Remote data control method for laminating machine technological process

Abstract

The invention provides a data remote control method of a laminating machine technological process, and relates to the field of data remote control of the laminating machine technological process. The data remote control method for the technical process of the laminating machine comprises the following steps: S1, setting a central control system for receiving and sending operation data of the laminating machine, configuring at least one edge processing unit near the process equipment of the laminating machine for real-time data processing and temporary storage, and S2, establishing an encrypted data transmission channel between the CCS and the EPU, the safety of data transmission is ensured, and the collected state data of the laminating machine is preliminarily analyzed through the EPU, so that the amount of data transmitted to the CCS is reduced, and the transmission efficiency is improved. The security and efficiency of data transmission are optimized by integrating advanced technologies, while the emergency response capability of the system is improved, specifically including establishing a central control system to receive and send operational data, and configuring an EPU to process and store data in a close range to reduce network dependence.

Description

Technical Field

[0001] The invention relates to the technical field of remote control of laminating machine processes, and in particular to a data remote control method for a laminating machine process. Background Art

[0002] A data remote control method for a laminating machine process mainly includes five key parts: a data acquisition module, a data processing module, a remote transmission module, a remote control module and a user interface module. The data acquisition module is responsible for real-time acquisition of the working parameters of each key component of the laminating machine, and obtains data such as temperature, pressure and speed through sensors and transmits them to the data processing module. The data processing module processes, analyzes and stores the collected raw data, and screens, filters and converts the data for monitoring and control. The remote transmission module transmits the processed data to the remote control center through the Internet or a dedicated network to ensure the real-time and security of data transmission. The remote control module is located in the control center, and monitors the working status of the laminating machine by receiving and analyzing the data transmitted remotely, and sends control instructions back to the site as needed to adjust the operating parameters of the laminating machine to ensure the stability and efficiency of the process. The user interface module provides operators and managers with a visual interface to display the real-time operating status and historical data of the laminating machine, and allows users to perform remote control and parameter adjustment.

[0003] Although this remote control method has significant advantages in improving the automation and intelligence of the laminating machine process, it also has some shortcomings. First, the problem of data transmission delay and reliability: due to the reliance on the Internet or dedicated network for data transmission, the instability of the network may cause data delay or loss, affecting the effect of real-time control; prolonged emergency response time: in the event of an emergency, reliance on remote control may prolong the response time, especially in the event of network failure or system failure, and on-site intervention cannot be carried out quickly. In summary, although this data remote control method for the laminating machine process can improve production efficiency and control accuracy, it still needs further optimization and improvement in data transmission, system security and emergency response. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides a method for remote control of data of a laminating machine process, which solves the problem that due to reliance on the Internet or a dedicated network for data transmission, the instability of the network may cause data delay or loss, thereby affecting the effect of real-time control; in the event of an emergency, reliance on remote control may prolong the response time, especially in the event of a network failure or system failure, and the problem of being unable to quickly perform on-site intervention.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a data remote control method for a laminating machine process, comprising:

[0008] S1. Set up a central control system for receiving and sending operation data of the laminator, and configure at least one edge processing unit near the laminator process equipment for real-time data processing and temporary storage;

[0009] S2. Establish an encrypted data transmission channel between CCS and EPU to ensure the security of data transmission. Perform a preliminary analysis on the collected laminator status data through EPU to reduce the amount of data transmitted to CCS and improve transmission efficiency.

[0010] S3. Set the priority rules for data transmission, adjust the transmission priority according to the urgency of the data to optimize the data processing flow, and implement a real-time fault detection algorithm in the EPU so that it can operate independently when the network is disconnected to ensure the continuous operation of the laminator;

[0011] S4. Deploy a machine learning model in CCS to predict the maintenance needs and possible failure points of the laminator to achieve preventive maintenance, and establish a cloud-based backup system to periodically synchronize data from CCS to cloud storage to prevent data loss and enhance data durability.

[0012] S5. Implement a fast switching mechanism to automatically switch from remote control to local control when the EPU detects that the network delay exceeds the set threshold to ensure the continuity of the laminator operation. Configure an emergency response system so that when the CCS receives an emergency signal from the EPU, it can quickly mobilize proximal support or start the backup control protocol to shorten the response time.

[0013] S6. Set up a multi-level user authentication system in CCS to enhance system security and data access control, prevent unauthorized access, and monitor data transmission between EPU and CCS in real time to promptly detect and resolve data delays and loss issues and improve data reliability.

[0014] Preferably, the CCS is a central control system and the EPU is an edge processing unit.

[0015] Preferably, the EPU is capable of operating independently for at least 72 hours without a network connection, maintaining basic laminator control and data logging functions.

[0016] Preferably, the encrypted data transmission channel uses at least 256-bit Advanced Encryption Standard encryption technology, and the machine learning model is able to predict the failure probability and maintenance cycle of the laminator based on historical data.

[0017] Preferably, the data transmission priority rules are automatically adjusted according to the type and generation time of the data, and the real-time fault detection algorithm in the EPU includes vibration analysis and temperature monitoring.

[0018] Preferably, the real-time fault detection algorithm implemented by the EPU utilizes a vibration data monitoring model based on time series analysis, which uses an autoregressive moving average model to identify abnormal patterns. The specific formula is as follows:

[0019]

[0020] Among them, Xt represents the vibration data at time point t, c is a constant term, φi and θj are the parameters of the autoregressive and moving average models respectively, and ∈t is the error term.

[0021] Preferably, the algorithm of the fast switching mechanism: design a fast switching algorithm based on a decision tree, which automatically switches to local control when the network delay exceeds a preset threshold. The algorithm can be described by the following formula:

[0022]

[0023] Among them, De l ay represents the current network delay, and τ is the set threshold.

[0024] Preferably, the machine learning model deployed in the CCS uses a support vector machine model based on supervised learning to predict the maintenance requirements and possible failure points of the laminator. The basic form of the SVM model is:

[0025] y=w·φ(x)+b

[0026] Among them, w is the weight vector, φ(x) is the function that maps the input vector x to a high-dimensional space, b is the bias term, and y is the prediction result.

[0027] (III) Beneficial effects

[0028] The present invention provides a data remote control method for a laminating machine process. It has the following beneficial effects:

[0029] The security and efficiency of data transmission are optimized by integrating advanced technologies, while the emergency response capability of the system is improved. Specifically, a central control system is established to receive and send operational data, and the EPU is configured to process and store data at close range to reduce network dependence. The EPU performs preliminary analysis on the laminator status data, adjusts the data transmission priority according to the degree of urgency, reduces the CCS load, and improves processing efficiency. A support vector machine model is deployed in the CCS to predict maintenance needs and potential failure points and implement preventive maintenance. When the EPU detects that the network delay exceeds the set threshold, it automatically switches to local control. Multi-level user verification is added to enhance data security, and real-time monitoring of data transmission is carried out to improve data reliability. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present invention are described clearly and completely. 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.

[0031] An embodiment of the present invention provides a method for remote data control of a laminating machine process, including: S1, setting a central control system for receiving and sending operating data of the laminating machine, and configuring at least one edge processing unit near the laminating machine process equipment for real-time data processing and temporary storage.

[0032] S2. Establish an encrypted data transmission channel between CCS and EPU to ensure the security of data transmission. Perform preliminary analysis on the collected laminator status data through EPU to reduce the amount of data transmitted to CCS and improve transmission efficiency. CCS is the central control system and EPU is the edge processing unit. EPU can operate independently for at least 72 hours without network connection to maintain basic laminator control and data recording functions. The encrypted data transmission channel uses at least 256-bit advanced encryption standard encryption technology. The machine learning model can predict the failure probability and maintenance cycle of the laminator based on historical data.

[0033] S3. Set the priority rules for data transmission. Adjust the transmission priority according to the urgency of the data to optimize the data processing flow. Implement a real-time fault detection algorithm in the EPU so that it can run independently when the network is disconnected to ensure the continuous operation of the laminator. The data transmission priority rules are automatically adjusted according to the type and generation time of the data. The real-time fault detection algorithm in the EPU includes vibration analysis and temperature monitoring. The real-time fault detection algorithm implemented by the EPU uses a vibration data monitoring model based on time series analysis. The model uses an autoregressive moving average model to identify abnormal patterns. The specific formula is as follows:

[0034]

[0035] Among them, Xt represents the vibration data at time point t, c is a constant term, φi and θj are the parameters of the autoregressive and moving average models respectively, and ∈t is the error term.

[0036] S4. Deploy a machine learning model in CCS to predict the maintenance needs and possible failure points of the laminator to achieve preventive maintenance. Establish a cloud-based backup system to periodically synchronize data from CCS to cloud storage to prevent data loss and enhance data persistence. The machine learning model deployed in CCS uses a support vector machine model based on supervised learning to predict the maintenance needs and possible failure points of the laminator. The basic form of the SVM model is:

[0037] y=w·φ(x)+b

[0038] Among them, w is the weight vector, φ(x) is the function that maps the input vector x to a high-dimensional space, b is the bias term, and y is the prediction result.

[0039] S5. Implement a fast switching mechanism to automatically switch from remote control to local control when the EPU detects that the network delay exceeds the set threshold to ensure the continuity of the laminator operation. Configure an emergency response system. When the CCS receives an emergency signal from the EPU, it can quickly mobilize proximal support or start the backup control protocol to shorten the response time. The algorithm of the fast switching mechanism designs a fast switching algorithm based on a decision tree. When the network delay exceeds the preset threshold, it automatically switches to local control. The algorithm can be described by the following formula:

[0040]

[0041] Among them, De l ay represents the current network delay, and τ is the set threshold.

[0042] S6. Set up a multi-level user authentication system in CCS to enhance system security and data access control, prevent unauthorized access, and monitor data transmission between EPU and CCS in real time to promptly detect and resolve data delays and loss issues and improve data reliability.

[0043] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A data remote control method for a laminating machine process, characterized in that: include: S1. Set up a central control system for receiving and sending operation data of the laminator, and configure at least one edge processing unit near the laminator process equipment for real-time data processing and temporary storage; S2. Establish an encrypted data transmission channel between CCS and EPU to ensure the security of data transmission. Perform a preliminary analysis on the collected laminator status data through EPU to reduce the amount of data transmitted to CCS and improve transmission efficiency. S3. Set the priority rules for data transmission, adjust the transmission priority according to the urgency of the data to optimize the data processing flow, and implement a real-time fault detection algorithm in the EPU so that it can operate independently when the network is disconnected to ensure the continuous operation of the laminator; S4. Deploy a machine learning model in CCS to predict the maintenance needs and possible failure points of the laminator to achieve preventive maintenance, and establish a cloud-based backup system to periodically synchronize data from CCS to cloud storage to prevent data loss and enhance data durability. S5. Implement a fast switching mechanism to automatically switch from remote control to local control when the EPU detects that the network delay exceeds the set threshold to ensure the continuity of the laminator operation. Configure an emergency response system so that when the CCS receives an emergency signal from the EPU, it can quickly mobilize proximal support or start the backup control protocol to shorten the response time. S6. Set up a multi-level user authentication system in CCS to enhance system security and data access control, prevent unauthorized access, and monitor data transmission between EPU and CCS in real time to promptly detect and resolve data delays and loss issues and improve data reliability.

2. The data remote control method for a laminating machine process according to claim 1, characterized in that: The CCS is a central control system, and the EPU is an edge processing unit.

3. The data remote control method for a laminating machine process according to claim 1, characterized in that: The EPU is capable of operating independently without a network connection for at least 72 hours, maintaining basic laminator control and data logging functions.

4. The data remote control method for a laminating machine process according to claim 1, characterized in that: The encrypted data transmission channel uses at least 256-bit Advanced Encryption Standard encryption technology, and the machine learning model can predict the failure probability and maintenance cycle of the laminating machine based on historical data.

5. The data remote control method for a laminating machine process according to claim 1, characterized in that: The data transmission priority rules are automatically adjusted according to the type and generation time of the data, and the real-time fault detection algorithm in the EPU includes vibration analysis and temperature monitoring.

6. The data remote control method for a laminating machine process according to claim 1, characterized in that: The real-time fault detection algorithm implemented by the EPU utilizes a vibration data monitoring model based on time series analysis, which uses an autoregressive moving average model to identify abnormal patterns. The specific formula is as follows: Among them, Xt represents the vibration data at time point t, c is a constant term, φi and θj are the parameters of the autoregressive and moving average models respectively, and ∈t is the error term.

7. The data remote control method for a laminating machine process according to claim 1, characterized in that: The algorithm of the fast switching mechanism designs a fast switching algorithm based on a decision tree. When the network delay exceeds a preset threshold, it automatically switches to local control. The algorithm can be described by the following formula: Among them, Delay represents the current network delay, and τ is the set threshold.

8. The data remote control method for a laminating machine process according to claim 1, characterized in that: The machine learning model deployed in the CCS uses a support vector machine model based on supervised learning to predict the maintenance needs and possible failure points of the laminator. The basic form of the SVM model is: y=w·φ(x)+b Among them, w is the weight vector, φ(x) is the function that maps the input vector x to a high-dimensional space, b is the bias term, and y is the prediction result.

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