value of the current sensor, and determine the zero-offset change amount of the current sensor according to the temperature value of the current sensor;

[0049] Multiply the reconstructed signal by a corresponding weight coefficient, multiply the noise-reduced signal by a corresponding weight coefficient, multiply the sensor zero-offset change amount by a corresponding weight coefficient, and add the products to obtain a revised output signal;

[0050] Send the revised output signal to a preset management terminal for display.

[0051] In this solution, the step of sending the original sensor signal data to a preset singular spectrum analysis module to obtain a reconstructed signal specifically includes:

[0052] Set the original sensor signal as x(t) and the length as N;

[0053] Based on a preset initial window length L, construct an L*A Hankel trajectory matrix P, where A = N - L + 1;

[0054] Perform singular value decomposition on the trajectory matrix P to obtain a singular value spectrum σ i and the corresponding left singular vector U i and right singular vector V i , where i = 1, 2,..., d, and d = min(L, A);

[0055] Set the reconstructed signal as x SSA (t), and its formula is where r represents the number of principal components to be retained, which is determined by the singular value energy ratio threshold.

[0056] This solution also includes:

[0057] Divide the original sensor signal into several segments to obtain signal segments;

[0058] Perform comparative analysis between the signal segments to obtain the local variance of the signal segments;

[0059] If the local variance of the signal segment is within the preset local variance range, the current initial window length is normal;

[0060] If the local variance of the signal segment is less than the minimum value in the preset local variance range, subtract the local variance of the signal segment from the minimum value in the preset local variance range to obtain the first difference of the local variance;

[0061] Determine the window length increase value of the current signal segment according to the first difference of the local variance;

[0062] Add the window length increase value of the current signal segment to the initial window length to obtain the window length revision value of the current signal segment;

[0063] If the local variance of the signal segment is greater than the maximum value in the preset local variance range, subtract the maximum value in the preset local variance range from the local variance of the signal segment to obtain the second difference of the local variance;

[0064] Determine the window length decrease value of the current signal segment according to the second difference of the local variance;

[0065] Subtract the window length decrease value from the initial window length of the current signal segment to obtain the window length revision value of the current signal segment.

[0066] A superior filtering error repair method and system for complex environmental disturbances disclosed by the present invention improve the data accuracy of sensors under vibration, wind vibration and temperature interference through SSA-CWT time-frequency domain collaborative processing, dynamic parameter optimization and multi-domain fusion compensation. Brief Description of the Drawings

[0067] Figure 1 Shows a flowchart of a superior filtering error repair method for complex environmental disturbances according to the present invention;

[0068] Figure 2 Shows a block diagram of a superior filtering error repair system for complex environmental disturbances according to the present invention. Detailed Embodiments

[0069] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0070] In the following description, many specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited by the specific embodiments disclosed below.

[0071] Figure 1 The flowchart of a superior filtering error repair method for complex environmental disturbances according to the present invention is shown.

[0072] As Figure 1 shown, the present invention discloses a superior filtering error repair method for complex environmental disturbances, including:

[0073] S101, obtaining the original sensor signal data information;

[0074] S102, sending the original sensor signal data to a preset singular spectrum analysis module to obtain a reconstructed signal;

[0075] S103, sending the reconstructed signal to a preset continuous wavelet transform module to obtain a noise-reduced signal;

[0076] S104, obtaining the temperature value of the current sensor and determining the zero-bias change amount of the current sensor according to the temperature value of the current sensor;

[0077] S105, multiplying the reconstructed signal by a corresponding weight coefficient, multiplying the noise-reduced signal by a corresponding weight coefficient, multiplying the sensor zero-bias change amount by a corresponding weight coefficient, and adding the products to obtain a revised output signal;

[0078] S106, sending the revised output signal to a preset management terminal for display.

[0079] According to the embodiments of the present invention, the time-domain low-dimensional reconstruction dominated by SSA (Singular Spectrum Analysis), the frequency-domain localization noise reduction driven by CWT (Continuous Wavelet Transform), and the multi-domain feature fusion and adaptive compensation are sequentially executed to construct a hierarchical progressive filtering framework; then, the sensor temperature-zero bias relationship is modeled by a polynomial to deduct the low-frequency limit drift, and then the dynamic weight is optimized to obtain a revised output signal.

[0080] According to the embodiments of the present invention, the step of sending the original sensor signal data to a preset singular spectrum analysis module to obtain a reconstructed signal specifically includes:

[0081] Setting the original sensor signal as x(t) and the length as N;

[0082] Based on a preset initial window length \(L\), construct an \(L\times A\) Hankel trajectory matrix \(P\), where \(A = N - L+1\).

[0083] Perform singular value decomposition on the trajectory matrix \(P\) to obtain the singular value spectrum \(\sigma\). i and the corresponding left singular vectors \(U\). i and right singular vectors \(V\). i where \(i = 1,2,\cdots,d\), and \(d=\min(L,A)\).

[0084] Set the reconstructed signal as \(x(t)\), and its formula is SSA where \(r\) represents the number of principal components to be retained, which is determined by the singular value energy ratio threshold. It should be noted that by performing singular value decomposition on the Hankel trajectory matrix \(P\), we get \(P = U\Sigma V\).

[0085] where \(U\) is the left singular vector matrix with dimension \(L\times L\); \(\Sigma\) is the singular value diagonal matrix with dimension \(L\times A\); \(V\) is the right singular vector matrix with dimension \(A\times A\); select the first \(r\) principal components according to the singular value energy ratio, and set the remaining components as noise, and reconstruct the selected principal components to obtain the SSA reconstructed signal; set the singular value energy ratio as \(P\). T Its formula is i For example, if the singular value energy ratio threshold is 90%, then select the first \(r\) singular values, there is and the first \(r + 1\) singular values

[0086] According to the embodiments of the present invention, it further includes:

[0087] Divide the original sensor signal into several segments to obtain signal segments.

[0088] Perform comparative analysis between the signal segments to obtain the local variance of the signal segments.

[0089] If the local variance of the signal segment is within the preset local variance range, the current initial window length is normal.

[0090] If the local variance of the signal segment is less than the minimum value in the preset local variance range, subtract the local variance of the signal segment from the minimum value in the preset local variance range to obtain the first difference of the local variance.

[0091] According to the first difference of the local variance, determine the window length increase value of the current signal segment.

[0092] Add the window length increase value of the current signal segment to the initial window length to obtain the window length revised value of the current signal segment.

[0093] ​If the local variance of a signal segment is greater than the maximum value in the preset local variance range, subtract the maximum value in the preset local variance range from the local variance of the signal segment to obtain a second difference of the local variance;

[0094] Determine the window length reduction value of the current signal segment according to the second difference of the local variance;

[0095] Subtract the window length reduction value from the initial window length of the current signal segment to obtain the window length revision value of the current signal segment.

[0096] It should be noted that the original sensor signal x(t) is divided into several segments, each with a length of M. For example, M is set to 2*L min , where L min represents the minimum window length, and each signal segment is denoted as x m (t), where m = 1, 2,..., N / M; for each signal segment x m (t), calculate the local variance The formula is where μm represents the mean value of the m-th signal segment, and the formula is When the local variance is smaller, it indicates that the current signal is more stable; when the local variance is larger, it indicates that the corresponding signal is more unstable; the larger the first difference of the local variance, the larger the window length increase value of the corresponding signal segment, which is obtained by querying the window length increase value table. The window length increase value table contains the first difference ranges of different local variances, and each first difference range corresponds to a window length increase value; the larger the second difference of the local variance, the larger the window length reduction value of the corresponding signal segment, which is obtained by querying the window length reduction value table. The window length reduction value table contains the second difference ranges of different local variances, and each second difference range corresponds to a window length reduction value.

[0097] Furthermore, calculate the difference between the window lengths of adjacent signal segments to obtain the adjacent window length difference; if the adjacent window length difference is greater than the preset window length threshold, smooth the window length of the adjacent subsequent signal segment, and set the window length of the adjacent subsequent signal segment to L m , and the window length of the adjacent subsequent signal segment after smoothing is set to L ′ m , and the formula is L ′ m = θ*L m-1 +(1 - θ)*L m , where θ = 0.8; where L m-1 represents the window length of the adjacent previous signal segment.

[0098] According to an embodiment of the present invention, the step of sending the reconstructed signal to a preset continuous wavelet transform module to obtain a noise-reduced signal specifically includes:

[0099] Performing continuous wavelet transform on the reconstructed signal to obtain wavelet coefficients W x =(a, b), where a represents the scale parameter, which is inversely proportional to the frequency; b represents the translation parameter, indicating the position of the wavelet window on the time axis;

[0100] Dividing the wavelet coefficients into frequency bands according to the preset interference characteristics to obtain a vibration interference frequency band and a wind vibration interference frequency band; and performing hard threshold processing on the vibration interference frequency band and soft threshold processing on the wind vibration interference frequency band to obtain the wavelet coefficients after threshold processing;

[0101] Performing inverse transform on the wavelet coefficients after threshold processing to obtain a noise-reduced signal.

[0102] It should be noted that for different frequency band interference characteristics, a hybrid wavelet basis is used for CWT transform. For example, for high-frequency vibration interference, the Db4 wavelet is selected for processing; for low-frequency wind vibration interference, the Syml et5 wavelet is selected for processing.

[0103] According to an embodiment of the present invention, the step of determining the zero bias change amount of the current sensor according to the temperature value of the current sensor specifically includes:

[0104] Setting the temperature value of the current sensor as T current ;

[0105] Setting the zero bias value at the current temperature as B(T current ), and its formula is where a k represents the polynomial coefficient, and n represents the polynomial order;

[0106] Subtracting the zero bias value at the preset calibration temperature from the zero bias value at the current temperature to obtain the zero bias change amount of the current sensor.

[0107] According to an embodiment of the present invention, it further includes:

[0108] Under static conditions, obtaining the output values of the sensor at different time nodes;

[0109] Calculating the average value of the output values of the sensor at different time nodes to obtain an average output value, and setting the average output value as the static zero bias of the corresponding sensor;

[0110] Obtaining the output values of the corresponding sensor at different temperatures;

[0111] Subtracting the static zero bias from the output values of the corresponding sensor at different temperatures to obtain the zero bias values B(T) at different temperatures;

[0112] The formula for fitting the temperature T and the zero bias B(T) using a polynomial model is as follows: The polynomial coefficients a are obtained based on the least squares method k .

[0113] It should be noted that under the static condition, which is an environment without external interference, the static zero bias of the corresponding sensor can be determined by the sensor before installation.

[0114] According to an embodiment of the present invention, it further includes:

[0115] Based on a preset first time period, obtain the output value of the corresponding sensor under static conditions;

[0116] Calculate the difference between the output value of the corresponding sensor under static conditions and the static zero bias of the corresponding sensor to obtain a first value;

[0117] If the first value is greater than or equal to a preset first threshold, trigger the static zero bias adjustment information, and recalculate the static zero bias according to the static zero bias adjustment information.

[0118] It should also be noted that regularly repeat the static zero bias extraction experiment to determine the static zero bias of the corresponding sensor. When the first value is greater than or equal to the preset first threshold, adjust the static zero bias in a timely manner to adapt to sensor aging or environmental changes, thereby improving the accuracy of the data.

[0119] Figure 2 The block diagram of a superior filtering error repair system for complex environmental disturbances according to the present invention is shown.

[0120] As Figure 2 shown, a second aspect of the present invention provides a superior filtering error repair system 2 for complex environmental disturbances, including a memory 21 and a processor 22. A superior filtering error repair method program for complex environmental disturbances is stored in the memory. When the superior filtering error repair method program for complex environmental disturbances is executed by the processor, the following steps are implemented:

[0121] Obtain the original sensor signal data information;

[0122] Send the original sensor signal data to a preset singular spectrum analysis module to obtain a reconstructed signal;

[0123] Send the reconstructed signal to a preset continuous wavelet transform module to obtain a denoised signal;

[0124] Obtain the temperature value of the current sensor, and determine the zero bias change amount of the current sensor according to the temperature value of the current sensor;

[0125] Multiply the reconstructed signal by the corresponding weight coefficient, multiply the noise-reduced signal by the corresponding weight coefficient, multiply the sensor zero-bias change amount by the corresponding weight coefficient, and add the products to obtain the revised output signal;

[0126] Send the revised output signal to a preset management terminal for display.

[0127] According to an embodiment of the present invention, the step of sending the original sensor signal data to a preset singular spectrum analysis module to obtain a reconstructed signal specifically includes:

[0128] Set the original sensor signal as x(t) and the length as N;

[0129] Based on a preset initial window length L, construct an L*A Hankel trajectory matrix P, where A = N - L + 1;

[0130] Perform singular value decomposition on the trajectory matrix P to obtain the singular value spectrum σ i and the corresponding left singular vector U i and right singular vector V i , where i = 1, 2,..., d, and d = min(L, A);

[0131] Set the reconstructed signal as x SSA (t), and its formula is where r represents the number of principal components to be retained, which is determined by the singular value energy ratio threshold.

[0132] According to an embodiment of the present invention, it further includes:

[0133] Divide the original sensor signal into several segments to obtain signal segments;

[0134] Perform comparative analysis between the signal segments to obtain the local variance of the signal segments;

[0135] If the local variance of the signal segment is within the preset local variance range, the current initial window length is normal;

[0136] If the local variance of the signal segment is less than the minimum value in the preset local variance range, subtract the local variance of the signal segment from the minimum value in the preset local variance range to obtain the first difference of the local variance;

[0137] Determine the window length increase value of the current signal segment according to the first difference of the local variance;

[0138] Add the window length increase value of the current signal segment to the initial window length to obtain the window length revised value of the current signal segment;

[0139] If the local variance of a signal segment is greater than the maximum value in a preset local variance range, subtract the maximum value in the preset local variance range from the local variance of the signal segment to obtain a second difference of the local variance;

[0140] Determine a window length reduction value of the current signal segment according to the second difference of the local variance;

[0141] Subtract the window length reduction value from the initial window length of the current signal segment to obtain a window length revised value of the current signal segment.

[0142] A superior filtering error repair method and system for complex environmental disturbances disclosed by the present invention improve the data accuracy of sensors under vibration, wind vibration and temperature interference through SSA-CWT time-frequency domain collaborative processing, dynamic parameter optimization and multi-domain fusion compensation.

[0143] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed may be through some interfaces, and the indirect coupling or communication connection of the devices or units may be electrical, mechanical, or other forms.

[0144] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units; they may be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0145] In addition, each functional unit in the embodiments of the present invention can be all integrated in one processing unit, or each unit can be separately used as a unit, or two or more units can be integrated in one unit; the above integrated units can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0146] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including those of the above method embodiments. The foregoing storage medium includes: various media such as removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0147] Alternatively, if the above integrated units of the present invention are implemented in the form of software function modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as removable storage devices, ROM, RAM, magnetic disks, or optical discs that can store program codes.

Claims

1. An efficient filtering error repair method for complex environmental disturbances, characterized in that: include: Get raw sensor signal data information; The original sensor signal data is sent to a preset singular spectrum analysis module to obtain a reconstructed signal; The reconstructed signal is sent to a preset continuous wavelet transform module to obtain a noise reduction signal; Obtain the temperature value of the current sensor, and determine the zero bias change of the current sensor according to the temperature value of the current sensor; The reconstructed signal is multiplied by the corresponding weight coefficient, the noise reduction signal is multiplied by the corresponding weight coefficient, the sensor zero bias change is multiplied by the corresponding weight coefficient, and the products are added to obtain the revised output signal; The revised output signal is sent to a preset management terminal for display.

2. According to claim 1, a superior filtering error repair method for complex environmental disturbances is characterized in that: The step of sending the original sensor signal data to a preset singular spectrum analysis module to obtain a reconstructed signal specifically includes: Set the original sensor signal to x(t) and its length to N; Based on the preset initial window length L, construct the Hankel trajectory matrix P of L*A, where A=N-L+1; Perform singular value decomposition on the trajectory matrix P and obtain the singular value spectrum σ i , the corresponding left singular vector U i and the right singular vector V i , where i = 1, 2, ..., d, and d = min(L, A); Let the reconstructed signal be x SSA (t), its formula is Where r represents the number of principal components retained, which is determined by the singular value energy ratio threshold.

3. According to claim 2, a superior filtering error repair method for complex environmental disturbances is characterized in that: Also includes: Divide the original sensor signal into several segments to obtain signal segments; Compare and analyze the signal segments to obtain the local variance of the signal segments; If the local variance of the signal segment is within the preset local variance range, the current initial window length is normal; If the local variance of the signal segment is less than the minimum value in the preset local variance range, subtract the local variance of the signal segment from the minimum value in the preset local variance range to obtain a first difference value of the local variance; Determining a window length increase value of the current signal segment according to a first difference value of the local variance; The window length increase value of the current signal segment is added to the initial window length to obtain the revised window length value of the current signal segment; If the local variance of the signal segment is greater than the maximum value in the preset local variance range, subtracting the maximum value in the preset local variance range from the local variance of the signal segment to obtain a second difference value of the local variance; Determining a window length reduction value of the current signal segment according to a second difference value of the local variance; The window length revision value of the current signal segment is obtained by subtracting the window length reduction value from the initial window length of the current signal segment.

4. According to claim 1, a superior filtering error repair method for complex environmental disturbances is characterized in that: The step of sending the reconstructed signal to a preset continuous wavelet transform module to obtain a noise reduction signal specifically includes: The reconstructed signal is subjected to continuous wavelet transform to obtain the wavelet coefficient W x =(a, b), where a represents the scale parameter, which is inversely proportional to the frequency; b represents the translation parameter, which represents the position of the wavelet window on the time axis; The wavelet coefficients are divided into frequency bands according to the preset interference characteristics to obtain the vibration interference frequency band and the wind vibration interference frequency band; and the vibration interference frequency band is processed by hard threshold and the wind vibration interference frequency band is processed by soft threshold to obtain the wavelet coefficients after threshold processing; The wavelet coefficients after threshold processing are inversely transformed to obtain the noise reduction signal.

5. The method for repairing the optimal filtering error for complex environmental disturbances according to claim 1 is characterized in that: The step of determining the zero bias variation of the current sensor according to the temperature value of the current sensor specifically includes: Set the current sensor temperature value to T current ; Set the zero bias value at the current temperature to B(T current ), whose formula is where a k represents the polynomial coefficient, and n represents the polynomial order; Subtract the zero bias value at the preset calibration temperature from the zero bias value at the current temperature to obtain the zero bias change of the current sensor.

6. The method for repairing the optimal filtering error for complex environmental disturbances according to claim 5 is characterized in that: Also includes: Under static conditions, obtain the output values ​​of sensors at different time nodes; Calculate the average value of the output values ​​of the sensor at different time nodes to obtain an average output value, and set the average output value as the static zero bias of the corresponding sensor; Get the output values ​​of the corresponding sensors at different temperatures; Subtract the static zero bias from the output value of the corresponding sensor at different temperatures to obtain the zero bias value B(T) at different temperatures; The formula for fitting temperature T and zero deviation B(T) using the polynomial model is: Based on the least squares method, we can get the polynomial coefficient a k .

7. The method for repairing the optimal filtering error for complex environmental disturbances according to claim 6 is characterized in that: Also includes: Based on a preset first time period, obtaining an output value of a corresponding sensor under static conditions; Calculate the difference between the output value of the corresponding sensor under static conditions and the static zero bias of the corresponding sensor to obtain a first value; If the first value is greater than or equal to a preset first threshold, the static zero offset adjustment information is triggered, and the static zero offset is recalculated according to the static zero offset adjustment information.

8. An efficient filtering error repair system for complex environmental disturbances, characterized in that: The invention comprises a memory and a processor, wherein the memory stores a program of a superior filtering error repairing method for complex environmental disturbances, and when the program of the superior filtering error repairing method for complex environmental disturbances is executed by the processor, the following steps are implemented: Get raw sensor signal data information; The original sensor signal data is sent to a preset singular spectrum analysis module to obtain a reconstructed signal; The reconstructed signal is sent to a preset continuous wavelet transform module to obtain a noise reduction signal; Obtain the temperature value of the current sensor, and determine the zero bias change of the current sensor according to the temperature value of the current sensor; The reconstructed signal is multiplied by the corresponding weight coefficient, the noise reduction signal is multiplied by the corresponding weight coefficient, the sensor zero bias change is multiplied by the corresponding weight coefficient, and the products are added to obtain the revised output signal; The revised output signal is sent to a preset management terminal for display.

9. The efficient filtering error repair system for complex environmental disturbances according to claim 8, characterized in that: The step of sending the original sensor signal data to a preset singular spectrum analysis module to obtain a reconstructed signal specifically includes: Set the original sensor signal to x(t) and its length to N; Based on the preset initial window length L, construct the Hankel trajectory matrix P of L*A, where A=N-L+1; Perform singular value decomposition on the trajectory matrix P and obtain the singular value spectrum σ i , the corresponding left singular vector U i and the right singular vector V i , where i = 1, 2, ..., d, and d = min(L, A); Let the reconstructed signal be x SSA (t), its formula is Where r represents the number of principal components retained, which is determined by the singular value energy ratio threshold.

10. The efficient filtering error repair system for complex environmental disturbances according to claim 9, characterized in that: Also includes: Divide the original sensor signal into several segments to obtain signal segments; Compare and analyze the signal segments to obtain the local variance of the signal segments; If the local variance of the signal segment is within the preset local variance range, the current initial window length is normal; If the local variance of the signal segment is less than the minimum value in the preset local variance range, subtract the local variance of the signal segment from the minimum value in the preset local variance range to obtain a first difference value of the local variance; Determining a window length increase value of the current signal segment according to a first difference value of the local variance; The window length increase value of the current signal segment is added to the initial window length to obtain the revised window length value of the current signal segment; If the local variance of the signal segment is greater than the maximum value in the preset local variance range, subtracting the maximum value in the preset local variance range from the local variance of the signal segment to obtain a second difference value of the local variance; Determining a window length reduction value of the current signal segment according to a second difference value of the local variance; The window length revision value of the current signal segment is obtained by subtracting the window length reduction value from the initial window length of the current signal segment.

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