Operation and maintenance state monitoring method and system for seal cover punch forming equipment

Abstract

The invention relates to the technical field of data processing, in particular to an operation and maintenance state monitoring method and system for sealing cover punch forming equipment, and the method comprises the steps: obtaining a pressure data sequence of the sealing cover punch forming equipment; obtaining an observation value of a to-be-filtered data point in the pressure data sequence by using an improved EWMA algorithm, and carrying out weighted summation on the observation value and a filtering value of a previous data point of the to-be-filtered data point based on a preset attenuation weight to obtain a filtering value of the to-be-filtered data point, so as to obtain a filtering value of the to-be-filtered data point based on the filtering value of each data point in the pressure data sequence. And monitoring the operation and maintenance state of the equipment. According to the invention, accurate pressure data can be obtained, so that accurate monitoring of the equipment operation and maintenance state can be realized based on the accurate pressure data.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing. More specifically, the present invention relates to a method and system for monitoring the operation and maintenance status of a stamping and forming device for a sealing cover. Background Art

[0002] The stamping and forming device for a sealing cover is a mechanical device specifically used for producing sealing covers. The production principle is mainly based on the principle of metal plastic deformation. By applying pressure to the material through a stamping device and a mold, plastic deformation or separation is caused to obtain the required shape and size of the sealing cover. During the stamping process, pressure is one of the core process parameters, and its change can directly reflect the operating state of the device. By collecting and analyzing pressure data in real time, the abnormal operating state of the device can be effectively identified, potential faults can be predicted in advance, so as to achieve precise maintenance and efficient management, and further improve the reliability and production efficiency of the device.

[0003] Since the pressure data is easily disturbed by noise during the collection process, if the collected pressure data is directly used to monitor the operation and maintenance status of the device, the accuracy of the monitoring result will be poor. Therefore, it is necessary to clean the collected pressure data. The EWMA (Exponentially Weighted Moving Average) algorithm is a commonly used time series smoothing technique that can capture the latest change trend of data by assigning different weights to the observed values at different time points. Therefore, the EWMA algorithm can be used for data cleaning.

[0004] However, due to the uncertainty of the noise in the pressure data collected during the device production process, if the EWMA algorithm is directly applied for data cleaning, the filtering effect may be poor. Specifically, if the collected observed value is noise data, it will lead to a deviation in the filtering result, which will further affect the subsequent filtering effect, resulting in inaccurate pressure data and thus unable to achieve precise monitoring of the operation and maintenance status of the monitoring device. Summary of the Invention

[0005] In order to solve the problem that the filtering effect of the EWMA algorithm on the pressure data during the device production process is poor, resulting in the inability to accurately monitor the operation and maintenance status of the device based on the filtered data. The present invention provides a method and system for monitoring the operation and maintenance status of a stamping and forming device for a sealing cover.

[0006] According to the first aspect of the present invention, there is provided a method for monitoring the operation and maintenance status of a stamping and forming device for a sealing cover, including: Obtaining a pressure data sequence of the stamping and forming device for a sealing cover; Using an improved EWMA algorithm, obtain the observed value of the data point to be filtered in the pressure data sequence, and based on a preset decay weight, perform a weighted sum of the observed value and the filtered value of the previous data point of the data point to be filtered to obtain the filtered value of the data point to be filtered, so as to monitor the operation and maintenance status of the device based on the filtered values of each data point in the pressure data sequence; Among them, the method for obtaining the observed value of the data point to be filtered includes: calculating the noise degree of the data point to be filtered, where the noise degree represents the mutability of the data point to be filtered within the neighborhood range; Using the correlation coefficient between the data point to be filtered and the corresponding data points in the adjacent working cycles before and after, correct the noise degree, where the correction value is negatively correlated with the correlation coefficient, and use the correction values of the noise degrees of each data point within the neighborhood range of the data point to be filtered as weights to perform a weighted average on all data points within the neighborhood range to obtain the observed value of the data point to be filtered; Among them, the pressure data sequence contains data points of multiple working cycles.

[0007] The present invention can reduce the influence of noise data when filtering the pressure data in the device production process using the EWMA algorithm, thereby improving the filtering effect, obtaining accurate pressure data, and enabling precise monitoring of the operation and maintenance status of the sealing cover stamping equipment based on the accurate pressure data.

[0008] Preferably, the method for distinguishing the data points of each working cycle in the pressure data sequence includes: Screen out the data points that are the same as the adjacent data points before and after from the pressure data sequence to obtain a stable data point set; Perform DBSCAN clustering on the stable data point set, and define the time period formed by the corresponding moments of the first data point and the last data point in each clustering cluster in the time sequence as a pressure holding stage, so as to distinguish each working cycle through the time interval between adjacent pressure holding stages in the time sequence and obtain the working cycle corresponding to each data point.

[0009] The present invention uses the characteristics of the pressure data within the pressure holding stage to distinguish each working cycle in the pressure data sequence, which can reduce the difficulty of distinction.

[0010] Preferably, the method for obtaining the correlation coefficient between the data point to be filtered and the corresponding data points in the adjacent working cycles before and after includes: Calculate the Pearson correlation coefficient between the data points within the neighborhood range of the data point to be filtered and the data points within the neighborhood range of the corresponding data points in the adjacent working cycles before and after, and take the average value to obtain the correlation coefficient between the data point to be filtered and the corresponding data points in the adjacent working cycles before and after.

[0011] When calculating the correlation coefficient, the present invention can take into account that noise data will affect the values of surrounding data, increasing the amount of data of the correlation coefficient between the data points to be filtered and the corresponding data points in the adjacent working cycles before and after, so as to ensure the accuracy of the determined correlation coefficient.

[0012] Preferably, the noise level of the data point to be filtered satisfies the following relational expression: ; In the formula, is the noise level of the th data point in the pressure data sequence; is the th data point in the pressure data sequence; is the th data point within the neighborhood range of the th data point in the pressure data sequence; is the number of data points within the neighborhood range of the th data point in the pressure data sequence; is the absolute value symbol; is the hyperbolic tangent function.

[0013] Preferably, calculating the noise level of the data point to be filtered further includes: Taking the variance of the data points within the neighborhood range of the data point to be filtered as the noise level of the data point to be filtered.

[0014] Preferably, using the correlation coefficient between the data point to be filtered and the corresponding data points in the adjacent working cycles before and after to correct the noise level, which satisfies the following relational expression: ; In the formula, , are the noise levels before and after correction of the th data point in the pressure data sequence respectively; is the th data point in the pressure data sequence; is the average value of the Pearson correlation coefficients between the data points within the neighborhood range of the th data point in the pressure data sequence and the data points within the neighborhood ranges of the corresponding data points in the adjacent working cycles before and after.

[0015] The present invention can reduce the interference of normal data changes on the identification of noise data, so as to accurately evaluate the possibility that the data point to be filtered is noise data.

[0016] Preferably, the observed value of the data point to be filtered satisfies the following relational expression: ; In the formula, is the observed value of the th data point in the pressure data sequence; is the th data point within the neighborhood range of the th data point in the pressure data sequence; is the corrected noise level of the th data point in the pressure data sequence; is the number of data points within the neighborhood range of the th data point in the pressure data sequence; is the natural exponential function.

[0017] The observed value determined by the present invention can reduce the influence of noise data, thereby improving the filtering effect of the EWMA algorithm.

[0018] Preferably, the filtered value of the data point to be filtered satisfies the following relational expression: ; wherein, is the filtered value of the th data point in the pressure data sequence; is the observed value of the th data point in the pressure data sequence; is the filtered value of the th data point in the pressure data sequence; is the preset attenuation weight.

[0019] Preferably, the neighborhood range of the data point to be filtered is a length range centered on the data point to be filtered and enclosing a preset number of data points.

[0020] According to the second aspect of the present invention, there is provided an operation and maintenance status monitoring system for a stamping forming device of a sealing cover, the system comprising a memory and a processor, wherein a computer program is stored on the memory, and the processor executes the computer program to implement the steps of the first aspect of the present invention.

[0021] The present invention has the following effects: The present invention corrects the noise level, can eliminate the interference of normal data changes on the identification of noise data, ensures the accuracy of the weights of each data point within the neighborhood range of the data point to be filtered, thereby can reduce the influence of noise data on the observed value of the data point to be filtered, and further can realize the precise filtering of the data point to be filtered based on the accurate observed value, so as to realize the precise monitoring of the operation and maintenance status of the device based on the filtered values of each data point in the pressure data sequence. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 It is a schematic flowchart of the steps of a method for monitoring the operation and maintenance status of a stamping and forming device for a sealing cover according to an embodiment of the present invention. Detailed implementation manners

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0024] The following will describe the detailed implementation manners of the present invention in detail with reference to the accompanying drawings.

[0025] Referring to Figure 1 , a method for monitoring the operation and maintenance status of a stamping and forming device for a sealing cover includes steps S1 - S2, specifically as follows: S1: Obtain the pressure data sequence of the stamping and forming device for the sealing cover.

[0026] Specifically, a pressure sensor can be installed at the position of the punch of the stamping and forming device for the sealing cover, so that the pressure data during the stamping process can be monitored in real time at a set sampling frequency, such as 5Hz, to obtain the pressure data sequence. The sampling frequency in this embodiment is not particularly limited.

[0027] S2: Use the improved EWMA algorithm to obtain the observed values when filtering each data point in the pressure data sequence, and based on a preset attenuation weight, perform a weighted sum of the observed value of any data point and the filtered value of the previous data point of the any data point to obtain the filtered value of the any data point, so as to monitor the operation and maintenance status of the device based on the filtered values of each data point.

[0028] Among them, the pressure data sequence contains data points of multiple working cycles.

[0029] It should be noted that the EWMA (Exponentially Weighted Moving Average) algorithm obtains the filtered value by performing a weighted average by assigning exponentially decreasing weights to the data points, that is, the data points closer to the current moment have larger weights, while the data points farther from the current moment have smaller weights, so that the algorithm can more sensitively reflect the change trend of recent data.

[0030] However, due to the influence of factors such as the working environment, the pressure data collected during the stamping process may contain noise data. If the data points close to the current data point are noise data, there will be a large deviation in the filtering value determined based on the traditional EWMA algorithm, the filtering effect is poor, and this algorithm performs filtering in a recursive manner, that is, the accuracy of the filtering value at the current moment will affect the accuracy of the filtering value at the next moment, resulting in subsequent filtering effects deviating from the normal level and affecting the final filtering effect. Therefore, the present invention improves the traditional EWMA algorithm. The specific improvement content is as follows: First, measure the noise degree of each data point within the neighborhood range of the data point to be filtered; then correct the noise degree of each data point within the neighborhood range; finally, use the corrected value as a weight and calculate the weighted average of the data points within the neighborhood range as the observed value of the data point to be filtered, so as to improve the determination of the observed value of the data point to be filtered in the traditional EWMA algorithm.

[0031] Among them, the present invention only improves the determination of the observed value of the data point to be filtered in the EWMA algorithm and does not improve the determination process of other parameters.

[0032] Specifically, the process of obtaining the observed value of the data point to be filtered by using the improved EWMA algorithm includes the following steps: Step 1: Calculate the noise degree of the data point to be filtered. The noise degree represents the mutability of the data point to be filtered within the neighborhood range. It should be noted that noise data usually appears as prominent points within a local range. Therefore, the present invention uses this feature to measure the mutability of each data point within its neighborhood range, thereby evaluating the possibility of each data point being noise data.

[0033] In an exemplary embodiment of the present invention, the neighborhood range of the data point to be filtered is a length range centered on the data point to be filtered and enclosing a preset number of data points. In this embodiment, the preset number is 11, and the present invention does not make a special limitation on the size of the preset number.

[0034] Furthermore, after determining the neighborhood range of the data point to be filtered, the noise degree of the data point to be filtered can be calculated by evaluating the mutability of the data point to be filtered within its neighborhood range.

[0035] Specifically, the noise degree of the data point to be filtered satisfies the following relational expression: ; In the formula, is the noise degree of the th data point in the pressure data sequence; [[ID=,31]]is the th data point in the pressure data sequence; is the -th data point within the neighborhood range of the -th data point; is the number of data points within the neighborhood range of the -th data point in the pressure data sequence. In this embodiment, = 11; is the absolute value symbol; is the hyperbolic tangent function, which is used for normalization.

[0036] Among them, when is larger, it indicates that the deviation between the -th data point and the overall values of the data points within the neighborhood range of this data point is relatively large, which further indicates that this data point is likely to be noise data.

[0037] In another embodiment, the determination of the noise level of the data point to be filtered can also be achieved through the following steps: Take the variance of the data points within the neighborhood range of the data point to be filtered as the noise level of the data point to be filtered.

[0038] Furthermore, the noise level of each data point in the pressure data sequence can be obtained through any calculation method of the noise level.

[0039] Step 2: Use the correlation coefficient between the data point to be filtered and the corresponding data points in the adjacent working cycles before and after to correct the noise level, and the correction value is negatively correlated with the correlation coefficient; It should be noted that during the working process of the sealing cover stamping and forming equipment, some normal technological phenomena will cause sudden changes in the pressure data. For example, the transition of the material from the elastic deformation stage to the plastic deformation stage, and the switching between different stages during the stamping process, etc., will all cause fluctuations in the pressure data, resulting in a relatively large noise level for the normally changing pressure data. Therefore, it is necessary to correct the noise level of each data point to reduce the influence of normal data changes.

[0040] It should be further noted that the sealing cover stamping and forming equipment is periodic during the working process, that is, each production of a sealing cover is a working cycle, and the pressure data changes within each working cycle are similar. Among them, the pressure change in each working cycle is divided into three stages: the pre-tightening stage, where the mold applies an initial pressure to make the material start elastic deformation and the pressure rises slowly; the working stage, where the punch continues to apply pressure, the material enters plastic deformation and fits the mold, and the pressure rises significantly and reaches the peak; the pressure-holding stage, where after the material fits the mold, a certain pressure is maintained to ensure the dimensional accuracy and stability of the sealing cover, and the pressure remains stable. Therefore, the present invention uses the correlation coefficient between the data point to be filtered and the corresponding data points in the two adjacent working cycles before and after to correct the noise level of the data point to be filtered, so as to eliminate the influence of normal data changes.

[0041] In an exemplary embodiment of the present invention, the determination of the duty cycle of each data point in the pressure data sequence can be achieved through the following steps: Screen out the data points that are the same as the adjacent data points before and after from the pressure data sequence to obtain a stable data point set; perform DBSCAN clustering on the stable data point set, and define the time period formed by the corresponding moments of the first data point and the last data point in each clustering cluster in terms of time sequence as a pressure holding stage, so as to distinguish each duty cycle through the time interval between adjacent pressure holding stages in the time sequence, and obtain the duty cycle corresponding to each data point.

[0042] It should be noted that since the pressure data in the pressure holding stage in each duty cycle remains unchanged, therefore, by screening the stable data point set in the present invention, the data points in the pressure holding stage in all duty cycles can be obtained, so that when clustering using the density-based clustering algorithm, the data points in the pressure holding stage in each duty cycle can be clustered into one category respectively, thereby the pressure holding stages of each duty cycle can be obtained, and further each duty cycle can be distinguished, and the duty cycle corresponding to each data point can be obtained.

[0043] Optionally, when performing DBSCAN clustering on the stable data point set, the selected clustering radius is 2, and the present embodiment does not make a special limitation on the size of the clustering radius.

[0044] Further, after determining the duty cycle of each data point, the correlation coefficient between the data point to be filtered and the corresponding data points in the adjacent duty cycles before and after can be calculated, so as to correct the noise level of the data point to be filtered based on this correlation coefficient.

[0045] In an exemplary embodiment of the present invention, the determination of the correlation coefficient of the data point to be filtered can be achieved through the following steps: Calculate the Pearson correlation coefficient between the data points within the neighborhood range of the data point to be filtered and the data points within the neighborhood range of the corresponding data points in the adjacent duty cycles before and after, and take the average value to obtain the correlation coefficient between the data point to be filtered and the corresponding data points in the adjacent duty cycles before and after.

[0046] Exemplarily, the duty cycle where the data point to be filtered is located can be denoted as , then the adjacent duty cycle before the data point to be filtered is , and the adjacent duty cycle after the data point to be filtered is . If the data point to be filtered is the th data point in , then and the a data point, as the data point corresponding to the data point to be filtered in the corresponding working cycle, so that the data points within the neighborhood range of the data point to be filtered can be calculated, and and the Pearson correlation coefficient of the data points within the neighborhood range of the corresponding data points in

[0047] is calculated, and the average value is taken to obtain the correlation coefficient between the data point to be filtered and the corresponding data points in the adjacent working cycles before and after.

[0048] It should be noted that the determination method of the Pearson correlation coefficient between data sequences is a prior art, and this embodiment will not elaborate on it here. ; In the formula, and are the noise levels before and after the correction of the th data point in the pressure data sequence respectively; is the th data point in the pressure data sequence; is the average value of the Pearson correlation coefficients of the data points within the neighborhood range of the th data point in the pressure data sequence and the data points within the neighborhood ranges of the corresponding data points in the adjacent working cycles before and after.

[0049] It should be noted that since the value range of the Pearson correlation coefficient is from -1 to 1, and under normal circumstances, the corresponding data points in different working cycles are in a positive correlation relationship, that is, takes a positive value; then when takes a negative value, it indicates that the data points within the neighborhood range of the data point to be filtered may be interfered by noise data, and further indicates that the data point to be filtered may be noise data, and the closer the value of is to -1, the greater the possibility that the data point to be filtered is noise data, and the noise level of the data point to be filtered needs to be corrected with a smaller amplitude, or even not corrected. Therefore, the larger it is, the smaller the possibility that the data point to be filtered is a noise level,

[0050] It should be further noted that the present invention represents the negative correlation relationship between the correction value and the correlation coefficient through and restricts its value within 0-1 by using

[0051] ​In another embodiment, the Spearman rank correlation coefficient, Kendall rank correlation coefficient, etc. can also be used to determine the correlation coefficient between the data point to be filtered and the corresponding data points in the adjacent working cycles before and after.

[0052] Step Three: Use the correction value of the noise level of each data point within the neighborhood range of the data point to be filtered as the weight, and perform weighted averaging on all data points within the neighborhood range to obtain the observed value of the data point to be filtered.

[0053] Specifically, the observed value of the data point to be filtered satisfies the following relational expression: ; In the formula, is the observed value of the th data point in the pressure data sequence; is the th data point within the neighborhood range of the th data point in the pressure data sequence; is the corrected noise level of the th data point in the pressure data sequence; is the number of data points within the neighborhood range of the th data point in the pressure data sequence; is the natural exponential function, where the natural exponential function refers to the exponential function with the natural constant as the base.

[0054] Optionally, when the correction value of the noise level of any data point within the neighborhood range of the data point to be filtered is large, it indicates that the credibility of this data point is low. Setting a lower weight for this data point can avoid the interference of noise data, thereby ensuring the accuracy of the observed value of the data point to be filtered.

[0055] Furthermore, after determining the observed value of the data point to be filtered, a weighted sum can be performed on this observed value and the filtered value of the previous data point of the data point to be filtered based on a preset attenuation weight to obtain the filtered value of the data point to be filtered.

[0056] Specifically, the filtered value of the data point to be filtered satisfies the following relational expression: ; In the formula, is the filtered value of the th data point in the pressure data sequence; is the observed value of the th data point in the pressure data sequence; is the filtered value of the th data point in the pressure data sequence; is the preset attenuation weight. In this embodiment, = 0.7.

[0057] It should be noted that when filtering the pressure data sequence using the improved EWMA algorithm, the initial EWMA value is the observed value of the first data point in the pressure data sequence.

[0058] Furthermore, the data points in the pressure data sequence can be successively used as the data points to be filtered. Thus, the improved EWMA algorithm can be used to clean the obtained pressure data to obtain accurate pressure data, enabling the timely detection of abnormalities in the pressure data, such as too high or too low pressure. Consequently, the abnormal operation and maintenance status of the equipment can be promptly identified, and corresponding measures can be taken to repair the equipment to extend its service life and ensure product quality.

[0059] The present invention also provides an operation and maintenance status monitoring system for a sealing cap stamping and forming device. The system includes a memory and a processor, and a computer program is stored on the memory. The computer program integrates the functions of an operation and maintenance status monitoring method for a sealing cap stamping and forming device. When the computer program is executed, accurate pressure data can be obtained through an operation and maintenance status monitoring method for a sealing cap stamping and forming device. Thus, based on the accurate pressure data, precise monitoring of the equipment operation and maintenance status can be achieved.

[0060] In the description of this specification, the meanings of "a plurality of" and "several" are at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0061] Although this specification has shown and described multiple embodiments of the present invention, it is obvious to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will think of many changes, alterations, and alternative ways without departing from the spirit and concept of the present invention. It should be understood that various alternative solutions to the embodiments of the present invention described herein can be adopted in the process of practicing the present invention.

Claims

1. A method for monitoring the operation and maintenance status of a stamping and forming device for a sealing cover, characterized in that, Including: Obtain the pressure data sequence of the sealing cover stamping equipment; Using the improved EWMA algorithm, obtain the observed value of the data point to be filtered in the pressure data sequence, and based on the preset attenuation weight, perform weighted summation on the observed value and the filtered value of the previous data point of the data point to be filtered, to obtain the filtered value of the data point to be filtered, so as to monitor the operation and maintenance status of the equipment based on the filtered values of each data point in the pressure data sequence; Among them, the method for obtaining the observed value of the data point to be filtered includes: calculating the noise degree of the data point to be filtered, where the noise degree represents the mutability of the data point to be filtered within the neighborhood range; Using the correlation coefficient between the data point to be filtered and the corresponding data points in the adjacent front and rear working cycles to correct the noise degree, the correction value is negatively correlated with the correlation coefficient, and using the correction values of the noise degrees of each data point within the neighborhood range of the data point to be filtered as weights, perform weighted averaging on all data points within the neighborhood range to obtain the observed value of the data point to be filtered; Among them, the pressure data sequence contains data points of multiple working cycles.

2. The operation and maintenance status monitoring method of a stamping and forming device for a sealing cover according to claim 1, characterized in that [[ID=*6]]The method for distinguishing data points of each working cycle in the pressure data sequence includes: Screen out the data points that are the same as the adjacent front and rear data points from the pressure data sequence to obtain a stable data point set; Perform DBSCAN clustering on the stable data point set, and define the time period formed by the corresponding moments of the first data point and the last data point in the time sequence of each clustering cluster as a pressure holding stage, so as to distinguish each working cycle through the time interval between adjacent pressure holding stages in the time sequence, and obtain the working cycle corresponding to each data point.

3. The operation and maintenance status monitoring method of a stamping and forming device for a sealing cover according to claim 2, wherein, The method for obtaining the correlation coefficient between the data point to be filtered and the corresponding data points in the adjacent front and rear working cycles includes: Calculate the Pearson correlation coefficient between the data points within the neighborhood range of the data point to be filtered and the data points within the neighborhood ranges of the corresponding data points in the adjacent front and rear working cycles, and take the average value to obtain the correlation coefficient between the data point to be filtered and the corresponding data points in the adjacent front and rear working cycles.

4. The operation and maintenance status monitoring method of a stamping and forming device for a sealing cover according to claim 1, characterized in that, The noise degree of the data point to be filtered satisfies the following relational expression: ； Wherein, is the noise level of the th data point in the pressure data sequence; is the th data point in the pressure data sequence; is the th data point within the neighborhood range of the th data point in the pressure data sequence; is the number of data points within the neighborhood range of the th data point in the pressure data sequence; is the absolute value symbol; is the hyperbolic tangent function.

5. The operation and maintenance status monitoring method of a stamping and forming device for a sealing cover according to claim 1, characterized in that, The calculation of the noise degree of the data point to be filtered further includes: Taking the variance of the data points within the neighborhood range of the data point to be filtered as the noise degree of the data point to be filtered.

6. A method for monitoring the operation and maintenance status of a stamping and forming device for a sealing cover according to claim 3 or 4 or 5, characterized in that, The use of the correlation coefficient between the data point to be filtered and the corresponding data points in the adjacent front and rear working cycles to correct the noise degree satisfies the following relational expression: ； In the formula, and are the noise levels before and after correction of the th data point in the pressure data sequence, respectively; is the th data point in the pressure data sequence; is the average value of the Pearson correlation coefficients of the data points within the neighborhood of the th data point in the pressure data sequence and the data points within the neighborhood of the corresponding data points in the adjacent working cycles before and after.

7. A method for monitoring the operation and maintenance status of a stamping and forming device for a sealing cover according to claim 1, characterized in that, The observed value of the data point to be filtered satisfies the following relational expression: ； Wherein, is the observed value of the -th data point in the pressure data sequence; is the -th data point within the neighborhood range of the -th data point in the pressure data sequence; is the corrected noise level of the -th data point in the pressure data sequence; is the number of data points within the neighborhood range of the -th data point in the pressure data sequence; is the natural exponential function.

8. A method for monitoring the operation and maintenance status of a stamping and forming device for a sealing cover according to claim 7, characterized in that, The filtered value of the data point to be filtered satisfies the following relational expression: ； In the formula, is the filtered value of the -th data point in the pressure data sequence; is the observed value of the -th data point in the pressure data sequence; is the filtered value of the -th data point in the pressure data sequence; is the preset attenuation weight.

9. The operation and maintenance status monitoring method of a stamping forming device for a sealing cover according to claim 1, characterized in that, The neighborhood range of the data point to be filtered is a length range centered on the data point to be filtered and enclosing a preset number of data points.

10. An operation and maintenance status monitoring system for a stamping and forming device of a sealing cover, characterized in that, The operation and maintenance status monitoring system of a sealing cover stamping equipment includes a memory and a processor, and a computer program is stored on the memory, and the processor executes the computer program to implement the steps of the operation and maintenance status monitoring method of a sealing cover stamping equipment as described in any one of claims 1-9.

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