Operation state monitoring method of digital controller

Abstract

The invention relates to the technical field of data processing, and particularly discloses an operation state monitoring method of a digital controller, which comprises the following steps of: acquiring # imgabs0 # utilization rates of the digital controller at different moments; the sample entropy of the # imgabs1 # utilization rate sequence at any moment and the previous moment is calculated through a sample entropy algorithm, and the # imgabs2 # utilization rate sequence is the # imgabs3 # utilization rate of the preset duration before the corresponding moment; the abnormal degree of the # imgabs4 # utilization rate at any moment is calculated, when the abnormal degree is larger than a threshold value, it is judged that the # imgabs5 # utilization rate at the moment is abnormal, an alarm is given, and corresponding equipment is controlled to stop running. According to the operating state monitoring method of the digital controller provided by the invention, an abnormal detection result is more accurate, and an abnormal condition can be found in time, so that the operating state monitoring effect of the digital controller is better.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to a method for monitoring the operating status of a digital controller. Background Art

[0002] With the development of intelligent manufacturing, more and more industrial processes need to rely on accurate and efficient calculation and control. As a core component of industrial automation, the operating status of digital controllers directly affects the reliability and stability of equipment. The operating status of digital controllers is usually measured by their Utilization rate reflects The more complex the utilization rate changes over a period of time, the greater the possibility of potential risks in the digital controller and the higher the possibility of failure. It is necessary to be vigilant and take measures to ensure the high reliability of the equipment.

[0003] With the rapid development of computer technology and anomaly detection algorithms, the use of anomaly detection algorithms to monitor the operating status of digital controllers has achieved good results. Among them, the sample entropy algorithm is a measurement method for evaluating the complexity of time series data, which can be used for anomaly detection. This is because abnormal data usually increases the complexity of time series data. Therefore, by calculating the complexity of time series data through sample entropy, the abnormal situation of the time series data can be reflected, thereby realizing the monitoring of the operating status of the digital controller. Specifically, the sample entropy algorithm includes the following steps: constructing multiple subsequences of length m based on time series data; calculating the Chebyshev distance between any subsequence and other subsequences, obtaining the ratio of the number of Chebyshev distances less than the similarity tolerance threshold to the total number, and calculating the mean of the corresponding ratios of all subsequences as the similarity mean; constructing multiple subsequences of length m+1 based on time series data, and repeating the previous step; taking the difference between the natural logarithm of the similarity mean of the subsequence of length m and the natural logarithm of the similarity mean of the subsequence of length m+1 as the sample entropy. The larger the sample entropy, the higher the complexity of the time series data and the higher the possibility of abnormal data.

[0004] However, in collecting When using the data, the electromagnetic interference in the industrial environment will affect The normal collection of utilization data leads to Utilization data is inaccurate. When calculating the Chebyshev distance between two subsequences, noise will affect the accuracy of the result, which will lead to a decrease in the accuracy of the final sample entropy, that is, the accuracy of the anomaly detection result is low. In addition, the existing sample entropy algorithm can only determine whether there is an anomaly in the time series data by the complexity of the time series data, but cannot locate the time when the abnormal data occurs. Therefore, the anomaly cannot be discovered in time, resulting in late reporting. Summary of the invention

[0005] The present invention provides a method for monitoring the operating status of a digital controller, aiming to solve the technical problems in the prior art of low accuracy of abnormal detection results and failure to timely discover abnormal situations.

[0006] The operating state monitoring method of the digital controller of the present invention comprises the following steps: Get the digital controller at different times Utilization rate; The sample entropy algorithm is used to calculate the The sample entropy of the utilization sequence, The utilization sequence is the preset time before the corresponding moment. Utilization rate; The sample entropy algorithm includes an improved similarity tolerance threshold, which is positively correlated with the initial similarity tolerance threshold and the noise interference degree of the subsequence group; the subsequence group includes The noise interference degree is positively correlated with the prominence of the subsequence group and the The correlation between utilization rate and corresponding traffic data is negatively correlated; the prominence is positively correlated with the Chebyshev distance between two subsequences in the subsequence group, negatively correlated with the mean of the Chebyshev distance of all subsequence groups, and negatively correlated with the average distance of the subsequence group; the average distance is the distance between two subsequences in the subsequence group. The mean of the absolute values ​​of the utilization differences; Calculate the The abnormal degree of utilization. When the abnormal degree is greater than the threshold, the If there is an abnormality in utilization, an alarm will be issued and the corresponding equipment will be controlled to stop running; The abnormality degree is the difference between any moment and the previous moment. The normalized difference between the sample entropy of the utilization sequence and the value corresponding to any moment and the previous moment The product of the differences in the means of the utilization series normalized.

[0007] In the above scheme, by calculating the noise interference degree of the subsequence group, the corresponding improved similarity tolerance threshold is obtained, which can avoid the problem that the calculated sample entropy is inaccurate due to noise interference, and thus the abnormal situation of the digital controller operation state cannot be accurately judged. The abnormal degree of utilization can monitor the operating status of the digital controller in real time.

[0008] Preferably, the improved similarity tolerance threshold is: ; In the formula, For the of the moment The utilization rate sequence The improved similarity tolerance threshold for subsequence groups, For the of the moment The utilization rate sequence The noise interference degree of the subsequence groups is is the initial similarity tolerance threshold, is the standard normalization function.

[0009] In the above scheme, the improved similarity tolerance threshold is adaptively increased according to the noise interference degree of the subsequence group, thereby solving the problem of inaccurate sample entropy calculation.

[0010] Preferably, the noise interference degree is: ; In the formula, For the of the moment The utilization rate sequence The noise interference degree of the subsequence groups is For the of the moment The utilization rate sequence The prominence of the subsequence groups, For the of the moment The utilization rate sequence The first subsequence The correlation between utilization and corresponding traffic data, The natural constant The exponential function of base .

[0011] In the above scheme, the prominence of the subsequence group and the The correlation between the utilization rate and the corresponding flow data characterizes the degree of noise interference, thereby avoiding the problem of inaccurate calculation of the degree of noise interference due to flow data.

[0012] Preferably, the protrusion degree is: ; In the formula, For the of the moment The utilization rate sequence The prominence of the subsequence groups, For the of the moment The utilization rate sequence The Chebyshev distance between two subsequences in a subsequence group, For the of the moment The utilization rate sequence The average distance of subsequence groups, For the of the moment The total number of subsequence groups in the utilization sequence, is the standard normalization function.

[0013] In the above scheme, the prominence degree is characterized by the difference between the Chebyshev distance of two subsequences in a subsequence group and the average distance of the subsequence group and the difference between the Chebyshev distance of all subsequence groups, so that the calculation result is more accurate and the influence caused by accidental errors is prevented.

[0014] Preferably, the correlation is: ; In the formula, For the of the moment The utilization rate sequence The first subsequence The correlation between utilization and corresponding traffic data, For the of the moment The utilization rate sequence The first subsequence The time difference between the maximum utilization rate and the maximum corresponding flow data, For the of the moment The utilization rate sequence The first subsequence The absolute value of the difference between the utilization rate and the number of extreme points of the corresponding flow data, is the preset parameter, , The natural constant The exponential function of base .

[0015] In the above scheme, by calculating The difference between the time when the utilization rate reaches its maximum value and the time when the corresponding flow data reaches its maximum value, and the subsequence The difference between the number of extreme points of utilization and the number of extreme points of corresponding flow data can represent Correlation of utilization rate with corresponding traffic data.

[0016] Preferably, the correlation is: ; In the formula, For the of the moment The utilization rate sequence The first subsequence The correlation between utilization and corresponding traffic data, For the of the moment The utilization rate sequence The first subsequence Utilization rate, For the of the moment The utilization rate sequence The first The subsequences correspond to the traffic data. is the Pearson correlation coefficient function.

[0017] In the above scheme, the correlation is represented by the Pearson correlation coefficient, which makes the calculation simple, small in amount, intuitive and easy to explain.

[0018] Preferably, the sample entropy algorithm is used to calculate the corresponding time The sample entropy of the utilization sequence includes the following steps: Utilize the rate sequence to obtain multiple subsequences of preset length; Calculate the Chebyshev distance between any subsequence and the subsequence group composed of the remaining subsequences, obtain the ratio of the number of subsequence groups whose Chebyshev distance is less than the corresponding improved similarity tolerance threshold to the total number of subsequence groups, and calculate the average of the corresponding ratios of all subsequences as the similarity mean; Get multiple subsequences of the preset length plus 1 again, and repeat the previous step; The difference between the natural logarithm of the similarity mean of multiple subsequences of the preset length and the natural logarithm of the similarity mean of multiple subsequences of the preset length plus 1 is taken as the Sample entropy of the utilization series.

[0019] Preferably, the The utilization rate is collected once per second.

[0020] Preferably, the initial similarity tolerance threshold is a preset coefficient and Each of the utilization rate series The product of the standard deviation of the utilization rate; the value range of the preset coefficient is [0.10, 0.25].

[0021] Preferably, the threshold value ranges from [0.7, 0.8].

[0022] The beneficial effects are: The scheme of the present invention obtains an improved similarity tolerance threshold by introducing the degree of noise interference, which can avoid the problem that the Chebyshev distance of two subsequences is too large due to noise interference, affecting the accuracy of the calculated sample entropy, and thus accurately judge the abnormality of the running state of the digital controller. Moreover, by comparing any moment with the previous moment The sample entropy of the utilization sequence and the corresponding The mean of the utilization rate sequence can be obtained at any time The abnormal degree of utilization rate can realize real-time monitoring of the operating status of the digital controller. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 The present invention is a flowchart of a method for monitoring the operating status of a digital controller according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, but should not be construed as limiting the present invention.

[0025] like Figure 1 As shown, the present invention provides a method for monitoring the operating status of a digital controller, comprising the following steps: S1. Get the digital controller at different times Utilization rate.

[0026] In this step, The utilization factor reflects the digital controller’s The degree of occupancy by the program over a period of time, indicating the workload conditions. The more complex the utilization rate changes over a period of time, the more The greater the possibility of abnormal utilization, the greater the possibility of potential risks in the digital controller and the higher the possibility of failure. Utilization rate is calculated and analyzed to monitor the operating status of the digital controller.

[0027] It can be obtained by inputting corresponding instructions through the operating system of the digital controller. Utilization rate. The operating system of the digital controller is System, you can enter " "or" "Command to view Of course, the operating system of the digital controller can also be system. The collection frequency of utilization is 1 time per second. Of course, the collection frequency can be adjusted as needed.

[0028] S2, use the sample entropy algorithm to calculate any moment and its previous moment The sample entropy of the utilization sequence, The utilization sequence is the preset time before the corresponding moment. Utilization rate.

[0029] In this step, the sample entropy algorithm belongs to the prior art and is a method for measuring the complexity of time series. The complexity of a time series is evaluated by calculating the probability of a new pattern in the time series. The higher the sample entropy, the higher the complexity of the time series, and the greater the possibility that the time series contains abnormal data. Therefore, the sample entropy algorithm can be used to calculate the probability of a new pattern in a time series. The sample entropy of utilization rate reflects the size of sample entropy during that period of time. The complexity of utilization rate. When the sample entropy is greater than the set value, it is considered that the The utilization rate contains abnormal data, which enables the operating status of the digital controller to be monitored.

[0030] The sample entropy algorithm is used to calculate the corresponding time The specific steps of utilizing the sample entropy of the rate sequence are as follows: S21, based on Utilize the rate sequence to obtain multiple subsequences of preset length; In this step, the preset duration is set to Seconds, that is The utilization sequence is before any time Seconds Utilization. The preset length is set to , that is, the subsequence includes continuous moment Utilization rate. The utilization rate sequence can be obtained subsequences. Among them, Can be set to 150, It can be set to 20. Of course, the preset duration and preset length can be adjusted as needed.

[0031] S22. Calculate the Chebyshev distance between any subsequence and the subsequence group consisting of the remaining subsequences, obtain the ratio of the number of subsequence groups whose Chebyshev distance is less than the corresponding improved similarity tolerance threshold to the total number of subsequence groups, and calculate the average of the corresponding ratios of all subsequences as the similarity average.

[0032] The subsequence groups in this application are Any two subsequences of equal length in the utilization sequence, and the subsequences are composed of consecutive moments Utilization composition.

[0033] The purpose of this step is to calculate The preset length in the utilization sequence is The similarity mean between the subsequences of , specifically includes the following steps: S221. Calculate the Chebyshev distance between any subsequence and the subsequence group consisting of the remaining subsequences.

[0034] Among them, the Chebyshev distance is the correspondence between two subsequences in the subsequence group. The maximum absolute value of the difference in utilization rate represents the maximum distance between two subsequences.

[0035] S222, obtaining the ratio of the number of subsequence groups whose Chebyshev distance is less than the corresponding improved similarity tolerance threshold to the total number of subsequence groups. This step also includes the following steps: S2221. Obtain an initial similarity tolerance threshold.

[0036] In the prior art, the initial similarity tolerance threshold is an empirical value, generally Each of the utilization rate series 0.10 to 0.25 times the standard deviation of the utilization rate. Preferably, the initial similarity tolerance threshold in this application is Each of the utilization rate series 0.20 times the standard deviation of the utilization rate.

[0037] If the Chebyshev distance between two subsequences in a subsequence group is less than the initial similarity tolerance threshold, it is considered that the similarity between the two subsequences in the subsequence group is high. However, in industrial environments, there are noises such as electromagnetic interference and mechanical vibration, which can affect Normal collection of utilization leads to The utilization rate is not accurate. Due to the existence of noise, the The degree of confusion of utilization rate increases, which makes the calculated Chebyshev distance of two subsequences in the subsequence group larger, resulting in the Chebyshev distance being greater than the initial similarity tolerance threshold. The sample entropy algorithm will consider that the similarity of the two subsequences in the subsequence group is low. In other words, in the prior art, due to the presence of noise, the calculation of the Chebyshev distance of the two subsequences in the subsequence group will be affected, which will eventually lead to inaccurate sample entropy calculation results and fail to well implement the operation status monitoring of the digital controller. The reason for the above problem is noise interference. Therefore, according to the degree of noise interference of the subsequence group, an improved similarity tolerance threshold can be obtained to solve the problem of inaccurate sample entropy calculation.

[0038] S2222. Obtain the noise interference degree of the subsequence group.

[0039] The noise interference degree is positively correlated with the prominence of the subsequence group and The correlation between utilization and corresponding traffic data is inversely correlated. It can be understood that the greater the prominence of the subsequence group, the greater the impact of noise interference on the subsequence group, and the greater the noise interference of the subsequence group. In addition, some real large Utilization and noise interference The utilization rate will be similar, so when calculating these real larger The utilization rate will also lead to a greater degree of noise interference, which cannot truly reflect the impact of noise interference, making the calculation results inaccurate. There is a strong correlation between the utilization rate and the corresponding traffic data. This is because when the traffic data increases, The traffic data needs to be processed, including receiving, parsing, protocol processing, encryption / decryption, etc. The utilization rate will also increase accordingly. When there is a strong correlation between the utilization rate and the corresponding traffic data, it indicates that The higher utilization rate is caused by the processing of more traffic data, which is real data, not caused by noise interference, so the corresponding noise interference is also smaller. Therefore, this step also needs to obtain the prominence of the subsequence group and the The correlation between the utilization rate and the corresponding flow data. Specifically, this step also includes the following steps: First, obtain the prominence of the subsequence groups.

[0040] The prominence is positively correlated with the Chebyshev distance between two subsequences in the subsequence group, negatively correlated with the mean of the Chebyshev distances of all subsequence groups, and negatively correlated with the average distance of the subsequence group. The mean of the absolute values ​​of the utilization rate differences. This is because the larger the Chebyshev distance between two subsequences, the farther the distance between the two subsequences, and the more prominent the corresponding subsequence group. Specifically, the prominence is: ; In the formula, For the of the moment The utilization rate sequence The prominence of the subsequence groups, For the of the moment The utilization rate sequence The Chebyshev distance between two subsequences in a subsequence group, For the of the moment The utilization rate sequence The average distance of subsequence groups, For the of the moment The total number of subsequence groups in the utilization sequence, is the standard normalization function.

[0041] In this step, each subsequence is a sliding window of a preset length with a step size of 1. The first subsequence and the second subsequence form the first subsequence group, and the third subsequence and the second subsequence group, and so on, and the The subsequences constitute subsequence group, the second subsequence and the first subsequence form the subsequence group, together with the third subsequence, constitutes the subsequence groups, ..., The subsequence and The subsequences constitute subsequence groups. In the subsequence group, In this application, subsequences at other positions and The definitions of the subsequence groups are the same as here.

[0042] In this step, the prominence of the subsequence group is not only represented by the difference between the Chebyshev distance of two subsequences in the subsequence group and the average distance of the subsequence group, but also the difference between the Chebyshev distance of two subsequences in the subsequence group and the mean of the Chebyshev distance of all subsequence groups is used to represent the credibility, which can take into account the situation of other subsequence groups, prevent the influence of accidental errors, and make the calculation result more accurate.

[0043] Secondly, get the subsequence Correlation between utilization and corresponding traffic data.

[0044] The higher the correlation between the utilization rate and the corresponding traffic data, the more similar the change trends of the two data are. The smaller the difference between the time corresponding to the maximum utilization rate and the time corresponding to the maximum flow data, the smaller the subsequence The smaller the difference between the number of extreme points of utilization and the number of extreme points of corresponding flow data, the The more similar the change trends of the utilization rate and the corresponding traffic data are, the higher the correlation between the two is. Specifically, the correlation is: ; In the formula, For the of the moment The utilization rate sequence The first subsequence The correlation between utilization and corresponding traffic data, For the of the moment The utilization rate sequence The first subsequence The time difference between the maximum utilization rate and the maximum corresponding flow data, For the of the moment The utilization rate sequence The first subsequence The absolute value of the difference between the utilization rate and the number of extreme points of the corresponding flow data, is the preset parameter, , The natural constant is the exponential function of the base. Among them, the preset parameters The purpose of setting is to prevent and If one of the data is 0, it will affect the calculation of the other data.

[0045] In this step, by calculating The difference between the time when the utilization rate reaches its maximum value and the time when the corresponding flow data reaches its maximum value, and the subsequence The difference between the number of extreme points of utilization and the number of extreme points of corresponding flow data can reflect The similarity of the changing trends of utilization rate and corresponding traffic data further characterizes the correlation between the two.

[0046] In some alternative embodiments, the correlation may also be performed using subsequences of The Pearson correlation coefficient between the utilization rate and the corresponding flow data is expressed. Specifically, the correlation is: ; In the formula, For the of the moment The utilization rate sequence The first subsequence The correlation between utilization and corresponding traffic data, For the of the moment The utilization rate sequence The first subsequence Utilization rate, For the of the moment The utilization rate sequence The first The subsequences correspond to the traffic data. is the Pearson correlation coefficient function.

[0047] In this step, the Pearson correlation coefficient is used to represent the correlation, which is not only simple and intuitive, easy to explain, but also simple to calculate and requires little calculation.

[0048] In some alternative embodiments, the correlation can also be performed using the subsequence Utilization rate and corresponding traffic data Calculated by the algorithm. The Dynamic Time Warping algorithm is an algorithm used to calculate the similarity of two time series. It uses dynamic programming to find the best alignment path to calculate the "distance" between the two sequences.

[0049] Finally, the noise interference degree of the subsequence group is obtained.

[0050] The noise interference degree is: ; In the formula, For the of the moment The utilization rate sequence The noise interference degree of the subsequence groups is For the of the moment The utilization rate sequence The prominence of the subsequence groups, For the of the moment The utilization rate sequence The first subsequence The correlation between utilization and corresponding traffic data, The natural constant Exponential function with base In this step, the noise interference degree of the subsequence group is not only characterized by the prominence degree of the subsequence group, but also by the The correlation correction between utilization rate and corresponding flow data makes the calculation result more accurate and avoids the actual larger error caused by the increase of flow data. The utilization rate is mistakenly considered to be affected by noise interference, resulting in the calculated noise interference level being too large.

[0051] S2223. Obtain an improved similarity tolerance threshold.

[0052] The improved similarity tolerance threshold is positively correlated with the initial similarity tolerance threshold and the noise interference degree of the subsequence group. This is because the greater the noise interference degree of the subsequence group, the larger the Chebyshev distance calculation result of the two subsequences in the subsequence group will be. In order to prevent it from exceeding the initial similarity tolerance threshold due to noise interference, it is necessary to increase the initial similarity tolerance threshold to obtain the improved similarity tolerance threshold. Specifically, the improved similarity tolerance threshold is: ; In the formula, For the of the moment The utilization rate sequence The improved similarity tolerance threshold for subsequence groups, For the of the moment The utilization rate sequence The noise interference degree of the subsequence groups is is the initial similarity tolerance threshold, is the standard normalization function.

[0053] In this step, the initial similarity tolerance threshold and the noise interference degree of the subsequence group are used to obtain the improved similarity tolerance threshold of the corresponding subsequence group. Since the improved similarity tolerance threshold is adaptively increased relative to the initial similarity tolerance threshold, it can solve the problem of large Chebyshev distance between two subsequences in the subsequence group caused by noise interference, so that the final sample entropy calculation is accurate.

[0054] S2224. Calculate the ratio of the number of subsequence groups whose Chebyshev distance is less than the corresponding improved similarity tolerance threshold to the total number of subsequence groups, and calculate the average of the corresponding ratios of all subsequences as the similarity average.

[0055] Then the similarity mean is: ; In the formula, For the of the moment The preset length of the utilization sequence is The similarity mean of all subsequences of For the of the moment The preset length of the utilization sequence is No. The Chershev distance of the subsequence group composed of the subsequences and the remaining subsequences is less than the ratio of the number of the corresponding improved similarity tolerance threshold to the total number of subsequence groups. is the total number of subsequence groups, for The length of the utilization sequence is also the preset duration in step S21.

[0056] S23. Obtain multiple subsequences of the preset length plus 1 again, and repeat step S22.

[0057] In this step, is the new preset length of the subsequence, and step S22 is performed again to obtain , that is, of the moment The preset length of the utilization sequence is The calculation process of this step is the same as the calculation process of step S22, and will not be repeated here.

[0058] S24, taking the difference between the natural logarithm of the mean of multiple subsequences of the preset length and the natural logarithm of the mean of multiple subsequences of the preset length plus 1 as the corresponding Sample entropy of the utilization series.

[0059] Then the sample entropy is: ; In the formula, For the of the moment The sample entropy of the utilization sequence, For the of the moment The preset length of the utilization sequence is The similarity mean of all subsequences of For the of the moment The preset length of the utilization sequence is The similarity mean of all subsequences of The natural constant The logarithmic function of the base.

[0060] The sample entropy calculated in this step represents the of the moment The complexity of the utilization sequence. If the complexity is greater than the threshold, it means that the There are abnormal data in the utilization sequence, but it is impossible to locate the time when the abnormal data occurs, so the abnormality cannot be discovered in time, resulting in late reporting. Therefore, it is also necessary to determine the The abnormal degree of utilization rate makes it easy to detect abnormalities in time.

[0061] S3, calculate any moment The abnormal degree of utilization. When the abnormal degree is greater than the threshold, the If there is an abnormality in utilization, an alarm will be issued and the corresponding equipment will be controlled to stop running.

[0062] The degree of abnormality is the difference between any moment and the previous moment. The normalized difference between the sample entropy of the utilization sequence and the value corresponding to any moment and the previous moment The product of the normalized values ​​of the difference in the mean of the utilization sequence. Specifically, the abnormality degree is: ; In the formula, For the of the moment The degree of abnormal utilization, For the of the moment The sample entropy of the utilization sequence, For the of the moment The sample entropy of the utilization sequence, For the of the moment Each of the utilization rate series The mean utilization rate, For the of the moment Each of the utilization rate series The mean utilization rate, is the standard normalization function.

[0063] In this step, by calculating the difference between any moment and its previous moment The difference in sample entropy of the utilization sequence can reflect the Whether the utilization sequence has a mutation, the greater the difference in sample entropy between the two, the greater the mutation. In order to reflect whether the mutation is increasing or decreasing, we can calculate the difference between any moment and the previous moment. The utilization rate series The difference between the mean values ​​of the utilization rate is used for judgment. The greater the difference between the two means, the greater the mutation. The calculation formula of the abnormality degree can be used to obtain the abnormality at any time. The degree of abnormality in utilization.

[0064] The threshold value range is [0.7, 0.8], preferably, the threshold value is 0.75. Of course, the threshold value can also be adjusted as needed.

[0065] In this step, when determining When the utilization rate is abnormal, the alarm and control of the corresponding equipment to stop running can remind the staff to check in time to ensure production safety and prevent safety accidents. The corresponding equipment is controlled by a digital controller.

[0066] In the operation status monitoring method of the digital controller of the present invention, by calculating The prominence of the subsequence group in the utilization sequence, the The correlation between the utilization rate and the corresponding flow data is used to obtain the degree of noise interference, and then the improved similarity tolerance threshold is obtained, which can avoid the problem that the Chebyshev distance between the two subsequences in the subsequence group is too large due to noise interference, thereby affecting the accuracy of the sample entropy, so that the final sample entropy calculation is accurate, and then the abnormality of the running state of the digital controller can be accurately judged. Moreover, by comparing any moment with its previous moment The sample entropy of the utilization sequence and the corresponding The mean of the utilization rate sequence can be obtained at any time The abnormal degree of utilization can determine the Whether the utilization rate is abnormal, the operating status of the digital controller can be monitored in real time to detect abnormal situations in time and avoid late reporting.

[0067] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A method for monitoring the operating status of a digital controller, characterized in that: The steps include: Get the digital controller at different times Utilization rate; use the sample entropy algorithm to calculate the efficiency of any moment and the previous moment The sample entropy of the utilization sequence, The utilization sequence is the preset time before the corresponding moment. Utilization rate; wherein, the sample entropy algorithm includes an improved similarity tolerance threshold, which is positively correlated with the initial similarity tolerance threshold and the noise interference degree of the subsequence group; the subsequence group includes The noise interference degree is positively correlated with the prominence of the subsequence group and the The correlation between utilization rate and corresponding traffic data is negatively correlated; the prominence is positively correlated with the Chebyshev distance between two subsequences in the subsequence group, negatively correlated with the mean of the Chebyshev distance of all subsequence groups, and negatively correlated with the average distance of the subsequence group; the average distance is the distance between two subsequences in the subsequence group. The mean of the absolute values ​​of the utilization differences; Calculate any moment The abnormal degree of utilization. When the abnormal degree is greater than the threshold, the If there is an abnormality in utilization, an alarm will be issued and the corresponding equipment will be controlled to stop running; the abnormality degree is the difference between any moment and the previous moment. The normalized difference between the sample entropy of the utilization sequence and the value corresponding to any moment and the previous moment The product of the differences in the means of the utilization series normalized.

2. The method for monitoring the operating status of a digital controller according to claim 1, characterized in that: The improved similarity tolerance threshold is: ； In the formula, For the of the moment The utilization rate sequence The improved similarity tolerance threshold for subsequence groups, For the of the moment The utilization rate sequence The noise interference degree of the subsequence groups is is the initial similarity tolerance threshold, is the standard normalization function.

3. The method for monitoring the operating status of a digital controller according to claim 1, characterized in that: The noise interference degree is: ； In the formula, For the of the moment The utilization rate sequence The noise interference degree of the subsequence groups is For the of the moment The utilization rate sequence The prominence of the subsequence groups, For the of the moment The utilization rate sequence The first subsequence The correlation between utilization and corresponding traffic data, The natural constant The exponential function of base .

4. The method for monitoring the operating status of a digital controller according to claim 1, characterized in that: The degree of prominence is: ； In the formula, For the of the moment The utilization rate sequence The prominence of the subsequence groups, For the of the moment The utilization rate sequence The Chebyshev distance between two subsequences in a subsequence group, For the of the moment The utilization rate sequence The average distance of subsequence groups, For the of the moment The total number of subsequence groups in the utilization sequence, is the standard normalization function.

5. The method for monitoring the operating status of a digital controller according to claim 1, characterized in that: The correlation is: ； In the formula, For the of the moment The utilization rate sequence The first subsequence The correlation between utilization and corresponding traffic data, For the of the moment The utilization rate sequence The first subsequence The time difference between the maximum utilization rate and the maximum corresponding flow data, For the of the moment The utilization rate sequence The first subsequence The absolute value of the difference between the utilization rate and the number of extreme points of the corresponding flow data, is the preset parameter, , The natural constant The exponential function of base .

6. The method for monitoring the operating status of a digital controller according to claim 1, characterized in that: The correlation is: ； In the formula, For the of the moment The utilization rate sequence The first subsequence The correlation between utilization and corresponding traffic data, For the of the moment The utilization rate sequence The first subsequence Utilization rate, For the of the moment The utilization rate sequence The first The subsequences correspond to the traffic data. is the Pearson correlation coefficient function.

7. The method for monitoring the operating status of a digital controller according to claim 1, characterized in that: The sample entropy algorithm is used to calculate the corresponding time The sample entropy of the utilization sequence includes the following steps: Utilize the rate sequence to obtain multiple subsequences of preset length; Calculate the Chebyshev distance between any subsequence and the subsequence group composed of the remaining subsequences, obtain the ratio of the number of subsequence groups whose Chebyshev distance is less than the corresponding improved similarity tolerance threshold to the total number of subsequence groups, and calculate the average of the corresponding ratios of all subsequences as the similarity mean; Get multiple subsequences of the preset length plus 1 again, and repeat the previous step; The difference between the natural logarithm of the similarity mean of multiple subsequences of the preset length and the natural logarithm of the similarity mean of multiple subsequences of the preset length plus 1 is taken as the Sample entropy of the utilization series.

8. The method for monitoring the operating status of a digital controller according to claim 1, characterized in that: Said The utilization rate is collected once per second.

9. The method for monitoring the operating status of a digital controller according to claim 1, characterized in that: The initial similarity tolerance threshold is a preset coefficient and Each of the utilization rate series The product of the standard deviation of the utilization rate; the value range of the preset coefficient is [0.10, 0.25].

10. The method for monitoring the operating status of a digital controller according to claim 1, characterized in that: The threshold value range is [0.7, 0.8].

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