Intelligent control-based automated packaging production line optimization method and system

Abstract

The application relates to an intelligent control-based automatic packaging production line optimization method and system, wherein the method comprises the following steps: collecting running data streams of key equipment of a production line in real time and generating an equipment state vector; adjusting an abnormality detection threshold according to current production material characteristics, comparing the equipment state vector with the adjusted abnormality detection threshold, and identifying pseudo abnormal signals; when pseudo abnormal signals exceeding a preset shutdown threshold are continuously detected within a preset time window, intercepting a shutdown instruction triggered by the pseudo abnormal signals, and triggering risk calculation; and executing a control decision according to a risk level value output by the risk calculation, so as to achieve the purposes of eliminating false shutdown, accurately isolating faults and synergistically optimizing energy efficiency of the packaging production line under fluctuating material characteristic working conditions.

Description

Technical Field

[0001] This invention relates to the field of industrial automation control technology, and in particular to an optimization method and system for an automated packaging production line based on intelligent control. Background Technology

[0002] In the field of automated paper packaging production lines, the operational stability of equipment has long been severely affected by fluctuations in material properties. Dynamic changes in parameters such as cardboard basis weight and moisture content lead to abnormal fluctuations in vacuum adsorption pressure and harmonic distortion of the transmission system current, triggering frequent false shutdowns by traditional fixed threshold protection mechanisms. Although existing technologies attempt to improve monitoring accuracy through multi-sensor fusion, they have failed to establish a dynamic mapping relationship between material properties and equipment status, and lack the ability to accurately identify false anomaly signals. At the same time, rigid control strategies cannot distinguish between genuine faults and material disturbances, resulting in an average annual unplanned downtime of over 15% for the production line, causing huge capacity losses. Especially in high-speed precision packaging scenarios, the mechanical impact caused by sudden stops further aggravates equipment wear and tear, creating a vicious cycle. Summary of the Invention

[0003] The main objective of this invention is to provide an optimization method and system for automated packaging production lines based on intelligent control. Through dynamic threshold collaborative judgment, equipment status risk decoupling, and lifespan adaptive decision-making mechanism, the invention aims to eliminate accidental shutdowns, accurately isolate faults, and coordinately optimize energy efficiency of the packaging production line under fluctuating material characteristics.

[0004] To achieve the above objectives, this invention provides an optimization method for an automated packaging production line based on intelligent control, comprising the following steps:

[0005] Real-time acquisition of operational data streams from key equipment on the production line and generation of equipment status vectors;

[0006] Adjust the anomaly detection threshold according to the current characteristics of the production materials, compare the equipment state vector with the adjusted anomaly detection threshold, and identify false anomaly signals;

[0007] When false anomaly signals exceeding the preset shutdown threshold are continuously detected within the preset time window, the shutdown command triggered by the false anomaly signal is intercepted, and risk calculation is triggered.

[0008] Control decisions are made based on the risk level values ​​output by the risk calculation, which include vacuum leakage risk level values ​​and mechanical transmission risk level values.

[0009] Furthermore, the steps of real-time acquisition of operational data streams from key production line equipment and generation of equipment state vectors include:

[0010] A pressure sensing unit is deployed at the main gas path node of the vacuum adsorption device in the production line to acquire pressure fluctuation time series data through a preset sampling frequency.

[0011] A current sensing unit is integrated at the power input terminal of the servo drive mechanism to extract the harmonic spectrum characteristics of the motor operating current.

[0012] By time-synchronized fusion of pressure fluctuation time-series data and harmonic spectrum characteristics, a multi-dimensional equipment state vector containing time-domain pressure indicators and frequency-domain current indicators is generated.

[0013] Furthermore, the step of adjusting the anomaly detection threshold based on the current characteristics of the production materials includes:

[0014] Obtain the reference pressure fluctuation threshold and reference current harmonic threshold during the equipment calibration phase;

[0015] The humidity compensation offset of the pressure fluctuation threshold is calculated based on real-time environmental humidity data, and the compensated pressure fluctuation threshold is generated.

[0016] Calculate the weight scaling factor of the current harmonic threshold based on the paperboard weight data, and generate the current harmonic threshold after proportional adjustment.

[0017] The compensated pressure fluctuation threshold and the proportionally adjusted current harmonic threshold are output as anomaly detection thresholds.

[0018] Further, the step of comparing the device state vector with the adjusted anomaly detection threshold to identify false anomaly signals includes:

[0019] When the peak value of the sliding window of the vacuum pressure fluctuation sequence exceeds the compensated pressure fluctuation threshold, a pressure anomaly is marked.

[0020] When the integral value of the servo current harmonic energy in the harmonic frequency band exceeds the current harmonic threshold after proportional adjustment, the current is marked as abnormal.

[0021] When the pressure anomaly marker and the current anomaly marker are triggered synchronously within a preset time, it is determined to be a false anomaly signal caused by fluctuations in material properties.

[0022] Furthermore, when false anomaly signals exceeding a preset shutdown threshold are continuously detected within a preset time window, the shutdown command triggered by the false anomaly signal is intercepted, and a risk calculation is initiated, including:

[0023] Set a hardware signal interceptor at the stop command output terminal of the programmable logic controller;

[0024] When the number of false abnormal signals accumulates to a preset trigger threshold within a preset time window, the hardware signal interceptor is activated to block the shutdown command.

[0025] Simultaneously, a priority interrupt signal is sent to the coprocessor to initiate a real-time risk calculation process based on the device state vector within the sliding time window.

[0026] Furthermore, the steps for initiating a real-time risk calculation process based on the device state vector within a sliding time window include:

[0027] Construct a probabilistic graphical model that includes equipment state correlation factors, including vacuum sealing state factors and transmission stability factors;

[0028] The device state vector is input into the probabilistic graphical model for belief propagation calculation;

[0029] The output consists of two-channel probability values ​​representing the risks of the vacuum system and the mechanical system, which are used as risk level values.

[0030] Furthermore, the steps for executing control decisions based on the risk level values ​​output by the risk calculation include:

[0031] When the vacuum leakage risk level is lower than the vacuum risk threshold, a pulse width modulation waveform reconstruction command is generated for the vacuum solenoid valve. The pulse width modulation waveform reconstruction command drives the vacuum solenoid valve to generate an inverse compensation waveform to suppress pressure fluctuations.

[0032] When the mechanical transmission risk level value is higher than the mechanical risk threshold, a graded deceleration and shutdown command sequence for the servo drive is generated. The servo drive is controlled to decelerate and stop in stages according to a preset time gradient through the graded deceleration and shutdown command sequence.

[0033] The vacuum risk threshold and mechanical risk threshold mentioned therein are dynamically calibrated based on the equipment's operating years.

[0034] Furthermore, the step of generating the pulse width modulation waveform reconstruction instruction for the vacuum solenoid valve includes:

[0035] Based on the phase distribution characteristics of the pressure fluctuation sub-vector, an anti-phase compensation waveform for solenoid valve drive is constructed.

[0036] The compensation waveform is superimposed onto the original drive signal through the feedforward control channel, thereby converging the pressure fluctuation amplitude to a safe range.

[0037] This invention also provides an automated packaging production line optimization system based on intelligent control, comprising:

[0038] The data acquisition module is used to collect real-time operating data streams of key equipment on the production line and generate equipment status vectors;

[0039] The status judgment module is used to adjust the anomaly detection threshold according to the current characteristics of the production material, compare the equipment status vector with the adjusted anomaly detection threshold, and identify false anomaly signals.

[0040] The signal interception module is used to intercept the shutdown command triggered by the pseudo-abnormal signal when a pseudo-abnormal signal exceeding the preset shutdown threshold is continuously detected within a preset time window, and to trigger risk calculation.

[0041] The risk calculation unit is used to make control decisions based on the risk level value output by the risk calculation.

[0042] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described intelligent control-based automated packaging production line optimization method.

[0043] The intelligent control-based automated packaging production line optimization method and system provided by this invention have the following beneficial effects: This invention accurately identifies false anomaly signals caused by material characteristic fluctuations through a dual-threshold collaborative judgment and time window constraint mechanism, reducing the problem of erroneous shutdowns caused by traditional fixed threshold protection and ensuring production continuity; by constructing a probabilistic graphical model, the equipment state vector is decoupled into independent channels for vacuum leakage risk and mechanical failure risk, outputting quantified risk level values, providing a theoretical basis for hierarchical control decisions, and improving fault identification accuracy; a dynamic threshold calibration mechanism based on equipment operating years automatically adjusts risk response standards to adapt to equipment aging patterns, extending the lifespan of key components while reducing maintenance costs; and through inverse-phase compensation waveform reconstruction and feedforward control technology, it actively suppresses vacuum pressure fluctuations, simultaneously improving system stability and energy utilization efficiency. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating an automated packaging production line optimization method based on intelligent control in one embodiment of the present invention.

[0045] Figure 2 This is a structural block diagram of an automated packaging production line optimization system based on intelligent control in one embodiment of the present invention;

[0046] Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0047] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0049] Reference Figure 1This is a flowchart illustrating an automated packaging production line optimization method based on intelligent control proposed in this invention, comprising the following steps:

[0050] S1, real-time acquisition of operating data streams of key equipment on the production line and generation of equipment status vectors;

[0051] S2, adjust the anomaly detection threshold according to the current characteristics of the production material, compare the equipment state vector with the adjusted anomaly detection threshold, and identify false anomaly signals;

[0052] S3, when false anomaly signals exceeding the preset shutdown threshold are continuously detected within the preset time window, the shutdown command triggered by the false anomaly signal is intercepted, and risk calculation is triggered.

[0053] S4. Execute control decisions based on the risk level values ​​output by the risk calculation, whereby the risk level values ​​include the vacuum leakage risk level value and the mechanical transmission risk level value.

[0054] In one embodiment, step S1 includes:

[0055] The steps for real-time acquisition of operational data streams from key production line equipment and generation of equipment state vectors include:

[0056] A pressure sensing unit is deployed at the main gas path node of the vacuum adsorption device in the production line to acquire pressure fluctuation time series data through a preset sampling frequency.

[0057] A current sensing unit is integrated at the power input terminal of the servo drive mechanism to extract the harmonic spectrum characteristics of the motor operating current.

[0058] By time-synchronized fusion of pressure fluctuation time-series data and harmonic spectrum characteristics, a multi-dimensional equipment state vector containing time-domain pressure indicators and frequency-domain current indicators is generated.

[0059] In a specific embodiment, a piezoresistive pressure sensing unit is deployed at the main gas path node (gas source distributor outlet) of the vacuum adsorption device in the production line, using a preset sampling frequency. To acquire time-series data of pressure fluctuations, the sampling frequency must meet the following requirements. Constraints (The maximum separation frequency during high-speed paperboard transport, typically 8-12Hz); the original pressure signal is filtered by a Kalman filter. Noise reduction processing is performed, and the standard deviation of pressure fluctuation is calculated as a time-domain pressure index:

[0060]

[0061] in, The standard deviation of pressure fluctuation, To achieve average adsorption pressure, a Hall current sensing unit is integrated at the power input of the servo drive mechanism to collect three-phase current. The harmonic spectrum was extracted using Fast Fourier Transform (FFT), and the energy integral values ​​of the 2nd to 5th harmonic frequency bands were calculated. ( (Based on the power grid fundamental frequency); time synchronization and alignment of pressure data and harmonic data are performed using a delay compensation algorithm. ( (To compensate for sensor response) eliminate timing deviations; finally, the data is fused to generate a two-dimensional device state vector. ,in Quantify the stability of the vacuum system. Characterizes the load state of the mechanical transmission. This step provides key feature basis for subsequent pseudo-anomaly identification through two-parameter coupled changes.

[0062] In one embodiment, step S2 includes:

[0063] The steps for adjusting the anomaly detection threshold based on the current characteristics of the production materials include:

[0064] Obtain the reference pressure fluctuation threshold and reference current harmonic threshold during the equipment calibration phase;

[0065] The humidity compensation offset of the pressure fluctuation threshold is calculated based on real-time environmental humidity data, and the compensated pressure fluctuation threshold is generated.

[0066] Calculate the weight scaling factor of the current harmonic threshold based on the paperboard weight data, and generate the current harmonic threshold after proportional adjustment.

[0067] The compensated pressure fluctuation threshold and the proportionally adjusted current harmonic threshold are output as anomaly detection thresholds.

[0068] In a specific embodiment, the reference pressure fluctuation threshold determined during the equipment calibration phase is obtained. (Typical value 0.08 kPa) and reference current harmonic threshold (Typical value 0.25); Based on real-time ambient humidity sensor data H (unit %RH), the pressure threshold offset is calculated using a linear compensation model:

[0069]

[0070] In the formula, This is the humidity compensation coefficient. To calibrate humidity, a compensated pressure fluctuation threshold is generated. Simultaneously, based on the online measured cardboard basis weight G (unit: g / m²), a nonlinear scaling function is used. Calculate the harmonic threshold adjustment factor ( As the attenuation factor, Based on the standard weight, , (For process range boundaries), generate the current harmonic threshold after proportional adjustment. The final output is a dynamic anomaly detection threshold set. The signal comparison module. Through the dynamic threshold mechanism of this embodiment, the false anomaly misjudgment rate caused by material disturbance is reduced compared to the traditional fixed threshold scheme.

[0071] In one embodiment, the step of comparing the device state vector with an adjusted anomaly detection threshold to identify false anomaly signals includes:

[0072] When the peak value of the sliding window of the vacuum pressure fluctuation sequence exceeds the compensated pressure fluctuation threshold, a pressure anomaly is marked.

[0073] When the integral value of the servo current harmonic energy in the harmonic frequency band exceeds the current harmonic threshold after proportional adjustment, the current is marked as abnormal.

[0074] When the pressure anomaly marker and the current anomaly marker are triggered synchronously within a preset time, it is determined to be a false anomaly signal caused by fluctuations in material properties.

[0075] In a specific embodiment, the device state vector Feature decoupling is performed to extract the vacuum pressure fluctuation sequence. Using a sliding time window (window length) sliding step size Real-time calculation of peak pressure within the window ,when Exceeding the compensation pressure fluctuation threshold Time trigger pressure anomaly flag Synchronous analysis of current harmonic energy When its value exceeds the harmonic threshold after proportional adjustment Time-triggered current anomaly flag A synchronization time constraint mechanism is introduced, which only applies when... and exist When overlap occurs within the time window (i.e., when the conditions are met) ,in These are the trigger times for the anomaly markers, and are determined to be pseudo-anomaly signals caused by fluctuations in material properties. For example, when the moisture content of the cardboard increases from 8% to 15%, the pressure peak... Reaching 0.19 kPa ( Simultaneously, harmonic energy Furthermore, the two anomalies trigger synchronously within 5 seconds, and the system accurately identifies them as false anomalies rather than real faults; this mechanism reduces the false judgment rate of traditional single-parameter judgment schemes, among which the time constraint parameter Through optimization using 200 sets of material switching experiments, it was determined that transient interference (<2s) and continuous material disturbance (>5s) can be effectively distinguished. This demonstrates the strong coupling relationship between vacuum pressure fluctuations and current harmonic distortion in the paper packaging production line. Specifically, humidity-induced decrease in cardboard permeability leads to abnormal adsorption pressure, which is simultaneously accompanied by sudden current distortion in the transmission load caused by changes in basis weight. These two characteristics are... The spatiotemporal correlation within the window becomes a key feature for identifying material disturbances.

[0076] In one embodiment, step S3 includes:

[0077] When false anomaly signals exceeding a preset shutdown threshold are continuously detected within a preset time window, the shutdown command triggered by the false anomaly signal is intercepted, and a risk calculation step is initiated, including:

[0078] Set a hardware signal interceptor at the stop command output terminal of the programmable logic controller;

[0079] When the number of false abnormal signals accumulates to a preset trigger threshold within a preset time window, the hardware signal interceptor is activated to block the shutdown command.

[0080] Simultaneously, a priority interrupt signal is sent to the coprocessor to initiate a real-time risk calculation process based on the device state vector within the sliding time window.

[0081] In a specific embodiment, a hardware signal interceptor is connected in series at the stop command output terminal of the programmable logic controller (PLC) on the packaging production line. This interceptor adopts a high-speed optocoupler isolation circuit design to form a physical isolation barrier and block voltage. Ensure signal blocking response time The system ensures that the interception action is faster than the mechanical actuator response (typical solenoid valve action time is 20ms) by using response time constraints; it also sets up event-accumulation-based triggering logic to respond when a false abnormal signal occurs within a preset time window. (The typical duration of material disturbance is 15-25 seconds) within which a preset threshold number of disturbances is reached. Next time (i.e., when satisfied) ,in This is a pseudo-anomaly indicator function with a counting threshold. It can avoid momentary interference (such as single cardboard displacement) and immediately activate the hardware signal interceptor to cut off the transmission path of the stop command to the actuator, preventing unnecessary shutdowns caused by momentary material disturbances; simultaneously, it sends a high-priority interrupt signal (interrupt level ≥ 3, capable of preempting 90% of non-real-time tasks (according to IEC 61131-3 standard)) to the coprocessor. This interrupt forcibly suspends the current background task and starts the real-time risk calculation process, which is based on the latest Device state vector sequence within the time window (reserve (Construct a dynamic risk model using all internal state histories) and process activation delay. (Compared to operating system task switching latency of over 100ms). For example, during implementation, when a sudden change in cardboard weight triggers 5 consecutive pseudo-anomalies ( Within 0.8ms, the hardware interceptor blocked the stop signal output by the PLC, while the coprocessor responded to the interrupt within 5ms and loaded 128 sets of state vectors to start risk calculation.

[0082] In one embodiment, the step of initiating a real-time risk calculation process based on the device state vector within a sliding time window includes:

[0083] Construct a probabilistic graphical model that includes equipment state correlation factors, including vacuum sealing state factors and transmission stability factors;

[0084] The device state vector is input into the probabilistic graphical model for belief propagation calculation;

[0085] The output consists of two-channel probability values ​​representing the risks of the vacuum system and the mechanical system, which are used as risk level values.

[0086] In a specific embodiment, the key hidden state node in the probabilistic graphical model is defined as the vacuum sealing state factor. (0 indicates normal sealing, 1 indicates leakage) and transmission stability factor (0 indicates normal transmission, 1 indicates failure), and establish the equipment state vector. Conditional probability relationship with hidden nodes:

[0087]

[0088] in, The characteristic mean vector of vacuum leakage ( (significantly increased) The covariance matrix; the sliding window 128 sets of device state vectors Input the model and iteratively calculate the posterior probability of hidden nodes using the belief propagation algorithm:

[0089]

[0090] Final output dual-channel risk level value: Vacuum system risk value Risk value of mechanical system This implementation maps equipment state vectors to interpretable risk quantification values ​​using a probabilistic graphical model, providing a rigorous mathematical basis for control decisions and overcoming the limitations of traditional solutions that rely on empirical thresholds.

[0091] In one embodiment, step S4 includes:

[0092] The steps for making control decisions based on the risk level value output by risk calculation include:

[0093] When the vacuum leakage risk level is lower than the vacuum risk threshold, a pulse width modulation waveform reconstruction command is generated for the vacuum solenoid valve. The pulse width modulation waveform reconstruction command drives the vacuum solenoid valve to generate an inverse compensation waveform to suppress pressure fluctuations.

[0094] When the mechanical transmission risk level value is higher than the mechanical risk threshold, a graded deceleration and shutdown command sequence for the servo drive is generated. The servo drive is controlled to decelerate and stop in stages according to a preset time gradient through the graded deceleration and shutdown command sequence.

[0095] The vacuum risk threshold and mechanical risk threshold mentioned therein are dynamically calibrated based on the equipment's operating years.

[0096] In a specific embodiment, when the vacuum leakage risk level value Below the dynamically calibrated vacuum risk threshold When (i.e., satisfying) This generates a pulse width modulation (PWM) waveform reconstruction command for the vacuum solenoid valve. This command drives the vacuum solenoid valve to generate an inverse compensation waveform, specifically including: instantaneous phase characteristics based on the pressure fluctuation subvector. Generate a driving signal that is inversely phase to the original disturbance. And through an independent feedforward control channel (delay <0.1ms), it is injected into the vacuum control system to bring the pressure fluctuation amplitude to a safe range; when the mechanical transmission risk level value is higher than the mechanical risk threshold (i.e., meets the requirements) This generates a tiered deceleration and stop command sequence for the servo drive. This sequence controls the servo drive to perform a three-stage deceleration and stop, including three speed reduction curves to eliminate mechanical shock.

[0097]

[0098] The speed difference between adjacent stages (Less than 40% of rated speed), duration of each stage Risk threshold and Based on the equipment's service life (Unit: Year) Dynamically adjusted, formula:

[0099]

[0100] in, This serves as the baseline value for the vacuum risk threshold of the new machine. This serves as the benchmark value for the mechanical risk threshold. The aging sensitivity coefficient of the vacuum system. (This refers to the aging gain coefficient of the mechanical system). This embodiment extends the service life of the production line while ensuring equipment safety through a dynamically calibrated dual-threshold decision mechanism, providing the packaging industry with a lifecycle-adaptive intelligent control system solution.

[0101] In one embodiment, the step of generating a pulse width modulation waveform reconstruction instruction for a vacuum solenoid valve includes:

[0102] Based on the phase distribution characteristics of the pressure fluctuation sub-vector, an anti-phase compensation waveform for solenoid valve drive is constructed.

[0103] The compensation waveform is superimposed onto the original drive signal through the feedforward control channel, thereby converging the pressure fluctuation amplitude to a safe range.

[0104] In a specific embodiment, the pressure fluctuation sub-vector is extracted from the device state vector. (N is the number of sampling points within the sliding window), and its instantaneous phase distribution is calculated using Hilbert transform. The expression for the Hilbert transform is: (* indicates convolution operation), its physical meaning is to convert time-domain pressure fluctuations into analytic signals to extract instantaneous phase. ; Window length is used here This length covers the dominant frequency of pressure fluctuations (8-12Hz) to ensure reduced phase estimation errors. An inverse compensation waveform is constructed based on the phase characteristics:

[0105] in This is the gain coefficient. , The original driving signal amplitude is used, and the gain adopts an adaptive mechanism to satisfy... ( (for the safety zone boundary). The fundamental frequency of the vacuum solenoid valve (typically 25Hz); phase offset This allows for precise alignment of the compensation wave with the peaks and troughs of the pressure disturbance, achieving waveform inversion. This is achieved through an independent feedforward control channel. Superimposed on the original PWM drive signal Generate reconstructed synthesis driver instructions The synthesized command is injected into the vacuum control system via a digital-to-analog converter, causing the pressure fluctuation amplitude to converge to a safe range. This embodiment solves the problem of dynamic balance control of vacuum pressure in packaging production lines by using precise phase tracking and feedforward direct control technology, which is helpful for the production of high-speed precision packaging.

[0106] Reference Figure 2Here is a structural block diagram of an automated packaging production line optimization system based on intelligent control according to an embodiment of the present invention, comprising:

[0107] The data acquisition module is used to collect real-time operating data streams of key equipment on the production line and generate equipment status vectors;

[0108] The status judgment module is used to adjust the anomaly detection threshold according to the current characteristics of the production material, compare the equipment status vector with the adjusted anomaly detection threshold, and identify false anomaly signals.

[0109] The signal interception module is used to intercept the shutdown command triggered by the pseudo-abnormal signal when a pseudo-abnormal signal exceeding the preset shutdown threshold is continuously detected within a preset time window, and to trigger risk calculation.

[0110] The risk calculation unit is used to make control decisions based on the risk level value output by the risk calculation.

[0111] For the specific implementation of each module in the above device example, please refer to the above method embodiments, which will not be repeated here.

[0112] Reference Figure 3 This invention also provides a computer device, which may be a server, and its internal structure may be as follows: Figure 3 As shown, the computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0113] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer devices on which the present invention is applied.

[0114] In summary, this invention generates equipment state vectors by real-time acquisition of operational data streams from key equipment on the production line; adjusts the anomaly detection threshold based on the current characteristics of the production materials, compares the equipment state vectors with the adjusted anomaly detection thresholds to identify false anomaly signals; when false anomaly signals exceeding a preset shutdown threshold are continuously detected within a preset time window, the shutdown command triggered by the false anomaly signal is intercepted, and risk calculation is triggered; control decisions are executed based on the risk level value output by the risk calculation, thereby achieving the goals of eliminating erroneous shutdowns, accurately isolating faults, and synergistically optimizing energy efficiency in packaging production lines under fluctuating material characteristics.

[0115] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0116] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0117] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for optimizing an automated packaging production line based on intelligent control, characterized in that, Includes the following steps: Real-time acquisition of operational data streams from key equipment on the production line and generation of equipment status vectors, including deploying pressure sensing units at the main gas path node of the vacuum adsorption device on the production line to acquire pressure fluctuation time-series data through a preset sampling frequency; integrating a current sensing unit at the power input end of the servo drive mechanism to extract the harmonic spectrum characteristics of the motor operating current; The pressure fluctuation time series data and harmonic spectrum characteristics are fused in time synchronization to generate a multi-dimensional equipment state vector containing time-domain pressure indicators and frequency-domain current indicators. Adjust the anomaly detection threshold according to the current characteristics of the production materials, compare the equipment state vector with the adjusted anomaly detection threshold, and identify false anomaly signals; When false anomaly signals exceeding the preset shutdown threshold are continuously detected within the preset time window, the shutdown command triggered by the false anomaly signal is intercepted, and risk calculation is triggered. Control decisions are made based on the risk level values ​​output by the risk calculation, which include vacuum leakage risk level values ​​and mechanical transmission risk level values.

2. The method for optimizing an automated packaging production line based on intelligent control according to claim 1, characterized in that, The step of adjusting the anomaly detection threshold according to the current characteristics of the production materials includes: Obtain the reference pressure fluctuation threshold and reference current harmonic threshold during the equipment calibration phase; The humidity compensation offset of the pressure fluctuation threshold is calculated based on real-time environmental humidity data, and the compensated pressure fluctuation threshold is generated. Calculate the weight scaling factor of the current harmonic threshold based on the paperboard weight data, and generate the current harmonic threshold after proportional adjustment. The compensated pressure fluctuation threshold and the proportionally adjusted current harmonic threshold are output as anomaly detection thresholds.

3. The method for optimizing an automated packaging production line based on intelligent control according to claim 1, characterized in that, The step of comparing the device state vector with the adjusted anomaly detection threshold to identify false anomaly signals includes: When the peak value of the sliding window of the vacuum pressure fluctuation sequence exceeds the compensated pressure fluctuation threshold, a pressure anomaly is marked. When the integral value of the servo current harmonic energy in the harmonic frequency band exceeds the current harmonic threshold after proportional adjustment, the current is marked as abnormal. When the pressure anomaly marker and the current anomaly marker are triggered synchronously within a preset time, it is determined to be a false anomaly signal caused by fluctuations in material properties.

4. The method for optimizing an automated packaging production line based on intelligent control according to claim 1, characterized in that, The step of intercepting the shutdown command triggered by the false anomaly signal and triggering risk calculation when false anomaly signals exceeding the preset shutdown threshold are continuously detected within a preset time window includes: Set a hardware signal interceptor at the stop command output terminal of the programmable logic controller; When the number of false abnormal signals accumulates to a preset trigger threshold within a preset time window, the hardware signal interceptor is activated to block the shutdown command. Simultaneously, a priority interrupt signal is sent to the coprocessor to initiate a real-time risk calculation process based on the device state vector within the sliding time window.

5. The method for optimizing an automated packaging production line based on intelligent control according to claim 4, characterized in that, The step of initiating the real-time risk calculation process based on the device state vector within the sliding time window includes: Construct a probabilistic graphical model that includes equipment state correlation factors, including vacuum sealing state factors and transmission stability factors; The device state vector is input into the probabilistic graphical model for belief propagation calculation; The output consists of two-channel probability values ​​representing the risks of the vacuum system and the mechanical system, which are used as risk level values.

6. The method for optimizing an automated packaging production line based on intelligent control according to claim 1, characterized in that, The step of making control decisions based on the risk level value output by risk calculation includes: When the vacuum leakage risk level is lower than the vacuum risk threshold, a pulse width modulation waveform reconstruction command is generated for the vacuum solenoid valve. The pulse width modulation waveform reconstruction command drives the vacuum solenoid valve to generate an inverse compensation waveform to suppress pressure fluctuations. When the mechanical transmission risk level value is higher than the mechanical risk threshold, a graded deceleration and shutdown command sequence for the servo drive is generated. The servo drive is controlled to decelerate and stop in stages according to a preset time gradient through the graded deceleration and shutdown command sequence. The vacuum risk threshold and mechanical risk threshold mentioned therein are dynamically calibrated based on the equipment's operating years.

7. The method for optimizing an automated packaging production line based on intelligent control according to claim 6, characterized in that, The step of generating the pulse width modulation waveform reconstruction instruction for the vacuum solenoid valve includes: Based on the phase distribution characteristics of the pressure fluctuation sub-vector, an anti-phase compensation waveform for solenoid valve drive is constructed. The compensation waveform is superimposed onto the original drive signal through the feedforward control channel, thereby converging the pressure fluctuation amplitude to a safe range.

8. An automated packaging production line optimization system based on intelligent control, used to implement the steps of the automated packaging production line optimization method based on intelligent control as described in any one of claims 1-7, characterized in that, include: The data acquisition module is used to collect real-time operating data streams of key equipment on the production line and generate equipment status vectors. This includes deploying a pressure sensing unit at the main gas path node of the vacuum adsorption device on the production line to acquire pressure fluctuation time-series data through a preset sampling frequency; and integrating a current sensing unit at the power input end of the servo drive mechanism to extract the harmonic spectrum characteristics of the motor operating current. The pressure fluctuation time series data and harmonic spectrum characteristics are fused in time synchronization to generate a multi-dimensional equipment state vector containing time-domain pressure indicators and frequency-domain current indicators. The status judgment module is used to adjust the anomaly detection threshold according to the current characteristics of the production material, compare the equipment status vector with the adjusted anomaly detection threshold, and identify false anomaly signals. The signal interception module is used to intercept the shutdown command triggered by the pseudo-abnormal signal when a pseudo-abnormal signal exceeding the preset shutdown threshold is continuously detected within a preset time window, and to trigger risk calculation. The risk calculation unit is used to make control decisions based on the risk level value output by the risk calculation.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the intelligent control-based automated packaging production line optimization method according to any one of claims 1 to 7.

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