A cloud-based detection and automatic correction method for grating encoders for drive motors

Abstract

The present invention relates to the field of data processing technology, and in particular to a cloud-based detection and automatic correction method for a grating encoder for a drive motor. The content includes: filtering preprocessing and encryption processing of the operating data of the grating encoder, and intelligent processing of the encrypted operating data to obtain accurate operating data; abnormality detection of the accurate operating data to obtain abnormality detection results, and the abnormality detection results include abnormality types and characteristic parameters of abnormal data; based on the abnormality detection results, the abnormal data of the grating encoder is corrected to obtain total correction parameters; and the total correction parameters are converted into correction instructions and fed back to the grating encoder. When the grating encoder receives the correction instructions, it adjusts the abnormal data in real time. The problem of inaccurate processing of operating data and correction of abnormal data in the detection and correction process of the grating encoder for the drive motor is solved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a cloud-based detection and automatic correction method for a grating encoder for a drive motor. Background Art

[0002] In the drive motor system, the grating encoder is a widely used precision sensing device that can convert rotational or linear displacement into a measurable electrical signal for accurate measurement of equipment position, speed and other parameters. However, in actual operation, the signal quality of the grating encoder is easily affected by various factors, resulting in reduced data accuracy and stability. These factors include environmental interference, mechanical vibration of the equipment, electromagnetic interference, temperature fluctuations, etc., which will cause high-frequency noise, low-frequency drift, sudden errors and other problems in the data, thereby affecting the measurement accuracy of the grating encoder. Especially in complex industrial applications such as drive motors, there are high requirements for the high precision and high stability of the grating encoder signal, and traditional single correction methods are often difficult to achieve the expected results when faced with complex, multi-dimensional abnormal data.

[0003] In summary, in the detection and correction process of the grating encoder for the drive motor, there are problems of inaccurate processing of operating data and inaccurate correction of abnormal data. Summary of the invention

[0004] The present invention provides a cloud-based detection and automatic correction method for a grating encoder for a drive motor, so as to solve the problems of inaccurate processing of operating data and inaccurate correction of abnormal data in the detection and correction process of a grating encoder for a drive motor.

[0005] A cloud-based detection and automatic correction method for a grating encoder for a drive motor comprises the following steps:

[0006] S1. Collect the operation data of the grating encoder in real time, perform filtering preprocessing and encryption processing on the operation data to obtain the encrypted operation data; perform intelligent processing on the encrypted operation data to obtain accurate operation data; perform anomaly detection on the accurate operation data to obtain anomaly detection results, which include anomaly types and characteristic parameters of the anomaly data;

[0007] S2. Based on the abnormality detection results, the abnormal data of the grating encoder is corrected using the dynamic coupling modulation-differential control algorithm to obtain the total correction parameter; and the total correction parameter is converted into a correction instruction and fed back to the grating encoder. When the grating encoder receives the correction instruction, it adjusts the abnormal data in real time.

[0008] Preferably, the S1 specifically includes:

[0009] In the intelligent processing process, the encrypted operating data is decrypted to obtain the decrypted operating data; the decrypted operating data is subjected to multi-layer nested fusion filtering and dynamic reconstruction algorithms to obtain accurate operating data.

[0010] Preferably, the S1 specifically includes:

[0011] In the process of implementing the multi-layer nested fusion filtering and dynamic reconstruction algorithm, the decrypted running data is filtered by multi-layer nested filtering; the calculation formula is:

[0012] ,

[0013] in, It is Output data of layer filtering; Indicates the moment; It is Recursive adjustment coefficients of layer filtering; It is The period parameter of the layer filter; It is Phase shift of layer filtering; It is Output data of layer filtering; is the smoothing factor; is the window length; It is the decrypted running data within the symmetric window; is the displacement step size within the time window; is a tiny offset; is a recursive adjustment item; is the high frequency compensation term.

[0014] Preferably, the S1 specifically includes:

[0015] In the process of implementing multi-layer nested fusion filtering and dynamic reconstruction algorithm, low-noise filtering results are obtained through layer-by-layer recursive nested filtering processing. , Indicates the number of layers of multi-layer nested filtering; filtering results for low noise Perform dynamic reconstruction processing to obtain dynamically reconstructed output data.

[0016] Preferably, the S1 specifically includes:

[0017] In the process of implementing the multi-layer nested fusion filtering and dynamic reconstruction algorithm, the output data after dynamic reconstruction is adjusted to obtain accurate operating data; the specific adjustment formula is as follows:

[0018] ,

[0019] in, For accurate operation data; It is the output data after dynamic reconstruction; is the maximum order of the polynomial; are the polynomial fitting coefficients; is the degree of the polynomial; is the maximum number of layers of cosine terms; are the cosine decomposition coefficients; is the period parameter; is the phase adjustment parameter.

[0020] Preferably, the S1 specifically includes:

[0021] In the process of anomaly detection on accurate operating data, firstly, the statistical characteristics and frequency characteristics of the accurate operating data are extracted to obtain feature data; the feature data is subjected to dimensionality reduction processing to obtain optimized feature data; anomaly detection analysis is performed based on the optimized feature data to determine whether an abnormal state exists and obtain anomaly detection results; the anomaly detection results include the detected anomaly type and the feature parameters of the abnormal data, and the feature parameters of the abnormal data include abnormal amplitude, abnormal frequency and phase offset.

[0022] Preferably, the S2 specifically includes:

[0023] In the implementation process of the dynamic coupling modulation-differential control algorithm, dynamic coupling modulation factors are introduced based on the characteristic parameters of abnormal data, including amplitude modulation factor, frequency modulation factor and phase modulation factor.

[0024] Preferably, the S2 specifically includes:

[0025] Based on the amplitude modulation factor, frequency modulation factor and phase modulation factor, the parameter data is corrected in real time through dynamic differential feedback to obtain amplitude differential feedback correction parameter data, frequency differential feedback correction parameter data and phase differential feedback correction parameter data; the specific formula is:

[0026] ,

[0027] ,

[0028] ,

[0029] in, is the amplitude differential feedback correction parameter data; is the amplitude modulation factor; is the abnormal amplitude; is the frequency differential feedback correction parameter data; is the frequency modulation factor; is the abnormal frequency; is the phase differential feedback correction parameter data; is the phase modulation factor; Phase shift; It is the data value corresponding to the abnormal data, which is taken from the accurate operation data; is the integration variable; , , The distribution is the instantaneous rate of change of the amplitude modulation factor, frequency modulation factor, and phase modulation factor; , , They are the integrals of the amplitude modulation factor, frequency modulation factor, and phase modulation factor respectively.

[0030] Preferably, the S2 specifically includes:

[0031] In the process of implementing the dynamic coupling modulation-differential control algorithm, the total correction parameter is generated by combining the amplitude differential feedback correction parameter data, the frequency differential feedback correction parameter data and the phase differential feedback correction parameter data.

[0032] The beneficial effects of the technical solution of the present invention are:

[0033] 1. Through multi-layer nested fusion filtering and dynamic reconstruction algorithm, the decrypted operation data of the grating encoder is intelligently processed to effectively remove high-frequency noise, low-frequency drift and sudden errors in the operation data of the grating encoder, and obtain more accurate operation data. This layer-by-layer recursive nested filtering and dynamic reconstruction method greatly improves the accuracy of the operation data and ensures that the grating encoder can maintain stable output in complex environments.

[0034] 2. Through abnormal detection and abnormal data correction based on dynamic coupling modulation-differential control algorithm, multi-dimensional compensation of abnormal data of grating encoder is realized, including step-by-step correction of amplitude, frequency and phase. Different from the traditional single parameter correction method, the present invention adopts a complex algorithm combining dynamic coupling modulation factor and differential feedback to synchronously adjust the amplitude, frequency and phase of abnormal data, which improves the accuracy and robustness of correction and ensures that the grating encoder can still operate with high precision under abnormal conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart of a cloud-based detection and automatic correction method for a grating encoder for a drive motor described in the present invention. DETAILED DESCRIPTION

[0036] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all 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.

[0037] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0038] The following is a detailed description of a specific solution of a cloud-based detection and automatic correction method for a grating encoder for a drive motor provided by the present invention in conjunction with the accompanying drawings.

[0039] See attached Figure 1 , which shows a flow chart of a cloud-based detection and automatic correction method for a grating encoder for a drive motor provided by an embodiment of the present invention, the method comprising the following steps:

[0040] S1. Collect the operation data of the grating encoder in real time, and perform filtering preprocessing and encryption processing on the operation data to obtain the encrypted operation data; perform intelligent processing on the encrypted operation data to obtain accurate operation data; perform anomaly detection on the accurate operation data to obtain anomaly detection results, which include anomaly types and characteristic parameters of the anomaly data.

[0041] Collecting the operation data of the grating encoder in real time, performing filtering preprocessing on the collected operation data to obtain preprocessed operation data, wherein the filtering preprocessing adopts a preprocessing technology well known in the art and will not be described in detail here;

[0042] At the same time, the preprocessed operating data is encrypted using the existing AES encryption algorithm, and the encrypted operating data is sent to the intelligent processing link to ensure the privacy of the operating data; further, in the intelligent processing link, the existing decryption algorithm corresponding to the AES encryption algorithm is first used to decrypt the data to obtain the decrypted operating data.

[0043] The decrypted operation data is processed using a multi-layer nested fusion filter and dynamic reconstruction algorithm to obtain accurate operation data; the multi-layer nested fusion filter and dynamic reconstruction algorithm processes the decrypted operation data layer by layer through multi-layer recursion and dynamic reconstruction to remove noise, accurately restore the operation data, and ensure the accuracy and robustness of the operation data. The specific implementation process is as follows:

[0044] First, in order to reduce high-frequency noise, low-frequency drift and occasional sudden errors, the decrypted operation data is filtered through multi-layer nested filtering. The calculation formula is:

[0045] ,

[0046] in, It is Output data of layer filtering; Indicates the moment; It is The recursive adjustment coefficient of the layer filter is used to adjust the filter strength. The value range is ; It is The periodic parameter of the layer filter represents the main periodic component of the noise, ensuring that high-frequency noise can be effectively suppressed; It is The phase offset of the layer filter is used to adjust the phase of the decrypted running data to ensure timing consistency; is the cosine term, used for periodic filter modulation; It is Output data of layer filtering; is the smoothing factor, which is used to affect the smoothness of the decrypted running data during filtering. The value range is ; is the window length, which is used to determine the range of local smoothing during filtering; It is the decrypted running data within the symmetric window; is the displacement step size within the time window; is a small offset used to ensure that the denominator is not zero and to enhance the numerical stability of multi-layer nested filtering. is a recursive adjustment item used to maintain the main frequency component of the decrypted running data and filter out high-frequency noise; It is a high-frequency compensation item, which is used to smooth the high-frequency components of the decrypted operating data so that the decrypted operating data will not be seriously distorted due to multiple filtering.

[0047] Through layer-by-layer recursive nested filtering processing, noise is suppressed layer by layer to obtain a low-noise filtering result. , Indicates the number of layers of multi-layer nested filtering. The filtering result of low noise Dynamic reconstruction is performed to further eliminate the residual minor deviations and nonlinear noise components in the filtered operation data to ensure the integrity of the decrypted operation data. The dynamic reconstruction formula is defined as follows:

[0048] ,

[0049] in, It is the output data after dynamic reconstruction and is the result of low noise filtering. The correction result of is the initial reconstruction coefficient, used to control the amplitude; is the number of layers of dynamic reconstruction, ranging from 1 to 5. The higher the value, the higher the reconstruction accuracy; It is An amplitude control factor of a reconstruction layer, used to control the amplitude of the reconstructed data of the reconstruction layer; is the dynamically reconstructed nonlinear sinusoidal modulation term; It is The frequency coefficient of the reconstruction layer is used to compensate for the specific frequency noise and is related to the noise frequency; It is The phase offset of the reconstruction layer is used to adjust the phase of the reconstructed data of the reconstruction layer; is an exponential decay term, which is used to adjust the decay rate of the reconstructed data in the time domain; It is The attenuation rate parameter of the reconstruction layer is used to control the attenuation strength of the reconstructed data of each reconstruction layer; It is The time center point of the reconstruction layer is used to ensure that the reconstructed data of each reconstruction layer are aligned in time.

[0050] After dynamic reconstruction processing, the dynamically reconstructed output data is obtained. In order to further improve the accuracy and prevent the residual small offset from affecting the accuracy of the running data, fine adjustment processing is required. Fine adjustment processing is to dynamically reconstruct the output data. The residual low-frequency trend and high-frequency noise in the system are corrected twice to achieve the final accurate operation data output. The specific adjustment formula is as follows:

[0051] ,

[0052] in, is the reconstructed operating data, that is, accurate operating data; is the maximum order of the polynomial; are the polynomial fitting coefficients, which are used to adjust the amplitude of the low-frequency trend; is the order of the polynomial, used to describe the low-frequency trend characteristics; is the maximum number of layers of cosine terms, matching the frequency characteristics of the noise; is the cosine decomposition coefficient, which is used to adjust the amplitude of periodic noise; is the periodic parameter, which indicates the periodic characteristics of the noise; is a phase adjustment parameter used to ensure that the phase of the periodic noise compensation matches the running data after dynamic reconstruction.

[0053] Furthermore, accurate operation data Anomaly detection is performed to identify potential anomalies in the operation of the encoder (such as data deviation, sudden noise or system error). First, the accurate operation data Use the existing feature extraction algorithm to analyze data features, obtain feature data by extracting statistical features and frequency features, and use the existing dimensionality reduction technology to reduce the dimension of the feature data to obtain optimized feature data. Based on the optimized feature data, apply the existing rule-based anomaly detection model and machine learning algorithm to analyze and determine whether there is an abnormal state, and obtain the anomaly detection result, including the detected anomaly type (amplitude anomaly, frequency anomaly or multiple anomalies), and the characteristic parameters of the abnormal data (including abnormal amplitude, abnormal frequency and phase offset).

[0054] S2. Based on the abnormality detection results, the abnormal data of the grating encoder is corrected using the dynamic coupling modulation-differential control algorithm to obtain the total correction parameter; and the total correction parameter is converted into a correction instruction and fed back to the grating encoder. When the grating encoder receives the correction instruction, it adjusts the abnormal data in real time.

[0055] Based on the abnormal detection results, the abnormal data of the grating encoder is automatically corrected through the dynamic coupling modulation-differential control algorithm to obtain the total correction parameters and convert them into correction instructions. The dynamic coupling modulation-differential control algorithm gradually compensates for the amplitude, frequency and phase deviation of the abnormal data through coupling modulation and dynamic differential feedback structure, thereby realizing adaptive correction of the abnormal data of the grating encoder. The specific implementation process is as follows:

[0056] First, based on the anomaly detection results, the characteristic parameters of the abnormal data are obtained, including the abnormal amplitude , abnormal frequency and phase shift , introduce dynamic coupling modulation factors, including amplitude modulation factors , frequency modulation factor and phase modulation factor , used to generate correction parameter data of amplitude, frequency and phase to compensate for the deviation of abnormal data. Specifically:

[0057] Abnormal amplitude Constructing the Amplitude Modulation Factor , which is used to compensate the amplitude deviation of abnormal data in real time. The calculation formula is as follows:

[0058] ,

[0059] in, is an amplitude modulation factor, which consists of multiple nonlinear modulation items and is used to generate real-time compensation data for abnormal amplitude; is the nonlinear gain control coefficient of the abnormal amplitude; is the logarithmic gain amplification control coefficient; is the attenuation control coefficient; is the anomaly amplitude, which indicates the amplitude deviation of known anomaly data; is the amplitude modulation period, which is set to the basic period of the grating encoder to match the periodic characteristics of the operating data; is the phase offset of the amplitude modulation, which is used to adjust the synchronization of abnormal data; is the attenuation coefficient, used to control the amplitude modulation factor The attenuation rate of the modulation gradually weakens over time. Represents the double nonlinear amplification effect, through Enhance the influence of abnormal amplitude on the amplitude modulation factor, and at the same time Suppress excessive abnormal amplitude. The logarithmic function is used to amplify and stabilize the influence of abnormal amplitude. The entire amplitude modulation factor is obtained by periodically changing the term Achieve synchronous modulation. The last item It is used to dynamically control the rapid attenuation of amplitude to avoid the continuous abnormal amplitude affecting the stability of grating encoder correction.

[0060] Abnormal frequency The corresponding frequency modulation factor It is used for dynamic control of abnormal frequency, and its calculation formula is as follows:

[0061] ,

[0062] in, It is the frequency modulation factor, which is used to dynamically control the abnormal frequency; Is the reference modulation intensity coefficient, which mainly controls the frequency modulation factor The baseline strength determines the basic amplitude of the frequency modulation factor in the initial state and is the basic amplification factor of the abnormal frequency; is the abnormal frequency attenuation control coefficient, which is used to control the attenuation degree of abnormal data to high-frequency anomalies; is the frequency modulation periodic term Amplitude control coefficient, used to convert abnormal frequency into periodic compensation; is the nonlinear amplification factor of the dynamic modulation effect; is the abnormal frequency, which indicates the frequency offset of known abnormal data; is the phase compensation factor of frequency modulation.

[0063] Phase modulation factor For phase shift Perform dynamic compensation, the calculation formula is as follows:

[0064] ,

[0065] in, is the phase modulation factor, used to compensate for phase offset; is the phase modulation intensity control parameter; It is the high phase anomaly modulation range control parameter; It is a dynamic phase adjustment amplitude parameter, which is used to dynamically control the amplification effect of phase modulation so that the phase offset can be compensated in real time; Phase shift indicating known abnormal data; is the phase modulation period, set to the basic period of the grating encoder. The inverse tangent function is used to control the change of phase anomaly within a limited range, reducing the impact of large phase offset on the grating encoder. Large phase shifts are suppressed by fractional modulation. The total phase modulation factor is Realize periodic phase modulation.

[0066] Furthermore, based on the amplitude modulation factor , frequency modulation factor and phase modulation factor , and the real-time correction parameter data is realized through dynamic differential feedback. , frequency modulation factor and phase modulation factor As input, generate amplitude differential feedback correction parameter data Accurately compensate for abnormal amplitude and generate frequency differential feedback correction parameter data Used to adjust abnormal frequency in real time and generate phase differential feedback correction parameter data Used to compensate for phase offset in real time. The formula is as follows:

[0067] ,

[0068] ,

[0069] ,

[0070] in, It is the amplitude differential feedback correction parameter data, which is used to compensate abnormal amplitude in real time; It is the frequency differential feedback correction parameter data, which is used to compensate abnormal frequency in real time; It is the phase differential feedback correction parameter data, which is used to compensate the phase offset in real time; is the data value corresponding to the abnormal data, i.e., the abnormal output of the grating encoder to be corrected, which is taken from the accurate operating data; is the integration variable; , , The distribution is the instantaneous rate of change of the amplitude modulation factor, frequency modulation factor, and phase modulation factor, which represents the differential feedback of amplitude, frequency, and phase modulation; , , They are the integrals of the amplitude modulation factor, frequency modulation factor, and phase modulation factor, respectively, and represent the cumulative compensation effects of amplitude, frequency, and phase modulation;

[0071] Finally, the total correction parameter is generated by combining the differential feedback correction parameter data of amplitude, frequency and phase , used to drive the automatic correction of the grating encoder, the formula is:

[0072] ,

[0073] Total calibration parameters Dynamic correction instructions Feedback to the grating encoder system, the calculation formula is as follows:

[0074] ,

[0075] in, It is a correction instruction, which is used to feed back to the grating encoder to achieve automatic correction; , , are the control gain coefficients, which are proportional, integral and differential gains, respectively, and are used to control response speed and accuracy; is the integral of the total correction parameter, indicating the cumulative correction effect; is the derivative of the total correction parameter, indicating the instantaneous response speed; is the cosine period compensation term, used to compensate for periodic errors; is the number of periodic compensation terms; is the index of the periodic compensation term; is the amplitude coefficient of the compensation term for each cosine cycle; is the periodic constant, which matches the periodic characteristics of the grating encoder; is the phase compensation factor used to ensure phase consistency of periodic compensation.

[0076] When the grating encoder receives the correction instruction After that, the abnormal data is adjusted in real time to realize the automatic correction of the grating encoder.

[0077] In summary, a cloud-based detection and automatic correction method for grating encoders for drive motors has been completed.

[0078] The order of the embodiments of the invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0079] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0080] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A cloud-based detection and automatic correction method for grating encoders for drive motors, characterized in that: The following steps are involved: S1. Real-time collection of the operation data of the grating encoder, and filtering pre-processing and encryption processing of the operation data to obtain encrypted operation data; and intelligent processing of the encrypted operation data to obtain accurate operation data; in the intelligent processing process, the encrypted operation data is decrypted to obtain decrypted operation data; The decrypted operation data is subjected to multi-layer nested fusion filtering and dynamic reconstruction algorithm to obtain accurate operation data; In the process of realizing the multi-layer nested fusion filtering and dynamic reconstruction algorithm, the decrypted running data is filtered through multi-layer nested filtering; The calculation formula is: ， in, It is Output data of layer filtering; Indicates the moment; It is Recursive adjustment coefficients of layer filtering; It is The period parameter of the layer filter; It is Phase shift of layer filtering; It is Output data of layer filtering; is the smoothing factor; is the window length; It is the decrypted running data within the symmetric window; is the displacement step size within the time window; is a tiny offset; is a recursive adjustment item; is the high frequency compensation term; And perform anomaly detection on accurate operating data to obtain anomaly detection results, which include anomaly types and characteristic parameters of abnormal data; S2. Based on the abnormality detection results, the abnormal data of the grating encoder is corrected using the dynamic coupling modulation-differential control algorithm to obtain the total correction parameter; and the total correction parameter is converted into a correction instruction and fed back to the grating encoder. When the grating encoder receives the correction instruction, it adjusts the abnormal data in real time.

2. The cloud-based detection and automatic correction method for grating encoders for drive motors according to claim 1 is characterized in that: The S1 specifically includes: In the process of implementing multi-layer nested fusion filtering and dynamic reconstruction algorithm, low-noise filtering results are obtained through layer-by-layer recursive nested filtering processing. , Indicates the number of layers of multi-layer nested filtering; filtering results for low noise Perform dynamic reconstruction processing to obtain dynamically reconstructed output data.

3. The cloud-based detection and automatic correction method for grating encoders for drive motors according to claim 2 is characterized in that: The S1 specifically includes: In the process of implementing the multi-layer nested fusion filtering and dynamic reconstruction algorithm, the output data after dynamic reconstruction is adjusted to obtain accurate operating data; the specific adjustment formula is as follows: ， in, For accurate operation data; It is the output data after dynamic reconstruction; is the maximum order of the polynomial; are the polynomial fitting coefficients; is the degree of the polynomial; is the maximum number of layers of cosine terms; are the cosine decomposition coefficients; is the period parameter; is the phase adjustment parameter.

4. The cloud-based detection and automatic correction method for grating encoders for drive motors according to claim 3 is characterized in that: The S1 specifically includes: In the process of anomaly detection on accurate operating data, firstly, the statistical characteristics and frequency characteristics of the accurate operating data are extracted to obtain feature data; the feature data is subjected to dimensionality reduction processing to obtain optimized feature data; anomaly detection analysis is performed based on the optimized feature data to determine whether an abnormal state exists and obtain anomaly detection results; the anomaly detection results include the detected anomaly type and the feature parameters of the abnormal data, and the feature parameters of the abnormal data include abnormal amplitude, abnormal frequency and phase offset.

5. The cloud-based detection and automatic correction method for grating encoders for drive motors according to claim 1, characterized in that: The S2 specifically includes: In the implementation process of the dynamic coupling modulation-differential control algorithm, dynamic coupling modulation factors are introduced based on the characteristic parameters of abnormal data, including amplitude modulation factor, frequency modulation factor and phase modulation factor.

6. The cloud-based detection and automatic correction method for grating encoders for drive motors according to claim 5, characterized in that: The S2 specifically includes: Based on the amplitude modulation factor, frequency modulation factor and phase modulation factor, the parameter data is corrected in real time through dynamic differential feedback to obtain amplitude differential feedback correction parameter data, frequency differential feedback correction parameter data and phase differential feedback correction parameter data; the specific formula is: ， ， ， in, is the amplitude differential feedback correction parameter data; is the amplitude modulation factor; is the abnormal amplitude; is the frequency differential feedback correction parameter data; is the frequency modulation factor; is the abnormal frequency; is the phase differential feedback correction parameter data; is the phase modulation factor; Phase shift; It is the data value corresponding to the abnormal data, which is taken from the accurate operation data; is the integration variable; , , The distribution is the instantaneous rate of change of the amplitude modulation factor, frequency modulation factor, and phase modulation factor; , , They are the integrals of the amplitude modulation factor, frequency modulation factor, and phase modulation factor respectively.

7. The cloud-based detection and automatic correction method for grating encoders for drive motors according to claim 6, characterized in that: The S2 specifically includes: In the process of implementing the dynamic coupling modulation-differential control algorithm, the total correction parameter is generated by combining the amplitude differential feedback correction parameter data, the frequency differential feedback correction parameter data and the phase differential feedback correction parameter data.

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