A method and system for digital management of a color box printing process

Abstract

This application relates to the field of color box printing technology, specifically disclosing a digital management method and system for the color box printing process. The method includes the following steps: acquiring and calculating a quantitative index of the supply risk of printing materials based on the supply chain information of the printing materials; acquiring and calculating a printability prediction value and a quality risk quantification value based on the microscopic physical characteristics of the printing materials when they are put into storage; adjusting the production scheduling strategy and matching the target printing equipment for color box printing orders using the printing materials based on the quantitative index of supply risk, the quantitative index of printability, and the quantitative index of quality risk; generating adjustment instructions for printing parameters of the target printing equipment according to the quantitative index of printability; and controlling the target printing equipment to perform trial printing according to the adjustment instructions and verifying the quality of the trial prints. Implementing this application can reduce production plan disruptions and equipment idle time caused by unexpected delays in printing materials.

Description

Technical Field

[0001] This application relates to the field of color box printing technology, and in particular to a digital management method and system for the color box printing process. Background Technology

[0002] In the color box printing industry, digital management systems play a crucial role in production scheduling and resource optimization. However, existing digital management systems face numerous challenges when handling high-value, customized color box printing orders with stringent delivery and quality requirements, especially when these orders rely on scarce or special printing materials. These challenges mainly manifest in opaque supply chain information, dynamic changes in the material transportation environment, and the difficulty in effectively identifying the micro-printability of materials upon warehousing.

[0003] Specifically, existing systems struggle to obtain real-time detailed information about the entire process of printing materials, from supplier production to warehousing. This includes real-time deviations from supplier production schedules and the potential impact of environmental factors (such as temperature and humidity) on material performance during transportation. This information lag and deficiency prevents the system from accurately assessing material supply risks. Furthermore, at the material warehousing stage, conventional quality inspection methods often fail to capture subtle differences in the material's microscopic physical properties, such as potential fluctuations in printability parameters like ink penetration and dot gain trends. Consequently, they cannot effectively predict the impact on the final printing effect or quantify potential quality risks.

[0004] Lacking the ability to perceive and accurately predict material supply risks and inherent quality fluctuations in real time, existing systems often rely solely on preset standard parameters and historical experience when formulating production scheduling strategies and matching printing equipment. Once material supply is delayed or undiscovered quality defects exist in the materials themselves, production plans are disrupted, equipment idles, product quality fails to meet standards, and even significant waste of expensive printing materials occurs. This delayed problem-solving mechanism not only reduces production efficiency and increases operating costs but also severely impacts customer satisfaction, contradicting the original intention of digital management. Summary of the Invention

[0005] This application proposes a digital management method and system for the color box printing process, aiming to solve the technical problems faced by the existing color box printing industry when handling high-value, customized orders, such as opaque supply chain information, dynamic changes in the material transportation environment, and difficulty in effectively identifying the microscopic printability of materials upon warehousing.

[0006] In a first aspect, this application provides a digital management method for the color box printing process, used to manage color box printing orders that depend on printing materials, the method comprising the following steps: The supply chain information of the printed material is obtained and a quantitative index of supply risk of the printed material is calculated; wherein, the supply chain information includes production progress information obtained from the production management system of the supplier of the printed material, and transportation environment information obtained from environmental sensors attached to the transport carrier of the printed material. When the printed materials are put into storage, the microscopic physical characteristics information of the printed materials are acquired and the printability prediction value used to predict the printing effect and the quality risk quantification value reflecting potential quality deviation are calculated. Based on the supply risk quantification index, printability prediction value, and quality risk quantification value, adjust the production scheduling strategy and match the target printing equipment for color box printing orders using the printing materials. Based on the printability prediction value, an adjustment instruction for the printing parameters of the target printing equipment is generated; According to the adjustment instructions, the target printing equipment is controlled to perform a trial print, and the quality of the trial print is verified.

[0007] According to some embodiments of this application, the supply risk quantification index is calculated through the following process: Real-time monitoring of whether the production progress information deviates from the predetermined production plan, and whether the transportation environment information exceeds the preset safety threshold; When an event that deviates from or exceeds the limit is detected, a preset initial risk score is assigned to each event; The initial risk score is multiplied by a scarcity coefficient determined based on the scarcity of the printed materials and an urgency coefficient determined based on the urgency of the printed order delivery to calculate the contribution value of the event corresponding to the initial risk score. The contribution values ​​of all current events are accumulated and calculated to generate and continuously update a numerical supply risk quantification index; and when the supply risk quantification index exceeds a preset risk warning threshold, a supply risk warning is triggered.

[0008] According to some embodiments of this application, the step of acquiring and calculating a printability prediction value for predicting printing effects and a quality risk quantification value reflecting potential quality deviations based on the microscopic physical property information of the printed materials when the printed materials are put into storage includes: When the printed materials are put into storage, the surface of the printed materials is scanned using a non-contact detection device to obtain information on the microscopic physical properties of the surface of the printed materials; wherein, the non-contact detection device includes: a hyperspectral imaging device for obtaining spectral reflectance data of the surface of the printed materials, and / or a laser scattering scanning device for obtaining microscopic morphology data of the surface of the printed materials. Based on a predefined physical property conversion model, the microscopic physical property information is converted into printability prediction values ​​that are directly related to the printing effect; wherein, the printability prediction values ​​include one or more of the following: ink penetration depth prediction value, dot gain trend prediction value, and drying time prediction value; the physical property conversion model defines the mapping relationship between the microscopic physical property information and the printability prediction values. The predicted printability value is compared with a preset standard printability parameter range. Based on the position of the predicted printability value within the standard printability parameter range or the direction and degree of deviation from the standard printability parameter range, a numerical quality risk quantification value is calculated and output.

[0009] According to some embodiments of this application, the non-contact detection device further includes a surface acoustic wave resonance scanning device; the surface acoustic wave resonance scanning device is used to emit high-frequency sound waves to the surface of the printed material and acquire acoustic resonance characteristic data of the surface of the printed material as reference information for calibrating the microscopic physical characteristic information.

[0010] According to some embodiments of this application, the calibration process for the microscopic physical property information includes: Based on preset association rules, the acoustic resonance characteristic data and the microscopic physical characteristic information are verified for consistency to obtain verification results; wherein, the association rules define the expected correspondence between the acoustic resonance characteristic data and the microscopic physical characteristic information under interference-free conditions; When the verification result determines that the microscopic physical property information is deviated due to environmental interference, the impact of the environmental interference on the microscopic physical property information is estimated based on the deviation between the acoustic resonance characteristic data and the microscopic physical property information, combined with a preset interference model. The influence quantity is subtracted from the microscopic physical property information to obtain the corrected microscopic physical property information, which is used as the input data for the physical property conversion model.

[0011] According to some embodiments of this application, the printed material is pre-installed with an internal stress feedback element during the manufacturing process; The microscopic physical properties information of the printed material includes: detecting the physical signal changes generated by the internal stress feedback element due to the internal micro-stress of the printed material through non-contact sensing scanning; The quality risk quantification value is: the intrinsic fatigue index of the printed material generated based on the internal micro-stress level reflected by the changes in the physical signal.

[0012] According to some embodiments of this application, the adjustment of the production scheduling strategy includes: When the supply risk quantification index exceeds the preset supply risk threshold, the production priority of the printing order in the current production queue is reduced, and a suggestion to start sourcing spare materials or alternative suppliers is sent. When the quality risk quantification value exceeds the preset quality risk threshold, when scheduling the production of the printing order, an additional buffer time for small-batch trial printing and parameter debugging is reserved in the planned production time. The target printing equipment for matching includes: Based on the quality risk quantification value, a set of candidate printing equipment that meets the quality requirements is selected for the printing order; Based on the material characteristics indicated by the printability prediction value, a target printing machine is matched from the candidate printing machine set; wherein, when the printability prediction value indicates that the ink absorption of the material deviates from the standard, a printing machine equipped with an automatic ink volume control system is preferentially matched as the target printing machine; and / or when the printability prediction value indicates that the drying characteristics of the material are abnormal, a printing machine with the ability to quickly adjust drying parameters is preferentially matched as the target printing machine.

[0013] According to some embodiments of this application, the step of generating adjustment instructions for printing parameters of the target printing equipment based on the printability prediction value includes: Based on the predicted printability value and combined with the target printing equipment's control capabilities in ink, pressure, and drying, adjustment instructions for the printing parameters of the target printing equipment are generated; wherein, the adjustment instructions include at least one parameter among ink viscosity, printing pressure, and drying temperature, and the adjustment amount relative to the preset standard printing parameters of the color box printing order.

[0014] According to some embodiments of this application, the step of controlling the target printing equipment to perform a trial print according to the adjustment instruction and verifying the quality of the trial print includes: According to the adjustment instructions, the target printing equipment is controlled to complete the setting of printing parameters and perform a trial print; During the trial printing process, the ink uniformity and dot reproduction of the test prints are monitored in real time using an online visual inspection device, and the color values ​​of the standard color blocks specified in the printing order on the test prints are measured in real time using a spectrophotometer. The ink uniformity, dot reproduction, and color values ​​of the standard color blocks are used as real-time quality data. The real-time quality data is compared with the quality standards of the printing order in real time to obtain the comparison results. If the comparison results meet the standards, then formal production will be approved to begin. Otherwise, the reasons for the deviation are analyzed based on the real-time quality data and the printability prediction value, and new adjustment instructions are generated until the comparison results meet the standards.

[0015] Secondly, this application provides a digital management system for the color box printing process, used to manage color box printing orders that depend on printing materials, the system comprising: The information acquisition module is used to acquire and calculate the supply risk quantification index of the printing material based on the supply chain information of the printing material; wherein, the supply chain information includes production progress information acquired from the production management system of the supplier of the printing material, and transportation environment information acquired from environmental sensors attached to the transport carrier of the printing material. The feature acquisition module is used to acquire and calculate, based on the microscopic physical characteristic information of the printed material when the printed material is put into storage, a printability prediction value for predicting the printing effect and a quality risk quantification value reflecting potential quality deviation. The equipment matching module is used to adjust the production scheduling strategy and match the target printing equipment for color box printing orders that use the printing materials, based on the supply risk quantification index, printability prediction value and quality risk quantification value. The parameter setting module is used to generate adjustment instructions for printing parameters of the target printing equipment based on the printability prediction value. The verification module is used to control the target printing equipment to perform a trial print according to the adjustment instructions, and to verify the quality of the trial print.

[0016] The technical solution according to the embodiments of this application has at least the following beneficial effects: The digital management method for the color box printing process in this application achieves early warning and avoidance of supply and quality risks through in-depth mining and intelligent analysis of material supply chain information and micro-physical characteristics; it achieves pre-identification and quantification of quality risks through real-time detection of micro-characteristics and prediction of printability; and it avoids batch printing failures and waste of high-value materials due to deviations in the micro-characteristics of materials by combining adaptive parameter adjustment based on predicted values ​​and closed-loop trial printing verification.

[0017] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0018] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0019] Figure 1 This is a flowchart illustrating a digital management method for the color box printing process provided in an embodiment of this application.

[0020] Figure 2 This is a schematic diagram of the architecture of a digital management system for the color box printing process provided in an embodiment of this application. Detailed Implementation

[0021] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0022] In the color box printing industry, digital management systems play a crucial role in production scheduling and resource optimization. However, traditional digital management systems for color box printing struggle to handle the challenges of high-value, customized orders, especially when dealing with orders that rely on special materials. They are unable to perceive and accurately predict material supply and quality risks in real time. Specifically, the system struggles to obtain transparent information across the entire supply chain (production progress, transportation environment) to assess supply risks, and it also cannot predict printability fluctuations and quality risks by detecting microscopic characteristics (such as ink permeability) when materials are received into the warehouse. Due to the lack of this forward-looking data, the system can only rely on static parameters for lagging scheduling and resource matching. Once material supply is delayed or has hidden defects, it can easily lead to production interruptions, equipment idleness, substandard quality, and material waste, increasing costs and defeating the original purpose of digital management: improving efficiency and ensuring on-time delivery.

[0023] In this regard, such as Figure 1 As shown, this application discloses a digital management method for the color box printing process, used to manage color box printing orders that depend on printing materials. The method includes the following steps: S110, Obtain and calculate the supply risk quantification index of the printing material based on the supply chain information of the printing material; wherein, the supply chain information includes production progress information obtained from the production management system of the supplier of the printing material, and transportation environment information obtained from environmental sensors attached to the transport carrier of the printing material. S120, when the printed materials are put into storage, the microscopic physical characteristics information of the printed materials are obtained and the printability prediction value for predicting the printing effect and the quality risk quantification value reflecting potential quality deviation are calculated. S130, based on the supply risk quantification index, printability prediction value and quality risk quantification value, adjust the production scheduling strategy and match the target printing equipment for color box printing orders using the printing materials; S140, Based on the printability prediction value, generate adjustment instructions for the printing parameters of the target printing equipment; S150, according to the adjustment instruction, control the target printing equipment to perform a trial print and verify the quality of the trial print.

[0024] First, the key terms and implementation environment of this application will be explained.

[0025] "Printing materials" refers to various substrates used for color box printing, such as paper, cardboard, and composite materials, whose physical and chemical properties directly affect the printing effect.

[0026] "Supply chain information" covers data on the entire process of printed materials from production source to warehousing, including but not limited to production progress, transportation status, environmental conditions, etc. This information is the basis for assessing supply risks.

[0027] The "Supply Risk Quantification Index" is a numerical indicator used to measure the degree of risk that printed materials may encounter during the supply process, such as delays and interruptions.

[0028] "Microscopic physical property information" refers to the physical property data of the surface or interior of the printed material at the microscopic level, such as surface roughness, porosity, and fiber structure. These properties are directly related to the printability of the material.

[0029] "Printability prediction value" is a numerical value that predicts the adaptability of a material during the printing process based on information about its microscopic physical properties, such as ink absorption and drying speed.

[0030] "Quality Risk Quantification Value" is a numerical indicator used to assess the extent to which printed materials have potential quality defects or deviate from standard ranges when they are received into the warehouse.

[0031] "Production scheduling strategy" refers to the adjustment and optimization of production plans, process arrangements, priorities, etc., based on factors such as order demand, material status, and equipment capacity.

[0032] "Target printing equipment" refers to the printing machine that is ultimately selected to perform the printing task based on the characteristics of the materials and the order requirements, after screening and matching.

[0033] "Printing parameter adjustment instructions" are specific operational instructions generated based on printability prediction values ​​for the target printing equipment, used to optimize printing results, such as ink volume, pressure, drying temperature, etc.

[0034] The implementation environment of this application is typically a digital management platform integrating various sensors, data acquisition systems, data processing servers, and printing equipment control interfaces. This platform can receive and process data from various links in the supply chain in real time, analyze and make decisions through algorithmic models, and finally issue instructions to the printing equipment for execution.

[0035] Supply chain information is crucial for assessing the stability of material supply. For example, production plans and actual production progress reports provided by suppliers, as well as logistics tracking information from transportation companies, can be entered. Supply chain information can include production batches, estimated completion dates, actual completion dates, shipping times, and estimated arrival times. Furthermore, environmental sensors, such as temperature, humidity, and vibration sensors, can be installed on the transport vehicles to monitor environmental conditions in real time. This sensor data can be periodically uploaded to the management system as part of the transportation environmental information. Based on supply chain information, a supply risk quantification index can be calculated. For example, a simple rule can be set: if production delays exceed a preset number of days, or if temperatures exceed safe ranges during transportation, the supply risk quantification index is increased by a fixed value. Alternatively, risk scores can be calculated linearly or non-linearly based on the severity of delays or the extent of environmental exceedances, and then accumulated to obtain the total supply risk quantification index.

[0036] The microscopic physical properties of printing materials are fundamental to predicting printing results and assessing quality risks. For example, upon receiving printing materials, sampling inspections can be used to measure surface roughness, porosity, and ink absorption. This measurement data can be input into the system as microscopic physical property information. Based on the acquired microscopic physical property information, preset empirical formulas or lookup tables can be used to calculate printability prediction values. For example, ink penetration depth can be predicted based on the material's ink absorption data; dot gain trends can be predicted based on surface roughness data. Simultaneously, these predicted values ​​can be compared with preset standard ranges. If they exceed the range, a quality risk quantification value is calculated based on the degree of deviation. For example, the greater the deviation, the higher the quality risk quantification value.

[0037] For box printing orders using printed materials, the system adjusts production scheduling strategies and matches target printing equipment. For example, when the supply risk quantification index is high, the system can suggest lowering the production priority of the order or initiating a spare material procurement process. When the quality risk quantification value is high, the system can suggest reserving additional trial printing and debugging time in the production plan. When matching target printing equipment, a set of printing equipment that meets basic conditions can be filtered based on the printing order's color requirements, printing accuracy requirements, etc. Then, based on printability prediction values, such as the material's ink absorption and drying speed, the most suitable printing equipment is selected from this set. For example, if the material has poor ink absorption, printing presses equipped with pre-treatment units or special ink path systems are preferred.

[0038] In the process of generating adjustment instructions for printing parameters specific to the target printing equipment, customized parameter adjustments are a crucial step in ensuring print quality. For example, if the material is predicted to have high ink absorption, it might be advisable to appropriately increase the ink viscosity or decrease the printing pressure. If the material is predicted to have a long drying time, it might be advisable to appropriately increase the drying temperature or extend the drying time.

[0039] Trial printing verification is essential to ensure the quality of final production. For example, operators can manually set the corresponding printing parameters on the target printing equipment according to the adjustment instructions generated by the system, and then perform small-batch trial printing. After the trial printing is completed, tools such as magnifying glasses and color charts can be used to conduct a preliminary evaluation of the ink uniformity, dot reproduction, and color accuracy of the trial prints. If the evaluation results meet the requirements, formal production is approved to begin; otherwise, the causes of the problems found in the trial printing are analyzed, the printing parameters are readjusted, and trial printing is performed again until the quality standards are met.

[0040] This application operates on a closed-loop control logic of "state perception - behavior prediction - decision compensation." Before printing materials are put into production, supply chain information and microscopic physical characteristic information are converted into a series of quantifiable future behavior predictions and risk indices. Instead of relying on fixed experience, these dynamically generated predictions and risk indices are used as the sole input to drive all subsequent production decisions, including dynamically adjusting production plans, selecting the most suitable production equipment, and calculating and pre-setting compensatory printing process parameters. Finally, through a mandatory small-batch trial production closed loop that verifies parameters with actual printing results, the quality stability of large-scale production is ensured, thereby transforming traditionally uncontrollable material variables into controllable parameters that can be predicted, quantified, and compensated for before production.

[0041] In summary, the digital management method for the color box printing process proposed in this application achieves early warning and avoidance of supply and quality risks through in-depth mining and intelligent analysis of material supply chain information and microscopic physical characteristics. By real-time detection of microscopic characteristics and prediction of printability, it enables the pre-identification and quantification of quality risks. Combined with a closed-loop system of parameter adaptive adjustment based on predicted values ​​and trial printing verification, it avoids batch printing failures and waste of high-value materials due to deviations in material microscopic characteristics. This makes the formulation of production scheduling strategies and the matching of printing equipment more scientific and reasonable, and the adjustment of printing parameters more refined. It reduces production plan disruptions and equipment idling caused by unexpected delays in printing materials, improves the reliability of production plans and resource utilization, and enhances the production efficiency, product quality, and customer satisfaction of color box printing.

[0042] In a specific embodiment of this application, the supply risk quantification index is preferably calculated through the following process: Real-time monitoring of whether the production progress information deviates from the predetermined production plan, and whether the transportation environment information exceeds the preset safety threshold; When an event that deviates from or exceeds the limit is detected, a preset initial risk score is assigned to each event; The initial risk score is multiplied by a scarcity coefficient determined based on the scarcity of the printed materials and an urgency coefficient determined based on the urgency of the printed order delivery to calculate the contribution value of the event corresponding to the initial risk score. The contribution values ​​of all current events are accumulated and calculated to generate and continuously update a numerical supply risk quantification index; and when the supply risk quantification index exceeds a preset risk warning threshold, a supply risk warning is triggered.

[0043] The automated system continuously tracks production progress information obtained from the supplier's production management system, such as production batches, completion rates, and delays, and compares this information with the predetermined production plan. Simultaneously, it performs real-time analysis of transportation environment information, such as temperature, humidity, and vibration, to identify potential risks during transportation.

[0044] The initial risk score assigned to the identified event can be preset based on the severity or potential impact of the event. For example, a one-day production delay may correspond to a lower score, while a severe temperature exceedance during transportation may correspond to a higher score.

[0045] The scarcity coefficient reflects the substitutability or supply tightness of the printed materials in the market; the higher the scarcity, the larger the coefficient. The urgency coefficient reflects the order's sensitivity to delivery time; the more urgent the delivery, the larger the coefficient. This makes the calculated contribution values ​​of each event more targeted and prioritized.

[0046] The supply risk quantification index is a dynamically changing indicator that reflects the overall risk level of the current supply chain in real time. Furthermore, when the supply risk quantification index exceeds a preset risk warning threshold, the system will automatically trigger a supply risk warning, for example, by sending a notification or changing the interface color, to alert management and prompt them to take appropriate measures.

[0047] This application's solution introduces a mechanism of real-time monitoring, event score allocation, multi-dimensional coefficient weighting, and continuous accumulation and updating. Real-time monitoring ensures immediate awareness of anomalies at each stage of the supply chain; the initial risk score provides a basic risk assessment for different types of events; and the introduction of scarcity and urgency coefficients allows risk assessment to fully consider the strategic importance of the materials themselves and the business priority of orders, thus closely linking the risk quantification results with the actual business impact. Through continuous accumulation and updating, this supply risk quantification index can comprehensively reflect the current overall risk situation of the supply chain and trigger early warnings in a timely manner, providing data support for subsequent production scheduling adjustments.

[0048] In some embodiments of this application, the step of acquiring and calculating a printability prediction value for predicting printing effects and a quality risk quantification value reflecting potential quality deviations based on the microscopic physical characteristics of the printing materials upon their receipt into the warehouse preferably includes: When the printed materials are put into storage, the surface of the printed materials is scanned using a non-contact detection device to obtain information on the microscopic physical properties of the surface of the printed materials; wherein, the non-contact detection device includes: a hyperspectral imaging device for obtaining spectral reflectance data of the surface of the printed materials, and / or a laser scattering scanning device for obtaining microscopic morphology data of the surface of the printed materials. Based on a predefined physical property conversion model, the microscopic physical property information is converted into printability prediction values ​​that are directly related to the printing effect; wherein, the printability prediction values ​​include one or more of the following: ink penetration depth prediction value, dot gain trend prediction value, and drying time prediction value; the physical property conversion model defines the mapping relationship between the microscopic physical property information and the printability prediction values. The predicted printability value is compared with a preset standard printability parameter range. Based on the position of the predicted printability value within the standard printability parameter range or the direction and degree of deviation from the standard printability parameter range, a numerical quality risk quantification value is calculated and output.

[0049] Non-contact testing devices are equipment that can measure the physical properties of printed materials without touching their surface. Among them, hyperspectral imaging devices capture the reflectance spectra of the printed material's surface at different wavelengths to obtain spectral reflectance data, reflecting information such as the material's chemical composition and surface coating uniformity. Laser scattering scanning devices emit laser beams and analyze the scattering patterns of the printed material's surface to obtain microscopic morphological data, such as roughness and porosity. This microscopic physical property information is a key factor affecting printing quality.

[0050] A physical property conversion model can be understood as a pre-established mathematical model or algorithm that transforms microscopic physical property information obtained from non-contact detection devices, such as spectral reflectance data and microstructure data, into printability prediction values ​​directly related to the actual printing process. The physical property conversion model defines the quantitative mapping relationship between microscopic physical properties and printability. For example, by analyzing pore structure and surface energy, the ink penetration depth can be predicted; by assessing surface roughness and ink absorption, the dot gain trend during printing can be predicted; and by analyzing hygroscopicity and desiccant content, the ink drying time can be predicted. These printability prediction values ​​provide an objective quantitative assessment of the printability performance of printed materials.

[0051] A standard printability parameter range refers to a pre-defined set of acceptable printability parameter intervals for a specific printed material or printing order. Once the predicted printability value is calculated, it is compared to this standard range. This comparison determines whether the predicted value falls within the standard range, and the direction and extent of any deviation. From this, a numerical quality risk quantification value can be calculated and output, which intuitively reflects the potential risk of quality problems occurring during the printing process. For example, if the predicted ink penetration depth is lower than the standard range, it may lead to poor ink adhesion or difficulty in drying, resulting in a higher quality risk quantification value.

[0052] The technical solution of this application can predict the printability of materials and assess quality risks at the material receiving stage, thereby enabling early warning and proactive management of the color box printing process, improving the efficiency and accuracy of quality control, helping to reduce production interruptions, increased scrap rates and delivery delays caused by quality problems of printing materials, and thus improving overall production efficiency and product quality stability.

[0053] In a further embodiment of this application, the non-contact detection device preferably includes a surface acoustic wave resonance scanning device; the surface acoustic wave resonance scanning device is used to emit high-frequency sound waves to the surface of the printed material and acquire acoustic resonance characteristic data of the surface of the printed material as reference information for calibrating the microscopic physical characteristic information.

[0054] Surface acoustic wave (SAW) resonance scanning devices are equipment that utilize the propagation and reflection characteristics of sound waves on material surfaces for detection. Their working principle involves emitting high-frequency sound waves of a specific frequency onto the surface of the printed material. When these sound waves encounter the microstructure within the material or on its surface, reflection, scattering, or resonance occur. By analyzing these reflected or resonant signals, acoustic resonance characteristic data of the printed material can be obtained. This data reflects physical properties such as the material's elastic modulus, density, and internal structural uniformity, and typically exhibits strong robustness to environmental interference (such as light and dust). Therefore, using the acquired acoustic resonance characteristic data as a reference for calibrating microscopic physical characteristic information aims to provide an independent and reliable verification or correction benchmark for the microscopic physical characteristic information acquired by hyperspectral imaging devices and / or laser scattering scanning devices.

[0055] This application's solution, by introducing a surface acoustic wave (SAW) resonance scanning device, can acquire acoustic resonance characteristic data of printed materials. Since acoustic detection methods are less sensitive to certain environmental factors (such as light and surface dust) than optical or laser detection methods, the obtained acoustic resonance characteristic data can serve as an independent and relatively stable reference. When the microscopic physical characteristic information acquired by hyperspectral imaging devices or laser scattering scanning devices may be deviated due to environmental interference, the acoustic resonance characteristic data can be used for comparison and verification. For example, by establishing a correlation model between microscopic physical characteristic information and acoustic resonance characteristic data, it is possible to assess whether there are anomalies in the optical or laser data and correct the microscopic physical characteristic information accordingly, thereby ensuring that the data input into the physical characteristic conversion model is more accurate and reliable.

[0056] In a further embodiment of this application, the calibration process for the microscopic physical property information preferably includes: Based on preset association rules, the acoustic resonance characteristic data and the microscopic physical characteristic information are verified for consistency to obtain verification results; wherein, the association rules define the expected correspondence between the acoustic resonance characteristic data and the microscopic physical characteristic information under interference-free conditions; When the verification result determines that the microscopic physical property information is deviated due to environmental interference, the impact of the environmental interference on the microscopic physical property information is estimated based on the deviation between the acoustic resonance characteristic data and the microscopic physical property information, combined with a preset interference model. The influence quantity is subtracted from the microscopic physical property information to obtain the corrected microscopic physical property information, which is used as the input data for the physical property conversion model.

[0057] The association rules are pre-established, defining the expected correspondence between the acoustic resonance characteristic data and the microscopic physical characteristic information under ideal conditions without environmental interference. For example, for a certain type of printed material, there is a stable mapping relationship between the response at a preset acoustic resonance frequency and microscopic physical characteristics such as surface roughness, porosity, or density. By comparing the actually measured data with these expected correspondences, it is possible to preliminarily determine whether any anomalies exist.

[0058] Environmental interference may include various factors such as temperature, humidity, air pressure, and vibration, which can cause drift or distortion in the measurement results of hyperspectral imaging devices or laser scattering scanning devices. The interference model can be an empirical model or a machine learning-based model, which quantifies the specific impact of different environmental interference factors on the measurement of microscopic physical property information by analyzing historical data or experimental results. Finally, the impact factor is subtracted from the microscopic physical property information to obtain the corrected microscopic physical property information. This corrected microscopic physical property information is considered more accurate and reliable data and is used as input data for the physical property conversion model to ensure that subsequent calculations of printability prediction values ​​and quality risk quantification values ​​are based on high-quality raw data.

[0059] This application effectively eliminates the impact of environmental interference on measurement results by introducing acoustic resonance characteristic data as a calibration reference and combining it with an interference model for deviation estimation and correction. This ensures that subsequent calculations of printability prediction values ​​and quality risk quantification values ​​are based on more accurate data, thereby enabling more precise adjustments to production scheduling strategies and matching of target printing equipment. This improves the overall digital management level of the color box printing process and the quality stability of printed materials.

[0060] The following is a specific example to illustrate this.

[0061] Suppose that when printed materials are received into the warehouse, the ambient temperature in the workshop fluctuates, which may cause a slight drift in the surface spectral reflectance data of the printed materials acquired by the hyperspectral imaging device. Simultaneously, the surface acoustic wave (SAW) resonance scanning device continuously acquires the acoustic resonance characteristic data of the material. First, based on preset association rules, the consistency between the hyperspectral data and the acoustic resonance data is verified. For example, if the association rule indicates that a specific spectral absorption peak should correspond to a certain acoustic resonance frequency at normal temperature, but the verification results show a deviation, it is determined that the microscopic physical characteristic information is deviated due to environmental interference (temperature fluctuation). Further, a preset temperature interference model is invoked. This model, possibly established using experimental data, describes the relationship between temperature changes and the drift of the spectral reflectance data. Based on the current deviation between the acoustic resonance characteristic data and the hyperspectral data, combined with this temperature interference model, the impact of temperature fluctuations on the spectral reflectance data is estimated. For example, the estimated reflectance value for a certain band is 0.5% higher. Subsequently, this 0.5% impact is subtracted from the original hyperspectral reflectance data to obtain the corrected spectral reflectance data. These corrected data will be used as input to the physical property conversion model to calculate more accurate ink penetration depth predictions and dot gain trend predictions, thereby avoiding misjudgments of printing parameters caused by fluctuations in ambient temperature.

[0062] In embodiments of this application, the printed material preferably has an internal stress feedback element pre-installed during the manufacturing process; the microscopic physical property information of the printed material includes: detecting the physical signal change of the internal stress feedback element due to the internal micro-stress of the printed material by non-contact induction scanning; the quality risk quantification value is: the intrinsic fatigue index of the printed material generated based on the internal micro-stress level reflected by the physical signal change.

[0063] "Internal stress feedback elements" refer to special structures or micro-sensors intentionally embedded or integrated into the printed material during the manufacturing process. These elements are designed to produce detectable physical responses to mechanical stress or strain within the material, such as changes in resistance, capacitance, optical properties, or acoustic properties. The aim is to provide a non-invasive way to monitor the internal state of the material. "Non-contact inductive scanning" refers to a technique that detects changes in the physical signals of internal stress feedback elements without direct contact with the printed material. This can include, but is not limited to, electromagnetic wave scanning, ultrasonic scanning, infrared spectroscopy analysis, or magnetic resonance imaging, depending on the nature of the internal stress feedback element and its response mechanism. This method avoids physical damage to the material while achieving real-time or near-real-time monitoring of its internal state. "Internal micro-stress" refers to localized stresses existing at the microstructural level within the printed material. These stresses may be caused by uneven cooling, drying, calendering during manufacturing, or by the accumulation of external loads during storage and transportation. Internal micro-stress is a potential factor leading to material fatigue, deformation, or cracking. "Physical signal change" refers to the measurable alteration of the physical properties (such as electrical, optical, and acoustic signals) of an internal stress feedback element when subjected to internal micro-stress. These changes directly reflect the magnitude and distribution of micro-stress within the material. The "intrinsic fatigue index" is a quantitative indicator used to assess the potential fatigue damage to printed materials caused by the accumulation of internal micro-stress. A higher index indicates poorer internal structural integrity of the material and a greater risk of quality problems during subsequent printing or use.

[0064] This application's solution, by pre-installing internal stress feedback elements during the printing material manufacturing stage, allows for a deeper assessment of the material's intrinsic quality, moving beyond traditional surface inspection or macroscopic physical property analysis to directly sensing the material's internal microscopic stress state. While microscopic physical property information primarily focuses on surface smoothness, porosity, and ink absorption, these, though crucial for printing quality, may not reveal deep-seated structural fatigue or potential defects. Through non-contact sensing scanning, changes in the physical signals generated by these internal stress feedback elements due to internal micro-stress can be detected in real-time or upon warehousing, thereby obtaining precise data on the material's internal stress distribution and cumulative damage. Based on the internal micro-stress level reflected by these physical signal changes, a numerical intrinsic fatigue index can be generated, which more comprehensively and accurately reflects the potential quality deviations and long-term reliability of the printed material.

[0065] The following is a specific example to illustrate this.

[0066] Imagine that during the manufacturing of printing materials for color boxes (such as high-strength cardboard), a micro-sensor array with piezoelectric effect is uniformly embedded into the internal fiber layer of the cardboard using micro-encapsulation technology. These micro-sensors, acting as internal stress feedback elements, generate measurable charge or voltage signals in response to changes in the micro-stress within the cardboard. When the printing materials are received, a non-contact electrostatic induction scanning device can rapidly scan the entire batch of cardboard. This scanning device can accurately detect the micro-stress levels in different areas of the cardboard by sensing changes in the charge or voltage signals of the micro-sensor array within the cardboard. For example, if a batch of cardboard experiences uneven pressure or vibration during manufacturing or transportation, its internal micro-sensors will show abnormal changes in electrical signals in localized areas, indicating high internal micro-stress. Based on the internal micro-stress levels reflected by these physical signal changes, the system can calculate a numerical intrinsic fatigue index. If this intrinsic fatigue index exceeds a preset threshold, it indicates a high inherent quality risk in this batch of cardboard, potentially leading to unexpected deformation, cracking, or poor ink adhesion during printing. In this situation, the system can adjust its production scheduling strategy accordingly, such as allocating the batch of materials to orders with low requirements for the intrinsic quality of the materials, or performing additional pre-processing before printing, thereby effectively avoiding potential quality problems.

[0067] Based on the above implementation methods, the preferred method for adjusting the production scheduling strategy includes: When the supply risk quantification index exceeds the preset supply risk threshold, the production priority of the printing order in the current production queue is reduced, and a suggestion to start using spare materials or find alternative suppliers is sent. When the quality risk quantification value exceeds the preset quality risk threshold, when scheduling production for printing orders, an extra buffer time is reserved in the planned production time for small-batch trial printing and parameter debugging. The target printing equipment for matching preferably includes: Based on the quality risk quantification value, a set of candidate printing equipment that meets the quality requirements is selected for printing orders; Based on the material characteristics indicated by the printability prediction value, a target printing machine is matched from the candidate printing machine set; wherein, when the printability prediction value indicates that the material's ink absorption deviates from the standard, a printing machine equipped with an automatic ink volume control system is preferentially matched as the target printing machine; and / or when the printability prediction value indicates that the material's drying characteristics are abnormal, a printing machine with the ability to quickly adjust drying parameters is preferentially matched as the target printing machine.

[0068] The production scheduling strategy aims to dynamically adjust order production arrangements based on material supply and quality risks. When the supply risk quantification index rises due to factors such as supplier production delays or abnormal transportation conditions, exceeding a preset supply risk threshold, the system automatically lowers the priority of the printing order in the production queue. Simultaneously, to avoid production halts caused by material supply interruptions, suggestions to initiate backup material procurement or find alternative suppliers will be sent to ensure production continuity. Furthermore, when the quality risk quantification value of the printing materials rises due to factors such as potential quality deviations indicated by microscopic physical characteristic testing, exceeding a preset quality risk threshold, the system will allocate an additional buffer period beyond the original planned production time when scheduling the printing order. This buffer period allows for small-batch trial printing and fine-tuning of printing parameters to address potential quality issues and ensure the quality of the final product.

[0069] The target printing equipment matching function aims to select the most suitable printing equipment based on the actual characteristics of the material and potential quality risks. First, the system filters all available printing equipment according to the quantified quality risk value, constructing a candidate set of printing equipment that meets the quality requirements of the printing order. For example, for orders with high quality risks, well-maintained and higher-precision equipment may be prioritized. Further, the system matches the most suitable target printing equipment from this candidate set based on the specific material characteristics indicated by the printability prediction value. For example, if the printability prediction value indicates that the ink absorption of the printing material deviates from the standard, meaning that the material's ink absorption capacity may be too strong or too weak, the system will prioritize matching a printing press equipped with an automatic ink volume control system, which can automatically adjust the ink supply according to the material's ink absorption characteristics, thereby ensuring ink uniformity and print quality. As another example, if the printability prediction value indicates abnormal drying characteristics of the material, meaning that the ink drying speed on the material surface may be too fast or too slow, the system will prioritize matching a printing press with rapid adjustment capabilities for drying parameters, which can flexibly adjust the drying temperature and time to adapt to the drying needs of the printing material, avoiding problems such as ink layer adhesion or incomplete drying.

[0070] This application's solution, through dynamic adjustment of production scheduling strategies, effectively reduces production interruptions and scrap rates caused by material supply and quality risks, ensuring on-time order delivery and product quality. Simultaneously, based on material printability predictions, it intelligently matches target printing equipment, enabling the printing process to better adapt to the characteristics of different materials, thereby optimizing printing results and reducing trial-and-error costs and resource waste. This proactive, preventative management approach not only improves production efficiency and product qualification rates but also enhances the resilience and responsiveness of the entire supply chain.

[0071] In some embodiments of this application, the step of generating adjustment instructions for printing parameters of the target printing equipment based on the printability prediction value preferably includes: Based on the predicted printability value and combined with the target printing equipment's control capabilities in ink, pressure, and drying, adjustment instructions for the printing parameters of the target printing equipment are generated; wherein, the adjustment instructions include at least one parameter among ink viscosity, printing pressure, and drying temperature, and the adjustment amount relative to the preset standard printing parameters of the color box printing order.

[0072] Control capability refers to the performance indicators of a target printing equipment, including its adjustable range, adjustment accuracy, response speed, and stability when adjusting key printing parameters such as ink viscosity, printing pressure, and drying temperature. For example, some high-end printing presses may have more refined ink volume control systems or wider drying temperature adjustment ranges, while others may have limitations in adjusting specific parameters. By acquiring and integrating this control capability information, it can be ensured that the generated adjustment instructions are executable and optimized. The adjustment amount can be understood as the specific numerical increase or decrease of preset standard printing parameters needed during the printing process to compensate for deviations in the characteristics of the printed material. For example, if the printability prediction value indicates that the material has excessive ink absorption, it may be necessary to increase the ink viscosity; in this case, the adjustment amount is the increase in ink viscosity. The adjustment amount can be an absolute value or a percentage value, with the aim of making the actual printing effect as close as possible to the quality requirements of the color box printing order. The preset standard printing parameters are usually benchmark parameters determined under normal production conditions based on factors such as the specifications of the color box printing order, the type of ink used, and the characteristics of common materials.

[0073] This application's solution combines the predicted printability of the printing material with the specific control capabilities of the target printing equipment, achieving intelligent and precise adjustment instructions. Compared to adjustments based solely on material characteristics, this solution fully considers the actual performance limitations and advantages of the target printing equipment, thereby avoiding low production efficiency or unstable quality caused by a mismatch between instructions and equipment capabilities. This improves the success rate of the first trial print, reduces the number of trial prints and material waste, shortens production preparation time, and ultimately enhances the overall quality and production efficiency of color box printing.

[0074] The following is a specific example to illustrate this.

[0075] Suppose the printability prediction for a batch of printing materials indicates a high ink penetration depth, meaning this batch of materials has strong ink absorption, potentially leading to pale ink colors or dot gain in the printed product. In this case, the system needs to generate an adjustment instruction to compensate for this characteristic. If the target printing equipment is equipped with an advanced automatic ink volume control system with an ink viscosity adjustment range of ±10% and an adjustment accuracy of 0.1%, the system will calculate, based on the high ink penetration depth prediction, that the ink viscosity needs to be increased by 5% to ensure ink adhesion and color development on the material surface. This adjustment amount is within the equipment's ±10% adjustment range, and the 0.1% accuracy meets the 5% adjustment requirement. Therefore, the generated adjustment instruction is "Increase ink viscosity by 5%". Conversely, if the target printing equipment can only perform coarse ink volume adjustment, such as an adjustment range of ±3%, the system will generate an optimal adjustment instruction within that range, such as "Increase ink viscosity by 3%", and may prompt the operator to consider other compensatory measures. This method of adjusting instruction generation based on equipment control capabilities ensures the effectiveness and executability of the instructions.

[0076] In an embodiment of this application, the step of controlling the target printing equipment to perform a trial print according to the adjustment instruction and verifying the quality of the trial print preferably includes: According to the adjustment instructions, the target printing equipment is controlled to complete the setting of printing parameters and perform a trial print; During the trial printing process, the ink uniformity and dot reproduction of the test prints are monitored in real time using an online visual inspection device, and the color values ​​of the standard color blocks specified in the printing order on the test prints are measured in real time using a spectrophotometer. The ink uniformity, dot reproduction, and color values ​​of the standard color blocks are used as real-time quality data. The real-time quality data is compared with the quality standards of the printing order in real time to obtain the comparison results. If the comparison results meet the standards, then formal production will be approved to begin. Otherwise, the reasons for the deviation are analyzed based on the real-time quality data and the printability prediction value, and new adjustment instructions are generated until the comparison results meet the standards.

[0077] Trial printing refers to automatically or semi-automatically configuring parameters such as ink viscosity, printing pressure, and drying temperature onto the target printing equipment according to pre-generated adjustment instructions, and initiating a small-batch trial production. Its purpose is to verify the effectiveness of the adjustment instructions and the performance of the printing equipment in actual operation before formal production.

[0078] During the trial printing process, key quality indicators of the trial prints are evaluated in real time and objectively using an online visual inspection device and spectrophotometer integrated into the printing production line. The online visual inspection device captures images of the trial prints and analyzes the uniformity of ink distribution and the degree of reproduction of dot shape and size through image processing algorithms. The spectrophotometer is used to accurately measure the color space data of specific standard color patches to ensure that the colors meet the requirements of the printing order. This data is collected and used as real-time quality data to provide a basis for subsequent quality judgment.

[0079] The monitored ink uniformity, dot gain, and color values ​​of the standard color patches are compared with preset quality thresholds or target values ​​in the printing order. For example, the maximum permissible deviation for ink uniformity, the range of dot gain, and the upper limit of the Delta E value for the standard color patches can be set. The comparison results will clearly indicate whether the trial prints meet production requirements.

[0080] If the comparison results meet the standards, it means that the system has confirmed that the current printing parameters and equipment status can produce products that meet the quality requirements, thus allowing the system to safely and efficiently enter the mass production stage.

[0081] When test prints fail to meet standards, the system performs a comprehensive analysis using real-time quality data (e.g., uneven ink coverage, excessive dot gain, color deviation) and previously calculated printability predictions (e.g., ink penetration depth prediction, dot gain trend prediction, drying time prediction). For example, if real-time quality data shows uneven ink coverage and printability predictions indicate that the material's ink absorption deviates from the standard, the system may infer that the problem is caused by improper ink volume control. Based on this analysis, the system will intelligently generate new adjustment instructions, such as fine-tuning the ink volume, pressure, or drying temperature, to correct the deviation. This process will be iterative until the test print quality reaches the preset standard, ensuring the quality of the final production.

[0082] Through the above technical solutions, this application can significantly improve the quality control level and production efficiency of the color box printing process. Firstly, real-time, objective quality data acquisition avoids errors caused by subjective judgment, ensuring the accuracy of trial printing results. Secondly, the instant comparison and intelligent feedback mechanism makes the adjustment of printing parameters more scientific and efficient, quickly converging to the optimal printing state, thereby significantly shortening production preparation time and reducing material waste and energy consumption caused by trial and error. Furthermore, this adaptive adjustment capability allows the system to better cope with potential changes in material characteristics and environmental conditions, ensuring the stability and consistency of quality in formal production, ultimately improving the delivery quality of color box printing orders and customer satisfaction.

[0083] The following is a specific example to illustrate this.

[0084] Suppose a color box printing order requires the printed materials to have a specific blue color and strict requirements for ink uniformity and dot reproduction. The system first generates a set of initial printing parameter adjustment instructions based on printability prediction values, such as adjusting the ink viscosity to value X, the printing pressure to value Y, and the drying temperature to value Z. After receiving the instructions, the target printing equipment performs a small-batch trial print.

[0085] During the test print, the online visual inspection device detected a slight deviation in the ink uniformity of the test print, and the dot reproduction did not reach the ideal state. Simultaneously, the spectrophotometer measured a Delta E deviation of 3 between the color value of the blue standard patch and the standard color value required by the order, exceeding the preset quality standard of Delta E less than 2. The system immediately compared these real-time quality data with the quality standards of the printing order and determined that the comparison result was substandard.

[0086] At this point, the system does not stop directly but initiates feedback analysis. It combines real-time quality data (uneven ink coverage, poor dot reproduction, color deviation) with previously acquired printability predictions (e.g., predicting slightly high ink absorption and moderate drying time). The system analysis suggests that uneven ink coverage and color deviation may be related to ink volume control, while poor dot reproduction may be related to printing pressure or ink viscosity. Based on this analysis, the system generates new adjustment instructions, such as fine-tuning the ink viscosity to X-0.5, fine-tuning the printing pressure to Y+0.2, and keeping the drying temperature constant.

[0087] After receiving the new adjustment instructions, the target printing equipment conducted another trial print. During this second trial print, the online visual inspection device and spectrophotometer again collected real-time quality data. This data showed a significant improvement in ink uniformity, dot reproduction meeting standards, and the Delta E value of the blue standard color patch decreasing to 1.5, satisfying the quality standards. The system determined that the comparison results met the standards, thus approving the commencement of formal production. This iterative, data-driven feedback adjustment ensured precise optimization of printing parameters before formal production, preventing the production of defective products.

[0088] This application also discloses a digital management system for the color box printing process, used to manage color box printing orders that depend on printing materials, the system comprising: The information acquisition module 210 is used to acquire and calculate the supply risk quantification index of the printing material based on the supply chain information of the printing material; wherein, the supply chain information includes production progress information acquired from the production management system of the supplier of the printing material, and transportation environment information acquired from environmental sensors attached to the transport carrier of the printing material. The feature acquisition module 220 is used to acquire and calculate, based on the microscopic physical characteristic information of the printing material, a printability prediction value for predicting the printing effect and a quality risk quantification value reflecting potential quality deviation when the printing material is put into storage. Equipment matching module 230 is used to adjust the production scheduling strategy and match the target printing equipment for color box printing orders using the printing materials based on the supply risk quantification index, printability prediction value and quality risk quantification value. The parameter setting module 240 is used to generate adjustment instructions for printing parameters of the target printing equipment based on the printability prediction value. The verification module 250 is used to control the target printing equipment to perform a trial print according to the adjustment instruction, and to verify the quality of the trial print.

[0089] The information acquisition module 210 can be configured as a software component capable of communicating with external systems through a pre-defined data interface. For example, it can periodically retrieve production progress data from a supplier's production management system via an application programming interface (API), or receive real-time environmental information uploaded by environmental sensors on the transport vehicle via the Message Queuing Telemetry Transmission (MQTT) protocol. Furthermore, the information acquisition module 210 may also include a user interface allowing operators to manually input or verify supply chain-related information, such as entering production batches and expected completion dates from paper reports. In some implementations, the information acquisition module 210 can also integrate data cleaning and preliminary analysis functions to ensure the accuracy and consistency of the input data and calculate a supply risk quantification index based on pre-defined rules or algorithms.

[0090] The characteristic acquisition module 220 can be designed as a hardware unit integrated into the material receiving and inspection station. For example, it may include one or more sensor arrays for scanning printed materials. The characteristic acquisition module 220 can employ contact sensors, for example, measuring macroscopic physical properties of the material through physical contact, such as thickness and surface roughness. Alternatively, the characteristic acquisition module 220 can be equipped with a manually operated inspection device, allowing operators to sample and inspect the materials and input the measurement results into the system via a data input interface. After receiving microscopic physical characteristic information, the characteristic acquisition module 220 inputs it into a predefined calculation model to generate printability prediction values ​​and quality risk quantification values. This calculation model can be a simple algorithm based on empirical formulas or a statistical model trained using historical data.

[0091] The equipment matching module 230 can be implemented as a decision support system, the core of which is a rule engine. This rule engine adjusts the production priority of color box printing orders based on the supply risk quantification index provided by the information acquisition module, the printability prediction value and quality risk quantification value provided by the characteristic acquisition module, and preset production scheduling rules. When matching target printing equipment, the equipment matching module 230 can filter a preliminary candidate equipment list based on the basic requirements of the printing order (such as size and number of colors), and then select one or more recommended equipment from this list based on the printability prediction value, such as the ink absorption or drying characteristics of the material.

[0092] The parameter setting module 240 can be implemented as a software application that receives printability prediction values ​​from the characteristic acquisition module and, in conjunction with the model and capability parameters of the target printing equipment, generates adjustment instructions for printing parameters. For example, the parameter setting module 240 may contain a parameter adjustment database storing the mapping relationships between different material properties and printing parameter adjustment amounts. When a specific printability prediction value is received, the parameter setting module 240 queries the database and outputs corresponding adjustment suggestions for ink viscosity, printing pressure, or drying temperature. These adjustment instructions can be displayed to the operator in text form for manual input into the printing equipment.

[0093] The verification module 250 can be configured as a control unit capable of sending trial printing commands to the target printing equipment and receiving feedback information during the trial printing process. After the trial printing is completed, the verification module 250 can integrate a manual quality assessment interface, allowing operators to visually inspect or use simple measuring tools (such as magnifying glasses or color charts) to evaluate the ink uniformity, dot reproduction, and color accuracy of the trial prints, and manually enter the assessment results into the system. The verification module 250 compares the entered real-time quality data with preset quality standards and determines whether to approve formal production based on the comparison results. If the standards are not met, the verification module 250 can prompt the operator to readjust the parameters and perform another trial printing.

[0094] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0095] The preferred embodiments of this application have been described in detail above, but this application is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application.

Claims

1. A method for digital management of a carton printing process for managing carton printing orders that rely on printing materials, characterized in that, The method includes the following steps: The supply chain information of the printed material is obtained and a quantitative index of supply risk of the printed material is calculated; wherein, the supply chain information includes production progress information obtained from the production management system of the supplier of the printed material, and transportation environment information obtained from environmental sensors attached to the transport carrier of the printed material. When the printed materials are put into storage, the microscopic physical characteristics information of the printed materials are acquired and the printability prediction value used to predict the printing effect and the quality risk quantification value reflecting potential quality deviation are calculated. Based on the supply risk quantification index, printability prediction value, and quality risk quantification value, adjust the production scheduling strategy and match the target printing equipment for color box printing orders using the printing materials. Based on the printability prediction value, an adjustment instruction for the printing parameters of the target printing equipment is generated; According to the adjustment instructions, the target printing equipment is controlled to perform a trial print, and the quality of the trial print is verified.

2. The method according to claim 1, wherein, The supply risk quantification index is calculated through the following process: Real-time monitoring of whether the production progress information deviates from the predetermined production plan, and whether the transportation environment information exceeds the preset safety threshold; When an event that deviates from or exceeds the limit is detected, a preset initial risk score is assigned to each event; The initial risk score is multiplied by a scarcity coefficient determined based on the scarcity of the printed materials and an urgency coefficient determined based on the urgency of the printed order delivery to calculate the contribution value of the event corresponding to the initial risk score. The contribution value of all current events is accumulated and calculated to generate and continuously update a numerical supply risk quantification index; Furthermore, when the supply risk quantification index exceeds the preset risk warning threshold, a supply risk warning is triggered.

3. The method of claim 1, wherein, The step of acquiring and calculating a printability prediction value for predicting printing effects and a quality risk quantification value reflecting potential quality deviations based on the microscopic physical characteristics of the printed materials upon their entry into the warehouse includes: When the printed materials are put into storage, the surface of the printed materials is scanned using a non-contact detection device to obtain information on the microscopic physical properties of the surface of the printed materials; wherein, the non-contact detection device includes: a hyperspectral imaging device for obtaining spectral reflectance data of the surface of the printed materials, and / or a laser scattering scanning device for obtaining microscopic morphology data of the surface of the printed materials. Based on a predefined physical property conversion model, the microscopic physical property information is converted into printability prediction values ​​that are directly related to the printing effect; wherein, the printability prediction values ​​include one or more of the following: ink penetration depth prediction value, dot gain trend prediction value, and drying time prediction value; the physical property conversion model defines the mapping relationship between the microscopic physical property information and the printability prediction values. The predicted printability value is compared with a preset standard printability parameter range. Based on the position of the predicted printability value within the standard printability parameter range or the direction and degree of deviation from the standard printability parameter range, a numerical quality risk quantification value is calculated and output.

4. The method according to claim 3, wherein, The non-contact detection device also includes a surface acoustic wave resonance scanning device; the surface acoustic wave resonance scanning device is used to emit high-frequency sound waves to the surface of the printed material and acquire acoustic resonance characteristic data of the surface of the printed material as reference information for calibrating the microscopic physical characteristic information.

5. The method of claim 4, wherein, The calibration process for the microscopic physical property information includes: Based on preset association rules, the acoustic resonance characteristic data and the microscopic physical characteristic information are verified for consistency to obtain verification results; wherein, the association rules define the expected correspondence between the acoustic resonance characteristic data and the microscopic physical characteristic information under interference-free conditions; When the verification result determines that the microscopic physical property information is deviated due to environmental interference, the impact of the environmental interference on the microscopic physical property information is estimated based on the deviation between the acoustic resonance characteristic data and the microscopic physical property information, combined with a preset interference model. The influence quantity is subtracted from the microscopic physical property information to obtain the corrected microscopic physical property information, which is used as the input data for the physical property conversion model.

6. The method of claim 1, wherein, The printed material is pre-loaded with an internal stress feedback element during the manufacturing process; The microscopic physical properties information of the printed material includes: detecting the physical signal changes generated by the internal stress feedback element due to the internal micro-stress of the printed material through non-contact sensing scanning; The quality risk quantification value is: the intrinsic fatigue index of the printed material generated based on the internal micro-stress level reflected by the changes in the physical signal.

7. The digital management method for the color box printing process according to claim 1, characterized in that, The adjusted production scheduling strategy includes: When the supply risk quantification index exceeds the preset supply risk threshold, the production priority of the printing order in the current production queue is reduced, and a suggestion to start sourcing spare materials or alternative suppliers is sent. When the quality risk quantification value exceeds the preset quality risk threshold, when scheduling the production of the printing order, an additional buffer time for small-batch trial printing and parameter debugging is reserved in the planned production time. The target printing equipment for matching includes: Based on the quality risk quantification value, a set of candidate printing equipment that meets the quality requirements is selected for the printing order; Based on the material characteristics indicated by the printability prediction value, a target printing machine is matched from the candidate printing machine set; wherein, when the printability prediction value indicates that the ink absorption of the material deviates from the standard, a printing machine equipped with an automatic ink volume control system is preferentially matched as the target printing machine; and / or when the printability prediction value indicates that the drying characteristics of the material are abnormal, a printing machine with the ability to quickly adjust drying parameters is preferentially matched as the target printing machine.

8. The method of claim 1, wherein, The step of generating adjustment instructions for printing parameters of the target printing equipment based on the printability prediction value includes: Based on the predicted printability value and combined with the target printing equipment's control capabilities in ink, pressure, and drying, adjustment instructions for the printing parameters of the target printing equipment are generated; wherein, the adjustment instructions include at least one parameter among ink viscosity, printing pressure, and drying temperature, and the adjustment amount relative to the preset standard printing parameters of the color box printing order.

9. The method of claim 1, wherein, The step of controlling the target printing equipment to perform a trial print according to the adjustment instruction and verifying the quality of the trial print includes: According to the adjustment instructions, the target printing equipment is controlled to complete the setting of printing parameters and perform a trial print; During the trial printing process, the ink uniformity and dot reproduction of the test prints are monitored in real time using an online visual inspection device, and the color values ​​of the standard color blocks specified in the printing order on the test prints are measured in real time using a spectrophotometer. The ink uniformity, dot reproduction, and color values ​​of the standard color blocks are used as real-time quality data. The real-time quality data is compared with the quality standards of the printing order in real time to obtain the comparison results. If the comparison results meet the standards, then formal production will be approved to begin. Otherwise, the reasons for the deviation are analyzed based on the real-time quality data and the printability prediction value, and new adjustment instructions are generated until the comparison results meet the standards.

10. A digital management system for a carton printing process, for managing carton printing orders that rely on printing materials, characterized in that, The system includes: The information acquisition module is used to acquire and calculate the supply risk quantification index of the printing material based on the supply chain information of the printing material; wherein, the supply chain information includes production progress information acquired from the production management system of the supplier of the printing material, and transportation environment information acquired from environmental sensors attached to the transport carrier of the printing material. The feature acquisition module is used to acquire and calculate, based on the microscopic physical characteristic information of the printed material when the printed material is put into storage, a printability prediction value for predicting the printing effect and a quality risk quantification value reflecting potential quality deviation. The equipment matching module is used to adjust the production scheduling strategy and match the target printing equipment for color box printing orders that use the printing materials, based on the supply risk quantification index, printability prediction value and quality risk quantification value. The parameter setting module is used to generate adjustment instructions for printing parameters of the target printing equipment based on the printability prediction value. The verification module is used to control the target printing equipment to perform a trial print according to the adjustment instructions, and to verify the quality of the trial print.

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