Situation awareness-based laser control terminal data acquisition method

Abstract

The invention discloses a laser control terminal data acquisition method based on situation awareness, and relates to the technical field of control terminals. S01, configuring an intelligent sensing terminal; s02, carrying out real-time data acquisition; s03, carrying out edge calculation processing; s04, submitting a data API (Application Program Interface); s05, multi-path data transmission is carried out; s06, situation awareness and analysis are carried out; s07, performing closed-loop control and optimization; by integrating an intelligent sensing terminal and an edge computing technology, the method can perform real-time data acquisition and processing near a data source, and reduces the demand of data transmission to a remote data center, thereby reducing delay, improving the efficiency and response speed of data processing, and reducing the cost. According to the method, multi-path transmission of data is realized through a 5G network and a special physical interface, and in combination with a data format conversion module of a data API, rapid and reliable data transmission is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of control terminals, and in particular to a laser control terminal data acquisition method based on situation awareness. Background Art

[0002] With the rapid development of industrial automation and intelligent manufacturing, the demand for data collection and real-time control in the production process is growing. Especially in key production links such as laser coding, sorting and packaging, precise control and real-time monitoring of equipment have become particularly important. Traditional data collection and processing methods often rely on centralized data processing systems, which not only increases the delay of data transmission, but also makes it difficult to meet real-time requirements due to bandwidth limitations;

[0003] In order to solve the above problems, the present invention provides a laser control terminal data collection method based on situational awareness. The method realizes local data collection, processing and analysis by integrating intelligent sensing terminals and edge computing technology, significantly improving the efficiency and response speed of data processing. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a laser control terminal data acquisition method based on situation awareness, which solves the problems raised in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a laser control terminal data acquisition method based on situation awareness, the steps of which are as follows:

[0006] S01. Configure intelligent sensing terminal: Configure an embedded intelligent sensing terminal, which integrates the functional modules of the original repeater and adds a sorting real-time data acquisition module to achieve real-time monitoring of the laser coding equipment;

[0007] S02. Real-time data collection: Utilize the serial port function of the intelligent sensing terminal to collect data, status and instructions of the laser coding equipment in real time, including the signal status of cigarette carton IN, cigarette carton OUT, household box IN, and household box OUT;

[0008] S03. Edge computing processing: The collected data is processed in real time at the millisecond level through edge computing technology to achieve rapid analysis and response to sorting and coding data;

[0009] S04. Data API submission: Submit the processed data to the central database through the data API to achieve centralized management and sharing of data;

[0010] S05. Multi-channel data transmission: Multi-channel data transmission through intelligent terminals, including 5G industrial interconnection, to ensure fast and reliable data transmission;

[0011] S06. Situational awareness and analysis: Combine 5G, IoT, and edge computing technologies to realize the application of intelligent perception systems based on the industrial Internet, and perceive and analyze order sorting process data, industry engineering cigarette code segment data, and UV laser coding equipment operating status information in real time;

[0012] S07. Closed-loop control and optimization: Use the collected and analyzed data to perform closed-loop control and optimization of laser coding equipment to ensure the safety, controllability and real-time closed-loop operation of production systems and industry projects.

[0013] Preferably, in the step of configuring the intelligent sensing terminal in S01, the intelligent sensing terminal includes at least one physical interface for connecting additional sensors or actuators, as well as an operation status indicator light connected through a 5G network, a field communication device, a UV laser machine, a trigger sensing device, a control unit, a display interface for real-time acquisition and analysis of the high-speed sorting process and laser marking, and a mechanism for data security isolation based on edge computing technology, which together realize high-speed, real-time intelligent perception and control of the system, improve the system's response speed, data processing capability, security and reliability, and allow the system to be flexibly expanded to adapt to different application scenarios.

[0014] Preferably, in the real-time data collection step of S02, the real-time data collection includes mean filtering and denoising processing of the collected signal, and the formula of mean filtering can be expressed as:

[0015]

[0016] Among them, x[n] is the original signal, y[n] is the filtered signal, N is the window size of the filter, that is, the number of adjacent data points considered, and i is the index variable for summation, which is used to traverse all data points in the window; for the value of the original signal x[n] at time point n, take the N-1 data points before and after it (including point n itself), calculate the average of these N data points, and then assign this average to the filtered signal y[n] to improve the accuracy and reliability of the data.

[0017] Preferably, the edge computing in S03 includes feature extraction: extracting key features from the original data, such as the peak value, mean value, variance, etc. of the signal, for subsequent analysis;

[0018] Peak detection: Peak = max(x[n]), where Peak is the maximum value of the signal, i.e., the peak value; x[n] is the value of the signal at time point n;

[0019] Mean calculation: Where, μ: mean value of the signal; N: total number of data points of the signal; x[n]: value of the signal at time point n;

[0020] Variance calculation: Among them, σ 2 : Variance of the signal, indicating the degree of discreteness of the data; N: the total number of data points of the signal; x[n]: the value of the signal at time point n; μ: the mean of the signal.

[0021] Preferably, the edge computing of S03 also includes real-time analysis: using statistical methods or machine learning models to analyze features to identify patterns or anomalies; based on the analysis results, the system automatically makes decisions or recommends operations; the system performs corresponding operations according to the decision results, such as adjusting device parameters or triggering alarms, wherein the linear regression and logistic regression formulas are as follows:

[0022] Linear regression: y = β0 + β1x + ∈

[0023] Among them, y: dependent variable, that is, the predicted output; x: independent variable, that is, input feature; β0: intercept term, which represents the value of y when x=0; β1: slope, which represents the change in y for every unit change in x; ∈: error term, which represents the difference between the model predicted value and the actual value;

[0024] Logistic Regression:

[0025] Among them, P(y=1): the probability that the dependent variable y is 1; x: independent variable, that is, input feature; β0: intercept term; β1: slope; e: base of natural logarithm.

[0026] Preferably, in the data API submission step of S02, the data API includes a data format conversion module for converting the collected data into a standardized format to facilitate compatibility and integration between different systems.

[0027] Preferably, in the multi-channel data transmission step of S05, the intelligent terminal supports at least two different network protocols to adapt to different data transmission environments and improve the flexibility of data transmission.

[0028] Preferably, in the situation awareness and analysis step of S06, the intelligent perception system includes a data analysis tool, which can perform statistical analysis and pattern recognition on the collected data to provide deeper insights.

[0029] Preferably, in the closed-loop control and optimization step of S07, the closed-loop control system includes a feedback mechanism for monitoring the execution effect of the control instructions and adjusting the control strategy according to the feedback to achieve more precise control.

[0030] Preferably, the specific method of the feedback mechanism is:

[0031] Monitoring: The system monitors the output of sensors or measuring devices. Monitoring sensors or measuring devices can capture the current state or output of the system in real time and convert it into a processable signal.

[0032] Comparison: The monitored output signal is compared with the desired output (or set point) of the system;

[0033] Error calculation: Calculate the difference between the actual output and the expected output to get the error;

[0034] Control decision: Based on the size and direction of the error, the system decides what control action to take; this is done through a control algorithm, such as a proportional-integral-derivative (PID) controller, which calculates a control signal based on the proportion, integral, and derivative of the error.

[0035] Execute control actions: control signals are sent to the actuators, which adjust their operations based on the control signals to reduce the error;

[0036] Continuous monitoring and adjustment: The system continuously monitors its output and adjusts the control strategy based on new error signals; the process is iterative, ensuring that the system can adapt to changing conditions or disturbances.

[0037] The present invention provides a laser control terminal data collection method based on situation awareness, which has the following beneficial effects:

[0038] (1) Improve data processing efficiency: By integrating intelligent sensing terminals and edge computing technology, this method can perform real-time data collection and processing near the data source, reducing the need to transmit data to remote data centers, thereby reducing latency and improving data processing efficiency and response speed.

[0039] (2) Enhance the reliability and security of the system: This method realizes multi-channel data transmission through 5G network and dedicated physical interface, and combines the data format conversion module of data API to ensure fast and reliable data transmission. At the same time, the data security isolation mechanism based on edge computing technology enhances the data security and privacy protection of the system.

[0040] (3) Optimize the production process and equipment performance: The feedback mechanism in the closed-loop control and optimization steps enables the system to automatically adjust the control strategy based on the difference between the real-time monitored output signal and the expected output, thereby achieving precise control and optimization of the laser coding equipment, improving the automation level of the production process and equipment performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the steps of the method of the present invention. DETAILED DESCRIPTION

[0042] 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.

[0043] Example 1: Integration and configuration of intelligent sensing terminals

[0044] In this embodiment, the embedded intelligent sensing terminal is designed as a multifunctional data acquisition and processing unit; the terminal integrates the functions of the original repeater, including signal forwarding and basic control logic, and adds modules specifically used for sorting real-time data acquisition; these modules can capture and record the operating parameters of the laser coding equipment, such as coding speed, laser intensity and material properties;

[0045] To expand data collection capabilities, the intelligent sensing terminal is equipped with multiple physical interfaces, including analog and digital input and output ports; through these interfaces, the terminal can connect additional sensors or actuators, such as temperature sensors, pressure sensors, and vibration sensors; these sensors provide detailed information about the equipment's operating status and working environment, such as temperature fluctuations, pressure changes, and vibration patterns, which are key indicators for evaluating equipment performance and predicting potential failures;

[0046] In addition, the intelligent sensing terminal is connected to the key components such as the operating status indicator lights, communication equipment, and UV laser machines on site through the 5G network. The high bandwidth and low latency characteristics of the 5G network ensure fast data transmission and real-time processing. Through this high-speed connection, the intelligent sensing terminal can achieve comprehensive monitoring of the laser coding equipment and its working environment, providing a solid foundation for subsequent data processing and analysis.

[0047] Intelligent sensing terminal performance monitoring table:

[0048]

[0049] According to the above table, the integration and configuration of intelligent sensing terminals significantly improve the real-time performance of data collection, the response speed and reliability of the system, and provide accurate data support for production process optimization and equipment maintenance, thereby effectively improving the efficiency and reliability of industrial automation and intelligent manufacturing.

[0050] Example 2: Real-time data collection and edge computing processing

[0051] In this embodiment, the intelligent sensing terminal uses its serial port function to collect key operation information of the laser coding equipment in real time, including signal states such as cigarette carton IN and cigarette carton OUT. These signal states reflect the real-time performance of the equipment during the sorting and coding process, and are important data for evaluating equipment performance and optimizing production processes.

[0052] The collected data is first preprocessed in the edge computing unit to ensure the quality and availability of the data. The preprocessing steps include mean filtering and denoising, which can effectively reduce random noise and interference in the data and improve the accuracy and reliability of the data. Mean filtering smoothes the data by calculating the local average of the data points, while denoising purifies the data by identifying and eliminating outliers. Edge computing also includes feature extraction, which is a key step in data analysis. Feature extraction algorithms identify and calculate key features from the raw data, such as the peak, mean, and variance of the signal. These features can provide in-depth insights into the operating status of the equipment and support subsequent analysis and decision-making.

[0053] Finally, linear and logistic regression models are used to analyze feature data in real time; these models can identify patterns and trends in the data and help predict the behavior and performance of the equipment; for example, by analyzing the relationship between coding speed and laser intensity, coding parameters can be optimized to improve coding quality and efficiency;

[0054] Real-time data collection and edge computing processing experimental data table:

[0055]

[0056] According to the experimental data table, it can be concluded that the real-time data collection and edge computing processing of the intelligent sensing terminal effectively improve the accuracy and reliability of the data. The random noise in the signal is reduced through mean filtering and denoising processing. Feature extraction reveals the key indicators of the equipment operation status. The application of linear regression and logistic regression models provides strong data analysis support for predicting equipment behavior and optimizing production processes, thereby improving the intelligence level and production efficiency of the entire system.

[0057] Example 3: Closed-loop control and optimized feedback mechanism

[0058] In this embodiment, the core of the closed-loop control system is an efficient feedback mechanism that can monitor the execution effect of the control instructions in real time; the system first monitors the actual output of the laser coding equipment through sensors, and then compares these outputs with the preset expected outputs to calculate the errors; based on the error analysis, the system uses advanced control algorithms such as PID controllers to calculate the necessary control signals, which are sent to the actuators to automatically adjust the operating parameters of the laser coding equipment to reduce the errors;

[0059] The PID controller is a commonly used feedback controller that calculates the control signal based on the proportion (P), integral (I) and differential (D) of the error; the proportional term responds to the current error, the integral term responds to the accumulation of the error, and the differential term predicts the future trend of the error; by adjusting the parameters of the PID controller, fine-tuning of the equipment performance can be achieved;

[0060] The system continuously monitors the adjusted output and further adjusts the control strategy based on the new error signal, forming a continuously optimized closed loop; this closed-loop control method ensures the stability and efficiency of the production process, and can maintain the optimal operating state of the equipment even in the face of changes or interference in production conditions;

[0061] Through this closed-loop control and optimized feedback mechanism, this embodiment can achieve precise control of the operation of laser coding equipment, improve production efficiency and product quality, while reducing failures and downtime, and provide comprehensive, real-time data support and intelligent control for industrial production.

[0062] Closed-loop control and optimized feedback mechanism experimental data table:

[0063]

[0064] According to the experimental data table, it can be concluded that the closed-loop control system effectively reduces the error between the actual output and the expected output through the PID controller, realizes the precise control of the operation of the laser coding equipment, and the fast system response time and continuous optimization process ensure the stability and efficiency of the production process, thereby improving production efficiency and product quality.

[0065] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0066] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A laser control terminal data acquisition method based on situation awareness, characterized in that: The steps are as follows: S01. Configure intelligent sensing terminal: Configure an embedded intelligent sensing terminal, which integrates the functional modules of the original repeater and adds a sorting real-time data acquisition module to achieve real-time monitoring of the laser coding equipment; S02. Real-time data collection: Utilize the serial port function of the intelligent sensing terminal to collect data, status and instructions of the laser coding equipment in real time, including the signal status of cigarette carton IN, cigarette carton OUT, household box IN, and household box OUT; S03. Edge computing processing: The collected data is processed in real time at the millisecond level through edge computing technology to achieve rapid analysis and response to sorting and coding data; S04. Data API submission: Submit the processed data to the central database through the data API to achieve centralized management and sharing of data; S05. Multi-channel data transmission: Multi-channel data transmission through intelligent terminals, including 5G industrial interconnection, to ensure fast and reliable data transmission; S06. Situational awareness and analysis: Combine 5G, IoT, and edge computing technologies to realize the application of intelligent perception systems based on the industrial Internet, and perceive and analyze order sorting process data, industry engineering cigarette code segment data, and UV laser coding equipment operating status information in real time; S07. Closed-loop control and optimization: Use the collected and analyzed data to perform closed-loop control and optimization of laser coding equipment to ensure the safety, controllability and real-time closed-loop operation of production systems and industry projects.

2. According to the laser control terminal data acquisition method based on situation awareness as described in claim 1, it is characterized by: In the step of configuring the intelligent sensing terminal in S01, the intelligent sensing terminal includes at least one physical interface for connecting additional sensors or actuators, as well as an operating status indicator light connected through a 5G network, a field communication device, an ultraviolet laser machine, a trigger sensor device, a control unit, a display interface for real-time collection and analysis of high-speed sorting process and laser marking, and a mechanism for data security isolation based on edge computing technology, which together realize high-speed, real-time intelligent perception and control of the system, improve the system's response speed, data processing capability, security and reliability, and allow the system to be flexibly expanded to adapt to different application scenarios.

3. The laser control terminal data acquisition method based on situational awareness according to claim 1 is characterized in that: In the real-time data collection step of S02, the real-time data collection includes mean filtering and denoising processing of the collected signal. The formula of mean filtering can be expressed as: Among them, x[n] is the original signal, y[n] is the filtered signal, N is the window size of the filter, that is, the number of adjacent data points considered, and i is the index variable for summation, which is used to traverse all data points in the window; for the value of the original signal x[n] at time point n, take the N-1 data points before and after it (including point n itself), calculate the average of these N data points, and then assign this average to the filtered signal y[n] to improve the accuracy and reliability of the data.

4. The laser control terminal data acquisition method based on situation awareness according to claim 1 is characterized in that: The edge computing in S03 includes feature extraction: extracting key features from the original data, such as the peak value, mean value, variance, etc. of the signal, for subsequent analysis; Peak detection: Peak = max(x[n]), where Peak is the maximum value of the signal, i.e., the peak value; x[n] is the value of the signal at time point n; Mean calculation: Where, μ: mean value of the signal; N: total number of data points of the signal; x[n]: value of the signal at time point n; Variance calculation: Among them, σ 2 : Variance of the signal, indicating the degree of discreteness of the data; N: the total number of data points of the signal; x[n]: the value of the signal at time point n; μ: the mean of the signal.

5. The laser control terminal data acquisition method based on situational awareness according to claim 1, characterized in that: The edge computing of S03 also includes real-time analysis: using statistical methods or machine learning models to analyze features to identify patterns or anomalies; based on the analysis results, the system automatically makes decisions or recommends operations; the system performs corresponding operations based on the decision results, such as adjusting device parameters or triggering alarms, where the linear regression and logistic regression formulas are as follows: Linear regression: y = β0 + β1x + ∈ Among them, y: dependent variable, that is, the predicted output; x: independent variable, that is, input feature; β0: intercept term, which represents the value of y when x=0; β1: slope, which represents the change in y for every unit change in x; ∈: error term, which represents the difference between the model predicted value and the actual value; Logistic Regression: Among them, P(y=1): the probability that the dependent variable y is 1; x: independent variable, that is, input feature; β0: intercept term; β1: slope; e: base of natural logarithm.

6. The laser control terminal data acquisition method based on situation awareness according to claim 1, characterized in that: In the data API submission step of S02, the data API includes a data format conversion module for converting the collected data into a standardized format to facilitate compatibility and integration between different systems.

7. The laser control terminal data acquisition method based on situation awareness according to claim 1, characterized in that: In the multi-channel data transmission step of S05, the intelligent terminal supports at least two different network protocols to adapt to different data transmission environments and improve the flexibility of data transmission.

8. The laser control terminal data acquisition method based on situation awareness according to claim 1, characterized in that: In the situation awareness and analysis step of S06, the intelligent perception system includes a data analysis tool that can perform statistical analysis and pattern recognition on the collected data to provide deeper insights.

9. The laser control terminal data collection method based on situation awareness according to claim 1, characterized in that: In the closed-loop control and optimization step of S07, the closed-loop control system includes a feedback mechanism for monitoring the execution effect of the control instructions and adjusting the control strategy according to the feedback to achieve more precise control.

10. The laser control terminal data collection method based on situation awareness according to claim 9, characterized in that: The specific method of the feedback mechanism is: Monitoring: The system monitors the output of sensors or measuring devices. Monitoring sensors or measuring devices can capture the current state or output of the system in real time and convert it into a processable signal. Comparison: The monitored output signal is compared with the desired output (or set point) of the system; Error calculation: Calculate the difference between the actual output and the expected output to get the error; Control decision: Based on the size and direction of the error, the system decides what control action to take; this is done through a control algorithm, such as a proportional-integral-derivative (PID) controller, which calculates a control signal based on the proportion, integral, and derivative of the error. Execute control actions: control signals are sent to the actuators, which adjust their operations based on the control signals to reduce the error; Continuous monitoring and adjustment: The system continuously monitors its output and adjusts the control strategy based on new error signals; The process is iterative, ensuring that the system can adapt to changing conditions or disturbances.

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