A packaging film production intelligent control system based on multi-source detection

Abstract

This invention relates to the field of intelligent control technology, and more particularly to an intelligent control system for packaging film production based on multi-source detection. The invention uses a data acquisition module to collect barrier performance characteristics of the target packaging film and detection quality characteristics of the target workshop's testing equipment over a historical period. An analysis module analyzes the barrier performance characteristics and detection quality characteristics. A monitoring module determines whether the packaging film produced in the target workshop meets microbial barrier standards based on the barrier performance characteristics and whether the operational stability of the target workshop's testing equipment is abnormal based on the detection quality characteristics. A diagnostic module determines the cause of the abnormal operational stability of the target workshop's testing equipment and its corresponding handling strategies based on the difference between the detection quality characteristics and a predetermined detection quality characteristic threshold. This invention improves the accuracy of quality control in packaging film production.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and in particular to an intelligent control system for packaging film production based on multi-source detection. Background Technology

[0002] In the production of soluble packaging films used for fabric infection prevention, online testing of the integrity of the sterile barrier of the packaging film is crucial to ensuring product quality. However, due to the characteristics of soluble materials, such as transparency, gloss, and surface texture, the optical properties of the packaging film are highly susceptible to batch-to-batch fluctuations caused by process variations. This interference with the optical path signal of the testing equipment leads to systematic biases in the test results. Existing testing technologies rely on fixed parameters and cannot dynamically adapt to product changes, resulting in frequent misjudgments of qualified products or the outflow of defective products, severely hindering the stable improvement of production quality and efficiency.

[0003] Chinese Patent Publication No. CN118992207A discloses a method, system, device, and medium for laminating packaging boxes, comprising the following steps: Step 1: Placing the packaging box to be laminated on the conveyor belt of the conveying module; after setting the conveying direction and speed through the control module, starting the conveying module to convey the packaging box to be laminated; Step 2: When the packaging box is conveyed to the identification and positioning module area, the size, shape, and position of the packaging box are detected and identified in real time; Step 3: The identification and positioning module feeds back the detected packaging box information to the control module in real time; the control module automatically adjusts the operating parameters of the laminating module based on the received packaging box information and controls the conveying module to convey the packaging box to be laminated. Step 4: The packaging box is conveyed to the laminating area; Step 5: The laminating module automatically adjusts its position and angle to ensure the laminating material adheres tightly to the packaging box; Step 6: During the laminating process, the monitoring and alarm module monitors the entire process in real time and automatically alarms when abnormalities occur; Step 7: The laminated packaging box continues to be conveyed out by the conveyor belt of the conveying module and enters the cutting module area. The cutting module automatically adjusts the position and angle of the cutting blades according to the size and shape information of the packaging box to precisely cut the laminated packaging box; Step 8: The data management module collects various data during the laminating process in real time and stores them in the database; Step 9: The data analysis module analyzes the collected data and generates statistical information.

[0004] Therefore, it is evident that the existing technology has the following problems:

[0005] Existing technologies do not consider the production scenario of soluble packaging films used for fabric infection prevention. Due to the unavoidable batch-to-batch fluctuations in the optical properties of the packaging film surface, measurement deviations occur in the testing equipment, leading to misjudgments of qualified products and missed detection of defective products, resulting in low production quality of packaging film products. Summary of the Invention

[0006] To address this issue, the present invention provides an intelligent control system for packaging film production based on multi-source detection. This system overcomes the problem in the prior art that it does not consider the unavoidable batch-to-batch fluctuations in the optical properties of soluble packaging films used for fabric infection prevention. These fluctuations cause measurement deviations in the detection equipment, leading to misjudgments of qualified products and missed detections of defective products, resulting in low production quality of packaging film products.

[0007] To achieve the above objectives, the present invention provides an intelligent control system for packaging film production based on multi-source detection, comprising:

[0008] The data acquisition module is used to collect barrier performance characteristics of the target packaging film and detection quality characteristics of the target workshop testing equipment within a historical period.

[0009] The analysis module is used to analyze the barrier performance characteristic characterization value based on the barrier performance characteristic information and to analyze the detection quality characteristic characterization value based on the detection quality characteristic information.

[0010] The monitoring module is used to determine whether the packaging film produced in the target workshop meets the microbial barrier standard based on the barrier performance characteristic characterization value, and to determine whether the operational stability of the detection equipment in the target workshop is abnormal based on the detection quality characteristic characterization value.

[0011] The diagnostic module is used to determine the cause of abnormal operational stability of the target workshop's testing equipment and its corresponding handling strategy based on the difference between the detection quality feature characterization value and the predetermined detection quality feature characterization threshold: determining to generate a confidence decay coefficient to determine the increase of the feature threshold;

[0012] The barrier performance characteristics include thickness uniformity and surface pore diameter, while the detection quality characteristics include the signal-to-noise ratio of the laser thickness gauge and the contrast of the image measured by the camera.

[0013] Furthermore, the analysis module is used to analyze the barrier performance characteristic representation value based on the barrier performance characteristic information, wherein the barrier performance characteristic representation value is determined based on the sum of a first performance factor and a second performance factor according to a predetermined first weighting ratio.

[0014] The first performance factor is determined based on the ratio of the thickness uniformity to a predetermined thickness uniformity threshold.

[0015] The second performance factor is determined based on the ratio of a predetermined pore diameter threshold to the pore diameter.

[0016] Furthermore, the monitoring module is used to determine whether the packaging film produced in the target workshop meets the microbial barrier standard based on the barrier performance characteristic characterization value, including:

[0017] If the barrier performance characteristic characterization value is less than or equal to the predetermined barrier performance characteristic characterization threshold, the packaging film is determined to be non-compliant with the microbial barrier standard.

[0018] If the barrier performance characteristic value is greater than the predetermined barrier performance characteristic threshold, the packaging film is determined to meet the microbial barrier standard.

[0019] Furthermore, the analysis module is used to analyze the detection quality feature characterization value based on the detection quality feature information, wherein the detection quality feature characterization value is determined based on the sum of a first quality factor and a second quality factor according to a predetermined second weighting ratio.

[0020] The first quality factor is determined based on the ratio of the signal-to-noise ratio to a predetermined signal-to-noise ratio threshold;

[0021] The second quality factor is determined based on the ratio of the contrast ratio to a predetermined contrast threshold.

[0022] Furthermore, the monitoring module is used to determine whether the operational stability of the target workshop's testing equipment is abnormal based on the detection quality characteristic values, including:

[0023] If the detection quality characteristic value is less than or equal to the predetermined detection quality characteristic threshold, it is determined to be an operational instability anomaly.

[0024] If the detection quality characteristic value is greater than the predetermined detection quality characteristic threshold, then the operation stability is determined to be normal.

[0025] Furthermore, the diagnostic module is used to determine the cause of abnormal operational stability of the target workshop testing equipment based on the difference, including:

[0026] If the difference is less than or equal to a predetermined difference threshold, it is determined to be the first cause feature label;

[0027] If the difference is greater than a predetermined difference threshold, it is determined to be a second cause feature label.

[0028] Furthermore, the diagnostic module is used to determine the corresponding processing strategy for the cause of abnormal operational stability of the target workshop testing equipment based on the difference, including:

[0029] If it is the primary cause feature label, then a confidence decay coefficient is generated and the feature threshold is increased to offset the decrease in the sensitivity of the detection system and prevent qualified products from being misjudged.

[0030] If it is a second cause feature label, then the noise generated by the complex texture of the current batch of products is removed from the original signal to obtain the true signal.

[0031] Furthermore, the acquisition module includes a laser thickness gauge and a high-resolution line scan camera, used to acquire barrier performance characteristic information.

[0032] Furthermore, the acquisition module also includes an A / D converter and an embedded standard contrast test target, used to acquire detection quality characteristic information.

[0033] Furthermore, when the monitoring module determines that the operational stability of the target workshop's testing equipment is abnormal based on the detection quality characteristic characterization value, it is further configured to:

[0034] The barrier performance characteristic values ​​currently output by the analysis module are marked as data to be verified;

[0035] The diagnostic module is triggered to diagnose the cause of the operational instability and execute the corresponding handling strategy;

[0036] A traceability verification window period is defined with the current time as the end point. The length of the traceability verification window period is defined by preset window parameters. The original test data of the packaging film batches that have been judged according to the microbial barrier standard within the traceability verification window period and before the operating status of the testing equipment in the target workshop is judged to be abnormal are extracted from the historical data.

[0037] In response to the completion of the diagnostic module, the data to be verified and the original test data within the traceability verification window period of the packaging film of the target production batch are re-evaluated using the adjusted feature threshold or the processed real signal.

[0038] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention provides an intelligent control system for packaging film production based on multi-source detection. The acquisition module first collects the barrier performance characteristic information of the target packaging film and the detection quality characteristic information of the detection equipment in the target workshop within a historical period; the analysis module analyzes and obtains the barrier performance characteristic characterization value and the detection quality characteristic characterization value accordingly; the monitoring module determines whether the packaging film produced in the target workshop meets the microbial barrier standard based on the comparison result of the barrier performance characteristic characterization value and the predetermined barrier performance characteristic characterization threshold, and determines whether the operational stability of the detection equipment in the target workshop is abnormal based on the comparison result of the detection quality characteristic characterization value and the predetermined detection quality characteristic characterization threshold; the diagnostic module further determines the cause of the abnormal operational stability of the detection equipment in the target workshop and its corresponding handling strategy based on the difference between the detection quality characteristic characterization value and the predetermined detection quality characteristic characterization threshold. This invention improves the accuracy of packaging film production quality control in the production scenario of soluble packaging film used for fabric infection prevention.

[0039] In particular, this invention uses a laser thickness gauge and a high-resolution line scan camera to extract the thickness uniformity and pore diameter of the packaging film through a data acquisition module, thereby obtaining barrier performance characteristic information of the target packaging film produced within a historical period. It also uses an A / D converter and an embedded standard contrast test target to monitor the signal-to-noise ratio of the laser thickness gauge and the contrast of the camera image, reflecting the detection quality characteristic information of the target workshop's testing equipment, thus providing a complete data foundation for subsequent analysis.

[0040] In particular, this invention uses an analysis module to normalize the two original data feature information, barrier performance feature information and detection quality feature information, by weighting and summing them according to a predetermined weight ratio. The analysis yields barrier performance feature characterization value and detection quality feature characterization value, which can unify data of different dimensions into a quantifiable basis for judging the quality of the produced packaging film products and the normal status of the workshop testing equipment. This provides a quantitative decision-making basis for the production line to achieve fully automated closed-loop intelligent control.

[0041] In particular, the present invention compares the barrier performance characteristic values ​​with predetermined barrier performance characteristic thresholds through a monitoring module to determine whether the packaging film produced in the target workshop meets the microbial barrier standard, and compares the detection quality characteristic values ​​with predetermined detection quality characteristic thresholds to determine whether the operating stability of the detection equipment in the target workshop is abnormal. It can distinguish whether the problem exists in the product or originates from the performance fluctuation of the detection equipment itself, thereby improving the accuracy of product quality diagnosis.

[0042] In particular, when the diagnostic module determines that the operational stability of the testing equipment in the target workshop is abnormal, the present invention distinguishes whether the abnormality is caused by temporary interference from the abnormal gloss of the current batch of products or by strong interference from the complex texture of the current batch of products, based on the difference between the calculated detection quality feature characterization value and the predetermined detection quality feature characterization threshold. Based on this, a differentiated processing strategy is matched: a confidence attenuation coefficient is generated, and the feature threshold is increased to offset the decrease in the sensitivity of the detection system and prevent the misjudgment of qualified products; noise generated by the complex texture of the current batch of products is removed from the original signal to obtain the true signal, thereby ensuring the timeliness of intelligent control of packaging film production. Attached Figure Description

[0043] Figure 1 This is a structural block diagram of the intelligent control system for packaging film production based on multi-source detection, according to an embodiment of the present invention.

[0044] Figure 2 This invention provides a logical decision diagram for determining whether packaging films produced in a target workshop meet microbial barrier standards based on the barrier performance characteristic characterization values.

[0045] Figure 3 This invention provides a logical decision diagram for determining whether the operational stability of the target workshop's testing equipment is abnormal based on the aforementioned detection quality characteristic characterization values.

[0046] Figure 4 This invention provides a logical decision diagram for determining the cause of abnormal operational stability of the target workshop testing equipment and its corresponding handling strategy based on the difference. Detailed Implementation

[0047] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0048] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0049] It should be clearly stated that, in the description of this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0050] Please see Figure 1 The diagram shown is a structural block diagram of an intelligent control system for packaging film production based on multi-source detection, according to an embodiment of the present invention. The present invention provides an intelligent control system for packaging film production based on multi-source detection, comprising:

[0051] The data acquisition module is used to collect barrier performance characteristics of the target packaging film and detection quality characteristics of the target workshop testing equipment within a historical period.

[0052] An analysis module, which is connected to the acquisition module, is used to analyze the barrier performance characteristic characterization value based on the barrier performance characteristic information and to analyze the detection quality characteristic characterization value based on the detection quality characteristic information.

[0053] The monitoring module is connected to the acquisition module and the analysis module respectively, and is used to determine whether the packaging film produced in the target workshop meets the microbial barrier standard based on the barrier performance characteristic characterization value, and to determine whether the operational stability of the detection equipment in the target workshop is abnormal based on the detection quality characteristic characterization value.

[0054] The diagnostic module, connected to the monitoring module, is used to determine the cause of abnormal operational stability of the target workshop testing equipment and its corresponding handling strategy based on the difference between the detection quality feature characterization value and the predetermined detection quality feature characterization threshold: determining to generate a confidence attenuation coefficient to determine the increase of the feature threshold;

[0055] The barrier performance characteristics include thickness uniformity and surface pore diameter, while the detection quality characteristics include the signal-to-noise ratio of the laser thickness gauge and the contrast of the image measured by the camera.

[0056] It is understood that the steps of the intelligent control system for packaging film production based on multi-source detection in this embodiment of the invention include:

[0057] Step S1: Collect barrier performance characteristic information of the target packaging film within the historical period;

[0058] Step S2: Analyze the barrier performance characteristic characterization value based on the barrier performance characteristic information;

[0059] Step S3: Determine whether the packaging film produced in the target workshop meets the microbial barrier standard based on the comparison result between the barrier performance characteristic characterization value and the predetermined barrier performance characteristic characterization threshold.

[0060] Step S4: In response to the packaging film meeting the microbial barrier standard, collect the detection quality characteristic information of the target workshop testing equipment within the historical period;

[0061] Step S5: Analyze the detection quality characteristic representation value based on the detection quality characteristic information;

[0062] Step S6: Determine whether the operational stability of the detection equipment in the target workshop is abnormal based on the comparison result between the detection quality feature characterization value and the predetermined detection quality feature characterization threshold.

[0063] Step S7: In response to abnormal operation stability of the target workshop testing equipment, calculate the difference between the test quality feature characterization value and the predetermined test quality feature characterization threshold.

[0064] Step S8: Based on the difference, determine the cause of the abnormal operation stability of the target workshop testing equipment and its corresponding handling strategy: determine to generate a confidence attenuation coefficient and determine to increase the feature threshold;

[0065] The barrier performance characteristics include thickness uniformity and surface pore diameter, while the detection quality characteristics include the signal-to-noise ratio of the laser thickness gauge and the contrast of the image measured by the camera.

[0066] It's understandable that thickness uniformity refers to the standard deviation of the packaging film thickness measured by a laser thickness gauge over a historical period. Uneven thickness can easily lead to localized barrier failure. This unevenness originates from single-point thickness measurements acquired at fixed sampling intervals during continuous operation of the production line by the laser thickness gauge. These measurements are collected and stored in real-time by the data processing unit in the acquisition module, with the unit being mm. A smaller value indicates more uniform packaging film thickness, while a larger value can lead to laser multiple scattering, distorting the detection signal.

[0067] It is understandable that pore diameter refers to the average diameter of pores on the surface of the packaging film measured directly using a high-resolution line scan camera over a historical period, measured in mm. A smaller value indicates fewer impurities in the packaging film.

[0068] In essence, the signal-to-noise ratio (SNR) refers to the ratio of the effective thickness signal intensity received within a historical period to the background noise intensity. It is acquired separately by the signal processing unit built into the laser thickness gauge and the image processing unit of the camera, originating from the digital signal after A / D conversion of the image, and is measured in arcseconds (log). A higher SNR indicates more accurate measurement. When there are abrupt changes or slight undulations in the thickness of the packaging film in localized areas, the incident laser will experience changes in its scattering angle and optical path. For the laser thickness gauge, this introduces high-frequency random components into the received light intensity signal, leading to a decrease in the SNR. For the camera, the uneven thickness of the packaging film surface creates microscopic height differences, resulting in changes in the local reflected light path length and scattering characteristics. In the acquired image, this manifests as localized high-frequency perturbations in pixel grayscale values, further reducing the image SNR.

[0069] It is understandable that contrast refers to the average difference in grayscale values ​​between the porous region and the surrounding smooth background region over a historical period. This is calculated by an image processing unit after images are acquired by a high-resolution line scan camera. The source is the grayscale image of the membrane surface captured by the camera in a single detection period, and the unit is nits. Large pores cause light to undergo multiple, irregular reflections, refractions, and scatterings inside and at the edges of the pores. The light entering the lens becomes diffuse, and scattered light spills out into areas that should be dark. Originally clear black-and-white boundaries become blurred, and the dark interior of the pores is illuminated by stray light. This reduces the grayscale difference between the pores and the bright background, making the overall image appear grayish and washed out, blurring details, making edges difficult to distinguish, and degrading image quality.

[0070] In this embodiment, a single cycle is preset, and preferably, the single cycle is 1 hour.

[0071] This invention provides an intelligent control system for packaging film production based on multi-source detection. Through a data acquisition module, it collects barrier performance characteristics of the target packaging film and detection quality characteristics of the target workshop's testing equipment over a historical period. Through an analysis module, it analyzes barrier performance characteristic values ​​based on the barrier performance characteristics and detection quality characteristic values ​​based on the detection quality characteristics. Through a monitoring module, it determines whether the packaging film produced in the target workshop meets microbial barrier standards based on the barrier performance characteristic values ​​and whether the operational stability of the target workshop's testing equipment is abnormal based on the detection quality characteristic values. Through a diagnostic module, it determines the cause of abnormal operational stability of the target workshop's testing equipment and its corresponding handling strategies based on the difference between the detection quality characteristic values ​​and predetermined detection quality characteristic thresholds, thereby improving the accuracy of packaging film production quality control.

[0072] Specifically, the analysis module is used to analyze the barrier performance characteristic representation value based on the barrier performance characteristic information, wherein the barrier performance characteristic representation value is determined by summing a first performance factor and a second performance factor according to a predetermined first weighting ratio.

[0073] The first performance factor is determined based on the ratio of the thickness uniformity to a predetermined thickness uniformity threshold.

[0074] The second performance factor is determined based on the ratio of a predetermined pore diameter threshold to the pore diameter.

[0075] In this embodiment, the predetermined thickness uniformity threshold is preset. Specifically, thickness uniformity samples from 10 historical periods (i.e., 10 hours) are predetermined. The predetermined thickness uniformity threshold is determined based on the average value of the thickness uniformity samples. In this embodiment, the predetermined thickness uniformity threshold is determined within the range [3.5, 5.5]. Based on the analysis of the distribution of historical data from the production line, under stable process conditions, the statistical distribution of thickness uniformity shows an average value of approximately 4.5 mm. Preferably, the predetermined thickness uniformity threshold in this embodiment is 4.5 mm.

[0076] In this embodiment, the predetermined pore diameter threshold is preset. Specifically, pore diameter samples from 10 historical periods (i.e., 10 hours) are predetermined, and the predetermined pore diameter threshold is determined based on the average value of the pore diameter samples. In this embodiment, the predetermined pore diameter threshold is determined within the range [1.8, 2.4]. Based on the investigation and analysis of compliance requirements for microbial barrier experiments and industry standards, the predetermined pore diameter threshold is preferably 2.1 mm.

[0077] In this embodiment, the predetermined first weighting ratio is 6:4, that is, the barrier performance characteristic value = 0.6 × first performance factor + 0.4 × second performance factor.

[0078] This invention normalizes and weights the two barrier performance characteristics—thickness uniformity and pore diameter—to obtain a barrier performance characteristic value. This transforms characteristic parameters with different dimensions into a unified quantitative index, comprehensively reflecting the overall quality level of packaging film products in terms of barrier performance. The more uniform the actual thickness of the packaging film, the higher the value of the first performance factor; conversely, the smaller the actual pore diameter, the higher the value of the second performance factor. Through weighted calculation, a consistent quality evaluation standard can be established for packaging film products from different production batches and under different process conditions, providing a quantifiable decision-making basis for subsequent microbial barrier standard determination.

[0079] Please see Figure 2 As shown, this is a logic diagram illustrating the determination of whether packaging films produced in a target workshop meet microbial barrier standards based on the barrier performance characteristic values ​​according to an embodiment of the present invention. The monitoring module of the present invention is used to determine whether packaging films produced in a target workshop meet microbial barrier standards based on the barrier performance characteristic values, including:

[0080] If the barrier performance characteristic characterization value is less than or equal to the predetermined barrier performance characteristic characterization threshold, the packaging film is determined to be non-compliant with the microbial barrier standard.

[0081] If the barrier performance characteristic value is greater than the predetermined barrier performance characteristic threshold, the packaging film is determined to meet the microbial barrier standard.

[0082] In this embodiment, the predetermined barrier performance characteristic representation threshold is preset. Specifically, the average value of the barrier performance characteristic representation value over 20 periods, i.e., 20 hours, is predetermined. The predetermined barrier performance characteristic representation threshold is determined based on the average value of the barrier performance characteristic representation value. The barrier performance characteristic representation threshold is determined within the range [0.82, 1.20]. The barrier performance characteristic representation threshold is selected based on the safety margin coefficient of 88% of the upper limit of the barrier performance characteristic representation threshold. In this embodiment, the preferred barrier performance characteristic representation threshold is 1.06.

[0083] This invention improves the consistency of production quality by applying barrier performance characteristic thresholds determined based on historical data to assess the quality of packaging film products. By directly comparing the quantitative characterization values ​​reflecting thickness uniformity and porosity with the predetermined barrier performance characteristic thresholds, batches of products that fail to meet barrier performance standards can be identified. This allows for rapid assessment of packaging film product batches that meet microbial barrier standards without relying on manual experience, enabling timely removal of unqualified products on the production line. This ensures that only packaging films that meet preset aseptic requirements can proceed to subsequent processes, thereby improving the reliability of the final product quality.

[0084] Specifically, the analysis module is used to analyze the detection quality feature characterization value based on the detection quality feature information, wherein the detection quality feature characterization value is determined based on the sum of a first quality factor and a second quality factor according to a predetermined second weighting ratio.

[0085] The first quality factor is determined based on the ratio of the signal-to-noise ratio to a predetermined signal-to-noise ratio threshold;

[0086] The second quality factor is determined based on the ratio of the contrast ratio to a predetermined contrast threshold.

[0087] In this embodiment, the predetermined signal-to-noise ratio (SNR) threshold is preset. Specifically, SNR samples from 10 historical periods (i.e., 10 hours) are predetermined. The predetermined SNR threshold is determined based on the average value of the SNR samples. The predetermined SNR threshold is determined within the range [45, 52]. Based on the assumption that the actual working SNR is not lower than 90% of the theoretical value, the preferred predetermined SNR threshold in this embodiment is 50 μC.

[0088] In this embodiment, the predetermined contrast threshold is preset. Contrast samples from 10 historical periods (i.e., 10 hours) are predetermined, and the predetermined contrast threshold is determined based on the average value of the contrast samples. The predetermined contrast threshold is determined within the range [0.68, 0.82]. Considering interference such as dust and vibration in the production environment, a safety margin of 9.5% should be increased on the basis of the ultimate performance to ensure that pores with a diameter greater than or equal to 2 mm can still be stably identified under typical working conditions. In this embodiment, the predetermined contrast threshold is preferably 0.75 nits.

[0089] In this embodiment, the predetermined second weighting ratio is 5:5, that is, the detection quality feature characterization value = 0.5 × first quality factor + 0.5 × second quality factor.

[0090] This invention normalizes two detection quality characteristics—signal-to-noise ratio and contrast ratio—to quantitatively evaluate the operational quality of two types of detection equipment: laser thickness gauges and cameras. The detection quality characteristic value is obtained by weighting a first quality factor and a second quality factor with equal weights, reflecting the reliability of the equipment's detection data. The construction of this detection quality characteristic value unifies previously independent signal strength and image sharpness indicators into a comparable dimension, providing a clear quantitative basis for objectively judging whether the detection equipment is operating stably. A decrease in the detection quality characteristic value indicates a decline in the equipment's measurement accuracy, laying the foundation for subsequent equipment anomaly diagnosis and adaptive processing strategies.

[0091] Please see Figure 3As shown, this is a logic diagram for determining whether the operational stability of the target workshop testing equipment is abnormal based on the detection quality characteristic characterization value according to an embodiment of the present invention. The monitoring module of the present invention is used to determine whether the operational stability of the target workshop testing equipment is abnormal based on the detection quality characteristic characterization value, including:

[0092] If the detection quality characteristic value is less than or equal to the predetermined detection quality characteristic threshold, it is determined to be an operational instability anomaly.

[0093] If the detection quality characteristic value is greater than the predetermined detection quality characteristic threshold, then the operation stability is determined to be normal.

[0094] In this embodiment, the predetermined detection quality feature characterization threshold is preset. Specifically, the average value of the detection quality feature characterization values ​​over 20 historical periods (i.e., 20 hours) is predetermined. The predetermined detection quality feature characterization threshold is determined based on the average value of the detection quality feature characterization values. The detection quality feature characterization threshold is determined within the range [0.91, 1.07]. The detection quality feature characterization threshold is selected based on the safety deviation coefficient, which is 95% of the upper limit of the detection quality feature characterization threshold. In this embodiment, the predetermined detection quality feature characterization threshold is preferably 1.02.

[0095] This invention, through setting a detection quality characteristic representation threshold calculated based on historical data and comparing it with the current detection quality characteristic representation value, can determine in real time whether the detection equipment is in a reliable and stable operational state. Only when the detection quality characteristic representation value is lower than the predetermined detection quality characteristic representation threshold is the operational stability of the detection equipment deemed abnormal. This ensures that when the detection equipment itself is in poor condition, identifying the abnormal situation provides an accurate basis for further diagnosis and handling measures, thus guaranteeing the reliability of intelligent production control.

[0096] Please see Figure 4 As shown, this is a logical decision diagram of an embodiment of the present invention for determining the cause of abnormal operational stability of the target workshop testing equipment based on the difference and its corresponding processing strategy. The diagnostic module of the present invention is used to determine the cause of abnormal operational stability of the target workshop testing equipment based on the difference, including:

[0097] If the difference is less than or equal to a predetermined difference threshold, it is determined to be the first cause feature label;

[0098] If the difference is greater than a predetermined difference threshold, it is determined to be a second cause feature label.

[0099] In this embodiment, the predetermined difference threshold is pre-set. Specifically, the average difference between the detection quality characteristic characterization value and the predetermined detection quality characteristic characterization threshold over 25 historical periods (i.e., 25 hours) is predetermined. The predetermined difference threshold is determined based on the product of the average difference and a tolerance coefficient. The tolerance coefficient is selected based on the historical statistical characteristics of equipment performance fluctuations and is selected within the range of [1.62, 1.73]. In this embodiment, the tolerance coefficient is preferably 1.68. The average difference is determined within the range of [0.05, 0.11]. In this embodiment, the predetermined difference threshold is preferably 0.10.

[0100] Understandably, the primary cause feature label indicates that the testing equipment is temporarily affected by the abnormal gloss of the current batch of products.

[0101] Understandably, the second reason for the feature label is to determine that the detection equipment is strongly interfered with by the complex texture of the current batch of products.

[0102] This invention compares the difference between the detected quality feature characterization value and a predetermined detected quality feature characterization threshold with a difference threshold set based on a tolerance coefficient. This enables intelligent classification and diagnosis of the causes of abnormal operating stability of the detection equipment. By analyzing the magnitude of the difference, it distinguishes whether the equipment abnormality is due to a general decrease in sensitivity caused by temporary optical interference on the product surface or due to strong interference caused by the complex texture of the product. This triggers differentiated adaptive processing strategies, improving the pertinence of the processing measures.

[0103] Specifically, the diagnostic module uses the difference to determine the corresponding processing strategy for the cause of the abnormal operational stability of the target workshop testing equipment, including:

[0104] If it is the first cause feature label, then a confidence decay coefficient β is generated, and the feature threshold is increased to offset the decrease in the sensitivity of the detection system and prevent qualified products from being misjudged.

[0105] If it is a second cause feature label, then the noise generated by the complex texture of the current batch of products is removed from the original signal to obtain the true signal.

[0106] Understandably, the specific strategy of determining a confidence decay coefficient β and increasing the feature threshold to offset the decrease in the sensitivity of the detection system and prevent qualified products from being misjudged is as follows: adjust the predetermined barrier performance feature characterization threshold of the current production batch to Q'=β×Q, where Q' is the current barrier performance feature characterization threshold and Q is the original barrier performance feature characterization threshold.

[0107] In this embodiment, the confidence attenuation coefficient β is preset and determined within the range [1.08, 1.22]. Considering that when the detection quality feature characterization value drops to 90% of the detection quality characterization threshold, the false judgment rate increases from 0.7% to 1.9%, in order to control the false judgment rate within 1.2%, the judgment standard is relaxed by 15%. In this embodiment, the confidence attenuation coefficient β is preferably 1.15.

[0108] Understandably, by implementing a process strategy that generates a confidence attenuation coefficient, the criteria for judging the quality of the current batch of products can be relaxed. When the overall sensitivity of the testing equipment decreases due to temporary optical interference, the requirements for the characterization values ​​of the product's barrier performance characteristics can be reduced. This prevents products that should be qualified from being mistakenly judged as unqualified by equipment with insufficient sensitivity, thereby reducing the false detection rate of qualified products caused by fluctuations in the state of the testing equipment and stabilizing the continuity of the production process.

[0109] Understandably, the specific processing strategy for extracting noise generated by the complex texture of the current batch of products from the original signal to obtain the real signal is: S'=S-λN, where S' is the real signal, S is the original signal, N is the noise, and λ is the noise coupling coefficient.

[0110] In this embodiment, the noise coupling coefficient λ is preset and determined within the range [0.68, 0.75]. Repeated measurements are performed on packaging film samples with known texture features, and the signal is decomposed using independent component analysis. Statistically, the average proportion of the texture noise component in the variance of the original signal is found to be 72%. In this embodiment, the preferred noise coupling coefficient λ is 0.72.

[0111] Understandably, by implementing a processing strategy that removes noise generated by the complex texture of the current batch of products from the original signal, the strong interference caused by the complex texture of the product surface can be directly weakened at the signal level. This allows the noise component related to the texture to be subtracted from the original detection data, thereby recovering the signal that reflects the true physical characteristics of the product. This improves the effectiveness of the signal and the accuracy of the detection results under complex working conditions.

[0112] This invention employs two synergistic processing strategies to address different root causes of anomalies. For temporary decreases in sensitivity, the threshold values ​​representing barrier performance characteristics are adjusted to mitigate risks and maintain smooth production. For signal interference, signal purification is used to improve data quality and ensure accurate detection. The combined effect of these two strategies enables the intelligent production control process to maintain reliable judgment and production stability even when equipment operating conditions or product characteristics fluctuate.

[0113] Specifically, the acquisition module includes a laser thickness gauge and a high-resolution line scan camera, used to acquire barrier performance characteristic information.

[0114] Understandably, laser thickness gauges continuously measure the thickness of packaging films during production in a non-contact manner. The emitted laser beam penetrates the film and is received by a receiver. The film thickness data is obtained by calculating the optical path difference, which can be directly used to calculate thickness uniformity. A high-resolution line scan camera is mounted perpendicular to the surface of the packaging film. To enhance surface topological contrast, a low-angle ring light is used to continuously image the surface of the packaging film. The acquired high-definition images are analyzed by a subsequent image processing unit to identify and quantify the morphology and size of surface pores.

[0115] This invention integrates two complementary sensing technologies—laser thickness measurement and high-resolution optical imaging—to establish a direct measurement capability for the physical barrier performance of packaging films. Laser thickness measurement enables online quantitative monitoring of thickness, while line scan imaging provides non-destructive visual detection of surface micro-defects. The multi-source simultaneous detection method can comprehensively acquire key physical characteristic data that determine the reliability of the product's sterile barrier from both the thickness distribution and porosity characteristics dimensions.

[0116] Specifically, the acquisition module also includes an A / D converter and an embedded standard contrast test target, used to acquire detection quality characteristic information.

[0117] Understandably, the A / D converter is responsible for converting the analog voltage signal output by the laser thickness gauge and the analog video signal output by the line scan camera into digital signals for processing by the analysis module; its conversion accuracy, sampling rate, and noise level directly affect the quality of signal digitization. An embedded standard contrast test target is moved to the center of the camera's field of view by a mechanical device at fixed intervals. Its surface has pre-calibrated standard patterns of different gray levels. After the camera images the target, it calculates the actual contrast value of the image by analyzing the image and compares it with the target to evaluate the camera's imaging performance.

[0118] The embodiments of this invention ensure that the conversion from physical signals to digital information can be evaluated through an A / D converter, while the embedded standard contrast test target provides a stable reference benchmark for camera imaging. This constitutes a direct basis for evaluating whether the operating status of the testing equipment is abnormal and whether the data is reliable. It means that the system not only focuses on whether the packaging film product is qualified, but also monitors whether the testing itself is accurate, thus forming a dual guarantee mechanism for product production quality control.

[0119] Specifically, when the monitoring module determines that the operational stability of the target workshop's testing equipment is abnormal based on the detection quality characteristic values, it is further configured to:

[0120] Step S11: Mark the barrier performance characteristic characterization value currently output by the analysis module as data to be verified;

[0121] Step S12: Trigger the diagnostic module to perform the diagnosis of the cause of the abnormal operation stability and the execution of the corresponding handling strategy;

[0122] Step S13: Determine a traceability verification window period ending at the current time. The length of the traceability verification window period is defined by the preset window parameter W. Extract the original test data of the packaging film batches that have undergone microbial barrier standard judgment within the traceability verification window period and before the operating status of the testing equipment in the target workshop is judged to be abnormal from the historical data.

[0123] Step S14: In response to the completion of the diagnostic module, the data to be verified and the original test data within the traceability verification window period of the packaging film of the target production batch are re-evaluated using the adjusted feature threshold or the processed real signal.

[0124] Understandably, in step S11, from the moment the abnormal stability of the equipment operation is detected until the diagnosis and processing are completed, the barrier performance characteristic value of the corresponding production batch packaging film is marked and the final judgment is temporarily suspended to prevent a final decision from being made based on inaccurate detection data.

[0125] It is understandable that in step S12, by calling the diagnostic module, the cause of the anomaly is analyzed based on the difference between the detection quality feature characterization value and the predetermined detection quality feature characterization threshold, and the corresponding processing strategy is executed: generating a confidence attenuation coefficient β to adjust the feature threshold, wherein the feature threshold in the embodiment is the judgment threshold, or denoising the original signal to extract the real signal.

[0126] In this embodiment, the preset window parameter W is pre-set and selected based on a comprehensive consideration of equipment stability and quality risk. In this embodiment, the preset window parameter W is preferably 2 hours.

[0127] It is understandable that in step S14, the data to be verified marked in step S11 is recalculated or re-judged based on the new feature threshold Q' output by the diagnostic module or the purified real signal S', so as to obtain a quality conclusion of the packaging film product based on the corrected judgment conditions.

[0128] This invention, through a closed-loop process of temporary storage of data to be verified, diagnosis and adaptive processing of anomalies, and data re-judgment, achieves self-healing and decision correction in the quality control process when the testing equipment itself malfunctions. When an abnormality in the operational stability of the testing equipment is detected, a diagnostic and repair program is initiated. This program can intervene and correct product quality misjudgments caused by equipment status fluctuations without stopping the production line. This ensures that even when temporary problems occur in the testing process, the final product quality judgment is still based on reliable data or a reasonably compensated standard, thus improving the fault tolerance of the entire intelligent control system.

[0129] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A smart control system for packaging film production based on multi-source detection, characterized in that, include: The data acquisition module collects barrier performance characteristics of the target packaging film produced within a historical period and detection quality characteristics of the target workshop's testing equipment. The analysis module analyzes the barrier performance characteristic characterization value based on the barrier performance characteristic information and analyzes the detection quality characteristic characterization value based on the detection quality characteristic information. The monitoring module determines whether the packaging film produced in the target workshop meets the microbial barrier standard based on the comparison between the barrier performance characteristic characterization value and the predetermined barrier performance characteristic characterization threshold, and determines whether the operational stability of the detection equipment in the target workshop is abnormal based on the detection quality characteristic characterization value. The diagnostic module determines the cause of abnormal operational stability of the target workshop's testing equipment and its corresponding handling strategies based on the difference between the detection quality characteristic characterization value and the predetermined detection quality characteristic characterization threshold. The reasons include the first reason feature label and the second reason feature label; If it is the first cause feature label, then a confidence decay coefficient β is generated, and the barrier performance feature representation threshold is increased, Q'=β×Q, where Q' is the current barrier performance feature representation threshold and Q is the original barrier performance feature representation threshold. If it is a second cause feature label, then the noise generated by the complex texture of the current batch of products in the original signal is determined to be removed; The barrier performance characteristics include thickness uniformity and surface pore diameter, and the detection quality characteristics include the signal-to-noise ratio of the laser thickness gauge and the contrast of the image measured by the camera. The barrier performance characteristic value is determined by summing a first performance factor and a second performance factor according to a predetermined first weighting ratio; The first performance factor is determined based on the ratio of the thickness uniformity to a predetermined thickness uniformity threshold; The second performance factor is determined based on the ratio of a predetermined pore diameter threshold to the pore diameter; The detection quality characteristic value is determined based on the summation of the first quality factor and the second quality factor according to a predetermined second weighting ratio; The first quality factor is determined based on the ratio of the signal-to-noise ratio to a predetermined signal-to-noise ratio threshold; The second quality factor is determined based on the ratio of the contrast to a predetermined contrast threshold.

2. The intelligent control system for packaging film production based on multi-source detection according to claim 1, characterized in that, The monitoring module is used to determine whether the packaging film produced in the target workshop meets the microbial barrier standard based on the barrier performance characteristic values, including: If the barrier performance characteristic characterization value is less than or equal to the predetermined barrier performance characteristic characterization threshold, the packaging film is determined to be non-compliant with the microbial barrier standard. If the barrier performance characteristic value is greater than the predetermined barrier performance characteristic threshold, the packaging film is determined to meet the microbial barrier standard.

3. The intelligent control system for packaging film production based on multi-source detection according to claim 1, characterized in that, The monitoring module is used to determine whether the operational stability of the target workshop's testing equipment is abnormal based on the detection quality characteristic values, including: If the detection quality characteristic value is less than or equal to the predetermined detection quality characteristic threshold, it is determined to be an operational instability anomaly. If the detection quality characteristic value is greater than the predetermined detection quality characteristic threshold, then the operation stability is determined to be normal.

4. The intelligent control system for packaging film production based on multi-source detection according to claim 1, characterized in that, The diagnostic module is used to determine the cause of abnormal operational stability of the target workshop testing equipment based on the difference, including: If the difference is less than or equal to a predetermined difference threshold, it is determined to be the first cause feature label; If the difference is greater than a predetermined difference threshold, it is determined to be a second cause feature label.

5. The intelligent control system for packaging film production based on multi-source detection according to claim 1, characterized in that, The acquisition module includes a laser thickness gauge and a high-resolution line scan camera, which are used to acquire barrier performance characteristic information, respectively.

6. The intelligent control system for packaging film production based on multi-source detection according to claim 1, characterized in that, The acquisition module also includes an A / D converter and an embedded standard contrast test target, which are used to acquire detection quality characteristic information.

7. The intelligent control system for packaging film production based on multi-source detection according to claim 3, characterized in that, When the monitoring module determines that the operational stability of the target workshop's testing equipment is abnormal based on the detection quality characteristic values, it also includes: The barrier performance characteristic values ​​currently output by the analysis module are marked as data to be verified; The diagnostic module is triggered to diagnose the cause of the operational instability and execute the corresponding handling strategy; A traceability verification window period is determined with the current time as the end point. The length of the traceability verification window period is defined by a preset window parameter W. The original test data of the packaging film batches that have been judged according to the microbial barrier standard within the traceability verification window period and before the operating status of the detection equipment is judged to be abnormal are extracted from the historical data. In response to the completion of the diagnostic module, the data to be verified and the original test data within the traceability verification window period of the packaging film of the target production batch are re-evaluated using the adjusted feature threshold or the processed real signal.

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