Simulation interaction process implementation method based on PLC and MES

Abstract

The invention discloses a simulation interaction process implementation method based on a PLC and an MES, and particularly relates to the technical field of simulation interaction, and the method comprises the steps: building a signal state reset monitoring model through obtaining network communication information, hardware fault information, electromagnetic interference information and mechanical vibration information when a target PLC carries out the signal state reset; the method comprises the following steps: analyzing and evaluating potential hidden dangers existing in a reset state of a target PLC signal, classifying the potential hidden dangers existing in the reset state of the target PLC signal, effectively identifying the potential hidden dangers existing in the reset state of the PLC signal, and according to a classification result of the potential hidden dangers and de-jittering information after the state of the target PLC signal is reset, judging whether the reset state of the target PLC signal is the reset state of the target PLC signal. The overall stability of PLC signal state reset is early warned in time through clustering analysis, the overall stability of PLC signal state reset is improved, continuity and efficient operation of a production line are ensured, and downtime and production interruption are reduced.

Description

Technical Field

[0001] The present invention relates to the field of simulation interaction technology, and more specifically, to a method for realizing a simulation interaction process based on PLC and MES. Background Art

[0002] The simulation interaction process implementation method based on PLC and MES is an important method in modern manufacturing. With the continuous development of industrial automation and informatization, manufacturing enterprises have put forward higher requirements for the control and management of production processes. The application of PLC and MES as two key technologies in the production process has greatly improved production efficiency and product quality. The interaction between PLC and MES is an important means to realize the automation and informatization of the manufacturing process. By passing the real-time production data collected by PLC to MES, MES can analyze and process these data to provide decision support in production planning, scheduling, quality control and other aspects. At the same time, the instructions and plans of MES can be passed to specific production equipment and production lines through PLC to achieve precise control of the production process. This interactive method can greatly improve the automation and informatization level of the production process and meet the needs of modern manufacturing enterprises for efficient, flexible and transparent production management.

[0003] In order to improve the interaction efficiency between PLC and MES, so that data exchange and operation control can be completed faster and more reliably on the production line where every second counts, a one-time interaction method is usually used to implement the interaction process between PLC and MES. When the pallet enters the workstation, PLC will read the RFID information on the pallet and prepare relevant workstation information (such as pallet ID, product type, task, etc.). PLC sets the pallet entry trigger point to 1 to notify the MES system that the pallet has entered the workstation and is ready. The MES system reads the pallet entry trigger point of the PLC at a fixed period (such as 100 milliseconds). When the trigger point is detected to change from 0 to 1, the trigger event is recorded and the relevant workstation information is read. After processing the workstation information, MES feeds back to PLC and sets the feedback trigger point to 1 to notify PLC that the information has been received and processed. After processing the workstation information, MES feeds back the result to PLC. After receiving the feedback result from MES, PLC resets the pallet entry trigger point and relevant workstation information to 0. After MES detects the PLC reset signal, it resets the feedback trigger point and entry result to 0. In the above interaction process, PLC is the first reset party, and the stability of its reset signal state directly affects the continuity of subsequent interactions. If the PLC reset signal state is unstable, the MES system may not be able to correctly identify the signal change, may process the signal at an incorrect time point, or miss the change in the signal state, causing the MES system to process the same event multiple times, repeat operations or miss certain trigger events, affecting the continuity and efficiency of production. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for realizing a simulation interaction process based on PLC and MES to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: The method for realizing the simulation interaction process based on PLC and MES includes the following steps: Step S1, obtaining network communication information, hardware fault information, electromagnetic interference information, and mechanical vibration information when the target PLC performs signal state reset; Step S2, constructing a signal state reset monitoring model, analyzing the potential hidden dangers of the target PLC signal reset state, and outputting a signal state reset evaluation index; Step S3, comparing the signal state reset evaluation index with a preset signal state reset evaluation index threshold, and classifying the potential hidden dangers existing in the target PLC signal reset state; Step S4, based on the classification result of potential hidden dangers and the de-jitter information after the target PLC signal state is reset, an early warning is issued on the overall stability of the PLC signal state reset through cluster analysis.

[0006] In a preferred embodiment, the network communication information includes the network link transmission rate valley coefficient, the hardware fault information includes the wiring looseness coefficient, the electromagnetic interference information includes the electromagnetic interference coefficient, and the mechanical vibration information includes the vibration rise rate; The network link transmission rate valley coefficient, wiring looseness coefficient, electromagnetic interference coefficient, and vibration rise rate are marked as , , , .

[0007] In a preferred embodiment, the logic for obtaining the network link transmission rate valley coefficient is as follows: Get the network transmission rate of the target PLC area and mark the network transmission rate as , i represents the order number of different sampling points, ; Calculate the average network transmission rate , the expression is as follows ; Get the network transmission rate valley threshold based on the average network transmission rate , the expression is as follows , where Represents the proportionality coefficient, which is used to determine the ratio of the network transmission rate valley threshold to the average transmission rate; Compare the network transmission rate of the target PLC area with the network transmission rate valley threshold, mark the network transmission rate less than the network transmission rate valley threshold as the network transmission valley rate, and calculate the network link transmission rate valley coefficient. The expression is as follows , where represents the jth network transmission trough rate, .

[0008] In a preferred embodiment, the logic for obtaining the wiring looseness coefficient is as follows:

[0009] Install a pressure sensor at the terminal screw position, measure the pressure of the terminal screw, and calculate the rate of change of pressure over time , the expression is as follows , where Indicates the pressure change, Indicates the amount of change over time; Calculate pressure deviation , the expression is as follows , where Indicates the pressure under ideal conditions when the terminal screws are not loose. represents the actual measured pressure at time t; Calculate the wiring looseness coefficient, the expression is as follows , where q represents the serial number of the terminal screws, .

[0010] In a preferred embodiment, the logic for obtaining the electromagnetic interference coefficient is as follows: Install an electromagnetic interference detector in the area where the target PLC is located to obtain discrete electromagnetic interference time domain signals in the area where the target PLC is located. , the electromagnetic interference time domain signal is converted into the electromagnetic interference frequency domain signal through fast Fourier transform, the expression is as follows , where represents the electromagnetic interference frequency domain signal, k represents the frequency index, and G represents the imaginary unit; Calculate frequency components , the expression is as follows , where Indicates the adopted frequency of electromagnetic interference signal; Calculate the amplitude of electromagnetic interference frequency domain signal , the expression is as follows , where express The real part of express The imaginary part of Calculate power spectral density , the expression is as follows ; Compare the frequency component with the preset frequency component threshold, sort the frequency components greater than the frequency component threshold from large to small, and obtain the frequency component sequence , and its corresponding power spectral density sequence ; The expression for calculating the electromagnetic interference coefficient is as follows , where Represents the sum of all power spectral densities, expressed as follows .

[0011] In a preferred embodiment, the logic for obtaining the vibration rise rate is: Install a vibration sensor on the target PLC to obtain the vibration intensity value of the target PLC and calculate the vibration rise rate. The expression is as follows , where express The maximum value of the vibration intensity value within the time period, express The minimum value of the vibration intensity within the time period.

[0012] In a preferred embodiment, in step S2, a signal state reset monitoring model is constructed to analyze the potential hazards of the target PLC signal reset state, and output a signal state reset evaluation index, as follows: The network link transmission rate trough coefficient, wiring loose coefficient, electromagnetic interference coefficient, and vibration rise rate are normalized, and a signal state reset monitoring model is constructed based on the normalized network link transmission rate trough coefficient, wiring loose coefficient, electromagnetic interference coefficient, and vibration rise rate, and the signal state reset evaluation index is output. The model is based on the following formula , where , , , They represent the preset proportional coefficients of the network link transmission rate valley coefficient, wiring looseness coefficient, electromagnetic interference coefficient, and vibration rise rate, respectively. , , , Both are greater than 0.

[0013] In a preferred embodiment, in step S3, the signal state reset evaluation index is compared with a preset signal state reset evaluation index threshold, and the potential hidden dangers existing in the target PLC signal reset state are classified into categories, as follows: If the signal state reset evaluation index is greater than the signal state reset evaluation index threshold, a potential hidden danger signal is generated; if the signal state reset evaluation index is less than or equal to the signal state reset evaluation index threshold, there is no need to generate a potential hidden danger signal.

[0014] In a preferred embodiment, in step S4, based on the classification result of potential hidden dangers and the de-jitter information after the target PLC signal state is reset, the overall stability of the PLC signal state reset is warned through cluster analysis, as follows: When a potential hidden danger signal is generated, the de-jitter information of the corresponding target PLC signal state after reset is obtained. The de-jitter information includes the de-jitter misrecognition rate. The de-jitter misrecognition rate is marked as ; The calculation expression of de-jitter misrecognition rate is as follows , where Indicates the number of valid signals that were not correctly identified and processed by the debounce mechanism. Indicates the number of valid signals that the de-jitter mechanism correctly identifies and processes; When a potential hidden danger signal is generated, the signal state reset evaluation index of the corresponding output is obtained, and the signal state reset evaluation index and the de-jitter misrecognition rate are used as the characteristic data of each PLC to perform cluster analysis on the overall stability of the PLC signal state reset using the k-means clustering algorithm, and an early warning prompt is given based on the clustering results, as follows: Step S41, using the silhouette coefficient method to determine the number K of cluster centers of the K-means algorithm; Step S42: clustering the PLCs using the KMEANS algorithm to obtain K PLC category clusters.

[0015] In a preferred embodiment, the standard deviation of the signal state reset evaluation index and the standard deviation of the de-jitter false recognition rate of the PLC in each cluster are calculated, and the standard deviation of the signal state reset evaluation index and the standard deviation of the de-jitter false recognition rate are compared with the standard deviation threshold of the signal state reset evaluation index and the standard deviation threshold of the de-jitter false recognition rate, respectively. When the standard deviation of the signal state reset evaluation index is greater than the standard deviation threshold of the signal state reset evaluation index and the standard deviation of the de-jitter false recognition rate is greater than the standard deviation threshold of the de-jitter false recognition rate, an early warning signal is issued to warn of the stability of the signal state reset of the PLC in the cluster; when the standard deviation of the signal state reset evaluation index is less than or equal to the standard deviation threshold of the signal state reset evaluation index and the standard deviation of the de-jitter false recognition rate is less than or equal to the standard deviation threshold of the de-jitter false recognition rate, there is no need to issue a early warning signal.

[0016] Technical effects and advantages of the present invention: 1. The present invention acquires network communication information, hardware fault information, electromagnetic interference information, and mechanical vibration information when the target PLC performs signal state reset, constructs a signal state reset monitoring model, analyzes and evaluates potential hidden dangers in the signal reset state of the target PLC, compares the signal state reset evaluation index with the signal state reset evaluation index threshold, categorizes the potential hidden dangers in the signal reset state of the target PLC, effectively identifies the potential hidden dangers in the PLC signal reset state, and according to the potential hidden danger categorization results and the de-jitter information after the target PLC signal state reset, timely warns the overall stability of the PLC signal state reset through cluster analysis, thereby improving the overall stability of the PLC signal state reset, preventing potential problems, ensuring the continuity and efficient operation of the production line, and reducing downtime and production interruptions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 Schematic diagram of the structure of the method of the embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Example: Figure 1 The present invention provides a method for implementing a simulation interaction process based on PLC and MES, comprising the following steps: Step S1, obtaining network communication information, hardware fault information, electromagnetic interference information, and mechanical vibration information when the target PLC performs signal state reset; Step S2, constructing a signal state reset monitoring model, analyzing the potential hidden dangers of the target PLC signal reset state, and outputting a signal state reset evaluation index; Step S3, comparing the signal state reset evaluation index with a preset signal state reset evaluation index threshold, and classifying the potential hidden dangers existing in the target PLC signal reset state; Step S4, based on the classification result of potential hidden dangers and the de-jitter information after the target PLC signal state is reset, an early warning is issued on the overall stability of the PLC signal state reset through cluster analysis.

[0020] In actual production applications, the MES system usually interacts and communicates with multiple PLCs. Different PLCs are deployed in different links of production to collect workstation information in different links, such as pallet ID, product type, production task, equipment status, working parameters and other information. Since the PLC is the first reset party during the interaction process, the stability of its reset signal state directly affects the continuity of subsequent interactions. If the PLC reset signal state is unstable, the MES system may not be able to correctly identify the signal change. Different PLCs are usually located in different production links. The network conditions, electromagnetic interference conditions, production environment conditions and their own fault conditions in the area where they are located may be different, which affects the stability of the PLC reset signal state. Therefore, by obtaining the network communication information, hardware fault information, electromagnetic interference information, and mechanical vibration information when the target PLC resets the signal state, the potential hidden dangers of the PLC signal reset state are predicted; The target PLC refers to any one of the many PLCs that interact and communicate with the MES system; Network communication information includes the network link transmission rate valley coefficient, hardware failure information includes the wiring looseness coefficient, electromagnetic interference information includes the electromagnetic interference coefficient, and mechanical vibration information includes the vibration rise rate; The network link transmission rate valley coefficient, wiring looseness coefficient, electromagnetic interference coefficient, and vibration rise rate are marked as , , , ; The network link transmission rate valley coefficient is used to measure the frequency of the network transmission rate valley in the area where the target PLC is located. A larger network link transmission rate valley coefficient indicates that the network condition in the area where the target PLC is located is worse, which may affect the stability of the PLC reset signal and increase the potential hidden dangers of the PLC when performing signal reset. By calculating the network link transmission rate valley coefficient, the network condition in the area where the target PLC is located can be effectively detected and optimized, thereby improving the network transmission rate in the area where the target PLC is located. The logic for obtaining the network link transmission rate valley coefficient is as follows: Get the network transmission rate of the target PLC area and mark the network transmission rate as , i represents the order number of different sampling points, ; It should be noted that the network transmission rate can be obtained in real time by using professional performance monitoring tools such as top, Prometheus, Grafana, etc. These tools can provide detailed information about the network status; Calculate the average network transmission rate , the expression is as follows ; Get the network transmission rate valley threshold based on the average network transmission rate , the expression is as follows , where Represents the proportionality coefficient, which is used to determine the ratio of the network transmission rate valley threshold to the average transmission rate; It should be noted that the proportionality coefficient It can be set according to the actual situation. For example, when the preset When , the expression for obtaining the network transmission rate valley threshold is: ; Compare the network transmission rate of the target PLC area with the network transmission rate valley threshold, mark the network transmission rate less than the network transmission rate valley threshold as the network transmission valley rate, and calculate the network link transmission rate valley coefficient. The expression is as follows , where represents the jth network transmission trough rate, ; As a programmable logic controller, PLC is usually composed of multiple components with different functions. The components are connected through wiring and terminals. When the terminal screws are loose, it may cause indirect interruption of the internal signal transmission of the PLC, resulting in inaccurate reset, thus program execution error or logic confusion. The wiring looseness coefficient is used to measure the looseness of the terminal screws inside the PLC. The larger the wiring looseness coefficient, the greater the looseness of the terminal screws inside the target PLC, which may affect the stability of the PLC reset signal and increase the potential hidden dangers of the PLC when performing signal reset; The logic for obtaining the wiring looseness coefficient is as follows: Install a pressure sensor at the terminal screw position, measure the pressure of the terminal screw, and calculate the rate of change of pressure over time , the expression is as follows , where Indicates the pressure change, Indicates the amount of change over time; Calculate pressure deviation , the expression is as follows , where Indicates the pressure under ideal conditions when the terminal screws are not loose. represents the actual measured pressure at time t; It should be noted that the greater the pressure, the tighter the terminal screws are, and the smaller the pressure, the looser the terminal screws are; Calculate the wiring looseness coefficient, the expression is as follows , where q represents the serial number of the terminal screws, ; The electromagnetic interference coefficient is used to measure the electromagnetic interference situation in the area where the target PLC is located. The larger the electromagnetic interference coefficient, the greater the degree of electromagnetic interference to the target PLC in the area, which may affect the stability of the PLC reset signal and increase the potential hidden dangers of the PLC when resetting the signal. By calculating the electromagnetic interference coefficient, the electromagnetic interference situation in the area where the target PLC is located can be effectively detected and optimized to reduce the electromagnetic interference intensity in the area where the target PLC is located. The logic for obtaining the electromagnetic interference coefficient is as follows: Install an electromagnetic interference detector in the area where the target PLC is located to obtain discrete electromagnetic interference time domain signals in the area where the target PLC is located. , the electromagnetic interference time domain signal is converted into the electromagnetic interference frequency domain signal through fast Fourier transform, the expression is as follows , where represents the electromagnetic interference frequency domain signal, k represents the frequency index, and G represents the imaginary unit; Calculate frequency components , the expression is as follows , where Indicates the adopted frequency of electromagnetic interference signal; Calculate the amplitude of electromagnetic interference frequency domain signal , the expression is as follows , where express The real part of express The imaginary part of Calculate power spectral density , the expression is as follows ; Compare the frequency component with the preset frequency component threshold, sort the frequency components greater than the frequency component threshold from large to small, and obtain the frequency component sequence , and its corresponding power spectral density sequence ; The expression for calculating the electromagnetic interference coefficient is as follows , where Represents the sum of all power spectral densities, expressed as follows ; The vibration rise rate is used to measure the rate of change of the mechanical vibration to which the target PLC is subjected. A higher vibration rise rate indicates that the target PLC is located in an environment where mechanical vibration is frequent and changes rapidly in intensity, which may affect the stability of the PLC reset signal and increase the potential hidden dangers of the PLC when resetting the signal. By calculating the vibration rise rate, the mechanical vibration conditions in the area where the target PLC is located can be effectively detected and optimized, reducing the impact of vibration on the reset of the PLC signal state.

[0021] The logic of obtaining the vibration rise rate: Install a vibration sensor on the target PLC to obtain the vibration intensity value of the target PLC and calculate the vibration rise rate. The expression is as follows , where express The maximum value of the vibration intensity value within the time period, express The minimum value of the vibration intensity within the time period; In step S2, a signal state reset monitoring model is constructed to analyze the potential risks of the target PLC signal reset state, and a signal state reset evaluation index is output, as follows: The network link transmission rate trough coefficient, wiring loose coefficient, electromagnetic interference coefficient, and vibration rise rate are normalized, and a signal state reset monitoring model is constructed based on the normalized network link transmission rate trough coefficient, wiring loose coefficient, electromagnetic interference coefficient, and vibration rise rate, and the signal state reset evaluation index is output. The model is based on the following formula , where , , , They represent the preset proportional coefficients of the network link transmission rate valley coefficient, wiring looseness coefficient, electromagnetic interference coefficient, and vibration rise rate, respectively. , , , All are greater than 0; It can be seen from the above calculation expression that the larger the trough coefficient of the network link transmission rate, the larger the wiring loose coefficient, the larger the electromagnetic interference coefficient, and the larger the vibration rise rate, the larger the signal state reset evaluation index, indicating that the probability of potential hidden dangers in the target PLC signal reset state is greater; conversely, the smaller the trough coefficient of the network link transmission rate, the smaller the wiring loose coefficient, the smaller the electromagnetic interference coefficient, and the smaller the vibration rise rate, the smaller the signal state reset evaluation index, indicating that the probability of potential hidden dangers in the target PLC signal reset state is smaller; In step S3, the signal state reset evaluation index is compared with a preset signal state reset evaluation index threshold, and the potential hidden dangers existing in the target PLC signal reset state are classified into categories, as follows: If the signal state reset evaluation index is greater than the signal state reset evaluation index threshold, it indicates that there is a potential hidden danger in the target PLC signal reset state, and a potential hidden danger signal is generated; if the signal state reset evaluation index is less than or equal to the signal state reset evaluation index threshold, it indicates that there is no potential hidden danger in the target PLC signal reset state, and there is no need to generate a potential hidden danger signal; In step S4, based on the classification results of potential hidden dangers and the de-jitter information after the target PLC signal state is reset, the overall stability of the PLC signal state reset is warned through cluster analysis, as follows: When a potential hidden danger signal is generated, the de-jitter information of the corresponding target PLC signal state after reset is obtained. The de-jitter information includes the de-jitter misrecognition rate. The de-jitter misrecognition rate is marked as ; In the simulation interaction process between PLC and MES, a de-jitter mechanism is usually used to filter short-term signal changes caused by electrical noise, mechanical vibration or other interference sources. The de-jitter misrecognition rate is used to measure the probability of misrecognition when the de-jitter mechanism is applied to the target PLC. The calculation expression of the de-jitter misrecognition rate is as follows: , where Indicates the number of valid signals that were not correctly identified and processed by the debounce mechanism. Indicates the number of valid signals that the de-jitter mechanism correctly identifies and processes; When a potential hidden danger signal is generated, the signal state reset evaluation index of the corresponding output is obtained, and the signal state reset evaluation index and the de-jitter misrecognition rate are used as the characteristic data of each PLC to perform cluster analysis on the overall stability of the PLC signal state reset using the k-means clustering algorithm, and an early warning prompt is given based on the clustering results, as follows: Step S41, using the silhouette coefficient method to determine the number K of cluster centers of the K-means algorithm; Step S41 further includes the following steps: Step S411, given the K value, use the KMEANS algorithm to cluster the data and calculate the silhouette coefficient of each PLC , the calculation expression is as follows , where is the silhouette coefficient, is the average distance between each PLC and all other PLCs in the same cluster, The minimum value of the average distance between each PLC and all the points in all other clusters, where the distance between every two PLCs can be obtained by a common distance calculation method, for example, the Euclidean distance calculation method, the Manhattan distance calculation method, the Minkowski distance method, etc.

[0022] In this embodiment, the Euclidean distance calculation method is used to obtain the distance between two PLCs. , for easy distinction, the two PLCs are marked as A-PLC and B-PLC, then the distance between the two PLCs is obtained The specific expression is as follows , where , They represent the signal state reset evaluation index and de-jitter misrecognition rate of A-PLC respectively. , They represent the signal state reset evaluation index and debounce misrecognition rate of B-PLC respectively; Step S412, for each cluster, calculating the average silhouette coefficient of all PLCs in the cluster to obtain the average silhouette coefficient of the cluster; Step S413, calculating the average of the average silhouette coefficients of all clusters to obtain the average silhouette coefficient under the current K value; Step S414, repeating steps S411, S412, and S413 to calculate the average silhouette coefficient under different K values; Step S415, selecting the K value with the largest average silhouette coefficient as the final K value; Step S42: clustering the PLCs using the KMEANS algorithm to obtain K PLC category clusters.

[0023] Through cluster analysis, each PLC is clustered on a certain cluster center; The standard deviation of the signal state reset evaluation index and the standard deviation of the de-jitter false recognition rate of the PLC in each cluster are calculated, and the standard deviation of the signal state reset evaluation index and the standard deviation of the de-jitter false recognition rate are compared with the standard deviation threshold of the signal state reset evaluation index and the standard deviation threshold of the de-jitter false recognition rate respectively. When the standard deviation of the signal state reset evaluation index is greater than the standard deviation threshold of the signal state reset evaluation index and the standard deviation of the de-jitter false recognition rate is greater than the standard deviation threshold of the de-jitter false recognition rate, it indicates that the stability fluctuation of the signal state reset of the PLC in the cluster is large, and an early warning signal is issued to warn the stability of the signal state reset of the PLC in the cluster; when the standard deviation of the signal state reset evaluation index is less than or equal to the standard deviation threshold of the signal state reset evaluation index and the standard deviation of the de-jitter false recognition rate is less than or equal to the standard deviation threshold of the de-jitter false recognition rate, it indicates that the stability fluctuation of the signal state reset of the PLC in the cluster is small, and there is no need to issue an early warning signal.

[0024] The present invention acquires network communication information, hardware fault information, electromagnetic interference information, and mechanical vibration information when a target PLC performs signal state reset, constructs a signal state reset monitoring model, analyzes and evaluates potential hidden dangers in the signal reset state of the target PLC, compares a signal state reset evaluation index with a signal state reset evaluation index threshold, categorizes the potential hidden dangers in the signal reset state of the target PLC, effectively identifies the potential hidden dangers in the PLC signal reset state, and timely warns the overall stability of the PLC signal state reset through cluster analysis based on the potential hidden danger categorization results and the de-jitter information after the target PLC signal state reset, thereby improving the overall stability of the PLC signal state reset, preventing potential problems, ensuring the continuity and efficient operation of the production line, and reducing downtime and production interruptions.

[0025] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0026] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0027] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for realizing a simulation interaction process based on PLC and MES, characterized in that: The steps include: Step S1, obtaining network communication information, hardware fault information, electromagnetic interference information, and mechanical vibration information when the target PLC performs signal state reset; Step S2, constructing a signal state reset monitoring model, analyzing the potential hidden dangers of the target PLC signal reset state, and outputting a signal state reset evaluation index; Step S3, comparing the signal state reset evaluation index with a preset signal state reset evaluation index threshold, and classifying the potential hidden dangers existing in the target PLC signal reset state; Step S4, based on the classification result of potential hidden dangers and the de-jitter information after the target PLC signal state is reset, an early warning is issued on the overall stability of the PLC signal state reset through cluster analysis.

2. The method for realizing a simulation interaction process based on PLC and MES according to claim 1, characterized in that: Network communication information includes the network link transmission rate valley coefficient, hardware failure information includes the wiring looseness coefficient, electromagnetic interference information includes the electromagnetic interference coefficient, and mechanical vibration information includes the vibration rise rate; The network link transmission rate valley coefficient, wiring looseness coefficient, electromagnetic interference coefficient, and vibration rise rate are marked as , , , .

3. The method for realizing the simulation interaction process based on PLC and MES according to claim 2, characterized in that: The logic for obtaining the network link transmission rate valley coefficient is as follows: Get the network transmission rate of the target PLC area and mark the network transmission rate as , i represents the order number of different sampling points, ; Calculate the average network transmission rate , the expression is as follows ; Get the network transmission rate valley threshold based on the average network transmission rate , the expression is as follows , where Represents the proportionality coefficient, which is used to determine the ratio of the network transmission rate valley threshold to the average transmission rate; Compare the network transmission rate of the target PLC area with the network transmission rate valley threshold, mark the network transmission rate less than the network transmission rate valley threshold as the network transmission valley rate, and calculate the network link transmission rate valley coefficient. The expression is as follows , where represents the jth network transmission trough rate, .

4. The method for realizing the simulation interaction process based on PLC and MES according to claim 2, characterized in that: The logic for obtaining the wiring looseness coefficient is as follows: Install a pressure sensor at the terminal screw position, measure the pressure of the terminal screw, and calculate the rate of change of pressure over time , the expression is as follows , where Indicates the pressure change, Indicates the amount of change over time; Calculate pressure deviation , the expression is as follows , where Indicates the pressure under ideal conditions when the terminal screws are not loose. represents the actual measured pressure at time t; Calculate the wiring looseness coefficient, the expression is as follows , where q represents the serial number of the terminal screws, .

5. The method for realizing the simulation interaction process based on PLC and MES according to claim 2, characterized in that: The logic for obtaining the electromagnetic interference coefficient is as follows: Install an electromagnetic interference detector in the area where the target PLC is located to obtain discrete electromagnetic interference time domain signals in the area where the target PLC is located. , the electromagnetic interference time domain signal is converted into the electromagnetic interference frequency domain signal through fast Fourier transform, the expression is as follows , where represents the electromagnetic interference frequency domain signal, k represents the frequency index, and G represents the imaginary unit; Calculate frequency components , the expression is as follows , where Indicates the adopted frequency of electromagnetic interference signal; Calculate the amplitude of electromagnetic interference frequency domain signal , the expression is as follows , where express The real part of express The imaginary part of Calculate power spectral density , the expression is as follows ; Compare the frequency component with the preset frequency component threshold, sort the frequency components greater than the frequency component threshold from large to small, and obtain the frequency component sequence , and its corresponding power spectral density sequence ; The expression for calculating the electromagnetic interference coefficient is as follows , where Represents the sum of all power spectral densities, expressed as follows .

6. The method for realizing the simulation interaction process based on PLC and MES according to claim 2, characterized in that: The logic of obtaining the vibration rise rate: Install a vibration sensor on the target PLC to obtain the vibration intensity value of the target PLC and calculate the vibration rise rate. The expression is as follows , where express The maximum value of the vibration intensity value within the time period, express The minimum value of the vibration intensity within the time period.

7. The method for realizing the simulation interaction process based on PLC and MES according to claim 2, characterized in that: In step S2, a signal state reset monitoring model is constructed to analyze the potential risks of the target PLC signal reset state, and a signal state reset evaluation index is output, as follows: The network link transmission rate trough coefficient, wiring loose coefficient, electromagnetic interference coefficient, and vibration rise rate are normalized, and a signal state reset monitoring model is constructed based on the normalized network link transmission rate trough coefficient, wiring loose coefficient, electromagnetic interference coefficient, and vibration rise rate, and the signal state reset evaluation index is output. The model is based on the following formula , where , , , They represent the preset proportional coefficients of the network link transmission rate valley coefficient, wiring looseness coefficient, electromagnetic interference coefficient, and vibration rise rate, respectively. , , , Both are greater than 0.

8. The method for realizing the simulation interaction process based on PLC and MES according to claim 7, characterized in that: In step S3, the signal state reset evaluation index is compared with a preset signal state reset evaluation index threshold, and the potential hidden dangers existing in the target PLC signal reset state are classified into categories, as follows: If the signal state reset evaluation index is greater than the signal state reset evaluation index threshold, a potential hidden danger signal is generated; if the signal state reset evaluation index is less than or equal to the signal state reset evaluation index threshold, there is no need to generate a potential hidden danger signal.

9. The method for realizing the simulation interaction process based on PLC and MES according to claim 8, characterized in that: In step S4, based on the classification results of potential hidden dangers and the de-jitter information after the target PLC signal state is reset, the overall stability of the PLC signal state reset is warned through cluster analysis, as follows: When a potential hidden danger signal is generated, the de-jitter information of the corresponding target PLC signal state after reset is obtained. The de-jitter information includes the de-jitter misrecognition rate. The de-jitter misrecognition rate is marked as ; The calculation expression of de-jitter misrecognition rate is as follows , where Indicates the number of valid signals that were not correctly identified and processed by the debounce mechanism. Indicates the number of valid signals that the de-jitter mechanism correctly identifies and processes; When a potential hidden danger signal is generated, the signal state reset evaluation index of the corresponding output is obtained, and the signal state reset evaluation index and the de-jitter misrecognition rate are used as the characteristic data of each PLC to perform cluster analysis on the overall stability of the PLC signal state reset using the k-means clustering algorithm, and an early warning prompt is given based on the clustering results, as follows: Step S41, using the silhouette coefficient method to determine the number K of cluster centers of the K-means algorithm; Step S42: clustering the PLCs using the KMEANS algorithm to obtain K PLC category clusters.

10. The method for realizing the simulation interaction process based on PLC and MES according to claim 9, characterized in that: The standard deviation of the signal state reset evaluation index and the standard deviation of the de-jitter false recognition rate of the PLC in each cluster are calculated, and the standard deviation of the signal state reset evaluation index and the standard deviation of the de-jitter false recognition rate are compared with the standard deviation threshold of the signal state reset evaluation index and the standard deviation threshold of the de-jitter false recognition rate respectively. When the standard deviation of the signal state reset evaluation index is greater than the standard deviation threshold of the signal state reset evaluation index and the standard deviation of the de-jitter false recognition rate is greater than the standard deviation threshold of the de-jitter false recognition rate, an early warning signal is issued to warn of the stability of the signal state reset of the PLC in the cluster; when the standard deviation of the signal state reset evaluation index is less than or equal to the standard deviation threshold of the signal state reset evaluation index and the standard deviation of the de-jitter false recognition rate is less than or equal to the standard deviation threshold of the de-jitter false recognition rate, there is no need to issue a early warning signal.

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