A method and system for monitoring the operating state of a programmable logic controller

Abstract

The present invention provides a method and system for monitoring the operating state of a programmable controller, relating to the technical field of controller state monitoring. The programmable controller is disassembled into modules, a module disassembly result is generated and function matching is performed to generate a module task list. A set of performance evaluation indicators is obtained for performance evaluation to generate a set of benchmark characteristic values of performance indicators. The actual characteristic values of performance indicators are collected, and a performance indicator deviation degree is generated by comparing the set of actual characteristic values of performance indicators and the set of benchmark characteristic values of performance indicators. When the performance indicator deviation degree meets the deviation degree threshold, an instruction for the normal operating state of the programmable controller is generated, solving the technical problem in the prior art that the monitoring of the operating state of the programmable controller mainly judges whether the control is in a stable state macroscopically, lacking microscopic operating state monitoring, resulting in insufficient fineness of the final monitoring result, and achieving comprehensive and accurate monitoring of the programmable controller.

Description

Technical Field

[0001] The present invention relates to the technical field of controller status monitoring, and particularly relates to a method and system for monitoring the operating status of a programmable controller. Background Art

[0002] With the development of technology and the increasing social demands, programmable controllers, as a new type of industrial control device, have emerged to meet the production demands of multiple varieties and small batches in the current industrial field. They can control various types of mechanical equipment and production processes. At the same time, to ensure control accuracy, high requirements are placed on the operating status of programmable controllers. By real-time monitoring the operating status of programmable controllers, normal operation can be ensured, and adjustments can be made in a timely manner when operating deviations occur. Currently, through real-time communication between a computer and a programmable controller, computer graphic display of fault monitoring can be achieved for real-time fault monitoring. However, there are still certain limitations in this monitoring method.

[0003] In the prior art, the monitoring of the operating status of programmable controllers mainly judges whether the control is in a stable state macroscopically, lacking microscopic operating status monitoring, resulting in insufficient fineness of the final monitoring results. Summary of the Invention

[0004] This application provides a method and system for monitoring the operating status of a programmable controller, aiming to solve the technical problem in the prior art that the monitoring of the operating status of programmable controllers mainly judges whether the control is in a stable state macroscopically, lacking microscopic operating status monitoring, resulting in insufficient fineness of the final monitoring results.

[0005] In view of the above problems, this application provides a method and system for monitoring the operating status of a programmable controller.

[0006] In a first aspect, this application provides a method for monitoring the operating status of a programmable controller. The method includes: splitting the programmable controller into modules to generate a module splitting result; traversing the module splitting result for function matching to generate a module task list; inputting the module task list into an index calibration table to generate a set of performance evaluation indicators; traversing the module splitting result according to the set of performance evaluation indicators for performance evaluation to generate a set of benchmark characteristic values of performance indicators; when the programmable controller is in an operating state, collecting characteristic values according to the set of performance evaluation indicators for the module splitting result to generate a set of actual characteristic values of performance indicators; comparing the set of actual characteristic values of performance indicators with the set of benchmark characteristic values of performance indicators to generate a deviation degree of performance indicators; when the deviation degree of performance indicators meets the deviation degree threshold, generating a normal instruction for the operating status of the programmable controller.

[0007] Second aspect, the present application provides a programmable controller operating status monitoring system, the system includes: a controller splitting module for splitting the programmable controller into modules to generate a module splitting result; a function matching module for traversing the module splitting result for function matching to generate a module task list; an index generation module for inputting the module task list into an index calibration table to generate a set of performance evaluation indexes; a performance evaluation module for performing performance evaluation by traversing the module splitting result according to the set of performance evaluation indexes to generate a set of performance index reference characteristic values; a characteristic value acquisition module for collecting characteristic values of the module splitting result according to the set of performance evaluation indexes when the programmable controller is in an operating state to generate a set of actual performance index characteristic values; a deviation degree generation module for comparing the set of actual performance index characteristic values and the set of performance index reference characteristic values to generate a performance index deviation degree; an instruction generation module for generating a normal operating state instruction for the programmable controller when the performance index deviation degree meets a deviation degree threshold.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0009] A programmable controller operating status monitoring method provided in an embodiment of the present application splits the programmable controller into modules, generates a module splitting result and performs function matching to generate a module task list, obtains a set of performance evaluation indexes and traverses the module splitting result for performance evaluation to generate a set of performance index reference characteristic values, collects actual performance index characteristic values when the programmable controller is in an operating state, generates a performance index deviation degree by comparing the set of actual performance index characteristic values and the set of performance index reference characteristic values, and generates a normal operating state instruction for the programmable controller when the performance index deviation degree meets a deviation degree threshold, solving the technical problem in the prior art that the monitoring of the operating state of the programmable controller mainly judges whether the control is in a stable state macroscopically and lacks microscopic operating state monitoring, resulting in insufficient fineness of the final monitoring result, and realizing comprehensive and accurate monitoring of the programmable controller. Description of the Drawings

[0010] Figure 1 It is a schematic flowchart of a programmable controller operating status monitoring method provided by the present application;

[0011] Figure 2 It is a schematic flowchart of generating a set of performance index reference characteristic values in a programmable controller operating status monitoring method provided by the present application;

[0012] Figure 3 This application provides a schematic diagram of the abnormal operation warning process for the operation status monitoring of a programmable logic controller;

[0013] Figure 4 This application provides a schematic diagram of the structure of a programmable logic controller operation status monitoring system.

[0014] Explanation of reference numerals: Controller splitting module 11, function matching module 12, index generation module 13, performance evaluation module 14, eigenvalue acquisition module 15, deviation degree generation module 16, instruction generation module 17. Detailed implementation manners

[0015] This application provides a method and system for monitoring the operation status of a programmable logic controller, splitting the programmable logic controller into modules, generating a module splitting result and performing function matching, generating a module task list, obtaining a set of performance evaluation indicators for performance evaluation, generating a set of benchmark characteristic values of performance indicators, collecting the actual characteristic values of performance indicators, and generating a performance indicator deviation degree by comparing the set of actual characteristic values of performance indicators and the set of benchmark characteristic values of performance indicators. When the performance indicator deviation degree meets the deviation degree threshold, a normal operation instruction for the programmable logic controller is generated, so as to solve the technical problem in the prior art that the monitoring of the operation status of the programmable logic controller mainly judges whether the control is in a stable state macroscopically, lacking the microscopic operation status monitoring, resulting in insufficient fineness of the final monitoring result.

[0016] Embodiment 1

[0017] As Figure 1 shown, this application provides a method for monitoring the operation status of a programmable logic controller. The method is applied to a programmable logic controller operation status monitoring system, and the method includes:

[0018] Step S100: Split the programmable logic controller into modules to generate a module splitting result;

[0019] Specifically, as a new type of industrial control device, the programmable logic controller integrates computer technology, automation technology and communication technology, and has extremely wide applications in various mechanical productions and automatic controls. First, split the programmable logic controller into modules. The programmable logic controller includes multiple structural modules, including a memory, an input / output interface, a central processing module, a communication interface, and a power supply. The module splitting result is generated by splitting the programmable logic controller. The acquisition of the modular splitting result provides a basic information basis for subsequent performance analysis and evaluation. Based on the split modules, modular analysis and performance evaluation can be carried out, effectively improving the analysis accuracy.

[0020] Step S200: Traverse the module splitting result for function matching to generate a module task list;

[0021] Specifically, traverse the module splitting result, and perform function matching on each module respectively. Exemplarily, as a module for storing system software, the memory can be used to store application programs and intermediate state information during program execution; the input / output interface, that is, the I / O module integrates input / output circuits and can perform state mapping of input / output signals or signal conversion. For example, when a signal is input, after passing through the input interface, the electrical signal can be converted into digital information for further system application analysis. Based on the functionality of each module in the module splitting result, determine the corresponding tasks of each module. Among them, one module can correspond to one to multiple types of tasks. For example, signal state determination, information recognition, signal conversion, etc. can be used as the module tasks corresponding to the input interface in the I / O module. Further perform mapping integration processing on the module splitting result and the module tasks to generate the module task list. The acquisition of the module task list lays a foundation for subsequent module performance evaluation.

[0022] Step S300: Input the module task list into the index calibration table to generate a performance evaluation index set;

[0023] Specifically, based on big data, perform custom setting of the index calibration table. The index calibration table is a measure standard for the indexes associated with each task in the module task list and corresponding multiple module models. For example, the corresponding program running speed, program running error recognition rate, etc. in the central processing module. Among them, one task corresponds to one to multiple performance evaluation indexes. Using industry-standardized index parameters as the measure standard, further input the module task list into the index calibration table, perform mapping correspondence between the module task list and the index calibration table, determine the index standards corresponding to each module task, which can be used as the basis for subsequent module performance evaluation of each module's performance analysis, and then perform classification and integration processing of the corresponding indexes to generate the performance evaluation index set.

[0024] Step S400: According to the performance evaluation index set, traverse the module splitting result for performance evaluation to generate a set of performance index reference feature values;

[0025] Step S500: When the programmable controller is in the running state, collect feature values of the module splitting result according to the performance evaluation index set to generate a set of actual performance index feature values;

[0026] Specifically, traverse the module splitting result, and perform performance evaluation on each module based on the performance evaluation index. In this embodiment, by obtaining the performance characteristic values of multiple different modules corresponding to the operating environment parameters of the programmable controller, since the operating environment of the programmable controller is in a real-time changing state, which will have a certain impact on the operating state, considering the environmental influence degree can improve the accuracy of data collection. Based on this, a performance index characteristic value analysis model is constructed for model analysis to generate the set of performance index reference characteristic values. The set of performance index reference characteristic values refers to the index characteristic values that the modules of the current model should reach, such as storage capacity, computing speed, number of concurrent threads, etc. Further, taking the set of performance evaluation indexes as the data collection basis, when the programmable controller is in the operating state, real-time characteristic values of each module are collected based on the module splitting result. The real-time characteristic values correspond one-to-one with the set of performance index reference characteristic values, and the set of actual performance index characteristic values is obtained. The acquisition of the set of performance index reference characteristic values and the set of actual performance index characteristic values lays a foundation for subsequent performance deviation analysis of the programmable controller.

[0027] Further, as Figure 2 shown, traversing the module splitting result according to the set of performance evaluation indexes for performance evaluation to generate the set of performance index reference characteristic values, step 400 of this application further includes:

[0028] Step 410: Collect environmental parameters of the programmable controller to generate working temperature parameters and working humidity parameters;

[0029] Step 420: According to the working temperature parameters and the working humidity parameters, collect performance calibration record data based on the set of performance evaluation indexes;

[0030] Step 430: Train a performance index characteristic value analysis model according to the performance calibration record data;

[0031] Step 440: Traverse the module splitting result and input it into the performance index characteristic value analysis model to generate the set of performance index reference characteristic values.

[0032] Specifically, parameter collection is performed on the operating environment of the programmable controller. The environmental parameters belong to real-time fluctuating data. Analyzing based on real-time data can effectively improve the accuracy of the analysis results. The working temperature parameter and the working humidity parameter are obtained. To a certain extent, the environmental parameters will affect the operating performance of the programmable controller. For example, too high a device temperature will affect the running smoothness, cause program calculation jams, and damage the controller hardware. Based on the working temperature parameter and the working humidity parameter, the corresponding performance data of the performance evaluation index set under the corresponding environmental parameters is collected to obtain the performance calibration record data. The performance calibration record data represents the performance characteristic values of different modules of the programmable controller corresponding to the environmental parameters calibrated by the expert group. Further, a performance index characteristic value analysis model is constructed, and the performance calibration record data is input into the performance index characteristic value analysis model for model training to improve the model. Then, the module splitting result is input into the performance index characteristic value analysis model, and the performance calibration record data corresponding to each module in the module splitting result is determined through information matching and mapping. Then, data integration and identification processing are performed based on the module as the basis, and the performance index reference characteristic value set is output for easy data identification and distinction.

[0033] Furthermore, according to the performance calibration record data, training the performance index characteristic value analysis model, step 430 of this application further includes:

[0034] Step 431: Split the performance calibration record data to generate module record type information and performance index characteristic value record data;

[0035] Step 432: Use the module record type information as input data and the performance index characteristic value record data as output identification data to construct a first decision tree;

[0036] Step 433: Extract the module record type information and the performance index characteristic value record data that do not meet the preset accuracy rate from the first decision tree to generate a first data volume;

[0037] Step 434: Determine whether the first data volume meets the preset data volume;

[0038] Step 435: If it is satisfied, set the first decision tree as the performance index characteristic value analysis model.

[0039] Specifically, by collecting the corresponding performance data of the performance evaluation index set under the corresponding environmental parameters, the performance calibration record data is obtained. The performance calibration record data includes the module record type information and the performance index eigenvalue record data. Using this as the data division standard, the performance calibration record data is split, and further data division is performed on the module record type information and the performance index eigenvalue record data to obtain the training data, iterative data, and verification data. Taking the module record type information in the training data as the input data and the performance index eigenvalue record data as the output identification data, the construction of the first decision tree is carried out. The weights of the parameter data in the first decision tree are equal, and the preset accuracy rate is obtained. The preset accuracy rate refers to the evaluation standard for determining whether the data is qualified. For example, 95% can be set as the preset accuracy rate. Extract the module record type information and the performance index eigenvalue record data in the first decision tree that do not meet the preset accuracy rate, and use them as the first data volume. Further, determine whether the first data volume meets the preset data volume. The maximum data volume that has no essential impact on the final analysis result can be used as the preset data volume. When the first data volume meets the preset data volume, it indicates that the amount of unqualified data in the first decision tree is small and has no impact on the subsequent constructed model. Then, the first decision tree is used as the finally determined performance index eigenvalue analysis model. The verification data is input into the model for model verification. By constructing a decision tree and performing data determination and analysis screening, the accuracy of the model simulation results can be effectively guaranteed.

[0040] Furthermore, step 434 of the present application further includes:

[0041] Step 4341: If the first data volume does not meet the preset data volume, generate a first weight adjustment instruction;

[0042] Step 4342: Increase the weights of the module record type information and the performance index eigenvalue record data that do not meet the preset accuracy rate to generate a second construction data set;

[0043] Step 4343: Train a second decision tree according to the second construction data set;

[0044] Step 4344: Extract the module record type information and the performance index eigenvalue record data that do not meet the preset accuracy rate from the second decision tree to generate a second data volume;

[0045] Step 4345: Determine whether the second data volume meets the preset data volume;

[0046] Step 4346: If not satisfied, repeat the iteration until the preset number of iterations is reached or the preset data volume is satisfied, and then stop. Merge the first decision tree, the second decision tree, up to the Mth decision tree to generate the performance metric eigenvalue analysis model.

[0047] Specifically, determine whether the first data volume meets the preset data volume. When the first data volume does not meet the preset data volume, generate the first weight adjustment instruction, which is an instruction to start adjusting the weights of the module record type information and the performance metric eigenvalue record data in the first decision tree. Reduce the weights of the module record type information and the performance metric eigenvalue record data in the first decision tree that meet the preset accuracy rate, and increase the weights of the corresponding data information that does not meet the rate. For example, the weight can be increased by increasing the quantity of such data to improve the model's analysis ability for this type of data. Update the training data by performing a secondary weight distribution, and construct the second decision tree based on the iterative data. Further, determine the data accuracy rate of the module record type information and the performance metric eigenvalue record data in the second decision tree, and integrate the data that does not meet the preset accuracy rate to generate the second data volume. Then, further determine 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, use the second decision tree as the finally determined performance metric eigenvalue analysis model. When the second data volume does not meet the preset data volume, repeat the above weight adjustment and decision tree construction verification steps until the preset number of iterations is reached or the preset data volume is satisfied, and then stop the iterative operation. Then, merge the first decision tree, the second decision tree, up to the Mth decision tree, perform a weighted sum of the data of the above multiple decision trees to generate the performance metric eigenvalue analysis model, make up for the deviation of the previous decision tree by constructing a new decision tree, and finally perform the merge to improve the simulation accuracy rate of the performance metric eigenvalue analysis model.

[0048] Step S600: Compare the actual eigenvalue set of the performance metric with the benchmark eigenvalue set of the performance metric to generate the performance metric deviation degree.

[0049] Step S700: When the performance metric deviation degree meets the deviation degree threshold, generate a programmable logic controller normal operation instruction.

[0050] Specifically, the set of actual characteristic values of the performance indicators corresponds one-to-one with the reference characteristic values of the performance indicators. The characteristic values of the two are mapped, and the eigenvalue mapping result is obtained and the deviation is calculated to generate the performance indicator deviation degree. Further, it is determined whether the performance indicator deviation satisfies the preset deviation degree threshold. The preset deviation degree threshold is the evaluation criterion for the overlap degree of the actual characteristic value of the set performance indicator and the reference characteristic value of the performance indicator. For example, the preset deviation degree can be set to 10%. When the performance deviation degree meets the deviation degree threshold, it indicates that the programmable logic controller is in a normal operating state and the processing control accuracy is qualified, and a normal operating state instruction for the programmable logic controller is generated. When the performance deviation degree does not meet the preset deviation degree threshold, it indicates that the programmable logic controller is in an abnormal operating state. Further, the analysis and determination of the abnormal configuration parameters of the programmable logic controller are carried out, and based on this, an early warning of abnormal operation is given, and then targeted adjustment and correction are carried out to ensure normal operation.

[0051] Furthermore, as Figure 3 shown, step 800 of this application further includes:

[0052] Step 810: When the performance indicator deviation degree does not meet the deviation degree threshold, generate an abnormal operation state instruction for the programmable logic controller, where the abnormal operation state instruction for the programmable logic controller includes abnormal module type information and abnormal indicator type information;

[0053] Step 820: Extract the set of module configuration parameters according to the abnormal module type information;

[0054] Step 830: Perform correlation analysis on the set of module configuration parameters according to the abnormal indicator type information to generate a correlation level analysis result;

[0055] Step 840: When the correlation level analysis result meets the correlation level threshold, add the set of module configuration parameters to the set of sensitive configuration parameters;

[0056] Step 850: Perform an early warning of abnormal operation on the programmable logic controller according to the set of sensitive configuration parameters.

[0057] Specifically, by performing the mapping comparison between the actual eigenvalue set of the performance index and the benchmark eigenvalue set of the performance index, the deviation degree of the performance index is obtained. When the deviation degree of the performance index does not meet the deviation threshold, an abnormal operation instruction of the programmable logic controller is generated. The abnormal operation instruction of the programmable logic controller refers to a warning instruction indicating abnormal operation, including the abnormal module type information and the abnormal index type information. Further, information anomaly analysis is performed to determine the sensitive configuration parameters of the abnormal module, so as to perform targeted adjustment to improve the operation accuracy. Based on the abnormal module type information, a set of module configuration parameters is extracted, such as the model of the hardware, the software program, the power supply type, etc. Then, the correlation analysis of the corresponding parameters between the abnormal index type information and the set of module configuration parameters is performed. Exemplarily, an information correlation level can be preset, and the correlation degree level of the relevant parameters is evaluated. Then, based on the correlation degree level, the corresponding information is arranged in order, and the correlation level analysis result is generated.

[0058] Further, a correlation level threshold is set. The correlation level threshold is the limit standard for defining the information correlation degree between the abnormal index type information and the set of module configuration parameters. For example, an information correlation degree of 5 is set as the correlation level threshold. The threshold determination is performed on the correlation level analysis result, and the module configuration parameters with an information correlation degree greater than or equal to 5 are used as sensitive configuration parameters and added to the set of sensitive configuration parameters. According to the set of sensitive configuration parameters, an abnormal operation warning of the programmable logic controller is performed, so as to make timely adjustments to ensure the operation accuracy.

[0059] Furthermore, for the correlation analysis of the set of module configuration parameters according to the abnormal index type information to generate a correlation level analysis result, step 630 of this application further includes:

[0060] Step 831: Input the abnormal index type information and the abnormal module type information into the module task list to match the abnormal module task type information;

[0061] Step 832: According to the abnormal module task type information, collect a dataset of the programmable logic controller operation records, where the dataset of the programmable logic controller operation records includes configuration parameter record data;

[0062] Step 833: Traverse the configuration parameter record data for feature analysis to generate a parameter record frequency eigenvalue;

[0063] Step 834: Perform correlation level division on the configuration parameter record data according to the parameter record frequency eigenvalue to generate a correlation level division result.

[0064] Specifically, input the abnormal module type information and the abnormal index type information in the generated programmable controller operation status abnormal instruction into the module task list, and obtain the abnormal module task type information through module task matching. For example, when the central processing module is an abnormal module, corresponding programming, program running, monitoring and control, etc. can be used as the abnormal module task type information. Based on the abnormal module task type information as the information collection basis, determine the data collection time interval, and collect the operation record data set of the programmable controller to obtain multiple groups of operation record data. The operation record data set includes configuration parameter record data, that is, module application models, operation parameters, etc. Any group of data includes the module task type and the configuration parameter record data set, and multiple groups of data can be repeated to improve the completeness of data information and ensure the accuracy of subsequent data analysis. Further traverse the configuration parameter record data, and generate the parameter record frequency feature value through feature analysis. The parameter record frequency feature value refers to the occurrence frequency of relevant features in the abnormal module. Among them, the parameter record frequency feature value is proportional to the association level. Then, based on the parameter record frequency feature value, divide the configuration parameter record data into association levels to generate an association level division result. By collecting multiple groups of record data and performing association level analysis based on the frequency feature value, the determination accuracy can be effectively improved.

[0065] Furthermore, for the step of dividing the configuration parameter record data into association levels based on the parameter record frequency feature value to generate an association level division result, step 634 of this application further includes:

[0066] Step 8341: When the configuration parameter record data meets the first frequency threshold, add it to the first association level;

[0067] Step 8342: When the configuration parameter record data meets the second frequency threshold, add it to the second association level;

[0068] Step 8343: When the configuration parameter record data meets the Nth frequency threshold, add it to the Nth association level, where the first frequency threshold > the second frequency threshold > the Nth frequency threshold;

[0069] Step 8344: Add the first association level, the second association level up to the Nth association level to the association level division result.

[0070] Specifically, based on the parameter recording frequency eigenvalues, the configuration parameter recording data is divided into association levels. The first frequency threshold, the second frequency threshold, and the Nth frequency threshold are set. The Nth frequency threshold is the determination criterion for judging the association level of the configuration parameter recording data. When the configuration parameter recording data meets the first frequency threshold, it is added to the first association level; the first frequency threshold represents the maximum limit value of the frequency, and the first association level represents the highest parameter association level. As the frequency decreases, the parameter association level changes synchronously. The higher the occurrence frequency, the more core the configuration parameter is for the corresponding module. When the configuration parameter recording data meets the second frequency threshold, the corresponding configuration parameter recording data is added to the second association level; when the configuration parameter recording data meets the Nth frequency threshold, the corresponding configuration parameter recording data is added to the Nth association level, where the first frequency threshold > the second frequency threshold > the Nth frequency threshold. Further, the first association level, the second association level, up to the Nth association level are added as the division criteria to the association level division result. By setting the frequency threshold for data classification and division, the division accuracy of the data can be effectively improved.

[0071] Embodiment 2

[0072] Based on the same inventive concept as a method for monitoring the operating state of a programmable logic controller in the foregoing embodiment, as Figure 4 shown, the present application provides a system for monitoring the operating state of a programmable logic controller, and the system includes:

[0073] A controller splitting module 11, which is used to split the programmable logic controller into modules and generate a module splitting result;

[0074] A function matching module 12, which is used to traverse the module splitting result for function matching and generate a module task list;

[0075] An index generating module 13, which is used to input the module task list into an index calibration table and generate a set of performance evaluation indexes;

[0076] A performance evaluation module 14, which is used to perform performance evaluation on the module splitting result according to the set of performance evaluation indexes and generate a set of performance index reference eigenvalue;

[0077] An eigenvalue acquisition module 15, which is used to collect eigenvalues of the module splitting result according to the set of performance evaluation indexes when the programmable logic controller is in an operating state and generate a set of actual performance index eigenvalues;

[0078] Deviation degree generation module 16, which is used to compare the actual eigenvalue set of the performance index and the benchmark eigenvalue set of the performance index to generate the performance index deviation degree;

[0079] Instruction generation module 17, which is used to generate a normal operation state instruction for the programmable logic controller when the performance index deviation degree meets the deviation degree threshold.

[0080] Furthermore, the system further includes:

[0081] Threshold judgment module, which is used to generate an abnormal operation state instruction for the programmable logic controller when the performance index deviation degree does not meet the deviation degree threshold, wherein the abnormal operation state instruction of the programmable logic controller includes abnormal module type information and abnormal index type information;

[0082] Parameter extraction module, which is used to extract the module configuration parameter set according to the abnormal module type information;

[0083] Correlation analysis module, which is used to perform correlation analysis on the module configuration parameter set according to the abnormal index type information to generate a correlation level analysis result;

[0084] Parameter addition module, which is used to add the module configuration parameter set into the sensitive configuration parameter set when the correlation level analysis result meets the correlation level threshold;

[0085] Abnormal warning module, which is used to perform an abnormal operation warning on the programmable logic controller according to the sensitive configuration parameter set.

[0086] Furthermore, the system further includes:

[0087] Information matching module, which is used to input the abnormal index type information and the abnormal module type information into the module task list to match the abnormal module task type information;

[0088] Data acquisition module, which is used to acquire the programmable logic controller operation record data set according to the abnormal module task type information, wherein the programmable logic controller operation record data set includes configuration parameter record data;

[0089] Feature analysis module, which is used to traverse the configuration parameter record data for feature analysis to generate a parameter record frequency eigenvalue;

[0090] An association level division module, which is used to divide the configuration parameter record data according to the parameter record frequency eigenvalue to generate an association level division result.

[0091] Furthermore, the system further includes:

[0092] A first frequency threshold judgment module, which is used to add the configuration parameter record data to the first association level when it meets the first frequency threshold;

[0093] A second frequency threshold judgment module, which is used to add the configuration parameter record data to the second association level when it meets the second frequency threshold;

[0094] An Nth frequency threshold judgment module, which is used to add the configuration parameter record data to the Nth association level when it meets the Nth frequency threshold, where the first frequency threshold > the second frequency threshold > the Nth frequency threshold;

[0095] An association level addition module, which is used to add the first association level, the second association level up to the Nth association level to the association level division result.

[0096] Furthermore, the system further includes:

[0097] A parameter acquisition module, which is used to acquire the environmental parameters of the programmable controller to generate a working temperature parameter and a working humidity parameter;

[0098] A performance data acquisition module, which is used to acquire performance calibration record data based on the performance evaluation index set according to the working temperature parameter and the working humidity parameter;

[0099] A model training module, which is used to train a performance index eigenvalue analysis model according to the performance calibration record data;

[0100] A model analysis module, which is used to traverse the module split result and input it into the performance index eigenvalue analysis model to generate the performance index benchmark eigenvalue set.

[0101] Furthermore, the system further includes:

[0102] A data splitting module, which is used to split the performance calibration record data to generate module record type information and performance index eigenvalue record data;

[0103] A decision tree construction module, which is used to use the module record type information as input data and the performance metric eigenvalue record data as output identification data to construct a first decision tree;

[0104] A data volume generation module, which is used to extract the module record type information and the performance metric eigenvalue record data that do not meet the preset accuracy rate from the first decision tree to generate a first data volume;

[0105] A data volume judgment module, which is used to judge whether the first data volume meets the preset data volume;

[0106] A model determination module, which is used to set the first decision tree as the performance metric eigenvalue analysis model if it is satisfied.

[0107] Furthermore, the system further includes:

[0108] An adjustment instruction generation module, which is used to generate a first weight adjustment instruction if the first data volume does not meet the preset data volume;

[0109] A second data set generation module, which is used to perform weight gain on the module record type information and the performance metric eigenvalue record data that do not meet the preset accuracy rate to generate a second construction data set;

[0110] A second decision tree training module, which is used to train a second decision tree according to the second construction data set;

[0111] A second data volume generation module, which is used to extract the module record type information and the performance metric eigenvalue record data that do not meet the preset accuracy rate from the second decision tree to generate a second data volume;

[0112] A second data volume judgment module, which is used to judge whether the second data volume meets the preset data volume;

[0113] A data volume iteration module, which is used to repeat the iteration if it is not satisfied until it meets the preset iteration times or the preset data volume and then stop, and merge the first decision tree, the second decision tree until the Mth decision tree to generate the performance metric eigenvalue analysis model.

[0114] Through the foregoing detailed description of a method for monitoring the operating state of a programmable controller, those skilled in the art can clearly know a method and system for monitoring the operating state of a programmable controller in this embodiment. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For the relevant parts, reference can be made to the description in the method part.

[0115] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring the operating state of a programmable controller, characterized in that, The method is applied to a programmable logic controller (PLC) operating status monitoring system. The method includes: Splitting the programmable logic controller into modules to generate a module splitting result; Traversing the module splitting result for function matching to generate a module task list; Inputting the module task list into an index calibration table to generate a set of performance evaluation indicators; Performing performance evaluation on the module splitting result according to the set of performance evaluation indicators to generate a set of benchmark characteristic values of performance indicators; When the programmable logic controller is in an operating state, collecting characteristic values of the module splitting result according to the set of performance evaluation indicators to generate a set of actual characteristic values of performance indicators; Comparing the set of actual characteristic values of performance indicators with the set of benchmark characteristic values of performance indicators to generate a deviation degree of performance indicators; When the deviation degree of performance indicators meets the deviation degree threshold, generating a normal instruction for the operating state of the programmable logic controller; When the deviation degree of performance indicators does not meet the deviation degree threshold, generating an abnormal instruction for the operating state of the programmable logic controller, where the abnormal instruction for the operating state of the programmable logic controller includes information on the type of abnormal module and information on the type of abnormal indicator; Extracting a set of module configuration parameters according to the information on the type of abnormal module; Performing correlation analysis on the set of module configuration parameters according to the information on the type of abnormal indicator to generate a correlation level analysis result; When the correlation level analysis result meets the correlation level threshold, adding the set of module configuration parameters to a set of sensitive configuration parameters; Performing an early warning of abnormal operation on the programmable logic controller according to the set of sensitive configuration parameters; The performing correlation analysis on the set of module configuration parameters according to the information on the type of abnormal indicator to generate a correlation level analysis result includes: Inputting the information on the type of abnormal indicator and the information on the type of abnormal module into the module task list to match the information on the type of abnormal module task; Collecting a dataset of PLC operation records according to the information on the type of abnormal module task, where the dataset of PLC operation records includes configuration parameter record data; Traversing the configuration parameter record data for feature analysis to generate a characteristic value of parameter record frequency; Performing correlation level division on the configuration parameter record data according to the characteristic value of parameter record frequency to generate a correlation level division result.

2. The method according to claim 1, characterized in that, The performing correlation level division on the configuration parameter record data according to the characteristic value of parameter record frequency to generate a correlation level division result includes: When the configuration parameter record data meets the first frequency threshold, adding it to the first correlation level; When the configuration parameter record data meets the second frequency threshold, adding it to the second correlation level; When the configuration parameter record data meets the Nth frequency threshold, adding it to the Nth correlation level, where the first frequency threshold > the second frequency threshold > the Nth frequency threshold; Adding the first correlation level, the second correlation level up to the Nth correlation level to the correlation level division result.

3. The method according to claim 1, wherein The performing performance evaluation on the module splitting result according to the set of performance evaluation indicators to generate a set of benchmark characteristic values of performance indicators includes: Collect environmental parameters of the programmable controller to generate working temperature parameters and working humidity parameters; According to the working temperature parameters and the working humidity parameters, based on the performance evaluation index set, collect performance calibration record data; Train a performance index eigenvalue analysis model according to the performance calibration record data; Traverse the module splitting result and input it into the performance index eigenvalue analysis model to generate the performance index benchmark eigenvalue set.

4. The method according to claim 3, wherein The training of the performance index eigenvalue analysis model according to the performance calibration record data includes: Split the performance calibration record data to generate module record type information and performance index eigenvalue record data; Use the module record type information as input data and the performance index eigenvalue record data as output identification data to construct a first decision tree; Extract the module record type information and the performance index eigenvalue record data that do not meet the preset accuracy rate from the first decision tree to generate a first data volume; Judge whether the first data volume meets the preset data volume; If it meets, set the first decision tree as the performance index eigenvalue analysis model.

5. The method according to claim 4, characterized in that, It also includes: If the first data volume does not meet the preset data volume, generate a first weight adjustment instruction; Perform weight gain on the module record type information and performance index eigenvalue record data that do not meet the preset accuracy rate to generate a second construction data set; Train a second decision tree according to the second construction data set; Extract the module record type information and the performance index eigenvalue record data that do not meet the preset accuracy rate from the second decision tree to generate a second data volume; Judge whether the second data volume meets the preset data volume; If it does not meet, repeat the iteration until it meets the preset iteration times or the preset data volume and then stop. Merge the first decision tree, the second decision tree until the Mth decision tree to generate the performance index eigenvalue analysis model.

6. A programmable controller operating state monitoring system, characterized in that, The system includes: A controller splitting module for splitting the programmable controller into modules to generate a module splitting result; A function matching module for traversing the module splitting result for function matching to generate a module task list; An index generation module for inputting the module task list into an index calibration table to generate a performance evaluation index set; A performance evaluation module for performing performance evaluation on the module splitting result according to the performance evaluation index set to generate a performance index benchmark eigenvalue set; An eigenvalue acquisition module for acquiring eigenvalues of the module splitting result according to the performance evaluation index set when the programmable controller is in an operating state to generate a set of actual performance index eigenvalues; A deviation degree generation module for comparing the set of actual performance index eigenvalues and the set of performance index benchmark eigenvalues to generate a performance index deviation degree. An instruction generation module, which is used to generate a normal operation state instruction of the programmable logic controller when the deviation degree of the performance index meets the deviation degree threshold; The system further includes: A threshold judgment module, which is used to generate an abnormal operation state instruction of the programmable logic controller when the deviation degree of the performance index does not meet the deviation degree threshold, wherein the abnormal operation state instruction of the programmable logic controller includes abnormal module type information and abnormal index type information; A parameter extraction module, which is used to extract a set of module configuration parameters according to the abnormal module type information; A correlation analysis module, which is used to perform correlation analysis on the set of module configuration parameters according to the abnormal index type information to generate a correlation level analysis result; A parameter addition module, which is used to add the set of module configuration parameters to the set of sensitive configuration parameters when the correlation level analysis result meets the correlation level threshold; An abnormal warning module, which is used to perform an abnormal operation warning on the programmable logic controller according to the set of sensitive configuration parameters; An information matching module, which is used to input the abnormal index type information and the abnormal module type information into the module task list to match the abnormal module task type information; A data acquisition module, which is used to acquire a set of programmable logic controller operation record data according to the abnormal module task type information, wherein the set of programmable logic controller operation record data includes configuration parameter record data; A feature analysis module, which is used to traverse the configuration parameter record data for feature analysis to generate a parameter record frequency feature value; A correlation level division module, which is used to perform correlation level division on the configuration parameter record data according to the parameter record frequency feature value to generate a correlation level division result.

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