A real-time monitoring method and system for the operation status of stamping equipment

Abstract

The present invention relates to the field of stamping equipment, and in particular to a method and system for real-time monitoring of the operating status of stamping equipment. The method comprises: obtaining the operating data of the stamping equipment, taking any operating data as the central data and presetting the distance, calculating the distance between the central data and other data, and determining the aggregation degree of each operating data within the preset distance to form an aggregation degree sequence under different preset distances; performing cluster analysis on the aggregation degree sequence to obtain multiple clusters, and determining the best clustering result through an evaluation function; obtaining the operating data of the stamping equipment collected in real time, and calculating the clustering cluster corresponding to the best clustering result to which the real-time operating data belongs, and judging the operating status of the stamping equipment according to the clustering cluster to which it belongs. The present invention accurately identifies the operating status of the stamping equipment by analyzing the abnormal patterns in the operating data and combining the clustering comparison of the real-time data, thereby achieving targeted maintenance and extending the service life of the stamping equipment.

Description

Technical Field

[0001] The present invention relates to the field of stamping equipment, and in particular to a method and system for real-time monitoring of the operating status of stamping equipment. Background Art

[0002] Stamping equipment is a type of mechanical equipment that uses dies and a stamping process to form metal or non-metallic sheets into parts of specific shapes and sizes through pressure. It is used in numerous fields, including automotive, aerospace, electronics, home appliances, and machinery. With the development of society, stamping equipment is developing towards intelligent, high-precision, energy-saving, and environmentally friendly features. Stamping processes include blanking, bending, stretching, and forming, enabling efficient processing of complex parts.

[0003] The existing Chinese patent application document with publication number CN117150333A discloses a method and system for operation and maintenance supervision of stamping and forming equipment, which relates to the field of data processing technology. The method includes: calling equipment operation data and establishing a mapping association between the call data and product data; compensating for stamping quality with input samples, and mapping the operation data of the forming equipment with abnormal identification; performing data clustering on the mapping results to generate N data clustering clusters; generating initial feature constraints, obtaining compensation data through linkage abnormal compensation, establishing an operation and maintenance supervision network, performing data analysis on real-time monitoring data, and generating operation and maintenance supervision results.

[0004] This application document uses raw material quality characteristics to compensate for equipment operating data, improving the accuracy of stamping equipment operation analysis. Currently, stamping equipment failure modes are complex, including mechanical failures, electrical failures, and software failures. Different failure types have different impacts on stamping equipment performance, and different failures may exhibit similar abnormal characteristics, making accurate differentiation and diagnosis difficult. Summary of the Invention

[0005] In order to solve the problem that different faults of stamping equipment may appear similar in data, making it difficult to distinguish the operating states of different faults, the present invention provides solutions in the following aspects.

[0006] In a first aspect, a method for real-time monitoring of the operating status of a stamping equipment comprises: obtaining operating data and data labels of historical stamping equipment, wherein the operating data include: temperature, current, feed speed, stamping speed, stamping pressure and motor load, and the data labels are mechanical fault, electrical fault and software fault; taking any operating data as central data and presetting a distance, calculating the distance between the central data and other data, judging whether the other data falls within the preset distance, calculating the aggregation degree of each operating data within the preset distance, and obtaining the aggregation degree sequence of each operating data at different preset distances; calculating the similarity between the aggregation degree sequences of each operating data, clustering the aggregation degree sequences of the operating data based on the similarity to obtain multiple cluster clusters, and calculating the evaluation function of each cluster cluster for obtaining the best clustering result; obtaining the operating data of the stamping equipment collected in real time, calculating the cluster cluster corresponding to the best clustering result to which the real-time operating data belongs, and judging the operating status of the stamping equipment according to the cluster cluster to which it belongs.

[0007] The effect is: by obtaining multi-dimensional operating data of the stamping equipment and combining it with cluster analysis of the aggregation sequence, accurate monitoring of the equipment's operating status and fault diagnosis are achieved; by calculating the weighted distance and aggregation sequence between data points, the operating data are classified into different clusters, and the best clustering results are screened out through the evaluation function. It can not only effectively distinguish complex failure modes such as mechanical failure, electrical failure and software failure, but also judge the equipment's operating status in real time, reducing false alarms and missed alarms. In addition, the real-time monitoring function of the present invention can detect potential faults in advance, reduce sudden downtime, optimize production efficiency, and facilitate timely maintenance and management of stamping equipment.

[0008] Preferably, the method further includes preprocessing the acquired operating data, wherein the preprocessing includes: aligning the operating data according to the acquisition timestamp, and sequentially using the exponential smoothing method for smoothing and the filtering algorithm for noise reduction, and normalizing each data point of the operating data.

[0009] The effect is: through the pre-processing methods of timestamp alignment, exponential smoothing, filtering noise reduction and normalization, the quality and consistency of operation data are effectively improved, an accurate time benchmark and consistent data format are provided, and a reliable data basis is provided for operation monitoring, thereby significantly enhancing the accuracy and reliability of fault diagnosis and operation status monitoring.

[0010] Preferably, the distance between the calculation center data and other data includes:

[0011] According to the operation data collected at each moment, the probability of occurrence of the operation data collected each time is obtained as the core degree of the corresponding operation data, the Euclidean distance between the central data and other data is calculated, and the sum of the core degree of the central data and the core degree of other data is used as an adjustment factor. The distance between the central data and other data is obtained according to the product of the adjustment factor and the Euclidean distance.

[0012] The effect is that by considering the probability of occurrence of each collected operating data, that is, the core degree, the importance of data points can be distinguished. Data points with high core degrees are more critical in the operation of stamping equipment and are given greater weight in the distance calculation. By introducing core degree as an adjustment factor, the distance measurement is made more accurate and can better reflect the significance of data points in the actual operating status.

[0013] Preferably, the aggregation degree sequence of each operation data includes:

[0014] Taking any preset distance as the target distance, calculate the number of other running data around the running data at the target distance, and take the ratio of the number of other running data to the total number of running data as the aggregation degree of the running data at the corresponding target distance, and obtain the aggregation degree sequence at each target distance.

[0015] The effect is that by counting the number of other data points around the running data at a preset target distance, the local density of each data point at that distance can be quantified. The aggregation sequence can reveal the distribution characteristics of data points at different distance scales and help identify dense and sparse areas in the data set.

[0016] Preferably, the aggregation degree sequence of each operation data further includes:

[0017] The maximum and minimum values ​​of the Euclidean distance between the central data and other data are obtained, and the ratio between the maximum and minimum values ​​is calculated as the aggregation degree of the central data. According to different preset distances, the aggregation degree sequence under different preset distances is obtained.

[0018] The effect is that by calculating the ratio of the maximum to minimum Euclidean distance between the central data and other data as the degree of clustering, and generating a sequence of clustering degrees based on different preset distances, it can quantify the degree of dispersion of data points in multidimensional space and intuitively reflect the density or sparseness of data distribution. This not only enhances the accuracy of anomaly detection by capturing the high discreteness of abnormal data through multi-dimensional analysis, but also optimizes the effectiveness of cluster analysis, improving the accuracy and robustness of clustering results.

[0019] Preferably, the calculating of the similarity between the aggregation degree sequences of each running data includes:

[0020] The aggregation sequence of any collected running data is used as the annotation sequence, the covariance between the annotation sequence and other aggregation sequences is calculated, the product of the standard deviation of the annotation sequence and the standard deviation of other aggregation sequences is calculated, and the ratio of the product between the covariance and the standard deviation is used as the similarity between the aggregation sequences.

[0021] Preferably, the calculating of the similarity between the aggregation degree sequences of each operation data further includes:

[0022] Take the aggregation sequence of any collected running data as the annotation sequence, and calculate the relationship between the annotation sequence and other aggregation sequences. distance, will The distance is calculated using a negative exponential function to obtain the similarity between the labeled sequence and other clustering sequences.

[0023] Preferably, the evaluation function satisfies the following relationship:

[0024] ;

[0025] Where, represents the evaluation function of the cluster, represents the overall entropy of all clusters, Indicates the total number of clusters. Indicates the The entropy of the clusters.

[0026] Preferably, the determining the operating status of the stamping equipment according to the clustering includes:

[0027] The distance between the real-time collected data and the cluster center of each cluster is calculated to obtain the matching cluster, and the label corresponding to the mode of the data in the matching cluster is used as the running status of the real-time collected data.

[0028] The effect is that based on the clustering results of real-time data, maintenance plans can be formulated more accurately, avoiding unnecessary inspections. By accurately judging the operating status of the stamping equipment, downtime caused by misjudgment can be reduced, and the efficiency of the production line can be improved.

[0029] In a second aspect, a real-time monitoring system for the operating status of a stamping equipment is provided, comprising: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned real-time monitoring method for the operating status of the stamping equipment is implemented.

[0030] The present invention has the following effects:

[0031] 1. By acquiring multi-dimensional operating data and utilizing aggregation degree sequences and cluster analysis, the present invention can more accurately distinguish and diagnose different types of faults, such as mechanical faults, electrical faults, and software faults. At the same time, it can quickly respond to the fault conditions of the historical data corresponding to each cluster and accurately judge the operating status of the stamping equipment based on past experience.

[0032] 2. The present invention quickly determines the current operating status of the stamping equipment by calculating the distance between the real-time operating data and the cluster center in the optimal clustering result, which is conducive to timely discovering potential anomalies, providing data support for preventive maintenance, reducing unexpected downtime, and improving production efficiency; at the same time, it helps to optimize the maintenance plan of the stamping equipment, give priority to stamping equipment with unstable operating status or frequent abnormalities, and then guide resource allocation to ensure that key equipment is properly maintained and monitored, thereby extending the service life of the stamping equipment and reducing operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a method flow chart of steps S1 to S4 in a method for real-time monitoring of the operating status of a stamping equipment according to an embodiment of the present invention.

[0034] Figure 2 This is a structural block diagram of a real-time monitoring system for the operating status of stamping equipment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0036] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0037] Reference Figure 1 A method for real-time monitoring of the operating status of a stamping equipment includes steps S1 to S4, specifically as follows:

[0038] S1: Acquire historical stamping equipment operation data and data tags, wherein the operation data includes: temperature, current, feed speed, stamping speed, stamping pressure and motor load, and the data tags are mechanical failure, electrical failure and software failure.

[0039] The method also includes preprocessing the acquired operation data, wherein the preprocessing includes: aligning the operation data according to the acquisition timestamp, and sequentially using the exponential smoothing method for smoothing and the filtering algorithm for noise reduction, and normalizing each data point of the operation data.

[0040] It should be noted that the operating data is a multi-dimensional spatial vector. By installing sensors such as temperature sensors, current sensors, speed sensors and load sensors on the stamping equipment, multi-dimensional data during the operation of the equipment can be collected in real time. The operating data includes but is not limited to parameters such as temperature, current, feed speed and motor load. In order to ensure data quality, the collected operating data of the stamping equipment is filtered and denoised to remove high-frequency interference, outliers are eliminated through statistical methods to avoid misjudgment, and normalization is performed to eliminate the dimensional and order of magnitude differences of different parameters.

[0041] The data labels correspond to the data features:

[0042] Mechanical failures are usually related to equipment components, such as molds, slides, guide rails, etc. Mechanical failures may manifest as abnormal temperature, unstable feed speed and unstable motor load;

[0043] Electrical faults are usually related to the equipment's electrical system, such as circuits, motors, and controllers. Electrical faults may manifest as abnormal current, voltage, frequency changes, and electromagnetic interference.

[0044] Software failures are usually related to the control system software of the equipment, such as programming errors, compatibility issues, etc. Software failures may manifest as: stamping speed and pressure data do not match the actual operating data.

[0045] In actual operation, the normal operation state of stamping equipment usually corresponds to a dense area of ​​data, while the abnormal state may be manifested as isolated or sparsely distributed data points. By analyzing the density or sparseness of the data points around each data point, it is possible to determine whether the data point is an abnormal point, thereby effectively distinguishing between normal operation and potential fault state, providing a basis for fault diagnosis. The specific implementation steps are as follows:

[0046] S2: Take any running data as the central data and preset the distance, calculate the distance between the central data and other data, determine whether the other data falls within the preset distance, calculate the aggregation degree of each running data within the preset distance, and obtain the aggregation degree sequence of each running data at different preset distances.

[0047] The Euclidean distance between the central data and other data is used as the distance calculation method. Specifically, the distance satisfies the following relationship:

[0048] ;

[0049] Where, Indicates the The operating data collected for the first time and the The distance between the running data collected, Indicates the The space vector of the running data collected, Indicates the The space vector of the running data collected, express Norm, which represents the Euclidean distance between two collected running data.

[0050] Further analysis shows that there are repeated data in the operating data. The repetition indicates that the data appears more frequently during the operation of the stamping equipment. The operating status corresponding to the data with more occurrences is more likely to be normal, because abnormal data is more random and less likely to appear the same. Therefore, the more repetitions occur, the greater the possibility that the point is a core point. Therefore, when calculating the distance between different operating data, the core degree of the data can be introduced into the calculation.

[0051] In addition, another embodiment further includes:

[0052] According to the operation data collected at each moment, the probability of occurrence of the operation data collected each time is obtained as the core degree of the corresponding operation data, the Euclidean distance between the central data and other data is calculated, and the sum of the core degree of the central data and the core degree of other data is used as the adjustment factor. The distance between the central data and other data is obtained according to the product of the adjustment factor and the Euclidean distance.

[0053] Specifically, the distance satisfies the following relationship:

[0054] ;

[0055] Where, Indicates the The operating data collected for the first time and the The distance between the running data collected, Indicates the The core degree of the collected operational data, Indicates the The core degree of the collected operational data, Indicates the The space vector of the running data collected, Indicates the The space vector of the running data collected, express Norm, which represents the Euclidean distance between two collected running data.

[0056] That is to say, the greater the core degree of the collected operating data, the greater the impact range of the data, and the farther away it is from the operating data with a high core degree, the greater the probability that it is an abnormal point. In fault diagnosis, weighted distance can help locate the cause of the fault more accurately, because data points with a high core degree are more likely to be associated with normal operating conditions.

[0057] Furthermore, the preset distance is a key parameter used to define the neighborhood range. By setting multiple preset distances, the distribution characteristics of data points can be analyzed at multiple scales. The preset distance can be adjusted based on the distribution range of the data and the operating characteristics of the equipment.

[0058] The aggregation degree series of each running data includes:

[0059] Taking any preset distance as the target distance, calculate the number of other running data around the running data at the target distance, and take the ratio of the number of other running data to the total number of running data as the aggregation degree of the running data at the corresponding target distance, and obtain the aggregation degree sequence at each target distance.

[0060] To further illustrate, the aggregation sequence reflects the characteristics of a certain running data among all the running data and how it is distributed among all the data. For example, if a certain running data is at the center point of all the data, the resulting aggregation sequence should grow smoothly, that is, the aggregation will not suddenly increase a lot or suddenly increase a little. If a certain running data is at an edge point or an abnormal point, the growth of the resulting aggregation sequence is not smooth, because as the distance increases, the increase of other data points within the distance range is sudden and unstable.

[0061] In addition, another embodiment further includes:

[0062] The maximum and minimum values ​​of the Euclidean distance between the central data and other data are obtained, and the ratio between the maximum and minimum values ​​is calculated as the aggregation degree of the central data. According to different preset distances, the aggregation degree sequence under different preset distances is obtained.

[0063] Specifically, the aggregation degree satisfies the following relationship:

[0064] ;

[0065] Where, Indicates the The aggregation of the collected operation data, Indicates the The space vector of the running data collected for the first time and the The maximum value of the Euclidean distance between the space vectors of the running data collected, Indicates the The space vector of the running data collected for the first time and the The minimum value of the Euclidean distance between the spatial vectors of the running data collected in the same time.

[0066] That is to say, in the same preset distance range, the larger the ratio between the maximum value of the Euclidean distance and the minimum value of the Euclidean distance, the more dispersed the data distribution in the preset distance range is, and the smaller the degree of aggregation is; conversely, the smaller the ratio, the more concentrated the data distribution in the preset distance range is, and the greater the degree of aggregation is.

[0067] It should be noted that by analyzing the similarity of the distribution characteristics of data points at different scales, data points with similar operating status are classified into one category, thereby achieving accurate classification of the operating status of the stamping equipment. Through the classification results between different data points, the operating status of the data point corresponding to the classification result can be determined, which is conducive to distinguishing the cause of the fault. The specific steps are as follows:

[0068] S3: Calculate the similarity between the aggregation degree sequences of each running data, cluster the aggregation degree sequences of the running data based on the similarity to obtain multiple clusters, and calculate the evaluation function of each cluster to obtain the best clustering result.

[0069] Similarity, including:

[0070] The aggregation sequence of any collected running data is used as the annotation sequence, the covariance between the annotation sequence and other aggregation sequences is calculated, the product of the standard deviation of the annotation sequence and the standard deviation of other aggregation sequences is calculated, and the ratio of the product between the covariance and the standard deviation is used as the similarity between the aggregation sequences.

[0071] Specifically, the similarity satisfies the following relationship:

[0072] ;

[0073] in, Indicates the The aggregation degree sequence of the running data collected for the first time and the The similarity between the aggregation sequences of the running data collected, Indicates the The aggregation degree sequence of the running data collected for the first time and the The covariance between the aggregation degree series corresponding to the running data collected, Indicates the The standard deviation of the aggregation series of the running data collected, Indicates the The standard deviation of the aggregation series of the collected run data.

[0074] That is, by dividing the covariance by the product of the standard deviations of the two series, we obtain a dimensionless measure, which makes the similarity The value of It is used to evaluate the similarity between data points, thereby helping to determine which data points should be grouped into the same cluster.

[0075] In addition, another embodiment further includes:

[0076] Take the aggregation sequence of any collected running data as the annotation sequence, and calculate the relationship between the annotation sequence and other aggregation sequences. distance, will The distance is calculated using a negative exponential function to obtain the similarity between the labeled sequence and other clustering sequences.

[0077] Specifically, the similarity satisfies the following relationship:

[0078] ;

[0079] Where, Indicates the The aggregation degree sequence of the running data collected for the first time and the The similarity between the aggregation sequences of the running data collected, Represented by natural numbers The exponential function with base , Indicates the The aggregation degree sequence of the running data collected for the first time and the The aggregation degree series between the running data collected distance.

[0080] That is to say, in similarity calculation, the negative exponential function can convert a non-negative DTW distance into a The similarity value between them is as follows: the greater the distance, the smaller the similarity; the smaller the distance, the closer the similarity is to 1.

[0081] Specifically, the evaluation function satisfies the following relationship:

[0082] ;

[0083] Where, represents the evaluation function of the cluster, Indicates the The entropy of the clusters, Represents the overall entropy of all clusters.

[0084] In other words, the larger the evaluation function, the better the clustering effect. It represents the overall entropy of all clusters. By calculating the overall entropy of the cluster, it reflects the degree of chaos of the cluster. The more consistent the running status of the running data in the cluster, the smaller the degree of chaos of the cluster.

[0085] Among them, the entropy of the cluster satisfies the following relationship:

[0086] ;

[0087] Where, Indicates the The entropy of the clusters, Indicates the The data belongs to The probability of clusters, Indicates Logarithmic function with base .

[0088] The overall entropy of all clusters satisfies the following relationship:

[0089] ;

[0090] Where, represents the overall entropy of all clusters, Indicates the first The probability of data appearing, Indicates Logarithmic function with base .

[0091] S4: Acquire and collect the real-time operation data of the stamping equipment, calculate the cluster corresponding to the best clustering result to which the real-time operation data belongs, and judge the operation status of the stamping equipment according to the cluster to which it belongs.

[0092] The distance between the real-time collected data and the cluster center of each cluster is calculated to obtain the matching cluster, and the label corresponding to the mode of the data in the matching cluster is used as the running status of the real-time collected data.

[0093] It should be noted that by calculating the distance between the real-time collected data and each cluster center, the position of the real-time data point in the multidimensional feature space can be determined, and then the closest cluster can be determined. The real-time data point is assigned to the cluster with the nearest distance, indicating that the characteristics of the real-time data point are most similar to the central characteristics of the cluster. The mode operating status of the matching cluster is used as the operating status of the real-time data point. According to the experience of historical regulation, there is more data basis for judging the abnormal operating status of the stamping equipment, and guiding adjustments can be made.

[0094] The present invention also provides a real-time monitoring system for the operation status of a stamping equipment. Figure 2As shown, the system includes a processor and a memory, the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a real-time monitoring method for the operating status of a stamping equipment according to the first aspect of the present invention is implemented.

[0095] The system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and therefore will not be described in detail here.

[0096] It should be noted that those skilled in the art may make various modifications and improvements without departing from the scope of the present invention, and these modifications and improvements fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be based on the appended claims.

Claims

1. A method for real-time monitoring of the operating status of a stamping equipment, characterized in that: include: Acquire historical stamping equipment operating data and data tags, wherein the operating data includes: temperature, current, feed speed, stamping speed, stamping pressure, and motor load, and the data tags are mechanical failure, electrical failure, and software failure; Taking any running data as the central data and presetting the distance, calculating the distance between the central data and other data, judging whether the other data falls within the presetting distance, calculating the aggregation degree of each running data within the presetting distance, and obtaining the aggregation degree sequence of each running data at different presetting distances, including: Taking any preset distance as the target distance, calculate the number of other running data around the running data at the target distance, and take the ratio of the number of other running data to the total number of running data as the aggregation degree of the running data at the corresponding target distance, and obtain the aggregation degree sequence at each target distance; Calculate the similarity between the aggregation sequences of each running data, cluster the aggregation sequences of the running data based on the similarity to obtain multiple clusters, and calculate the evaluation function of each cluster to obtain the best clustering result. The evaluation function includes: ； Where, represents the evaluation function of the cluster, represents the overall entropy of all clusters, Indicates the total number of clusters. Indicates the The entropy of the clusters; Acquire the real-time operating data of the stamping equipment, calculate the cluster to which the real-time operating data belongs in the best clustering result, and judge the operating status of the stamping equipment based on the cluster to which it belongs; The step of judging the operating status of the stamping equipment according to the cluster to which it belongs includes: The distance between the real-time collected data and the cluster center of each cluster is calculated to obtain the matching cluster, and the label corresponding to the mode of the data in the matching cluster is used as the running status of the real-time collected data.

2. A method for real-time monitoring of the operating status of a stamping equipment according to claim 1, characterized in that: The method further includes preprocessing the acquired operation data, wherein the preprocessing includes: aligning the operation data according to the acquisition timestamp, and sequentially using an exponential smoothing method to perform smoothing processing and a filtering algorithm to perform noise reduction processing, and normalizing each data point of the operation data.

3. A method for real-time monitoring of the operating status of a stamping equipment according to claim 1, characterized in that: The distance between the computing center data and other data includes: According to the operation data collected at each moment, the probability of occurrence of the operation data collected each time is obtained as the core degree of the corresponding operation data, the Euclidean distance between the central data and other data is calculated, and the sum of the core degree of the central data and the core degree of other data is used as an adjustment factor. The distance between the central data and other data is obtained according to the product of the adjustment factor and the Euclidean distance.

4. A method for real-time monitoring of the operating status of a stamping equipment according to claim 1, characterized in that: The calculating the similarity between the aggregation degree sequences of each running data includes: The aggregation sequence of any collected running data is used as the annotation sequence, the covariance between the annotation sequence and other aggregation sequences is calculated, the product of the standard deviation of the annotation sequence and the standard deviation of other aggregation sequences is calculated, and the ratio of the product between the covariance and the standard deviation is used as the similarity between the aggregation sequences.

5. The method for real-time monitoring of the operating status of a stamping equipment according to claim 1, characterized in that: The calculating of the similarity between the aggregation degree sequences of each running data further includes: Take the aggregation sequence of any collected running data as the annotation sequence, and calculate the relationship between the annotation sequence and other aggregation sequences. distance, will The distance is calculated using a negative exponential function to obtain the similarity between the labeled sequence and other clustering sequences.

6. A real-time monitoring system for the operation status of a stamping equipment, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the real-time monitoring method for the operating status of a stamping equipment according to any one of claims 1 to 5 is implemented.

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