Operation state sensing analysis method for industrial automatic assembly line

Abstract

The invention relates to the technical field of assembly line state sensing, in particular to an operation state sensing analysis method for an industrial automatic assembly line, which comprises the following steps: acquiring current of a driving motor at each moment in each verification period in the assembly line verification process of an electric energy meter, recording the starting time and the ending time of each electric energy meter in each verification link, the time of each feeding operation and each discharging operation on the assembly line, and the feeding quantity and the discharging quantity of each verification period; the transmission fluctuation degree and the current deviation degree of each verification period are determined, and the first anomaly degree, the unbalance degree, the second anomaly degree and the unbalance index of each verification period are obtained; and determining the unbalance saliency of the current verification period, and evaluating the operation state of the assembly line. According to the method, the composite fault problem of transmission abnormity and feeding imbalance can be effectively evaluated, and the sensing accuracy of the running state of the assembly line is improved.

Description

Technical Field

[0001] The present application relates to the technical field of pipeline state perception, and in particular to a method for perceiving and analyzing the operating state of an industrial automation pipeline. Background Art

[0002] As a key component of the power system, efficient calibration of energy meters is crucial. Automated meter calibration lines combine production lines with calibration technology to achieve efficient calibration. However, existing lines are prone to fluctuations in conveying rhythm and material supply, leading to rhythm mismatches, buffer congestion, or sparse buffers. This can cause production line stalls, equipment overloads, increased defective product rates, and even safety hazards.

[0003] When the existing technology detects the operating status of the electric energy meter automated assembly line, it usually directly compares the cache occupancy rate of the assembly line with the preset threshold to determine whether there is a production disorder problem of untimely or excessive material demand in the loading amount of the assembly line, and then evaluates the operating status of the assembly line. It does not fully consider that when an abnormality occurs in the transmission process of the assembly line, it will cause the transmission speed to be unstable. At this time, even if the loading amount is appropriate, it may cause abnormal actual transmission volume, which will lead to inaccurate perception of the assembly line operation status and false detection, thereby affecting the calibration efficiency of the electric energy meter. Summary of the Invention

[0004] In order to solve the above technical problems, a method for perceiving and analyzing the operating status of an industrial automation assembly line is provided to solve the existing problems.

[0005] The solution to the technical problem of this application is to provide a method for sensing and analyzing the operating status of an industrial automation assembly line, including the following steps:

[0006] Obtain the current of the drive motor at each moment in each calibration cycle during the assembly line calibration process of the energy meter, record the start and end time of each calibration link for each energy meter, the time of each loading and unloading operation on the assembly line, and the loading and unloading quantity in each calibration cycle;

[0007] Based on the discreteness and extreme range of the difference between the start time of each verification link and the end time of the previous verification link in each verification cycle, the transmission fluctuation degree of each verification cycle is determined; based on the discreteness of the peak-to-peak value of all currents in each verification cycle and the time interval between the corresponding moments of adjacent peaks, the current deviation degree of each verification cycle is determined, and combined with the transmission fluctuation degree, the first abnormality degree of each verification cycle is obtained;

[0008] The imbalance degree of each calibration cycle is determined by the difference in the discrete conditions between the time intervals of two adjacent loading operations and the time intervals of two adjacent unloading operations under each calibration cycle, as well as the difference between the loading quantity and the unloading quantity. The second abnormality degree of each calibration cycle is determined in combination with the discrete conditions of the time intervals of the first calibration of two adjacent electric energy meters; and the imbalance index of each calibration cycle is determined in combination with the first abnormality degree.

[0009] The imbalance indexes of all calibration cycles are clustered, and the affiliation between the cluster to which the current calibration cycle belongs and the remaining clusters is analyzed. Combined with the number of elements in the cluster and the imbalance index, the imbalance significance of the current calibration cycle is determined, and the operation status of the pipeline is evaluated.

[0010] Preferably, determining the transmission fluctuation of each verification period includes:

[0011] For each electric energy meter in each calibration cycle, in two adjacent calibration links, the time interval between the start time of the latter calibration link and the end time of the previous calibration link is recorded as the transmission time of the two adjacent calibration links;

[0012] Calculate the dispersion degree of the transmission time lengths of the two adjacent verification links for all electric energy meters in each verification cycle, and record it as the first dispersion degree;

[0013] The product of the range and the mean of the first discreteness of all the two adjacent verification links in each verification period is calculated as the transmission fluctuation of each verification period.

[0014] Preferably, determining the current deviation of each verification cycle includes:

[0015] Obtain the current peaks at all times within each test cycle; calculate the intervals between the corresponding moments of adjacent peaks;

[0016] The degree of dispersion of the interval durations of all adjacent peaks in each test period is recorded as the second dispersion;

[0017] The degree of dispersion of the peak values ​​corresponding to all the peaks in each test cycle is recorded as the third dispersion;

[0018] The current deviation is a product of the second dispersion and the third dispersion.

[0019] Preferably, the first abnormality degree is a normalized result of the product of the transmission fluctuation degree and the current deviation degree.

[0020] Preferably, determining the degree of imbalance in each verification cycle includes:

[0021] Calculate the discrete degrees of the time intervals between all two adjacent loading operations and all two adjacent unloading operations in each calibration cycle, and record them as the first discrete coefficient and the second discrete coefficient respectively;

[0022] The difference between the first dispersion coefficient and the second dispersion coefficient is recorded as the dispersion difference; the difference between the loading quantity and the unloading quantity is recorded as the quantity difference;

[0023] The imbalance degree is the product of the dispersion difference and the quantity difference.

[0024] Preferably, determining the second abnormality degree of each verification cycle includes:

[0025] The time interval between the start times of the first verification link of two adjacent electric energy meters in each verification cycle is recorded as the initial verification time difference; the dispersion degree of the initial verification time difference of all two adjacent electric energy meters in each verification cycle is recorded as the distribution dispersion;

[0026] The second abnormality degree is a normalized result of the product of the distribution dispersion and the imbalance degree.

[0027] Preferably, determining the imbalance index of each verification cycle includes:

[0028] Recording the difference between the second abnormality degree and the first abnormality degree as a relative difference; calculating the sum of the second abnormality degree and the first abnormality degree;

[0029] The imbalance index is the product of the sum value and the relative difference.

[0030] Preferably, the clustering process is: clustering the imbalance indexes of the current calibration cycle and all previous calibration cycles using a fuzzy clustering algorithm to obtain multiple clusters and the membership degree of the current calibration cycle to each cluster.

[0031] Preferably, determining the imbalance significance of the current verification cycle includes:

[0032] The mean of the difference between the membership of the current test cycle to the cluster to which it belongs and its membership to all other clusters is recorded as the membership difference;

[0033] Calculate the ratio of the imbalance index of the current calibration cycle to the maximum value of the imbalance index of all previous calibration cycles, and record it as a relative ratio;

[0034] Counting the number of elements in the cluster belonging to the current verification period, and calculating the product of the membership difference and the relative comparison;

[0035] The imbalance significance is a normalized result of the ratio of the product value to the quantity.

[0036] Preferably, the evaluating the running state of the pipeline includes: if the imbalance significance is greater than or equal to a preset threshold, then the running state of the pipeline is abnormal; otherwise, the running state of the pipeline is not abnormal.

[0037] This application has at least the following beneficial effects:

[0038] The present application determines the transmission fluctuation of each calibration cycle by analyzing the difference in transmission time between two adjacent calibration links of the electric energy meter, and determines the current deviation of each calibration cycle by the waveform deviation of the output current on the driving motor, and obtains the first abnormality of each calibration cycle. Its beneficial effect is that it reflects the abnormality of the pipeline during the transmission process by integrating the stable output of the current and the difference in the time interval of the electric energy meter transmission, so as to indicate the instability of the operating state of the pipeline and accurately identify non-steady-state transmission problems; secondly, the imbalance of each calibration cycle is calculated by the difference in the interval time of loading and unloading of the pipeline, as well as the difference in the loading quantity and the unloading quantity. Its beneficial effect is that it takes into account the imbalance degree of the pipeline in the loading process and the unloading process to reflect the uneven distribution degree of the electric energy meters on the pipeline; and then determines the second abnormality of each calibration cycle. Its beneficial effect is that it takes into account the unevenness of the calibration time interval of the electric energy meter. Regularity indicates the possibility of production disorder with insufficient or excessive feeding in the assembly line, and further reflects the irrationality of the feeding operation of the assembly line, and the possible degree of abnormality in the operation state of the assembly line; further, the imbalance index of each calibration cycle is determined, and its beneficial effect is that it comprehensively considers the irrationality of feeding in the assembly line and the abnormal transmission of the transmission line, and judges whether there is production disorder or transmission abnormality in the assembly line, so as to reflect the imbalance abnormality in the operation state of the assembly line; the imbalance significance of the current calibration cycle is determined, and the operation state of the assembly line is evaluated. Its beneficial effect is that it considers the abnormal degree of the imbalance significance of the current calibration cycle and the calibration cycle of the historical period, and then judges whether the assembly line deviates significantly from the normal state in the current calibration cycle, which can effectively evaluate the compound fault problem of transmission abnormality and feeding imbalance, improves the perception accuracy of the operation state of the assembly line, reduces the false detection phenomenon of the operation state of the assembly line, and improves the calibration efficiency of the electric energy meter of the assembly line. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The following is a detailed description of an operating status perception and analysis method for an industrial automation assembly line of the present application in conjunction with the accompanying drawings.

[0040] Figure 1 A flowchart of the steps of a method for perceiving and analyzing the operating status of an industrial automation assembly line provided in an embodiment of the present application;

[0041] Figure 2 A flowchart of the steps of the method for obtaining the second abnormality degree of each calibration cycle provided in an embodiment of the present application;

[0042] Figure 3 A flowchart of the steps of the method for obtaining the imbalance index provided in an embodiment of the present application. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the following, in conjunction with the accompanying drawings and implementation examples, further describes in detail a method for sensing and analyzing the operating status of an industrial automation assembly line proposed in this application. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0044] 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 application belongs.

[0045] See also Figure 1 , which shows a flowchart of a method for perceiving and analyzing the operating status of an industrial automation assembly line provided by one embodiment of the present application, the method comprising the following steps:

[0046] Step 1: Obtain the current of the driving motor at each moment in each calibration cycle during the assembly line calibration process of the electric energy meter, record the start time and end time of each calibration link of each electric energy meter, the time of each loading and unloading operation on the assembly line, and the loading and unloading quantity in each calibration cycle.

[0047] The automatic energy meter calibration line integrates meter transmission, meter identification, error verification, meter communication and multi-function testing, appearance inspection, voltage resistance testing, automatic labeling, and automatic grouping. The entire calibration process is computer-controlled, and coupled with the input and output pipeline, this significantly improves calibration efficiency and reduces the workload for calibration personnel. It allows for accurate positioning of each meter and process control, enabling refined management of calibration work.

[0048] Secondly, the automatic calibration line system of the electric energy meter tests the various performance indicators of the electric energy meter according to the calibration standards to test whether its various performance indicators are normal and can operate in a state where it can work. The purpose is to calibrate the electric energy meter.

[0049] The automatic calibration line system for electric energy meters includes: a loading unit, which is used to place the electric energy meters that need to be calibrated on the transmission line through a loading robot; a calibration link unit, which is used to calibrate the various performances of each electric energy meter; and an unloading unit, which is used to distinguish between qualified and unqualified electric energy meters, and then use an unloading robot to unload and store electric energy meters of different qualities from the transmission line. Therefore, the specific calibration process is as follows: the electric energy meters that need to be calibrated are transported to the automated assembly line connection port, and then reach the robot loading unit via a roller line. The robot places the electric energy meters on the transmission line, and then performs various performance calibrations on the electric energy meters in turn. The qualified electric energy meters are packaged and labeled, and the unqualified electric energy meters are sorted into the unqualified area. Finally, the unloading robot unloads the calibrated electric energy meters in a unified manner, thus realizing the automated assembly line process of electric energy meter calibration.

[0050] During the electric energy meter calibration process, the current of the transmission belt drive motor at different times in each calibration cycle is obtained in real time;

[0051] In this embodiment, the collection time interval is 1 second, and the duration of the verification cycle is 20 minutes. As other implementation methods, the implementer can set them according to actual conditions.

[0052] Obtain the start and end time of each calibration link for each electric energy meter in each calibration cycle;

[0053] It should be noted that, for the sake of ease of understanding, it is assumed that the various calibration links include three calibration links: voltage resistance test, power consumption test, and communication test. After the electricity meter A is transported to the voltage resistance test area via a conveyor belt for testing, the electricity meter A is transferred to the power consumption test area. After completing the power consumption test, the electricity meter A is transferred to the communication test area.

[0054] Obtain the time interval between two adjacent loading operations performed by the loading robot in each calibration cycle, and the time interval between two adjacent unloading operations performed by the unloading robot in each calibration cycle, and count the number of loading and unloading operations in each calibration cycle;

[0055] The collected data is normalized to eliminate the dimensionality effect. In this embodiment, the maximum-minimum normalization method is used for normalization. The maximum-minimum normalization method is a well-known technology and will not be described here. As other implementation methods, the implementer may adopt other methods of the existing technology, such as the Z-score normalization method, etc. This embodiment does not impose any special restrictions on this.

[0056] At this point, the current at each moment in each calibration cycle, the start and end time of each calibration link of each electric energy meter, as well as the time interval between two adjacent loading operations and two adjacent unloading operations, the loading quantity and the unloading quantity are obtained.

[0057] Step 2: Determine the transmission fluctuation of each calibration cycle based on the discreteness and extreme range of the difference between the start time of each calibration link and the end time of the previous calibration link of the electric energy meter in each calibration cycle; determine the current deviation of each calibration cycle based on the discreteness of the peak-to-peak value of all currents in each calibration cycle and the time intervals between the corresponding moments of adjacent peaks, and obtain the first abnormality of each calibration cycle in combination with the transmission fluctuation.

[0058] When the automated production line is operating normally and the transmission link is stable, the transmission time between adjacent performance verification links for different single-phase meters should be nearly identical. Conversely, if the transmission line experiences an anomaly such as slack, slippage, or jamming, the transmission time differences between the same adjacent performance verification links for different single-phase meters can significantly increase. Therefore, by analyzing the difference in transmission time between two adjacent verification links for different electricity meters, as well as the changes in current, we can calculate the first degree of abnormality and thus provide a preliminary understanding of whether there are any abnormalities in the automated production line's transmission function.

[0059] First, analyze the fluctuation of the transmission time between adjacent verification links of different electric energy meters under each verification cycle and calculate the transmission fluctuation degree, specifically:

[0060] For each electric energy meter in each calibration cycle, in two adjacent calibration links, the time interval between the start time of the latter calibration link and the end time of the previous calibration link is recorded as the transmission time of the two adjacent calibration links;

[0061] Calculate the dispersion degree of the transmission time lengths of the two adjacent verification links for all electric energy meters in each verification cycle, and record it as the first dispersion degree;

[0062] In this embodiment, the degree of discreteness is obtained by calculating the standard deviation of the transmission time of all the electric energy meters in the two adjacent calibration links under each calibration cycle. As other implementation methods, the implementer can adopt other methods of the existing technology, such as coefficient of variation, variance, etc. This embodiment does not impose any special restrictions on this.

[0063] Calculating the product of the range and the mean of the first dispersion of all the two adjacent verification links in each verification cycle as the transmission fluctuation of each verification cycle;

[0064] It should be noted that the larger the first discreteness is, the more significant the difference in transmission time between two adjacent calibration links of different electricity meters is. The larger the transmission fluctuation is, the poorer the transmission consistency between different electricity meters under the calibration cycle is. The worse the stability of the pipeline in the transmission link is, the greater the possibility of abnormality in the transmission line in the pipeline.

[0065] Secondly, when the transmission line is transporting the energy meter normally, the current fluctuation of the transmission line drive motor will be relatively stable. If the transmission line has an abnormal phenomenon such as a loose belt, the load of the drive motor will change, causing the drive motor current to fluctuate abnormally and no longer resemble a sine wave. Therefore, the current change trend within each calibration cycle is analyzed and the current deviation is calculated, specifically:

[0066] Obtain the peak value of the current at all times within each verification cycle;

[0067] In this embodiment, the AMPD (Automatic Multiscale-based Peak Detection) algorithm is used to obtain the peak, wherein the AMPD algorithm is a well-known technology and will not be described here. As other implementation methods, the implementer can adopt other methods of the existing technology, such as the differential method, etc. This embodiment does not impose any special restrictions on this.

[0068] Calculate the interval length between the corresponding moments of adjacent peaks; record the degree of dispersion of the interval lengths of all adjacent peaks in each verification period as the second dispersion;

[0069] The degree of dispersion of the peak values ​​corresponding to all the peaks in each test cycle is recorded as the third dispersion;

[0070] In this embodiment, the degree of dispersion is measured by calculating the standard deviation of the interval durations of all adjacent peaks in each verification period; the standard deviation of the peak values ​​corresponding to all peaks in each verification period is recorded as the third degree of dispersion.

[0071] The product of the second dispersion and the third dispersion is used as the current deviation of each test cycle;

[0072] It should be noted that the larger the second discreteness is, the greater the fluctuation of the time interval of the current peak is; the larger the third discreteness is, the greater the fluctuation of the current peak is, which means that the current output by the drive motor is unstable; the larger the current deviation is, the abnormal fluctuation of the current output by the drive motor is, the more likely there is an abnormality in the transmission line, and the worse the stability of the operation state of the assembly line is.

[0073] Furthermore, based on the transmission fluctuation and the current deviation, a first abnormality degree is determined, specifically:

[0074] Normalizing the product of the transmission fluctuation and the current deviation as the first abnormality of each test cycle;

[0075] In this embodiment, the sigmoid function is used for normalization processing, wherein the sigmoid function is a well-known technology and will not be described in detail here. As other implementation methods, the implementer can adopt other methods of the existing technology, such as the tanh function, etc. This embodiment does not impose any special restrictions on this.

[0076] It should be noted that the greater the first abnormality degree is, the greater the possibility that an abnormality occurs in the transmission line of the automated assembly line.

[0077] At this point, the first abnormality degree of each test period is obtained.

[0078] Step 3, determine the imbalance degree of each calibration cycle through the difference in the discrete conditions between the time intervals between two adjacent loading operations and the time intervals between two adjacent unloading operations in each calibration cycle, as well as the difference between the loading quantity and the unloading quantity. Combined with the discrete conditions of the time intervals between the first calibration of two adjacent electricity meters, determine the second abnormality of each calibration cycle.

[0079] Furthermore, in automated production lines, the amount of material loaded by the loading robot directly affects the calibration rhythm and efficiency of the entire production line. If the loading amount is insufficient, the distribution of energy meters on the production line will become sparse, resulting in a longer interval between the calibration of two adjacent energy meters in the first calibration step. On the other hand, if the loading amount is excessive, the distribution of energy meters on the production line will be dense, and the interval between the calibration of two adjacent energy meters in the first calibration step will be shortened.

[0080] Furthermore, the flowchart of the method for obtaining the second abnormality degree of each test cycle provided in the embodiment of the present application is as follows: Figure 2 shown.

[0081] First, by analyzing the time interval between two adjacent electric energy meters, the distribution dispersion is calculated, specifically:

[0082] The time interval between the start time of the first verification link of two adjacent electric energy meters in each verification cycle is recorded as the initial verification time difference;

[0083] The dispersion degree of the initial inspection time difference of all two adjacent electric energy meters in each inspection cycle is recorded as distribution dispersion;

[0084] In this embodiment, the degree of dispersion is obtained by calculating the variance of the initial detection time difference between all two adjacent electric energy meters in each detection period.

[0085] It should be noted that the distribution dispersion reflects the distribution of the electricity meters on the assembly line, indicating whether the loading rate of the loading robot is relatively stable. The larger the distribution dispersion, the more irregular the calibration time interval of the electricity meters, and the greater the change in the distribution of the electricity meters, indicating that the loading operation of the loading robot is more unreasonable.

[0086] Secondly, in automated production lines, a balance between loading and unloading is crucial for maintaining smooth operation. Normally, the loading and unloading rates should be consistent, ensuring a stable number of meters on the line and uniform transmission speeds, thus ensuring smooth verification. Conversely, an imbalance between loading and unloading rates can easily lead to sparse or congested meters on the line, compromising overall line efficiency and verification quality.

[0087] Therefore, by analyzing the balance between the loading and unloading amounts in each calibration cycle, as well as the difference in the time interval between two adjacent loading operations and the time interval between two adjacent unloading operations, the imbalance degree is calculated to reflect whether there is insufficient or excessive loading. Specifically:

[0088] Calculate the degree of dispersion of the time intervals between all two adjacent loading operations in each calibration cycle, and record it as the first dispersion coefficient;

[0089] Calculate the degree of dispersion of the time intervals between all two adjacent blanking operations in each calibration cycle, and record it as the second dispersion coefficient;

[0090] In this embodiment, the degree of dispersion is measured by calculating the standard deviation of the time intervals between two adjacent loading operations in each verification cycle and the standard deviation of the time intervals between two adjacent unloading operations in each verification cycle.

[0091] Calculate the difference between the first dispersion coefficient and the second dispersion coefficient, and record it as dispersion difference;

[0092] Calculate the difference between the loading quantity and the unloading quantity in each verification cycle, and record it as the quantity difference;

[0093] In this embodiment, the absolute value of the difference between the first discrete coefficient and the second discrete coefficient is calculated and recorded as the discrete difference; the absolute value of the difference between the loading quantity and the unloading quantity in each calibration cycle is calculated and recorded as the quantity difference.

[0094] The product of the discrete difference and the quantity difference is used as the imbalance degree of each verification cycle;

[0095] It should be noted that the larger the discrete difference is, the less consistent the time intervals between the loading and unloading operations in the automated assembly line are; the larger the quantity difference is, the greater the difference between the loading amount of the loading robot and the unloading amount of the unloading robot in the automated assembly line is; the greater the resulting imbalance is, the more unbalanced the loading and unloading are in the automated assembly line.

[0096] Furthermore, based on the distribution dispersion and the imbalance, a second abnormality degree is calculated, specifically:

[0097] Normalizing the product of the distribution dispersion and the imbalance degree as the second abnormality degree of each test cycle;

[0098] In this embodiment, the sigmoid function is used for normalization processing, wherein the sigmoid function is a well-known technology and will not be described in detail here. As other implementation methods, the implementer can adopt other methods of the existing technology, such as the tanh function, etc. This embodiment does not impose any special restrictions on this.

[0099] It should be noted that the greater the second abnormality degree is, the greater the possibility of production disorder caused by imbalanced feeding in the assembly line is, and the more likely the operation status of the assembly line is to be abnormal.

[0100] At this point, the second abnormality degree of each test period is obtained.

[0101] Step 4: Based on the first abnormality and the second abnormality, determine the imbalance index of each calibration cycle; cluster the imbalance indices of all calibration cycles, analyze the affiliation between the current calibration cycle and the cluster to which it belongs and the remaining clusters, and determine the imbalance significance of the current calibration cycle based on the number of elements in the cluster and the imbalance index, and evaluate the operation status of the pipeline.

[0102] Furthermore, based on the second abnormality and the first abnormality, an imbalance index is determined, specifically:

[0103] Calculating a difference between the second abnormality degree and the first abnormality degree, and recording it as a relative difference;

[0104] In this embodiment, the absolute value of the difference between the second abnormality degree and the first abnormality degree is calculated and recorded as the relative difference.

[0105] Calculating a sum of the second abnormality degree and the first abnormality degree, and multiplying the sum by the relative difference as an imbalance index for each calibration cycle;

[0106] It should be noted that when insufficient feeding occurs and the pipeline transmission is normal, the distribution of electric energy meters on the pipeline will be sparse. The larger the second abnormality and the smaller the first abnormality, the larger the relative difference and the larger the imbalance index. When excessive feeding occurs, it will lead to local dense areas and sparse areas on the pipeline. The larger the second abnormality, the more congestion will be caused by excessive feeding, resulting in changes in the load, which will increase the first abnormality and the relative difference will be small, but the sum value will increase, and the imbalance index will increase relatively. Secondly, if the feeding operation is normal and the pipeline transmission is normal, the second abnormality and the first abnormality are small, the relative difference and the sum value are relatively small, and the imbalance index is also small. Therefore, on the whole, the sum value of the abnormal pipeline is relatively larger than the sum value of the normal state. The larger the imbalance index, the greater the possibility of abnormality in the automated pipeline, and thus the greater the possibility that the pipeline operation state is no longer stable and is in an abnormal state during the calibration cycle. Secondly, the flow chart of the steps of the method for obtaining the imbalance index provided in the embodiment of the present application is as follows: Figure 3 shown.

[0107] Furthermore, the imbalance significance is obtained by comparing the current calibration cycle's imbalance index with that of multiple calibration cycles, specifically:

[0108] Clustering the imbalance indexes of the current verification cycle and all previous verification cycles to obtain multiple clusters and the membership degree of the current verification cycle to each cluster;

[0109] In this embodiment, a fuzzy C-means clustering algorithm is used for clustering. The fuzzy C-means clustering algorithm is a well-known technology and will not be described in detail here. The elbow rule is used in the fuzzy C-means clustering algorithm to obtain the optimal number of clusters.

[0110] The mean of the difference between the membership of the current test cycle to the cluster to which it belongs and its membership to all other clusters is recorded as the membership difference;

[0111] In this embodiment, the average of the absolute values ​​of the differences between the membership of the current test period to the cluster to which it belongs and its membership to all other clusters is recorded as the membership difference.

[0112] Calculate the ratio of the imbalance index of the current calibration cycle to the maximum value of the imbalance index of all previous calibration cycles, and record it as a relative ratio;

[0113] Counting the number of elements in the clusters belonging to the current verification period, calculating the product of the membership difference and the relative ratio, and taking the normalized result of the ratio of the product to the number as the imbalance significance of the current verification period;

[0114] In this embodiment, the sigmoid function is used for normalization processing, wherein the sigmoid function is a well-known technology and will not be described in detail here. As other implementation methods, the implementer can adopt other methods of the existing technology, such as the tanh function, etc. This embodiment does not impose any special restrictions on this.

[0115] It should be noted that the affiliation difference reflects the correlation between the current calibration cycle and all clusters. The larger the affiliation difference, the stronger the correlation of the current calibration cycle to the cluster to which it belongs, and the weaker the correlation to the other clusters. The greater the difference between the data of the current calibration cycle and the data of the other calibration cycles in the historical period, the more likely there is a significant disordered feeding phenomenon. Secondly, the relative contrast reflects the degree of abnormality of the operating status of the pipeline in the current calibration cycle and its proximity to the other calibration cycles in the historical period. The larger the relative contrast, the more likely it is that the operating status of the pipeline in the current calibration cycle is more abnormal. The smaller the number, the more likely the cluster to which the current calibration cycle belongs is a significantly prominent cluster, the more likely the operating status of the pipeline in the corresponding calibration cycle in the cluster is less common, and the greater the significance of the imbalance, the greater the difference between the operating status of the pipeline in the current calibration cycle and the operating status in the historical period, thereby reflecting the greater the possibility of abnormal operating status of the pipeline.

[0116] It should be noted that if the number of clusters after the final clustering is 1, it means that the operating status of the current calibration cycle is consistent with that of the historical period, reflecting that there is no significant abnormal change in the operating data of the pipeline under the current calibration cycle. At this time, the imbalance significance of the current calibration cycle is set to 0.

[0117] Based on the imbalance significance, the operating status of the pipeline is evaluated, specifically:

[0118] If the imbalance significance is greater than or equal to a preset threshold, then the pipeline is in an abnormal state; otherwise, the pipeline is in an abnormal state;

[0119] In this embodiment, the preset threshold value is 0.5. As for other implementation methods, the implementer can set it according to actual conditions.

[0120] It should be noted that when the operation status of the assembly line is abnormal, the operation of the assembly line should be adjusted or repaired in a timely manner to ensure stable and efficient calibration operation of the electricity meter.

[0121] It should be understood that although Figure 1The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0122] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0123] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the present application. It should be noted that a person skilled in the art can make various modifications and improvements without departing from the spirit of the present application. Therefore, any simple modifications, equivalent variations, and modifications to the above embodiments made in accordance with the technical essence of the present application without departing from the content of the present application's technical solution fall within the scope of protection of the present application's technical solution.

Claims

1. A method for perceiving and analyzing the operating status of an industrial automation assembly line, characterized in that: The method comprises the following steps: Obtain the current of the drive motor at each moment in each calibration cycle when the electric energy meter is being calibrated on the assembly line. Record the start and end time of each calibration link for each electric energy meter, the time of each loading and unloading operation on the assembly line, and the loading and unloading quantity in each calibration cycle. Based on the discreteness and extreme range of the difference between the start time of each verification link and the end time of the previous verification link in each verification cycle, the transmission fluctuation degree of each verification cycle is determined; based on the discreteness of the peak-to-peak value of all currents in each verification cycle and the time interval between the corresponding moments of adjacent peaks, the current deviation degree of each verification cycle is determined, and combined with the transmission fluctuation degree, the first abnormality degree of each verification cycle is obtained; The imbalance degree of each calibration cycle is determined by the difference in the discrete conditions between the time intervals of two adjacent loading operations and the time intervals of two adjacent unloading operations under each calibration cycle, as well as the difference between the loading quantity and the unloading quantity. The second abnormality degree of each calibration cycle is determined in combination with the discrete conditions of the time intervals of the first calibration of two adjacent electric energy meters; and the imbalance index of each calibration cycle is determined in combination with the first abnormality degree. The imbalance indexes of all calibration cycles are clustered, and the affiliation between the cluster to which the current calibration cycle belongs and the remaining clusters is analyzed. Combined with the number of elements in the cluster and the imbalance index, the imbalance significance of the current calibration cycle is determined, and the operation status of the pipeline is evaluated.

2. The method for sensing and analyzing the operating status of an industrial automation assembly line according to claim 1, wherein: Determining the transmission fluctuation of each verification period includes: For each electric energy meter in each calibration cycle, in two adjacent calibration links, the time interval between the start time of the latter calibration link and the end time of the previous calibration link is recorded as the transmission time of the two adjacent calibration links; Calculate the dispersion degree of the transmission time lengths of the two adjacent verification links for all electric energy meters in each verification cycle, and record it as the first dispersion degree; The product of the range and the mean of the first discreteness of all the two adjacent verification links in each verification period is calculated as the transmission fluctuation of each verification period.

3. The method for sensing and analyzing the operating status of an industrial automation assembly line according to claim 1, wherein: Determining the current deviation of each verification cycle includes: Obtain the current peaks at all times within each test cycle; calculate the intervals between the corresponding moments of adjacent peaks; The degree of dispersion of the interval durations of all adjacent peaks in each test period is recorded as the second dispersion; The degree of dispersion of the peak values ​​corresponding to all the peaks in each test cycle is recorded as the third dispersion; The current deviation is a product of the second dispersion and the third dispersion.

4. The method for sensing and analyzing the operating status of an industrial automation assembly line according to claim 1, wherein: The first abnormality degree is a normalized result of the product of the transmission fluctuation degree and the current deviation degree.

5. The method for sensing and analyzing the operating status of an industrial automation assembly line according to claim 1, wherein: Determining the imbalance degree of each verification cycle includes: Calculate the discrete degrees of the time intervals between all two adjacent loading operations and all two adjacent unloading operations in each calibration cycle, and record them as the first discrete coefficient and the second discrete coefficient respectively; The difference between the first dispersion coefficient and the second dispersion coefficient is recorded as the dispersion difference; the difference between the loading quantity and the unloading quantity is recorded as the quantity difference; The imbalance degree is the product of the dispersion difference and the quantity difference.

6. The method for sensing and analyzing the operating status of an industrial automation assembly line according to claim 1, wherein: Determining the second abnormality degree of each verification cycle includes: The time interval between the start times of the first verification link of two adjacent electric energy meters in each verification cycle is recorded as the initial verification time difference; the dispersion degree of the initial verification time difference of all two adjacent electric energy meters in each verification cycle is recorded as the distribution dispersion; The second abnormality degree is a normalized result of the product of the distribution dispersion and the imbalance degree.

7. The method for sensing and analyzing the operating status of an industrial automation assembly line according to claim 1, wherein: Determining the imbalance index of each verification cycle includes: Recording the difference between the second abnormality degree and the first abnormality degree as a relative difference; calculating the sum of the second abnormality degree and the first abnormality degree; The imbalance index is the product of the sum value and the relative difference.

8. The method for perceiving and analyzing the operating status of an industrial automation assembly line according to claim 1, wherein: The clustering process is: clustering the imbalance indexes of the current verification cycle and all previous verification cycles using a fuzzy clustering algorithm, obtaining multiple clusters and the membership degree of the current verification cycle to each cluster.

9. The method for sensing and analyzing the operating status of an industrial automation assembly line according to claim 8, wherein: Determining the imbalance significance of the current verification cycle includes: The mean of the difference between the membership of the current test cycle to the cluster to which it belongs and its membership to all other clusters is recorded as the membership difference; Calculate the ratio of the imbalance index of the current calibration cycle to the maximum value of the imbalance index of all previous calibration cycles, and record it as a relative ratio; Counting the number of elements in the cluster belonging to the current verification period, and calculating the product of the membership difference and the relative comparison; The imbalance significance is a normalized result of the ratio of the product value to the quantity.

10. The method for sensing and analyzing the operating status of an industrial automation assembly line according to claim 1, wherein: The evaluating the running state of the pipeline includes: if the imbalance significance is greater than or equal to a preset threshold, then the running state of the pipeline is abnormal; otherwise, the running state of the pipeline is not abnormal.

Images