CN115373370A - Method and system for monitoring running state of programmable controller - Google Patents
Method and system for monitoring running state of programmable controller Download PDFInfo
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Abstract
The invention provides a method and a system for monitoring the running state of a programmable controller, which relate to the technical field of controller state monitoring, the method comprises the steps of carrying out module splitting on the programmable controller, generating a module splitting result and carrying out function matching, generating a module task list, obtaining a performance evaluation index set to carry out performance evaluation, generating a performance index reference characteristic value set, collecting the actual characteristic value of a performance index, comparing the actual characteristic value set of the performance index with the performance index reference characteristic value set to generate a performance index deviation degree, and generating a normal instruction of the running state of the programmable controller when the deviation degree of the performance index meets a deviation degree threshold value.
Description
Technical Field
The invention relates to the technical field of controller state monitoring, in particular to a method and a system for monitoring the running state of a programmable controller.
Background
With the development of science and technology and the increasing of social requirements, a programmable controller is brought to the end as a novel industrial control device to meet the production requirements of various types and small batches in the current industrial field, control of various types of mechanical equipment and production processes can be carried out, meanwhile, in order to ensure control accuracy, the requirement on the running state of the programmable controller is higher, the running state of the programmable controller can be monitored in real time to ensure normal running, and adjustment can be carried out in time when running deviation occurs.
In the prior art, the running state monitoring of the programmable controller is mainly to judge whether the control is in a stable state from the macroscopic view, and the running state monitoring from the microscopic view is lacked, so that the fineness of the final monitoring result is insufficient.
Disclosure of Invention
The application provides a method and a system for monitoring the running state of a programmable controller, which are used for solving the technical problems that the running state monitoring of the programmable controller in the prior art is mainly to judge whether the control is in a stable state from the macroscopic view, and the running state monitoring from the microscopic view is lacked, so that the fineness of the final monitoring result is not enough.
In view of the foregoing, the present application provides a method and a system for monitoring an operating state of a programmable controller.
In a first aspect, the present application provides a method for monitoring an operating state of a programmable controller, where the method includes: carrying out module splitting on the programmable controller to generate a module splitting result; traversing the module splitting result to perform function matching, and generating a module task list; inputting the module task list into an index calibration table to generate a performance evaluation index set; traversing the module splitting result according to the performance evaluation index set to perform performance evaluation, and generating a performance index reference characteristic value set; when the programmable controller is in a running state, characteristic value collection is carried out on the module splitting result according to the performance evaluation index set, and a performance index actual characteristic value set is generated; comparing the performance index actual characteristic value set with the performance index reference characteristic value set to generate a performance index deviation degree; and when the deviation degree of the performance index meets the deviation degree threshold value, generating a normal instruction of the running state of the programmable logic controller.
In a second aspect, the present application provides a programmable controller operation status monitoring system, the system comprising: the controller splitting module is used for carrying out module splitting on the programmable controller to generate a module splitting result; the function matching module is used for traversing the module splitting result to perform function matching and generating a module task list; the index generation module is used for inputting the module task list into an index calibration table to generate a performance evaluation index set; the performance evaluation module is used for performing performance evaluation according to the performance evaluation index set by traversing the module splitting result to generate a performance index reference characteristic value set; the characteristic value acquisition module is used for acquiring the characteristic value of the module split result according to the performance evaluation index set when the programmable controller is in the running state, and generating a performance index actual characteristic value set; the deviation degree generating module is used for comparing the performance index actual characteristic value set with the performance index reference characteristic value set to generate a performance index deviation degree; and the instruction generating module is used for generating a normal instruction of the running state of the programmable controller when the deviation degree of the performance index meets the deviation degree threshold value.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the embodiment of the application provides a method for monitoring the running state of a programmable controller, which is used for splitting a module of the programmable controller, generating a module splitting result and performing function matching, generating a module task list to obtain a performance evaluation index set and traversing the module splitting result to perform performance evaluation, generating a performance index reference characteristic value set, collecting the actual characteristic value of a performance index when the programmable controller is in a running state, comparing the actual characteristic value set of the performance index with the performance index reference characteristic value set to generate a performance index deviation degree, and generating a normal running state instruction of the programmable controller when the performance index deviation degree meets a deviation degree threshold value.
Drawings
Fig. 1 is a schematic flow chart of a method for monitoring an operating state of a programmable controller according to the present application;
FIG. 2 is a schematic diagram illustrating a process of generating a set of performance index reference characteristic values in a method for monitoring an operating state of a programmable controller according to the present application;
fig. 3 is a schematic diagram illustrating an abnormal operation early warning process of a programmable controller in a method for monitoring an operation state of the programmable controller according to the present application;
fig. 4 is a schematic structural diagram of a system for monitoring an operating state of a programmable controller according to the present application.
Description of reference numerals: the system comprises a controller splitting module 11, a function matching module 12, an index generating module 13, a performance evaluating module 14, a characteristic value collecting module 15, a deviation degree generating module 16 and an instruction generating module 17.
Detailed Description
The application provides a method and a system for monitoring the running state of a programmable controller, the programmable controller is subjected to module splitting, a module splitting result is generated and is subjected to function matching, a module task list is generated, a performance evaluation index set is obtained for performance evaluation, a performance index reference characteristic value set is generated, performance index actual characteristic values are collected, performance index deviation degree is generated by comparing the performance index actual characteristic value set with the performance index reference characteristic value set, and a normal running state instruction of the programmable controller is generated when the performance index deviation degree meets a deviation degree threshold value and is used for solving the technical problem that in the prior art, the running state monitoring of the programmable controller mainly judges whether the control is in a stable state from a macroscopic view, the running state monitoring from a microscopic view is lacked, and the fineness of a final monitoring result is insufficient.
Example one
As shown in fig. 1, the present application provides a method for monitoring an operating state of a programmable controller, where the method is applied to a system for monitoring an operating state of a programmable controller, and the method includes:
step S100: carrying out module splitting on the programmable controller to generate a module splitting result;
specifically, as a novel industrial control device, a programmable controller integrates a computer technology, an automation technology and a communication technology, and has a very wide application in various mechanical production and automatic control.
Step S200: traversing the module splitting result to perform function matching, and generating a module task list;
specifically, the module splitting result is traversed, and the modules are respectively subjected to function matching, for example, the memory is used as a module for storing system software and can be used for storing an application program and intermediate state information in the program execution process; the input/output interface, i.e. the I/O module, integrates an input/output circuit, and can perform state mapping or signal conversion of input/output signals, for example, when a signal is input, the signal can convert an electrical signal into digital information through the input interface, and then perform system application analysis, and determine the corresponding task of each module based on the functionality of each module in the module splitting result, where one module may correspond to one or more types of tasks, for example, signal state determination, information identification, signal conversion, and the like may be used as the module task corresponding to the input interface in the I/O module, and further perform mapping integration processing of the module splitting result and the module task, so as to generate the module task list, and the acquisition of the module task list lays a foundation for performing subsequent module performance evaluation.
Step S300: inputting the module task list into an index calibration table to generate a performance evaluation index set;
specifically, the index calibration table is customized based on big data, and the index calibration table is a measurement standard of indexes associated with each task in the module task list and corresponding multiple module types, such as corresponding program running rate, program running error recognition rate, and the like in the central processing module, wherein one task corresponds to one or more performance evaluation indexes, and an intra-industry standardized index parameter is used as the measurement standard, the module task list is further input into the index calibration table, the module task list is mapped and corresponds to the index calibration table, the index standard corresponding to each module task is determined, and the index standard can be used as a basis for performance analysis of each module to perform subsequent module performance evaluation, and then classification and integration processing of corresponding indexes is performed to generate the performance evaluation index set.
Step S400: traversing the module splitting result according to the performance evaluation index set to perform performance evaluation, and generating a performance index reference characteristic value set;
step S500: when the programmable controller is in a running state, carrying out characteristic value acquisition on the module splitting result according to the performance evaluation index set to generate a performance index actual characteristic value set;
specifically, the module splitting result is traversed, performance evaluation is performed on each module based on the performance evaluation index, in this embodiment, by obtaining performance characteristic values of a plurality of groups of different modules corresponding to parameters of an operating environment of the programmable controller, since the operating environment of the programmable controller is in a real-time changing state, a certain influence is caused on the operating state, the environment influence degree is considered, and accuracy of data acquisition can be improved.
Further, as shown in fig. 2, the step 400 of traversing the module splitting result according to the performance evaluation index set to perform performance evaluation to generate a performance index reference feature value set further includes:
step 410: collecting environmental parameters of the programmable controller to generate a working temperature parameter and a working humidity parameter;
step 420: collecting performance calibration record data based on the performance evaluation index set according to the working temperature parameter and the working humidity parameter;
step 430: training a performance index characteristic value analysis model according to the performance calibration record data;
step 440: and traversing the module splitting result, inputting the splitting result into the performance index characteristic value analysis model, and generating the performance index reference characteristic value set.
Specifically, parameter collection is performed on an operating environment of the programmable controller, the environment parameter belongs to real-time fluctuation data, analysis based on the real-time data can effectively improve accuracy of an analysis result, the operating temperature parameter and the operating humidity parameter are obtained, the environment parameter can affect the operating performance of the programmable controller to a certain extent, for example, when the temperature of equipment is too high, operation smoothness can be affected, program calculation is blocked, controller hardware is damaged, and the like.
Further, according to the performance calibration record data, training a performance index feature value analysis model, in this step 430, the method further includes:
step 431: splitting the performance calibration recorded data to generate module recorded type information and performance index characteristic value recorded data;
step 432: taking the module record type information as input data, taking the performance index characteristic value record data as output identification data, and constructing a first decision tree;
step 433: extracting the module record type information and the performance index characteristic value record data which do not meet the preset accuracy from the first decision tree to generate a first data volume;
step 434: judging whether the first data volume meets a preset data volume or not;
step 435: and if so, setting the first decision tree as the performance index characteristic value analysis model.
Specifically, the performance calibration record data is acquired by collecting corresponding performance data of the performance evaluation index set under corresponding environmental parameters, the performance calibration record data includes the module record type information and the performance index characteristic value record data, the performance calibration record data is split by using the data as a data partitioning standard, the module record type information and the performance index characteristic value record data are further partitioned to acquire the training data, the iteration data and the verification data, the module record type information in the training data is used as input data, the performance index characteristic value record data is used as output identification data, the first decision tree is constructed, the weights of the parameter data in the first decision tree are equal to each other, and the preset accuracy rate is acquired, the preset accuracy refers to an evaluation criterion for determining whether data is qualified, for example, 95% of the data may be set as the preset accuracy, the module record type information and the performance index feature value record data which do not satisfy the preset accuracy in the first decision tree are extracted and used as the first data volume, whether the first data volume satisfies the preset data volume is further determined, the maximum data volume which cannot substantially affect a final analysis result may be used as the preset data volume, when the first data volume satisfies the preset data volume, it is indicated that the unqualified data volume in the first decision tree is small and cannot affect a subsequently constructed model, and the first decision tree is used as the finally determined performance index feature value analysis model to input the verification data into the model for model verification, by constructing a decision tree and carrying out data judgment, analysis and screening, the accuracy of the simulation result of the model can be effectively guaranteed.
Further, step 434 of the present application further includes:
step 4341: if the first data volume does not meet the preset data volume, generating a first weight adjusting instruction;
step 4342: performing weight gain on the module record type information and the performance index characteristic value record data which do not meet the preset accuracy rate to generate a second constructed data set;
step 4343: training a second decision tree based on the second constructed data set;
step 4344: extracting the module record type information and the performance index characteristic value record data which do not meet the preset accuracy from the second decision tree to generate a second data volume;
step 4345: judging whether the second data volume meets the preset data volume or not;
step 4346: if not, repeating iteration until a preset iteration number is met or the preset data quantity is met, and combining the first decision tree, the second decision tree and the Mth decision tree to generate the performance index characteristic value analysis model.
Specifically, it is determined whether the first data amount satisfies the preset data amount, when the first data amount does not satisfy the preset data amount, the first weight adjustment instruction is generated, where the first weight adjustment instruction is an instruction to start performing weight adjustment on the module record type information and the performance index feature value record data in the first decision tree, the module record type information and the performance index feature value record data satisfying the preset accuracy rate in the first decision tree are weighted down, corresponding data information that does not satisfy is weighted by, for example, increasing the number of such data to perform weight gain, thereby improving the analysis capability of a model on such data, performing update of training data by performing weight secondary distribution, and performing construction of the second decision tree based on iterative data, further performing data accuracy judgment on the module record type information and the performance index characteristic value record data in the second decision tree, integrating data which do not meet the preset accuracy to generate a second data volume, further judging whether the second data volume meets the preset data volume to evaluate whether the second decision tree meets the standard, when the second data volume meets the preset data volume, taking the second decision tree as the finally determined performance index characteristic value analysis model, when the second data volume does not meet the preset data volume, repeating the weight adjustment and decision tree construction verification steps until a preset iteration number is met or the preset data volume is met, stopping iteration operation, and further combining the first decision tree and the second decision tree until an Mth decision tree, and performing data weighted summation on the decision trees to generate the performance index characteristic value analysis model, performing deviation complementation on the previous decision tree by constructing a new decision tree, and finally combining to improve the simulation accuracy of the performance index characteristic value analysis model.
Step S600: comparing the performance index actual characteristic value set with the performance index reference characteristic value set to generate a performance index deviation degree;
step S700: and when the deviation degree of the performance index meets the deviation degree threshold value, generating a normal instruction of the running state of the programmable logic controller.
Specifically, the performance index actual characteristic value sets correspond to the performance index reference characteristic values one by one, characteristic value mapping is performed on the performance index actual characteristic value sets and the performance index reference characteristic values, a characteristic value mapping result is obtained and deviation calculation is performed, a performance index deviation degree is generated, whether the performance index deviations meet a preset deviation degree threshold value is further judged, the preset deviation degree threshold value is a judgment standard of the overlapping degree of the set performance index actual characteristic value and the performance index reference characteristic value, for example, the preset deviation degree can be set to be 10%, when the performance deviation degree meets the deviation degree threshold value, the programmable controller is indicated to be in a normal operation state, the processing control precision is qualified, a programmable controller operation state normal instruction is generated, when the performance deviation degree does not meet the preset deviation degree threshold value, the programmable controller is indicated to be in an abnormal operation state, further, abnormal configuration parameters are analyzed and determined, abnormal operation warning is performed based on the abnormal operation warning, and then targeted adjustment and correction are performed to ensure normal operation.
Further, as shown in fig. 3, the step 800 of the present application further includes:
step 810: when the deviation degree of the performance index does not meet the deviation degree threshold value, generating an abnormal instruction of the running state of the programmable controller, wherein the abnormal instruction of the running state of the programmable controller comprises abnormal module type information and abnormal index type information;
step 820: extracting a module configuration parameter set according to the abnormal module type information;
step 830: performing association analysis on the module configuration parameter set according to the abnormal index type information to generate an association level analysis result;
step 840: when the correlation level analysis result meets a correlation level threshold, adding the module configuration parameter set into a sensitive configuration parameter set;
step 850: and performing operation abnormity early warning on the programmable controller according to the sensitive configuration parameter set.
Specifically, the deviation degree of the performance index is obtained by performing mapping comparison between the actual characteristic value set of the performance index and the reference characteristic value set of the performance index, when the deviation degree of the performance index does not satisfy the deviation degree threshold, an abnormal operation state instruction of the programmable controller is generated, the abnormal operation state instruction of the programmable controller refers to a warning instruction for indicating abnormal operation, the warning instruction includes the type information of the abnormal module and the type information of the abnormal index, information abnormality analysis is further performed to determine a sensitive configuration parameter of the abnormal module, so as to perform targeted adjustment and improve operation accuracy, a module configuration parameter set, such as a model of hardware, a program of software, a power supply type and the like, is extracted based on the abnormal module type information, so as to perform correlation analysis on corresponding parameters between the abnormal index type information and the module configuration parameter set, exemplarily, an information correlation level can be preset, the correlation level evaluation is performed on the associated parameters, and further, the sequential arrangement of the corresponding information is performed based on the correlation level, so as to generate the correlation level analysis result.
Further, setting the correlation level threshold, where the correlation level threshold is a limit standard that defines an information correlation degree between the abnormal index type information and the module configuration parameter set, for example, setting an information correlation degree 5 as the correlation level threshold, performing threshold determination on the correlation level analysis result, adding a module configuration parameter with an information correlation degree greater than or equal to 5 as a sensitive configuration parameter into the sensitive configuration parameter set, and performing operation abnormality early warning on the programmable controller according to the sensitive configuration parameter set, so as to adjust in time and ensure operation accuracy.
Further, the performing, according to the abnormal index type information, a correlation analysis on the module configuration parameter set to generate a correlation level analysis result, in step 630 of the present application, further includes:
step 831: inputting the abnormal index type information and the abnormal module type information into the module task list, and matching the abnormal module task type information;
step 832: acquiring a running record data set of a programmable controller according to the abnormal module task type information, wherein the running record data set of the programmable controller comprises configuration parameter record data;
step 833: traversing the configuration parameter record data to perform characteristic analysis and generate a parameter record frequency characteristic value;
step 834: and performing association level division on the configuration parameter record data according to the parameter record frequency characteristic value to generate an association level division result.
Specifically, the abnormal module type information and the abnormal index type information in the generated abnormal command of the running state of the programmable controller are input into the module task list, the abnormal module task type information is obtained by performing module task matching, for example, when a central processing module is an abnormal module, corresponding programming, program running, monitoring control and the like can be used as the abnormal module task type information, a data acquisition time interval is determined by using the abnormal module task type information as an information acquisition basis, a running record data set of the programmable controller is acquired, a plurality of groups of running record data can be obtained, the running record data set comprises configuration parameter record data, namely module application models, running parameters and the like, any one group of data comprises a module task type and a configuration parameter record data set, the plurality of groups of data can be repeated to improve the completeness of data information and guarantee the accuracy of subsequent data analysis, the configuration parameter record data is further traversed, a parameter record frequency characteristic value is generated by performing characteristic analysis, the parameter record frequency characteristic value refers to the occurrence frequency of relevant features in the abnormal module, wherein the parameter record frequency characteristic value and the associated index level are further graded to generate the associated parameter record frequency characteristic value, and the associated parameter record frequency characteristic value is divided based on the classification, and the associated parameter record data can be classified based on the classification accuracy.
Further, the performing, according to the parameter record frequency characteristic value, association level division on the configuration parameter record data to generate an association level division result, step 634 of the present application further includes:
step 8341: adding into a first association level when the configuration parameter record data meets a first frequency threshold;
step 8342: adding a second association level when the configuration parameter record data meets a second frequency threshold;
step 8343: adding an Nth association level when the configuration parameter record data meets an Nth frequency threshold, wherein the first frequency threshold > the second frequency threshold > the Nth frequency threshold;
step 8344: adding the first association level, the second association level up to the Nth association level into the association level division result.
Specifically, the configuration parameter record data is subjected to association level division based on the parameter record frequency characteristic value, and the first frequency threshold, the second frequency threshold, and the nth frequency threshold are set, where the nth frequency threshold is a determination criterion for determining an association level of the configuration parameter record data, and is added to the first association level when the configuration parameter record data meets the first frequency threshold; the first frequency threshold value represents a maximum frequency limit value, the first association level represents the highest parameter association level, the parameter association level is synchronously changed along with the decreasing of the frequency, the higher the occurrence frequency is, the more core configuration parameters are for the corresponding module, and when the configuration parameter record data meet a second frequency threshold value, the corresponding configuration parameter record data are added into the second association level; when the configuration parameter record data meet an nth frequency threshold, adding the corresponding configuration parameter record data into the nth association level, wherein the first frequency threshold is greater than the second frequency threshold is greater than the nth frequency threshold, further adding the first association level, the second association level and up to the nth association level as division standards into the association level division result, and performing data classification division by setting a frequency threshold, thereby effectively improving the data division precision.
Example two
Based on the same inventive concept as the method for monitoring the running state of the programmable controller in the foregoing embodiment, as shown in fig. 4, the present application provides a system for monitoring the running state of the programmable controller, where the system includes:
the controller splitting module 11 is configured to split a programmable controller to generate a module splitting result;
the function matching module 12, the function matching module 12 is configured to traverse the module splitting result to perform function matching, and generate a module task list;
the index generation module 13 is configured to input the module task list into an index calibration table, and generate a performance evaluation index set;
the performance evaluation module 14 is configured to perform performance evaluation according to the performance evaluation index set by traversing the module splitting result, and generate a performance index reference feature value set;
the characteristic value acquisition module 15 is configured to, when the programmable controller is in an operating state, perform characteristic value acquisition on the module split result according to the performance evaluation index set to generate a performance index actual characteristic value set;
the deviation degree generating module 16 is configured to compare the performance index actual characteristic value set with the performance index reference characteristic value set to generate a performance index deviation degree;
and the instruction generating module 17, wherein the instruction generating module 17 is configured to generate a normal instruction of the running state of the programmable controller when the deviation degree of the performance index meets the deviation degree threshold.
Further, the system further comprises:
the threshold judgment module is used for generating an abnormal instruction of the running state of the programmable controller when the deviation degree of the performance index does not meet the deviation degree threshold, wherein the abnormal instruction of the running state of the programmable controller comprises abnormal module type information and abnormal index type information;
the parameter extraction module is used for extracting a module configuration parameter set according to the abnormal module type information;
the correlation analysis module is used for performing correlation analysis on the module configuration parameter set according to the abnormal index type information to generate a correlation level analysis result;
a parameter adding module for adding the module configuration parameter set into a sensitive configuration parameter set when the correlation level analysis result satisfies a correlation level threshold;
and the abnormity early warning module is used for carrying out operation abnormity early warning on the programmable controller according to the sensitive configuration parameter set.
Further, the system further comprises:
the information matching module is used for inputting the abnormal index type information and the abnormal module type information into the module task list and matching the abnormal module task type information;
the data acquisition module is used for acquiring a running record data set of the programmable controller according to the abnormal module task type information, wherein the running record data set of the programmable controller comprises configuration parameter record data;
the characteristic analysis module is used for traversing the configuration parameter record data to perform characteristic analysis and generating a parameter record frequency characteristic value;
and the association level division module is used for carrying out association level division on the configuration parameter record data according to the parameter record frequency characteristic value to generate an association level division result.
Further, the system further comprises:
a first frequency threshold determination module, configured to add a first association level when the configuration parameter record data satisfies a first frequency threshold;
a second frequency threshold determination module, configured to add a second association level when the configuration parameter record data satisfies a second frequency threshold;
an nth frequency threshold determination module, configured to add an nth association level when the configuration parameter record data meets an nth frequency threshold, where the first frequency threshold > the second frequency threshold > the nth frequency threshold;
an association level adding module for adding the first association level, the second association level up to the Nth association level into the association level division result.
Further, the system further comprises:
the parameter acquisition module is used for acquiring environmental parameters of the programmable controller to generate working temperature parameters and working humidity parameters;
the performance data acquisition module is used for acquiring performance calibration record data based on the performance evaluation index set according to the working temperature parameter and the working humidity parameter;
the model training module is used for training a performance index characteristic value analysis model according to the performance calibration record data;
and the model analysis module is used for traversing the module splitting result and inputting the module splitting result into the performance index characteristic value analysis model to generate the performance index reference characteristic value set.
Further, the system further comprises:
the data splitting module is used for splitting the performance calibration record data to generate module record type information and performance index characteristic value record data;
the decision tree construction module is used for constructing a first decision tree by taking the module record type information as input data and taking the performance index characteristic value record data as output identification data;
the data volume generating module is used for extracting the module recording type information and the performance index characteristic value recording data which do not meet the preset accuracy rate from the first decision tree to generate a first data volume;
the data volume judging module is used for judging whether the first data volume meets a preset data volume or not;
a model determination module to set the first decision tree as the performance indicator feature value analysis model if satisfied.
Further, the system further comprises:
the adjustment instruction generating module is used for generating a first weight adjustment instruction if the first data volume does not meet the preset data volume;
the second data set generating module is used for performing weight gain on the module record type information and the performance index characteristic value record data which do not meet the preset accuracy rate to generate a second construction data set;
a second decision tree training module to train a second decision tree based on the second constructed dataset;
the second data volume generation module is used for extracting the module record type information and the performance index characteristic value record data which do not meet the preset accuracy from the second decision tree to generate a second data volume;
the second data volume judging module is used for judging whether the second data volume meets the preset data volume or not;
and the data quantity iteration module is used for repeating iteration if the data quantity iteration module does not meet the requirement, stopping the iteration until a preset iteration number is met or the preset data quantity is met, combining the first decision tree and the second decision tree until an Mth decision tree, and generating the performance index characteristic value analysis model.
In the present specification, through the foregoing detailed description of the method for monitoring the operating state of the programmable controller, it is clear to those skilled in the art that a method and a system for monitoring the operating state of the programmable controller in the present embodiment are disclosed.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A method for monitoring the running state of a programmable controller is applied to a system for monitoring the running state of the programmable controller, and the method comprises the following steps:
carrying out module splitting on the programmable controller to generate a module splitting result;
traversing the module splitting result to perform function matching, and generating a module task list;
inputting the module task list into an index calibration table to generate a performance evaluation index set;
traversing the module splitting result according to the performance evaluation index set to perform performance evaluation, and generating a performance index reference characteristic value set;
when the programmable controller is in a running state, carrying out characteristic value acquisition on the module splitting result according to the performance evaluation index set to generate a performance index actual characteristic value set;
comparing the performance index actual characteristic value set with the performance index reference characteristic value set to generate a performance index deviation degree;
and when the deviation degree of the performance index meets the deviation degree threshold value, generating a normal running state instruction of the programmable logic controller.
2. The method of claim 1, further comprising:
when the deviation degree of the performance index does not meet the deviation degree threshold value, generating an abnormal instruction of the running state of the programmable controller, wherein the abnormal instruction of the running state of the programmable controller comprises abnormal module type information and abnormal index type information;
extracting a module configuration parameter set according to the abnormal module type information;
performing correlation analysis on the module configuration parameter set according to the abnormal index type information to generate a correlation level analysis result;
adding the set of module configuration parameters into a set of sensitive configuration parameters when the correlation level analysis result meets a correlation level threshold;
and performing abnormal operation early warning on the programmable controller according to the sensitive configuration parameter set.
3. The method of claim 2, wherein performing association analysis on the set of module configuration parameters according to the anomaly indicator type information to generate an association level analysis result comprises:
inputting the abnormal index type information and the abnormal module type information into the module task list, and matching the abnormal module task type information;
acquiring a programmable controller operation record data set according to the abnormal module task type information, wherein the programmable controller operation record data set comprises configuration parameter record data;
traversing the configuration parameter record data to perform characteristic analysis and generate a parameter record frequency characteristic value;
and performing association grade division on the configuration parameter record data according to the parameter record frequency characteristic value to generate an association grade division result.
4. The method of claim 3, wherein the performing association level division on the configuration parameter record data according to the parameter record frequency characteristic value to generate an association level division result comprises:
adding into a first association level when the configuration parameter record data meets a first frequency threshold;
when the configuration parameter record data meets a second frequency threshold, adding into a second association level;
adding an Nth association level when the configuration parameter record data meets an Nth frequency threshold, wherein the first frequency threshold > the second frequency threshold > the Nth frequency threshold;
adding the first association level, the second association level up to the Nth association level into the association level division result.
5. The method of claim 1, wherein performing performance evaluation according to the results of the performance evaluation index set obtained by traversing the module splitting module to generate a set of performance index baseline characteristic values comprises:
collecting environmental parameters of the programmable controller to generate working temperature parameters and working humidity parameters;
collecting performance calibration record data based on the performance evaluation index set according to the working temperature parameter and the working humidity parameter;
training a performance index characteristic value analysis model according to the performance calibration record data;
and traversing the module splitting result, inputting the splitting result into the performance index characteristic value analysis model, and generating the performance index reference characteristic value set.
6. The method of claim 5, wherein training a performance metric eigenvalue analysis model based on said performance calibration record data comprises:
splitting the performance calibration recorded data to generate module recorded type information and performance index characteristic value recorded data;
taking the module record type information as input data, taking the performance index characteristic value record data as output identification data, and constructing a first decision tree;
extracting the module record type information and the performance index characteristic value record data which do not meet the preset accuracy from the first decision tree to generate a first data volume;
judging whether the first data volume meets a preset data volume or not;
and if so, setting the first decision tree as the performance index characteristic value analysis model.
7. The method of claim 6, further comprising:
if the first data volume does not meet the preset data volume, generating a first weight adjusting instruction;
performing weight gain on the module record type information and the performance index characteristic value record data which do not meet the preset accuracy rate to generate a second constructed data set;
training a second decision tree based on the second constructed data set;
extracting the module record type information and the performance index characteristic value record data which do not meet the preset accuracy from the second decision tree to generate a second data volume;
judging whether the second data volume meets the preset data volume or not;
if not, repeating iteration until a preset iteration number is met or the preset data volume is met, and combining the first decision tree, the second decision tree and an Mth decision tree to generate the performance index characteristic value analysis model.
8. A programmable controller operating condition monitoring system, the system comprising:
the controller splitting module is used for carrying out module splitting on the programmable controller to generate a module splitting result;
the function matching module is used for traversing the module splitting result to perform function matching and generating a module task list;
the index generation module is used for inputting the module task list into an index calibration table to generate a performance evaluation index set;
the performance evaluation module is used for performing performance evaluation according to the split result of the performance evaluation index set traversing the module to generate a performance index reference characteristic value set;
the characteristic value acquisition module is used for acquiring the characteristic value of the module split result according to the performance evaluation index set when the programmable controller is in the running state, and generating a performance index actual characteristic value set;
the deviation degree generating module is used for comparing the performance index actual characteristic value set with the performance index reference characteristic value set to generate a performance index deviation degree;
and the instruction generation module is used for generating a normal instruction of the running state of the programmable controller when the deviation degree of the performance index meets the deviation degree threshold value.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115759684A (en) * | 2022-11-30 | 2023-03-07 | 南京贝迪新材料科技股份有限公司 | Preparation process optimization method and system of quantum dot diffusion plate |
| CN116303188A (en) * | 2023-01-30 | 2023-06-23 | 百仑生物科技(江苏)有限公司 | Module hot plug control method, device, equipment and medium of circuit board |
| CN116719271A (en) * | 2023-08-04 | 2023-09-08 | 深圳市华茂欧特科技有限公司 | Remote data monitoring system for PLC controller |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115759684A (en) * | 2022-11-30 | 2023-03-07 | 南京贝迪新材料科技股份有限公司 | Preparation process optimization method and system of quantum dot diffusion plate |
| CN116303188A (en) * | 2023-01-30 | 2023-06-23 | 百仑生物科技(江苏)有限公司 | Module hot plug control method, device, equipment and medium of circuit board |
| CN116303188B (en) * | 2023-01-30 | 2024-01-26 | 百仑生物科技(江苏)有限公司 | Module hot plug control method, device, equipment and medium of circuit board |
| CN116719271A (en) * | 2023-08-04 | 2023-09-08 | 深圳市华茂欧特科技有限公司 | Remote data monitoring system for PLC controller |
| CN116719271B (en) * | 2023-08-04 | 2023-10-13 | 深圳市华茂欧特科技有限公司 | Remote data monitoring system for PLC controller |
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