Operation state monitoring and analyzing system of PLC control cabinet

Abstract

The invention discloses an operation state monitoring analysis system of a PLC control cabinet, and relates to the technical field of PLC control cabinet monitoring, and the system comprises a target setting module, a preliminary evaluation module and an adjustment monitoring module. The technical key points are as follows: in the whole period of system monitoring, operating parameters of the PLC control cabinet are obtained, the operating parameters are compared with a preset threshold line, a state setting strategy is executed, a first supervision plan is formulated to perform maintenance period updating work, and a state adjusting strategy is executed in combination with an environment comprehensive index; a dynamic attribute network corresponding to the PLC control cabinet is designed, the state of each time node in the time period is monitored in real time, and a second supervision plan is made; in the process, a systematic supervision and maintenance mechanism is established, the operation state condition of the PLC control cabinet is accurately identified, a fault index is obtained based on fault state analysis, the residual life is obtained based on normal state analysis, a corresponding alarm calibration strategy is executed, and the requirement of dynamic evaluation is met.

Description

Technical Field

[0001] The present invention relates to the technical field of PLC control cabinet monitoring, and specifically relates to an operation status monitoring and analysis system for a PLC control cabinet. Background Art

[0002] A PLC control cabinet, that is, a programmable control cabinet, is an electronic device specially designed for industrial environments. It can execute preset program logics according to the changes of input signals to control output devices. In the actual environment, PLC control cabinets are widely used in various different fields and often face more complex working environments during operation. The operation status of a PLC control cabinet is an important link to ensure the stable and safe operation of an industrial automation system. In the prior art, the planned management of PLC control cabinets is generally carried out by setting fixed maintenance cycles, and the working status of the current PLC control cabinet is judged by monitoring the voltage or current values of some important devices inside the PLC control cabinet. When facing a harsh working environment, it is easy to cause faults in the PLC control cabinet. In the traditional judgment process, the analysis is not combined with the maintenance cycle, specifically manifested as: completely relying on a fixed maintenance plan or not relying on a maintenance plan, and only carrying out maintenance work when a fault occurs. There is a lack of a systematic supervision and maintenance mechanism to continuously analyze the abnormal data of the PLC control cabinet for a period of time, making it difficult to accurately identify the status of related devices or components of the PLC control cabinet. During the monitoring process, adjustments cannot be made in a timely manner, resulting in inaccurate evaluation results during the analysis process and reducing the accuracy of the analysis of the operation status of the PLC control cabinet. Summary of the Invention

[0003] Technical Problems to be Solved In view of the deficiencies of the prior art, the present invention provides an operation status monitoring and analysis system for a PLC control cabinet, which evaluates and adjusts the operation status of the PLC control cabinet in a full cycle, and also combines environmental parameters to achieve comprehensive analysis of the PLC control cabinet. During this process, a dynamic attribute network is designed. According to the dynamic attribute network, the maintenance cycle and maintenance frequency of the supervision plan are correspondingly adjusted to avoid a reduction in work efficiency caused by faults in related components or devices of the PLC control cabinet. By comparing the fault index with the standard fault threshold and comparing the remaining life with the standard life threshold, the effectiveness of the system can be monitored and adjusted to meet the requirements of dynamic evaluation, and the problems raised in the background art are solved.

[0004] (2) Technical Solutions To achieve the above objectives, the present invention is realized through the following technical solutions: An operation status monitoring and analysis system for a PLC control cabinet includes: a target setting module, a preliminary evaluation module, and an adjustment and monitoring module; The target setting module obtains various operating parameters and environmental parameters of the PLC control cabinet within the first preset time. When any operating parameter exceeds the preset threshold line, it executes the status setting strategy; according to the preprocessed environmental parameters, it generates corresponding comprehensive environmental indicators. When the comprehensive environmental indicator exceeds the environmental indicator threshold, it executes the status adjustment strategy. The preliminary evaluation module obtains various operating parameters of the PLC control cabinet again within the second preset time and compares them with each threshold line, extracts the number of operating parameters that do not meet the threshold line and the number of times of repeated operating parameters that do not meet the threshold line among various operating parameters, and generates a warning value in combination with the comprehensive environmental indicator. The adjustment monitoring module executes the regular inspection strategy when the warning value is less than the standard threshold, and vice versa, executes the variable frequency inspection strategy.

[0005] Furthermore, the execution of the regular inspection strategy or the variable frequency inspection strategy includes: Within the third preset time, based on the regular inspection strategy and the variable frequency inspection strategy, determine whether the operating state of the PLC control cabinet is in a normal state or a fault state. In the case of a fault state, trigger the evaluation and analysis mechanism to obtain a fault index. In the case of a normal state, trigger the life analysis mechanism to predict the remaining life. If the current fault index exceeds the standard fault threshold or the current remaining life is greater than the standard life threshold, execute the alarm calibration strategy.

[0006] Furthermore, the operating parameters include power supply status parameters, equipment status parameters, and communication status parameters; the power supply status parameters include the voltage value and frequency value of the input power supply, the equipment status parameters include the motor vibration value and the flow value of the electric valve, and the communication status parameters include the baud rate and the delay rate; then the executed status setting strategy is: According to the difference between the corresponding operating parameter exceeding the threshold line and the corresponding threshold line, and in accordance with the pre-established rule engine, formulate a first supervision plan. The formula for the estimated maintenance duration corresponding to the first supervision plan is: ; In the formula, Duration1 represents the estimated maintenance duration generated by the first supervision plan, Jb is the basic maintenance duration, i represents a certain corresponding operating parameter, Pm i is the corresponding value of a certain operating parameter, Th i is the threshold line corresponding to a certain operating parameter, G 1 is the first error correction factor, and its value range is 0 to 1.

[0007] Furthermore, the execution of the status adjustment strategy includes: Assign corresponding weights to each operating parameter, multiply each operating parameter by its corresponding weight and accumulate them to generate corresponding evaluation indicators, including the first evaluation indicator, the second evaluation indicator, and the third evaluation indicator; and construct relationship functions between each evaluation indicator and the comprehensive environmental indicator, including the first relationship function, the second relationship function, and the third relationship function; Sort the evaluation indicators determined to exceed the environmental indicator threshold, generate a first sorting table and output it, and the evaluation indicators in the first sorting table are sorted in descending order; Obtain the operating parameters corresponding to each evaluation indicator in the first sorting table, perform dimensionless processing to generate standardized operating parameters, sort the standardized operating parameters in descending order, generate a second sorting table and output it, and construct a dynamic attribute network based on the first sorting table and the second sorting table, including the first dynamic network, the second dynamic network, and the third dynamic network, and the names of the first dynamic network, the second dynamic network, and the third dynamic network are specified based on the order of the first sorting table; Based on the dynamic attribute network, extract corresponding feature subgraphs, including the first feature subgraph, the second feature subgraph, and the third feature subgraph, and the first feature subgraph corresponds to the first dynamic network, the second feature subgraph corresponds to the second dynamic network, and the third feature subgraph corresponds to the third dynamic network; determine the corresponding state vector based on the feature subgraph; According to the pre-established rule engine, formulate a second supervision plan, and the estimated maintenance duration corresponding to the second supervision plan is: ; In the formula, Duration2 represents the maintenance duration corresponding to the second supervision plan; Jb is the basic maintenance duration, c represents the state vector of the corresponding feature subgraph at a certain moment, represents the average state vector within the preset time, represents the fluctuation vector within the preset time, G 2 is the second error correction factor.

[0008] Further, the steps for designing the dynamic network include: Taking the PLC control cabinet as the target node, the equipment corresponding to each operating parameter as the object node, and the state of the PLC control cabinet and the corresponding equipment as the edge, and the weight of each edge is determined by the relationship function between each evaluation indicator and the comprehensive environmental indicator, to construct the corresponding dynamic attribute network.

[0009] Further, the formula for generating the warning value is: ; Wherein, warn represents the warning value, cs is the number of operating parameters that do not meet the threshold line, sl is the number of times the operating parameters repeatedly do not meet the threshold line, φ1 is the first constant correction coefficient, and its value is 1, and β1, β2 are weight coefficients.

[0010] Further, the implemented regular inspection strategy includes: regularly checking whether the maintenance supervision is carried out according to the first supervision plan in the target setting strategy, and the frequency of the regular inspection is a preset fixed value; The implemented variable-frequency inspection strategy includes: variable-frequency checking whether the maintenance supervision is carried out according to the second supervision plan in the state adjustment strategy, and the frequency of the variable-frequency inspection is set according to the fixed value, the warning value, and the standard threshold, and the frequency of the variable-frequency inspection is obtained by the following formula: ; Wherein, Dp represents the frequency of the variable-frequency inspection, gd represents the preset fixed value in the regular inspection, Ba represents the standard threshold, G 3 represents the third error correction factor, and int(·) is the rounding function.

[0011] Further, the formula for obtaining the fault index by the triggered evaluation and analysis mechanism is: ; Wherein, fault represents the fault index, pl represents the maintenance frequency, st represents the maintenance duration, φ2 is the second constant correction coefficient, and μ1, μ2 are weight coefficients; The triggered life analysis mechanism for predicting the remaining life includes: collecting the moments that have been determined to be in the fault state in history, randomly extracting two time series of them to obtain the risk time periods, sorting the risk time periods according to the time series and marking them as T1, T2,..., Tn; wherein, the time series is a dynamically changing quantity, and n represents the number of times of being judged to be in the fault state; equally dividing the risk time periods and marking them as r, and when there is a remainder, it is r + 1, r is a positive integer and is a dynamic value; Combined with the historical fault index, calculate the risk factors corresponding to the risk time periods under r or r + 1 respectively, and then combine the current operating parameters, environmental parameters, and the state vector corresponding to the dynamic attribute network, and import them all into the pre-constructed life analysis model to predict and output the remaining life.

[0012] (III) Beneficial effects The present invention provides an operating state monitoring and analysis system for a PLC control cabinet, which has the following beneficial effects: 1. The present invention monitors the operating states of the PLC control cabinet in real time, formulates personalized supervision and maintenance plans based on the comparison of various operating parameters with corresponding threshold lines, and then analyzes the environmental comprehensive index according to environmental parameters, including temperature value, humidity value, air pressure value, and dust density value. After comparison, the state adjustment strategy is executed. By constructing dynamic attributes, in-depth analysis of the data is carried out to monitor the state of the PLC control cabinet at each moment, improve the accuracy of fault prediction and diagnosis, discover and handle faults in a timely manner, send alarm information in a timely manner, and improve the reliability and safety of the system; 2. The present invention preliminarily evaluates the operating state of the PLC control cabinet, selectively adjusts the maintenance frequency of the supervision and maintenance plan according to the warning value obtained from the preliminary evaluation, and changes the frequency of variable frequency inspection in the case where the preliminary evaluation result does not conform, so as to update the supervision plan, reasonably allocate the maintenance cycle, and can effectively monitor and maintain the system. It can not only ensure the effective maintenance of the PLC control cabinet, but also conduct targeted inspections to save labor and time costs; 3. The present invention determines the operating state of the PLC control cabinet based on the regular inspection strategy and the variable frequency inspection strategy, which further facilitates the subsequent analysis of the operating state; in the fault state, the evaluation and analysis mechanism is triggered to obtain the fault index; in the normal state, the life analysis mechanism is triggered to predict the remaining life; by classifying the fault levels of the corresponding components or devices of the PLC control cabinet, it is beneficial for maintenance personnel to carry out corresponding maintenance work and improve the working efficiency of the PLC control cabinet to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a module schematic diagram of an operating state monitoring and analysis system for a PLC control cabinet in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0014] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. Embodiment

[0015] This embodiment provides an operating state monitoring and analysis system for a PLC control cabinet. Figure 1 It is a module schematic diagram of an operating state monitoring and analysis system for a PLC control cabinet in the present invention. Please refer to Figure 1 , the system includes: a target setting module, a preliminary evaluation module, and an adjustment and monitoring module, and the target setting module, the preliminary evaluation module, and the adjustment and monitoring module are communicatively connected; The following are the specific descriptions and explanations of each module or component: Target setting module: Within the first preset time, obtain the operating parameters of the PLC control cabinet, compare each operating parameter with its corresponding threshold line respectively. When any operating parameter exceeds the preset threshold line, execute the status setting strategy; The operating parameters include power supply status parameters, equipment status parameters, and communication status parameters; the power supply status parameters include the voltage value and frequency value of the input power supply, the equipment status parameters include the motor vibration value and the flow value of the electric valve, and the communication status parameters include the baud rate and the delay rate; It should be noted that the operation status monitoring of the PLC control cabinet mainly includes the following aspects: Power supply status monitoring: Check whether the voltage and frequency of the input power supply are normal; Equipment status monitoring: Monitor the operation status of various sensors and actuators (such as motors, valves) to determine whether they are working properly; Temperature and humidity monitoring: Monitor the temperature and humidity inside the PLC control cabinet to avoid equipment damage caused by environmental factors; Communication status monitoring: Ensure normal communication between the PLC and various external devices, including network communication and serial communication; Then the executed status setting strategy is: According to the difference between the corresponding operating parameter exceeding the threshold line and the corresponding threshold line, formulate the first supervision plan according to the pre-built rule engine. The formula for the estimated repair duration corresponding to the first supervision plan is: ; In the formula, Duration1 represents the estimated repair duration generated for the first supervision plan, Jb is the basic repair duration, which is set in advance. For example, as long as the basic repair work is carried out, the fixed repair is 3 hours per week. i represents a certain operating parameter, including: power supply status parameters, equipment status parameters, and communication status parameters, Pm i is the corresponding value of a certain operating parameter, G 1 is the first error correction factor, and its value range is 0 to 1, which is set according to needs. Usually, it is set according to the numerical size of the operating parameter. When the value is a four-digit number, it is 0.0005. When the value is a three-digit number, it is 0.005. When the value is a two-digit number, it is 0.05. When the value is a single-digit number, it is 0.5. It can also help adjust the corresponding estimated repair duration. When all operating parameters exceed the threshold line, then the first error correction factor G 1 needs to be adaptively increased, so as to make each operation status monitoring complete and ensure the safe operation of the PLC control cabinet, Th iIt is the threshold line corresponding to a certain operating parameter. Generally speaking, the value setting of the corresponding threshold line is based on the corresponding historical operating parameters. By analyzing the data exceeding the threshold line in the past operation of the PLC control cabinet, the data range in the normal operating state and the data range in the fault state are obtained. The collected data is statistically analyzed to determine the average value and standard deviation of the data range in the normal operating state, and the value of the threshold line is the sum of the average value and twice the standard deviation; Example: Suppose the operating parameters obtained at a certain moment = {power status parameter, equipment status parameter, communication status parameter} = {{voltage value, frequency value}, {motor vibration value, flow value of the electric valve}, {baud rate, delay rate}} = {{220, 50}, {0.5, 100}, {9600, 30}}, and suppose the threshold line corresponding to the operating parameters = {{260, 51}, {2.5, 520}, {9600, 50}}; Then the estimated maintenance duration generated by the first supervision plan is: Duration1 = 3 + (0.005 * (260 - 220) + 0.05 * (51 - 50) + 0.5 * (2.5 - 0.5) + 0.005 * (520 - 100) + 0.0005 * (9600 - 9600) + 0.05 * (50 - 30)) = 7.35h; Meanwhile, obtain the environmental parameters of the PLC control cabinet within the first preset time, and generate the corresponding environmental comprehensive index based on the preprocessed environmental parameters. Compare the environmental comprehensive index with the preset environmental index threshold, and execute the status adjustment strategy when the environmental comprehensive index exceeds the environmental index threshold; The environmental parameters include: temperature value, humidity value, air pressure value, and dust density value; The steps for preprocessing the environmental parameters are: perform dimensionless processing on the temperature value, humidity value, air pressure value, and dust density value to generate standardized environmental parameters. The formula used is: ; In the formula, Ii represents the environmental parameter, Ij represents the standardized environmental parameter, and I avg represents the average value corresponding to any environmental parameter within the time period T; Through relevant sensors, such as: a temperature sensor (such as: PT100) to monitor the temperature value, a humidity sensor (such as: DHT22) to monitor the humidity value, an air pressure sensor (such as: BME280) to monitor the air pressure value, and a dust density sensor (such as: PMS5003) to monitor the dust density value, and the relevant sensors are adaptively installed, which are not shown in the figure; The meanings of the relevant terms involved in the environmental parameters are explained as follows: Temperature value: Each PLC control cabinet has its specified operating temperature range. Exceeding this range may cause the device to crash or malfunction, and the higher the temperature, the faster the aging speed, shortening the service life. Humidity value: Each PLC control cabinet has its specified operating humidity range. When the humidity is too high, it may affect the electrical insulation performance and cause failures. Dust density value: Dust wears on the mechanical components of the PLC (such as relays and contactors), affecting its working efficiency, and the accumulation of dust may hinder heat dissipation, leading to a temperature rise and increasing the failure rate. Air pressure value: Changes in air pressure may affect the ventilation effect inside the PLC control cabinet, resulting in a temperature rise. Extremely low air pressure may affect the electrical insulation performance and cause equipment failures. It should be noted that the temperature value is the main factor affecting the operating state of the PLC control cabinet, followed by the humidity value, then the dust density value, and the influence of the air pressure value is relatively small. Then the corresponding environmental comprehensive index is generated, and the formula based on is: ; In the formula, EI represents the environmental comprehensive index, and I1, I2, I3, and I4 are the preprocessed temperature value, humidity value, air pressure value, and dust density value respectively; ω1, ω2, ω3, and ω4 are all weight coefficients, and usually ω1 = 40% > ω2 = 30% > ω4 = 20% > ω3 = 10% > 0, and ω1 + ω2 + ω3 + ω4 = 1; when the values of the temperature value, humidity value, air pressure value, and dust density value are larger, the corresponding environmental comprehensive index value is larger, and it is determined that the operating state of the PLC control cabinet is prone to failure, resulting in the PLC control cabinet being unable to operate normally and reducing work efficiency. Example: Assume that the temperature value I1, humidity value I2, air pressure value I3, and dust density value I4 are 0.1, 0.3, 0.5, and 0.7 respectively, then the environmental comprehensive index is: EI = 0.1 * 40% + 0.3 * 30% + 0.5 * 10% + 20% * 0.7 = 0.32; Then the executed state adjustment strategy is: First, assign corresponding weights to each operating parameter, multiply each operating parameter by its corresponding weight and accumulate to generate corresponding evaluation indicators, including the first evaluation indicator, the second evaluation indicator, and the third evaluation indicator; among them, the weight usually takes a value of 0.5. Then construct the relationship functions between each evaluation indicator and the environmental comprehensive index, including the first relationship function of the first evaluation indicator - environmental comprehensive index: F1(X), the second relationship function of the second evaluation indicator - environmental comprehensive index: F2(X), and the third relationship function of the third evaluation indicator - environmental comprehensive index: F3(X); For example: Through the environmental comprehensive index EI(0), the corresponding first evaluation indicator f 1 (0), the second evaluation indicator f 2(0) and the third evaluation index f 3 (0); Then, sort the evaluation indexes corresponding to those determined to exceed the environmental index threshold, generate a first sorting table and output it. Among the first sorting table, the evaluation indexes with larger numerical differences are ranked higher. It should be noted that the larger the difference between the environmental comprehensive index and the environmental index threshold, the worse the environmental state of the corresponding PLC control cabinet. The formula for calculating the difference is: numerical difference = environmental index threshold - environmental comprehensive index; Obtain the operating parameters corresponding to each evaluation index in the first sorting table, perform dimensionless processing to generate standardized operating parameters, and sort the standardized operating parameters in descending order, generate a second sorting table and output it. The higher the ranking, the more attention should be paid to the corresponding operating parameter, and the greater the probability of failure; Perform corresponding dynamic network design based on the first sorting table and the second sorting table; Among them, the process of dynamic network design is: Taking the PLC control cabinet as the target node, the equipment corresponding to each operating parameter as the object node, and the relationship between the PLC control cabinet and the corresponding equipment status as the edge, and the weight of each edge is determined by the relationship function between each evaluation index and the environmental comprehensive index, construct the corresponding dynamic attribute network, including the first dynamic network, the second dynamic network and the third dynamic network, and the first dynamic network, the second dynamic network and the third dynamic network are named according to the order of the first sorting table; Suppose in the first sorting table, the first evaluation index > the second evaluation index > the third evaluation index, and the first evaluation index corresponds to the power supply state parameter, the second evaluation index corresponds to the equipment state parameter, and the third evaluation index corresponds to the communication state parameter, then: The first dynamic network is a dynamic attribute network constructed with the PLC control cabinet as the target node, the power supply equipment as the object node, and the relationship between the PLC control cabinet and the power supply equipment as the edge; The second dynamic network is a dynamic attribute network constructed with the PLC control cabinet as the target node, the sensor or actuator as the object node, and the relationship between the PLC control cabinet and the sensor or actuator as the edge; The third dynamic network is a dynamic attribute network constructed with the PLC control cabinet as the target node, the serial port number or port number as the corresponding node, and the relationship between the PLC control cabinet and the serial port or network port as the edge; It should be noted that the first sorting table and the second sorting table can be regarded as structures for organizing and storing data, which can reflect data changes in real time. Through the first sorting table and the second sorting table, the node and attribute information of the dynamic attribute network can be analyzed in depth; The goal of the relationship function is to establish the connection between the evaluation indicators and the comprehensive environmental indicators, thereby affecting the interaction between the PLC control cabinet and each device. For example, machine learning models (support vector machine (SVM), decision tree model) are used to train the dynamic attribute network. Usually, non-linear activation functions (such as ReLU, Sigmoid, Tanh, etc.) are used to introduce non-linearity, enabling the neural network to learn and represent more complex relationships, and accurately learn and adjust the edge weights in a complex environment to adapt to changing systems and external conditions; The weight can be expressed as: ; In the formula, w t represents the corresponding weight at each time point, t represents time, F(X) represents the relationship function, and h(·) represents the non-linear activation function; Meanwhile, add timestamps and status change identifiers to the dynamic attribute network; Extract the corresponding feature subgraphs based on the dynamic attribute network, including the first feature subgraph, the second feature subgraph, and the third feature subgraph. The first feature subgraph corresponds to the first dynamic network, the second feature subgraph corresponds to the second dynamic network, and the third feature subgraph corresponds to the third dynamic network; among them, the feature subgraphs change dynamically with time nodes; Among them, the representation form of the feature subgraph is: Y = [NF, H], then ; In the formula, Y represents the feature subgraph, NF is the node matrix feature, H is the attribute feature of all edges, and H ∈ R, , and are the attributes of the PLC control cabinet, , and are the attributes of the corresponding devices; Finally, determine the corresponding state vector according to the feature subgraph. Usually, the state vector represents the current state of the system, contains information describing each state variable of the system, and can use GCN technology and matrix decomposition technology to process the feature subgraph, calculate the embedding vector of each node and aggregate it into a state vector; the specific method will not be elaborated; The representation form of the state vector is: ; In the formula, State represents the state vector, staus represents the current state, including normal state or fault state, parameter represents the number of times any operating parameter exceeds the threshold line, and lasttime is the last maintenance time during the execution of the state setting strategy, and is represented by a timestamp; According to the pre-built rule engine, formulate the second supervision plan, and the estimated maintenance duration corresponding to the second supervision plan is: ; In the formula, Duration2 represents the maintenance duration corresponding to the second supervision plan; Jb is the basic maintenance duration, c represents the state vector of the corresponding feature sub - graph at a certain moment, represents the average state vector within the preset time, represents the fluctuation vector within the preset time, G 2 is the second error correction factor, and its value range is 0 - 1, usually set to 0.5, similar to the function of the first error correction factor G 1 and will not be elaborated here; Preliminary evaluation module: Within the second preset time, obtain the operating parameters of the PLC control cabinet again, compare the operating parameters with each threshold line, extract the number of operating parameters that do not meet the threshold line in each operating parameter and the number of times of repeatedly non - compliant operating parameters, and generate a warning value in combination with the environmental comprehensive index; The formula for generating the warning value is: ; In the formula, warn represents the warning value, cs is the number of operating parameters that do not meet the threshold line, sl is the number of times of repeatedly non - compliant operating parameters, φ1 is the first constant correction coefficient, and its value is 1 to avoid the sum of cs and sl being 0, facilitating subsequent calculations and ensuring the formula holds. Both β1 and β2 are weight coefficients, and the value ranges of β1 and β2 are 0 - 1; Adjustment monitoring module: Compare and analyze the warning value with the preset standard threshold. If the warning value is less than the standard threshold, then implement the regular inspection strategy. Otherwise, send a warning signal and implement the variable - frequency inspection strategy; The implemented regular inspection strategy includes: regularly checking whether the maintenance supervision is carried out according to the first supervision plan in the target - setting strategy, and the frequency of regular inspection is a preset fixed value; The implemented variable - frequency inspection strategy includes: variable - frequency checking whether the maintenance supervision is carried out according to the second supervision plan in the state - adjustment strategy, and the frequency of variable - frequency inspection is set according to the fixed value, warning value, and standard threshold, and the frequency of variable - frequency inspection is obtained in the following way: ; In the formula, Dp represents the frequency of variable - frequency inspection, gd represents the preset fixed value in the regular inspection, Ba represents the standard threshold, and the standard threshold is set according to the historical warning value, G 3 represents the third error correction factor, and is related to the first error correction factor G 1 and the second error correction factor G 2They have similar functions and will not be elaborated here. Their value range is 0 to 1, and int(·) is the rounding function. It should be noted that the standard threshold and the threshold line are taken in a similar way and will not be elaborated here. It should be noted that the second supervision plan is usually a more specific maintenance strategy formulated based on the first supervision plan for the operation status monitoring and abnormal situation handling of the PLC control cabinet. To achieve more effective management of the PLC control cabinet, the inspection frequency should be flexibly adjusted in combination with the equipment operation status; on the basis of regular inspections, the equipment status is monitored in real time through the warning value and the standard threshold. Once a deviation in operation is found, the inspection frequency is changed, and the strategy is adjusted in combination with historical operation data. For example, the regular inspection once every 15 days is changed to once every 10 days. This is just an example, and it is set according to the actual situation specifically. Within the third preset time, based on the regular inspection strategy and the variable-frequency inspection strategy, determine whether the operation status of the PLC control cabinet is in a normal state or a fault state: When it is determined to be in a fault state, trigger the evaluation and analysis mechanism, including: obtaining the corresponding operation parameters in the fault state, extracting the last maintenance frequency and the last maintenance duration, and generating a fault index in combination with the comprehensive environmental indicators; The formula for generating the fault index is: ; In the formula, fault represents the fault index, pl represents the maintenance frequency, st represents the maintenance duration, φ2 is the second constant correction coefficient, and its value is 1 to avoid the sum of pl and st being 1. μ1 and μ2 are both weight coefficients, and the value ranges of μ1 and μ2 are 0 to 1; When it is determined to be in a normal state, trigger the life analysis mechanism, including: collecting the moments when the historical state is determined to be a fault state, randomly extracting two time series of them, obtaining the risk time periods, sorting the risk time periods in time series and marking them as T1, T2, ……, Tn respectively; among them, the time series is a dynamically changing quantity, and n represents the number of times the state is determined to be a fault; dividing the risk time periods equally and marking them as r, and when there is a remainder, it is r + 1, m is a positive integer and is a dynamic value; Combined with the historical fault index, calculate the risk factors corresponding to the risk time periods under r or r + 1 respectively; When it is the risk time period corresponding to r, ; When it is the risk time period corresponding to (r + 1), ; In the formula, risk represents the risk factor, T risk represents the risk time period, and the calculation method is the duration between the start time and the end time of the risk time period, and the value is T1, T2 …… Tn; represents the average value obtained for the fault index in any risk time period, ε represents the control parameter used to control the speed of the PLC control cabinet from failing, and the control parameter is a dynamically varying quantity. Both μ0 and μ1 are weight coefficients, and both μ0 and μ1 are greater than 0; Then, in combination with the current operating parameters, environmental parameters, and the state vector corresponding to the dynamic attribute network, all are imported into a pre-constructed life analysis model to predict and output the remaining life; It should be noted that the risk factor, various operating parameters, environmental parameters, and the state vector corresponding to the dynamic attribute network are used as input information and input into the life analysis model. The life analysis model can be a regression model. For example, using a long short-term memory network (LSTM), support vector regression (SVR), through multiple iterative trainings, a numerical type result is output. The result usually represents the remaining hours, days, or months, etc. The specific implementation method is set by professionals. For example, if 182h is output, it means the remaining life is 182 hours; when there is no historical fault state, the Weibull distribution is used to predict the remaining life of the PLC control cabinet, and the specific implementation method will not be elaborated; If the current fault index exceeds the standard fault threshold or the current remaining life is greater than the standard life threshold, execute the alarm calibration strategy; Specifically include: Compare the current fault index with the standard fault threshold. When the current fault index exceeds the standard fault threshold, execute the alarm calibration strategy to calibrate and display the fault level of the operating state of the PLC control cabinet; It should be noted that the standard fault threshold is obtained corresponding to the first sorting table, and the first sorting table is divided into four parts according to the quartiles, that is, the first sorting table is divided into four parts, and the separated data are located at the positions of 25%, 50%, and 75% of the first sorting table respectively. Calculate the average value and standard deviation of the corresponding standard fault threshold in each quantile interval to set the calibration threshold of the calibration strategy. The calculation formula of the calibration threshold is: ; In the formula, By k represents the calibration threshold of any quantile interval, Adav avg represents the average value of the standard fault threshold corresponding to any quantile interval, S k represents the standard deviation of the standard fault threshold corresponding to any quantile interval, σ represents the multiple, and the value range is 2 - 3; Then, the calibration thresholds corresponding to each quantile interval are respectively marked as By 1 、By 2 and By 3 And By 1 >By 2 >By 3, the range calibration of the corresponding fault level is as follows: If the current fault index fault < calibration threshold By 3 When, mark the current fault index fault as a, and assign a first-level character. Combine a with the first-level character to generate a first-level fault level, determine that the current operating state of the PLC control cabinet is in a minor fault, and match alarm policy one; If the calibration threshold By 2 < current fault index fault ≤ calibration threshold By 3 When, mark the current fault index fault as b, and assign a second-level character. Combine b with the second-level character to generate a second-level fault level, determine that the current operating state of the PLC control cabinet is in a general fault, and match alarm policy two; If the calibration threshold By 1 < current fault index fault ≤ calibration threshold By 2 When, mark the current fault index fault as c, and assign a third-level character. Combine c with the third-level character to generate a third-level fault level, determine that the current operating state of the PLC control cabinet is in a serious fault, and match alarm policy three; If the current fault index fault ≥ calibration threshold By 1 When, mark the current fault index fault as d, and assign a fourth-level character. Combine d with the fourth-level character to generate a fourth-level fault level, determine that the current operating state of the PLC control cabinet is in a major fault, and match alarm policy four; When receiving the corresponding fault level of the PLC control cabinet, immediately edit and display the text. The text content is "The current fault level is XX level, and alarm policy XX is executed". Professional personnel perform corresponding maintenance work according to the text content; also add the fault level as an attribute feature to the corresponding dynamic attribute network to realize the timely update of the dynamic attribute network; Compare and analyze the current remaining life with the standard life threshold. When the current remaining life exceeds the standard life threshold, execute the alarm calibration policy to calibrate and display the remaining life of the operating state of the PLC control cabinet; the specific comparison method is similar to the above description and will not be elaborated; It should be noted that the first preset time, the second preset time and the third preset time can be regarded as the full cycle of the PLC control cabinet. During the first preset time (initial period), the various operating parameters and environmental parameters of the PLC control cabinet are first obtained, and the state setting strategy and state adjustment strategy are executed as required. During the second preset time (after running for a period of time), the various operating parameters and environmental parameters are obtained again for corresponding judgments, and the regular inspection strategy or the frequency conversion inspection strategy is executed. The supervision plan is updated in time. During the third preset time (running until the system stops), the corresponding alarm calibration strategy is executed for the fault state or normal state based on the regular inspection strategy and the frequency conversion inspection strategy.

[0016] Based on the above technical solutions, the present invention includes a target setting module, a preliminary evaluation module and an adjustment monitoring module; within the full cycle of system monitoring, by obtaining the operating parameters of the PLC control cabinet, comparing them with the preset threshold line, executing the state setting strategy, and formulating the first supervision plan to update the maintenance cycle, then combining the comprehensive environmental indicators, executing the state adjustment strategy, designing the corresponding dynamic attribute network of the PLC control cabinet, real-time monitoring the state at each time node within the time period, and at the same time, formulating the second supervision plan; in this process, through preliminary evaluation, calculate the warning value within the preset time, and update the maintenance frequency of the supervision plan according to the size of the warning value, so as to realize the operation state of the PLC control cabinet within the full cycle, and also obtain the corresponding operation parameters under the fault state, and generate the fault index in combination with the comprehensive environmental indicators to analyze the fault level, and predict the remaining life under normal conditions; to a certain extent, improve the working efficiency of the PLC control cabinet and the accuracy of analysis.

[0017] In the application, the several formulas involved are all calculated by removing dimensions and taking their numerical values, and the formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The formula is set by technical personnel in this field according to actual conditions.

[0018] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0019] The unit described as the separation component may or may not be physically separated. The component displayed as a unit may or may not be a physical unit, and it may be located in one place or distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0020] As described above, the foregoing is only a specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application.

Claims

1. A PLC control cabinet operation status monitoring and analysis system, characterized in that: The system includes: a target setting module, a preliminary assessment module and an adjustment monitoring module; The target setting module obtains various operating parameters and environmental parameters of the PLC control cabinet within the first preset time. When any operating parameter exceeds the preset threshold line, the state setting strategy is executed; the corresponding environmental comprehensive index is generated according to the pre-processed environmental parameters, and when the environmental comprehensive index exceeds the environmental index threshold, the state adjustment strategy is executed; The preliminary evaluation module obtains the various operating parameters of the PLC control cabinet again within the second preset time and compares them with the various threshold lines, extracts the number of operating parameters that do not meet the threshold lines and the number of operating parameters that repeatedly do not meet the threshold lines, and generates a warning value in combination with the comprehensive environmental indicators; Adjust the monitoring module. When the warning value is less than the standard threshold, execute the regular inspection strategy. Otherwise, execute the variable frequency inspection strategy.

2. The operating status monitoring and analysis system of a PLC control cabinet according to claim 1 is characterized in that: The executing of the periodic inspection strategy or the executing of the variable frequency inspection strategy includes: Within a third preset time, determining whether the operating state of the PLC control cabinet is a normal state or a fault state based on the periodic inspection strategy and the frequency conversion inspection strategy; Under the fault state, the evaluation and analysis mechanism is triggered to obtain the fault index; Under the normal state, the life analysis mechanism is triggered to predict the remaining life; If the current fault index exceeds the standard fault threshold or the current remaining life is greater than the standard life threshold, the alarm calibration strategy is executed.

3. The operating status monitoring and analysis system of a PLC control cabinet according to claim 1 is characterized in that: The operating parameters include power state parameters, device state parameters and communication state parameters; the power state parameters include the voltage value and frequency value of the input power supply, the device state parameters include the motor vibration value and the flow value of the electric valve, and the communication state parameters include the baud rate and the delay rate; the state setting strategy to be executed is: According to the difference between the corresponding operating parameter exceeding the threshold line and the corresponding threshold line, a first supervision plan is formulated according to a pre-built rule engine. The formula for the estimated maintenance time corresponding to the first supervision plan is: ； In the formula, Duration1 represents the estimated maintenance duration generated by the first supervision plan, Jb is the basic maintenance duration, i represents a corresponding operating parameter, and Pm i is the value corresponding to a certain operating parameter, Th i is the threshold line corresponding to a certain operating parameter, G1 is the first error correction factor, and its value range is 0 to 1.

4. The operating status monitoring and analysis system of a PLC control cabinet according to claim 1 is characterized in that: The execution state adjustment strategy includes: Assigning corresponding weights to various operating parameters, multiplying and accumulating various operating parameters and their corresponding weights, generating corresponding evaluation indicators, including a first evaluation indicator, a second evaluation indicator, and a third evaluation indicator; and constructing relationship functions between various evaluation indicators and environmental comprehensive indicators, including a first relationship function, a second relationship function, and a third relationship function; Sort the corresponding evaluation indicators that are determined to exceed the environmental indicator threshold, generate a first sorting table and then output it, and sort the evaluation indicators in the first sorting table in descending order; Obtaining various operating parameters corresponding to various evaluation indicators under the first sorting table, and performing dimensionless processing to generate standardized operating parameters, sorting the standardized operating parameters in descending order, generating a second sorting table and then outputting it, and constructing a dynamic attribute network based on the first sorting table and the second sorting table, including a first dynamic network, a second dynamic network and a third dynamic network, and the first dynamic network, the second dynamic network and the third dynamic network are named based on the order of the first sorting table; Based on the dynamic attribute network, extract corresponding feature subgraphs, including a first feature subgraph, a second feature subgraph, and a third feature subgraph, wherein the first feature subgraph corresponds to the first dynamic network, the second feature subgraph corresponds to the second dynamic network, and the third feature subgraph corresponds to the third dynamic network; determine the corresponding state vector based on the feature subgraphs; According to the pre-built rule engine, a second supervision plan is formulated. The estimated maintenance time corresponding to the second supervision plan is: ； In the formula, Duration2 represents the corresponding maintenance duration under the second supervision plan; Jb is the basic maintenance duration, and c represents the state vector of the corresponding feature subgraph at a certain moment. represents the average state vector within a preset time, represents the fluctuation vector within the preset time, and G2 is the second error correction factor.

5. The operating status monitoring and analysis system of a PLC control cabinet according to claim 4 is characterized in that: The steps of dynamic network design include: The PLC control cabinet is taken as the target node, the equipment corresponding to each operating parameter is taken as the object node, and the status of the PLC control cabinet and the corresponding equipment is taken as the edge. The weight of each edge is determined by the relationship function between each evaluation index and the comprehensive environmental index, so as to construct the corresponding dynamic attribute network.

6. The operating status monitoring and analysis system of a PLC control cabinet according to claim 1 is characterized in that: The formula for generating the warning value is: ； Where warn is the warning value, cs is the number of operating parameters that do not meet the threshold line, sl is the number of operating parameters that repeatedly do not meet the threshold line, φ1 is the first constant correction coefficient, and β1 and β2 are weight coefficients.

7. The operating status monitoring and analysis system of a PLC control cabinet according to claim 2 is characterized in that: The executed periodic inspection strategy includes: regularly inspecting whether maintenance supervision is performed according to the first supervision plan in the target setting strategy, and the frequency of the regular inspection is a preset fixed value; The executed frequency conversion inspection strategy includes: whether the frequency conversion inspection is carried out according to the second supervision plan in the state adjustment strategy for maintenance supervision, and the frequency of the frequency conversion inspection is set according to the fixed value, the warning value and the standard threshold, and the frequency of the frequency conversion inspection is obtained by the following formula: ； Where Dp represents the frequency of variable frequency inspection, gd represents the preset fixed value in periodic inspection, Ba represents the standard threshold, G3 represents the third error correction factor, and int (·) is the rounding function.

8. The operating status monitoring and analysis system of a PLC control cabinet according to claim 2 is characterized in that: The triggering evaluation and analysis mechanism is used to obtain the fault index based on the formula: ； In the formula, fault represents the fault index, pl represents the maintenance frequency, st represents the maintenance time, φ2 is the second constant correction coefficient, μ1 and μ2 are weight coefficients; The triggering life analysis mechanism predicts and obtains the remaining life, including: collecting the moments judged as fault states in history, randomly extracting two time series thereof, obtaining risk time periods, and respectively sorting the risk time periods in time series and marking them as T1, T2, ..., Tn; wherein the time series is a dynamic change amount, and n represents the number of times judged as a fault state; dividing the risk time period into equal parts and marking them as r, and when there is a remainder, it is r+1, and r is a positive integer and a dynamic value; Combined with the historical failure index, the risk factors corresponding to the risk time period under r or r+1 are calculated respectively, and then combined with the current operating parameters, environmental parameters and the state vector corresponding to the dynamic attribute network, all are imported into the pre-built life analysis model to predict the remaining life.

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