Intelligent digital printing management method based on data processing

Abstract

The invention discloses an intelligent digital printing management method based on data processing. The method comprises the steps of receiving personalized customization order information and converting the personalized customization order information into a data structure in a preset format; performing efficient processing and sorting on the converted order data by using a big data analysis technology; making an optimal printing production plan based on a preprocessing result; the printing production plan is executed, and the efficiency and quality of the whole process are monitored; and a production plan is adjusted according to data feedback collected in the production process to ensure the consistency of data processing and production. According to the invention, the problem of efficiently, stably and accurately processing large-scale personalized customization order data in an intelligent digital printing management process can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of digital printing, and in particular to an intelligent digital printing management method based on data processing. Background Art

[0002] The core of intelligent digital printing management is to use data science technology to optimize all information processing processes involved in the entire process from receiving orders to finished product output. This method uses cloud computing, big data analysis and other technical means to centrally store and process information such as order data, production parameters, and equipment status, thereby improving the execution efficiency of printing tasks, reducing costs, and improving printing quality and satisfaction.

[0003] The challenge faced by this method is how to accurately process the increasing scale of personalized order data. As customer needs become more diversified and segmented, the business characteristics of "large-scale personalized customization" have become increasingly prominent. Massive single-copy or small-batch printing requirements mean that there are higher and more detailed requirements for the type complexity and processing accuracy of order data. In addition, data security and privacy protection must be considered to ensure that data quality and compliance issues in the entire processing chain are properly handled. This requires the design of corresponding technical mechanisms to support the automation and intelligent operation of the system while ensuring the accuracy, stability and reliability of processing to meet the specific needs of different customers. Furthermore, digital printing still has challenges in actual applications, such as quality control and defect detection, which are specifically manifested in how to accurately identify tiny defects in printed products and how to quickly and accurately locate the source of quality problems to achieve timely quality feedback and correction. Under existing technologies, especially when the printing batch is large, the speed is high and the types are complex, these problems are particularly difficult and need to be gradually improved and solved with the support of higher-precision sensor technology and advanced algorithms. Summary of the invention

[0004] The purpose of the present invention is to propose an intelligent digital printing management method based on data processing to overcome the above-mentioned deficiencies in the prior art.

[0005] To achieve the above technical objectives, the technical solution of the present invention is implemented as follows:

[0006] An intelligent digital printing management method based on data processing, the method comprising the following steps:

[0007] Step 1: Receive personalized order information and convert it into a data structure in a preset format;

[0008] Step 2: Use big data analysis technology to efficiently process and sort the converted order data;

[0009] Step 3: Formulate an optimal printing production plan based on the preprocessing results;

[0010] Step 4: Execute the printing production plan and monitor the efficiency and quality of the entire process;

[0011] Step 5: Adjust the production plan based on the data feedback collected during the production process to ensure consistency between data processing and production. Preferably, step 1 includes the following steps:

[0012] Receive personalized order information;

[0013] Parse personalized order information to extract key order fields;

[0014] Use the rule engine to select the corresponding order data conversion template based on product type;

[0015] Match personalized customized fields with conversion templates to form a standardized data structure;

[0016] The matched standardized data is verified and stored in the database queue for processing.

[0017] Preferably, step 2 comprises the following steps:

[0018] A program that performs batch import of data into a data warehouse for data structures in a preset format;

[0019] Desensitize and standardize data to eliminate noise and improve the quality of subsequent analysis;

[0020] Use stream computing technology for online processing and instant update of order sorting;

[0021] Apply machine learning algorithms to predict and prioritize the best delivery time for each order;

[0022] Output the optimization sorting results and recommended printing resource allocation plan to the system management layer.

[0023] Preferably, step three comprises the following steps:

[0024] Allocate resources to each task order based on the sorting results;

[0025] Predict the time window required to complete each task order based on historical data statistical models;

[0026] Use heuristic search methods to construct the shortest path problem to find the fastest task sequence combination;

[0027] The final execution schedule is determined by comprehensively considering the actual availability of the production line and the material preparation status;

[0028] Automatically schedule printers and other production equipment to execute planned tasks in a specified order.

[0029] Preferably, step four includes:

[0030] Smart printers automatically start and execute order processing tasks according to the pre-arranged task list;

[0031] Install sensors at every node of the processing process to collect data to monitor quality parameters;

[0032] Use closed-loop control systems to dynamically compare expected and measured results and adjust key process parameters in real time;

[0033] Arrange quality inspection robots to regularly extract completed samples and compare them with design documents to evaluate the pass rate level and record problem points; if a fault alarm occurs, the machine will be shut down immediately and a warning SMS will be sent to technicians through cloud services to repair the fault in time.

[0034] Preferably, step five includes:

[0035] The sensor automatically acquires the raw data generated by the printing equipment during the printing process;

[0036] Processing the raw data using a data cleaning and preprocessing algorithm to form a state parameter information set that can be used for analysis; constructing a dynamic model for detecting and predicting printing quality problems through machine learning based on the state parameter information set, the model being used for real-time monitoring and adjustment of printing press setting parameters;

[0037] adjusting printing parameters according to feedback provided by the model to achieve online closed-loop optimization;

[0038] Among them, the dynamic model for detecting and predicting printing quality problems constructed by machine learning based on the state parameter information set also takes into account the feature extraction of the impact of environmental factors and raw material properties on printing quality.

[0039] Preferably, the process of constructing a dynamic model based on the state parameter information set includes:

[0040] Extract printing environment temperature and humidity data as environmental factor input;

[0041] Extracting printing material characteristic data as raw material attribute input, the material characteristic data including paper thickness and glossiness;

[0042] A conditional judgment logic is set, and if the current environmental humidity is higher than a preset humidity threshold H, the next step of adjusting the setting parameters of the printing press based on the state parameter information set is executed;

[0043] A predefined machine learning algorithm is used to establish a mapping relationship model M between state parameters and quality.

[0044] Preferably, the process of adjusting the printing press setting parameters based on the state parameter information set includes:

[0045] Using model M to predict the printing quality score Q for a given combination of state parameters;

[0046] Determine whether the predicted quality score Q is lower than the predetermined minimum quality standard QS;

[0047] If Q < QS, adjust the key printing parameters according to the model's suggestions, such as pressure, inkjet head speed V, or ink volume I;

[0048] Repeat the calculation step of the quality score Q after each parameter adjustment until Q ≥ QS to stop the parameter adjustment loop.

[0049] Preferably, the basis for determining that the model suggests adjusting the key printing parameters is:

[0050] Compare whether there is a significant difference between the quality prediction Q under the current printing parameters and the standard quality score QS;

[0051] If a significant difference is detected and it is determined that the change in the current paper characteristics exceeds the preset allowable range, then dynamically calculate the adaptation parameter C according to the change in the material characteristics;

[0052] Perform a small-scale trial print using the adjusted parameters and further verify the adjustment effect E using the model;

[0053] If the verification result shows that the effect E is improved or not lower than the expected quality level T, update the machine parameter settings to the adjusted value and record the current optimization information.

[0054] Preferably, the key parameters for dynamic adjustment include printing speed, inkjet pressure, and nozzle opening size, specifically including the steps of:

[0055] Monitor the ambient temperature Tenv and compare it with the target operating temperature Topt. When Tenv ≠ Topt, adjust the ambient temperature to make it consistent with the target operating temperature;

[0056] Adjust the inkjet pressure of the printing press until the measured ink droplet volume matches the theoretical ideal value;

[0057] Observe and record whether the ink diffusion on the paper surface exceeds the allowable limit L, and correct the inkjet rate R based on these observations until the ink diffusion meets the requirements;

[0058] Perform a cross-validation process on the values of each finally determined printing parameter to ensure their consistency and achieve a stable state in mass production.

[0059] Preferably, the verification steps for whether the ink on the paper surface meets the quality requirements consist of:

[0060] Automated equipment measures actual inkjet volume v and ink penetration depth D;

[0061] If v falls within the pre-established ideal range and the penetration depth complies with the established rules, then proceed to the next stage;

[0062] Check if the condition (v belongs to [v_lower, v_upper], and D ≤ limit_depth) is true;

[0063] When all of the above checkpoints are marked as having passed successfully, the system determines that the current printing configuration is valid and the quality assurance meets the standards, and this configuration is confirmed as the optimal setting solution.

[0064] Preferably, the process of checking whether the printing quality meets the requirements and finally confirming the optimal setting scheme is as follows:

[0065] The actual measured inkjet point spread SD must be within the allowable error range δ, and further adjustments are determined based on this;

[0066] Assuming that the current condition is that the inkjet point spread SD is less than or equal to the maximum allowable error, then proceed to the next step;

[0067] The calculation logic includes evaluating whether the color uniformity Y after printing is within the acceptable range to ensure the accuracy of color reproduction;

[0068] The conclusive judgment is that if the SD meets the conditions and the color reproduction quality is proven to be good, the system will lock the optimal parameters obtained through the experiment and prepare the reference standard for the next round of printing.

[0069] Preferably, constructing a dynamic model through machine learning based on the state parameter information set includes:

[0070] Extracting key features associated with quality from the state parameter information set;

[0071] Applying a feature selection algorithm to remove non-important features to obtain a simplified feature set; and

[0072] Training an initial quality detection prediction model based on the simplified feature set;

[0073] It also includes a model update step based on a conditional judgment formula: when the accuracy of the model prediction result is lower than a predetermined threshold T, a retraining step is triggered to update the initial model M to a new version N, where T is a predetermined accuracy threshold value.

[0074] Preferably, the step of training the initial quality detection prediction model based on the simplified feature set further includes:

[0075] Select a specific machine learning algorithm and initialize the algorithm parameters P0;

[0076] Use the cross - validation technique CV to split the simplified feature set into a training subset C and a validation subset D;

[0077] Train a machine learning model based on the training subset C and obtain a preliminary prediction model M;

[0078] Apply the conditional formula: the performance B on the validation subset is less than the preset standard S (where B represents the model score and S is the baseline). If the evaluation is True, adjust the algorithm parameters Pi and restart the iteration until a satisfactory result is achieved or the maximum number of iterations is exceeded.

[0079] Preferably, during the model update process, determining whether to retrain according to specific formula conditions includes:

[0080] Set the validation data E for evaluation;

[0081] Calculate the actual performance Rn of the existing version N model based on E;

[0082] Compare with the predetermined accuracy threshold T using Rn;

[0083] If Rn < T, then execute the model update process.

[0084] Preferably, when the model needs to be updated, perform the following steps:

[0085] Reset the hyperparameter configuration Hp;

[0086] Select a new sample F and add it to the original training data;

[0087] Repeat the machine learning steps based on the enhanced training data set to generate an upgraded prediction model;

[0088] Set the iteration termination condition G: that is, Rm >= T, then stop the iteration; otherwise, readjust the hyperparameters and repeat the entire learning cycle to improve Rm until the end criterion G is met or the specified maximum learning iteration upper limit Limit is exceeded.

[0089] Preferably, the specific steps of the process of adjusting the algorithm parameters are:

[0090] Define the adjustable parameter space V of the algorithm and its range;

[0091] Determine the objective function J to evaluate the model performance under different parameter settings;

[0092] Based on the set search strategy such as grid search Grid, sample points X from V for multiple rounds of experiments to solve for the best point Z* that maximizes / minimizes J;

[0093] The initial parameter settings are updated according to the optimal point to the parameter Pz until the optimal solution of J is reached and Rn reaches or exceeds the predefined accuracy target T.

[0094] Preferably, the specific parameter range and candidate set of the sampling point X configured for searching should include but not be limited to the following types of operation modes:

[0095] Initialize a set of initial points Xo and add random noise N to these initial values ​​so that the distribution of X covers all possible exploration areas rather than just around the local minimum;

[0096] For each trial, the feedback information from the previous step is used to guide how to move to the new position Xi+1 and try to escape the current local extreme point by using the set rules such as random perturbation or heuristic search methods;

[0097] The loop is executed until all allowed attempts L are exhausted or the desired model convergence degree is reached;

[0098] After each adjustment, the corresponding historical optimal parameter set Yi and the corresponding quality score Qi are saved as basic reference and basis for selecting a more efficient path iteration in the next round.

[0099] Beneficial effects of the present invention: The present invention can solve the problem of efficiently, stably and accurately processing large-scale personalized customization order data in the intelligent digital printing management process. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] Figure 1 is a flow chart of the method described in an embodiment of the present invention;

[0101] Figure 2 It is a specific operation flow chart of the method described in the embodiment of the present invention. DETAILED DESCRIPTION

[0102] The technical solutions in the embodiments of the present invention are described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0103] like Figure 1 As shown, an intelligent digital printing management method based on data processing disclosed in an embodiment of the present invention includes the following steps:

[0104] Step S1, receiving personalized order information and converting it into a data structure in a preset format. Personalized orders may come from different customers or channels, such as email, online shopping malls, or APP orders. The initial forms of these diverse order data are diverse. For example, in a company called "Creative Printing Studio", they received 5 orders from their e-commerce platform, including two wedding invitations (each with independent content), three business card designs, and 12 T-shirts with different customizations. The first step is to unify these diverse formats of information, such as XML or JSON, which helps the back-end system to effectively process orders. Once this step is completed, the system will extract the key attributes of each order, such as printed content, size, and required printing color, so that each order is presented in the system in the same way. This not only makes the data more neat and standardized, but also prepares for the subsequent processing flow.

[0105] Step S2, use big data analysis technology to efficiently process and sort the converted order data. After all orders are converted into a consistent data format, use a framework such as Hadoop or Spark to efficiently analyze and process them in batches, and use specific rules to determine which types of projects can be given priority in production. For example, the printing company in the above example can give priority to printing orders with the same paper thickness and the same size, such as business card orders from three companies. Through these analysis methods, a large number of personalized customizations can be reasonably scheduled, and reasonable allocation and optimization can be made considering the actual equipment production capacity and material supply conditions, making the entire production line more compact and efficient. At the same time, the printing time is arranged according to the importance of customer needs, so that the delivery requirements of all customers can be met within the shortest time frame.

[0106] Step S3, formulate the optimal printing production plan based on the preprocessing results. In this stage, the most appropriate production plan is formulated through further analysis of the data information obtained in the previous step. Taking the "Creative Printing Studio" as an example, assuming that the number of colors selected for three business cards and two wedding customization orders is the same, the printing tasks that require the same paper type can be arranged first to ensure a smoother production process. In addition, at this stage, the status of the equipment (such as whether there is a need to replace the ink cartridge or equipment maintenance) and the inventory of raw materials (whether there is corresponding paper or other material inventory) must also be considered. According to the equipment status and resource situation, the arrangement of which personalized order is prioritized for production is adjusted, and the optimal production line combination is arranged under the condition of ensuring sufficient raw materials. After this stage is completed, a specific executable task table (including but not limited to specific requirements such as the number of prints, the required color type, and the expected production date) will be formed and given to each department to ensure that the production tasks can be carried out in an orderly manner. This process not only solves the scheduling conflict problem of different orders, but also greatly improves work efficiency and resource utilization, and realizes efficient allocation of production resources.

[0107] Step S4, execute the printing production plan and monitor the efficiency and quality of the entire process. After having a clear implementation plan, the actual production process will begin. The production department will follow the plan formulated by the upper level to arrange the production of each batch of products and track its progress and quality in real time. Taking wedding invitations as an example, this process may use a variety of process methods such as hot stamping and embossing to present the best appearance effect, and before production, it is necessary to confirm its final design style through pre-printed samples. In order to ensure the final quality and reduce the error rate as much as possible, the entire production chain needs to regularly sample the finished products and promptly correct or directly destroy the unqualified items to avoid losses and delays caused by subsequent rework; at the same time, the real-time production information generated by various devices is integrated into a unified data platform through the Internet of Things technology for comprehensive monitoring and analysis; abnormal problems (if any) are automatically identified through the intelligent system and fed back to the relevant personnel in the form of early warnings The email or mobile communication tool for quick resolution; finally, third-party evaluation standards can also be introduced as an important reference for measuring product quality. For example, after each production link is completed, a dedicated person will check it once and take photos and record it and upload it to the cloud for storage. These operations ensure that products meet high standards of quality, traceability and transparency throughout the entire process.

[0108] Step S5, adjust the production plan according to the data feedback collected during the production process to ensure the consistency of data processing and production. After the printing process is completed, it is necessary to further improve the original plan by analyzing the production results and make appropriate updates to meet the ever-changing demand changes. Based on the above case analysis, if the "Creative Printing Studio" finds that all the customized parts it produces are delivered to customers on time and within the expected time, it can respond more quickly when encountering similar orders in the future based on this successful experience. On the contrary, if some products are delayed due to shortage of raw materials, measures should be taken immediately, such as ordering additional materials or adjusting the production process to reduce the occurrence of such phenomena. In short, it is crucial to continuously accumulate experience and continuously optimize the plan in the production process to ensure the efficient and stable operation of the entire process.

[0109] Specifically, Figure 2 As shown, an intelligent digital printing management method based on data processing disclosed in an embodiment of the present invention includes the following steps:

[0110] A JSON file containing the customer order content is received from the client. For example, if a customer named John Doe submits a new order from the client, the order is packaged in a JSON file and transmitted to the system through a communication protocol. The key to this step is to ensure the security of the communication link and the integrity of the data packet.

[0111] The obtained JSON file is then parsed. The system reads the JSON file, identifies and separates the necessary order information items, including but not limited to the quantity of products ordered and the customer's special requests, such as specific details such as preferred color options. Assuming that the file in this case specifies that the number of copies of a specific book to be purchased is 35 and that it needs to be bound in red, these fields will be specifically extracted as important customer information for continued use in the next step.

[0112] On this basis, for further processing, a module called "rule engine" is used to select a data conversion model suitable for this product. This rule engine will select different templates for data processing according to different product characteristics. For example, the order in this example may correspond to the data structure of a customized book; therefore, the system selects specific rules to ensure that the correct template can be adapted later.

[0113] The subsequent process is to match and convert all the collected personalized demand data with the template. The system will convert the previously extracted order information into a unified data standard format according to the template format selected by the rule engine. For example, for the specific book order mentioned above, its information such as quantity and cover color preference will be accurately marked according to the selected model, which is convenient for the subsequent system to identify and further operate.

[0114] After completing the above work, a data verification process is required to prevent potential problems, confirm that there are no omissions or errors in the standardized data, and store it in the system database for use in the next task execution phase. After this example is completed, all relevant information will enter the data queue waiting to be processed after verification.

[0115] Batch import in the data warehouse is performed for a certain preset format data structure. This means reading data in a specific format from external files or other systems and storing it in batches in the data warehouse for further processing or analysis. For example, in the workflow of a printing company, the design drawing files uploaded by customers through an online platform may be in PDF format. After the customer submits these drawings, they automatically trigger a preset batch process and are imported into the company's database.

[0116] The desensitization and standardization cleaning process of the imported data mainly involves removing invalid data, reducing noise, and protecting customer privacy information. For example, after receiving the customer's design documents, the database of a printing company will automatically clean and anonymize them, delete the personal information fields in the documents, or blur the sensitive image content to ensure that the privacy protection policy is not violated.

[0117] Stream updates and sorts the latest status stored in the data warehouse to ensure that the current data warehouse content always reflects an accurate view of the current business scenario. For example, when a new order is received or the customer adjusts the existing order requirements, the processing order of the customer's order in the printing process is updated in real time.

[0118] Use a machine learning model based on historical delivery performance to estimate the best estimated completion time to complete printing and binding tasks. Assume that the machine learning system can analyze the actual execution of past orders and their associated influencing factors (including but not limited to work type, quantity and urgency, etc.), and then predict and recommend how similar requests should be scheduled and prioritized in the future to achieve the best service effect. This sorting forms the best scheduling result based on the information obtained by machine learning, and then provides an efficient production schedule for the printing department to refer to and arrange the actual production plan.

[0119] Generate the above optimized sequence results and recommended equipment or resource usage arrangements and send them to the company manager for final review and execution. Based on the results of all the above calculations, the system management will be notified of which customer's requirements should be met first and specific operation instructions, as well as estimates of the required input material quantity and machine load. This series of operations can greatly reduce the time waste and production costs caused by waiting.

[0120] The first concrete action is to allocate resources according to the sorting results. In this step, appropriate printing presses and the printing technologies used are matched to different tasks based on the priority or sorting results obtained in the previous analysis. For example, if a task has extremely high color requirements and the budget allows, it will be assigned to a high-end printing press using high-definition printing technology. This not only ensures that the quality of the finished product meets the requirements, but also improves the overall service efficiency and user satisfaction.

[0121] By analyzing historical data, we use statistical data modeling to predict the time range required to complete each task. This can be done by collecting the actual completion time of the same or similar work in the past as sample basic data, and combining it with factors such as the current environment and equipment conditions to establish a reasonable completion cycle prediction model. If the same type of work took about 12 hours to complete in the past, the estimated time this time will also be fine-tuned and evaluated based on this benchmark.

[0122] Use heuristic search techniques such as greedy algorithms to solve the problem of task sequence arrangement. The goal of this stage is to find the best schedule configuration to complete all tasks in the shortest time among all possibilities. For example, from many options, it is determined that doing tasks with smaller order quantities and faster production speeds first can more effectively compress the total time consumed by the entire production process and apply this result in practice.

[0123] After considering the actual situation of the production line itself and the material reserves, an overall execution schedule with feasible operations is formulated. For example, considering the problems such as equipment maintenance causing certain processes to be unable to proceed continuously or the shortage of key raw materials causing the inability to start specific processes, appropriate adjustments should be made to form a practical worksheet, and then the automatic control system should arrange all production devices (such as inkjet printers and other processing equipment) to operate in an orderly manner according to the schedule. Such a comprehensive automation management system not only helps to improve operational efficiency, save costs, and ensure delivery time, but also enhances customers' confidence in service quality and increases the opportunity for their own companies to gain an advantage in the fierce market environment.

[0124] Smart printers start working according to the preset task list. This means that once there is a new printing task, these smart devices can autonomously read the printing command entered in the system in advance to start the job. For example, in a digital printing company, the daily printing list is entered into the system in advance by the planning team, including the printing type, quantity and required schedule; at the set time, each printer automatically starts running the corresponding task.

[0125] Sensors are installed at key locations of printing to detect parameters such as actual work efficiency and product consistency. Sensors can capture various signals, such as speed fluctuations in time series, which can help us grasp the real-time progress of each batch of prints and the accuracy of the finished quality. For example, when printing large-format posters, during the inkjet printing process, pressure and displacement sensors installed on the side of the conveyor belt continuously detect the speed of the printing paper and the distribution of the ink, allowing the system to adjust in real time to maintain high-quality output.

[0126] An automatic control device with feedback adjustment capability adjusts the important operating factors of the equipment in real time, such as pressure control. If there is a phenomenon that does not match the pre-designed target state, this system can quickly correct itself to ensure stable output without relying on external human instructions. Suppose a batch of business cards needs to ensure that the back is smooth and not printed through. At this time, the closed-loop control can fine-tune the severity of the pressure parameters during printing based on the results of real-time inspection of the products being manufactured, so that all products meet consistent standards.

[0127] A robotic arm equipped with quality inspection is used to regularly sample and check whether the quality status is consistent with the original file, and then register the unqualified items or defects. When all tasks are completed, the reliability and compliance level of the product need to be ensured before the last step. The quality inspection robot can simulate human eye recognition technology for precise comparison. For example, it can randomly check the samples of books that have been produced to see if there are any misprints or omissions, and record and analyze them in order to track and correct the design problems of the source files or the causes of errors in the equipment itself.

[0128] In the event of a hardware failure, the system will immediately suspend operations and send abnormal information to management personnel to remind them to take repair measures to prevent further damage. It is very important to prevent accidents from causing greater losses. For example, when a printer restarts after a task execution cycle that has been running for many days, if the printer finds a problem with the internal memory during the self-check process, it will not only automatically stop production to prevent the flow of wrong products to customers, but also immediately notify the operation and maintenance experts on the remote server to pay attention to maintenance, thereby ensuring the continuous, stable and efficient development of the business.

[0129] The system automatically collects all relevant information generated during the printing operation from various related devices. The data sources can be data points collected by the printer sensors, image processors and other detection elements. This information may include color saturation, printing paper characteristics (such as paper thickness, glossiness), nozzle working status (whether it is blocked), machine temperature and humidity of the surrounding environment, and many other factors that may affect the final output quality. For example, in one embodiment, a four-color high-speed digital printing machine is running. At this time, the printing device will automatically generate and collect multiple indicator values ​​related to its current status, such as color temperature distribution and consistency of ink droplet ejection.

[0130] Clean the raw recorded data using data cleaning and preprocessing algorithms. This stage involves identifying and removing noise or outliers while ensuring that all remaining data is reliable and accurate. Data preprocessing usually involves converting the collected information into a unified format to ensure its usability and consistency. In one case, it may be necessary to filter out unexpected error signals first, so as to retain useful data for subsequent process analysis; for example, in the process of analyzing image samples, the noise interference points in the background are removed to make the image clearer and more accurate.

[0131] Based on the processed cleaning data, a predictive model for quality assessment and future quality forecasting is established. The specific operation is to use an appropriate machine learning framework to learn the rules from the cleaned status information data set, so as to create a technical model that can automatically identify whether there are quality risks in the current working state and predict and warn in advance of possible future failures. The state parameters referred to in this case include both the specific measured readings directly generated by the equipment and the impact of changes in external physical environmental factors on the printing results, including important external influencing factors such as temperature and humidity that may cause changes in the evaporation performance of the nozzle, which in turn have a direct impact on the quality of the final product. For example, when a batch of paper is given as the input material and the environmental conditions are determined to remain constant, the AI ​​system in training can identify and record the optimal printer setting parameters so that the equipment can be automatically configured to achieve the best working state when similar conditions occur repeatedly.

[0132] Apply the previously constructed dynamic model to the online printing operation scenario. This means guiding the actual printing activities on the production line based on proven algorithms. Specifically, it means timely fine-tuning the corresponding production parameters based on the model prediction feedback to ensure that the quality of the finished product is stable and up to standard; for example, once the monitoring finds that the color uniformity of the current printed product has a slight downward trend, the preset instructions can be triggered to adjust the ink supply concentration or paint spraying frequency, etc. to correct the problem immediately to prevent the accumulation of defects; this closed-loop control system allows continuous improvement of the process until the set accuracy level and production goals are achieved, and effectively avoids the various negative effects caused by human errors.

[0133] By tightly integrating automated collection, intelligent processing and predictive analysis functions, we achieve precise management of the entire production process, and ultimately ensure that each batch of printed products produced meets the expected quality standards; this not only solves the pain point of difficult quality control that was common in the industry in the past, but also greatly reduces the additional cost pressure caused by rework. In this way, we can ensure a high level of consistency throughout the entire cycle from pre-press to delivery.

[0134] Specifically, the specific steps of the process of constructing a dynamic model based on the state parameter information set of the present invention include:

[0135] Extracting specific types of data and applying machine learning algorithms to build mapping models. The first operation is to extract the temperature and humidity data of the printing environment as important environmental factor inputs in the model. In this link, for example, the sensor elements installed in the digital printing equipment are used to obtain real-time printing room environmental data. These factors such as temperature and relative humidity are key parameters because they significantly affect ink diffusion, paper deformation and other issues, and directly affect the final print output quality.

[0136] After that, extracting the printing material characteristic data such as paper thickness and glossiness is equally important as raw material attribute input. Specifically, changes in paper characteristics will lead to changes in ink absorption or color shift problems. These details need to be captured and considered when the final model is established.

[0137] The next step is to establish a conditional judgment logic to determine whether the humidity index in the current environment has reached a preset threshold value H. If the threshold value H is determined to exceed the standard (i.e., the current environmental humidity is higher than the preset humidity threshold value H), it is necessary to start the operation of adjusting the setting value of the printing device based on the aforementioned status information. In this case, the humidity threshold value H can be set to a specific value or an interval value according to different working scenarios, paper water absorption and ink characteristics.

[0138] Finally, by selecting a machine learning algorithm suitable for this type of data, all previously acquired and prepared state parameters are associated with the final product quality indicators to establish a mapping model between state parameters and quality. In an experimental case, the machine learning algorithm can be one of a series of algorithms such as regression trees (such as random forest algorithms), support vector machines, or deep neural networks, the purpose of which is to predict potential quality problems of products under the same conditions in the future; and when building the model, it may involve standardizing or regularizing certain state variables to enhance generalization performance.

[0139] No actual calculation formula is given directly here, but the principle of setting thresholds based on actual application scenarios and the general process of how to choose a modeling method are introduced. However, it is conceivable that there will be some mathematical expressions to define the operation mechanism and criteria of the above steps for specific application scenarios. For example, a function of a threshold condition \(H_{\mathrm{crit}}\)(humidity threshold) is defined, which is expressed as \[H_{\mathrm{crit}}=f(T,\,U)\] where T represents the temperature and U represents the relative humidity ratio in the air; after given certain boundary conditions (such as the optimal humidity level is between 45%-65%), the function can determine a reasonable target value based on factors such as the moisture resistance limit of the specific paper product. This setting is to ensure that the printing production process is always carried out under appropriate and controllable operating conditions.

[0140] Specifically, the specific steps of the process of adjusting the setting parameters of the printing press based on the state parameter information set of the present invention include:

[0141] First, the model M is used to predict the printing quality of the printed product under a given set of state parameters to obtain a predicted quality score Q. The model M in this process is usually developed based on machine learning or other data-driven methods. Its purpose is to extract key factors that affect printing quality from a large amount of printing history data, such as ink concentration, paper water absorption performance, and temperature changes, and establish a quantitative relationship between them and printing quality. For example, in a specific environment (such as high humidity weather conditions), if the model predicts that the current amount of ink and nozzle speed used may still cause the image boundary to be unclear even under ideal conditions (this is indicated by a low predicted score).

[0142] The system will then determine whether the quality prediction value Q is lower than the minimum standard score QS set by the system in advance. The minimum score here is usually an empirical value determined based on customer expectations or industry standards; if Q is indeed lower than the threshold QS, the system will continue to execute the automatic calibration procedure to correct the printing parameters. In one embodiment, the minimum score is defined as 75 points (the score range is 1 to 100 points). If the printing effect obtained through simulation is only 68 points and obviously does not meet the standard, further adjustments are required to improve the score performance.

[0143] If the predicted quality score Q is less than the set minimum qualified score QS, then several important variables such as the mechanical stress applied to the printing material, the operating speed V of the printing device to control the advance and retreat, or the change of the single injection volume I sprayed onto the surface of the substrate will be adjusted according to the optimization guidance provided by the M model until the final result meets or exceeds the minimum quality standard requirements. Here, it is assumed that the main problem detected during the printing process is insufficient image density, that is, the color of the final product appears too pale and not bright due to insufficient coverage of ink droplets. In order to improve this defect, it may be proposed to increase the inkjet volume as a countermeasure, thereby increasing the adhesion of pigment particles to the receiving medium and enhancing the fullness of the picture. At this time, the system may recommend increasing the inkjet volume to solve the aforementioned color depth defect.

[0144] After each parameter modification, the above process needs to be run again to perform quality estimation operations again, and such tests are repeated until the level acceptable to the customer group is achieved, that is, the final score is greater than or equal to the minimum limit value, and then the cycle of making further changes to the machine status configuration is stopped. For example, if the score remains at 69 after the first re-test after the ink capacity is replenished, it means that the parameter adjustment has not yet achieved the requirement of allowing the value to jump over the limit, and a more appropriate way must be found again to improve the printing quality - such as simultaneously speeding up the speed of the paper path transmission line to cooperate with the liquid ejection rate to reduce local aggregation and infiltration. Only after repeated iterations and finally observing that the results shown on the display panel are sufficient for commercial use or exceed the benchmark level can this automatic fine-tuning sequence be exited, marking the successful end of the intelligent production adjustment stage and the beginning of the continuation of the regular batch production process.

[0145] Specifically, the basis for determining that the model recommends adjusting key printing parameters is:

[0146] Compare whether there is a significant difference between the quality prediction Q under the current printing parameters and the standard quality score QS; if a significant difference is detected and it is determined that the current paper property change exceeds the preset allowable range, dynamically calculate the adaptation parameter C according to the change of material properties; use the adjusted parameters to perform a small test print, and use the model to further verify the adjustment effect E; if the verification result shows that the effect E is improved or not lower than the expected quality level T, then update the machine parameter settings to the adjusted value and record the specific steps of this optimization information. The process of this step is divided into four steps to ensure that the optimal configuration of printing parameters is automatically completed when the paper properties deviate significantly.

[0147] The first step is to compare the numerical difference between the quality prediction value Q under the currently used printing parameters and the defined standard quality score target QS. The key quantity Q involved in this process is estimated by an intelligent algorithm through analysis of the current process settings and past printing records, while QS reflects the most basic level required for the final printing effect. If the difference between the two exceeds a specific standard deviation threshold (such as ±2%), it is determined that it is difficult to meet the specification requirements under current production conditions. For example, in an actual operation scenario, the system predicts a quality score of Q = 88 and the standard expected value is QS ≥ 95%, so the difference between the two is calculated. If the gap is significantly beyond the expected error margin (+ / -7% or more), it indicates that measures need to be taken to narrow the gap and improve the qualified rate of finished products.

[0148] On the basis of confirming the above conditions, it is also necessary to study the causes of this state - in particular, the investigation of the physical properties of the raw materials becomes crucial. Specifically, it is to check whether there are any unusual changes in the basic properties of the current paper type, such as fiber structure, moisture content, and smoothness (such as moisture content changes by ±3%, and paper surface roughness fluctuates by more than 2 microns). If it is evaluated that the changes in these key factors do exceed the limit range that the equipment can adapt to and adjust, then the next step is to reconfigure the optimal process conditions based on the new substrate data: including but not limited to setting new coating ratios, modifying printing pressure levels and other variables, and finally generating an adaptive correction coefficient set C for use in subsequent processes.

[0149] Then, we entered the stage of trial production of small batch samples as a key transition period for the transformation from theory to application. Through the optimized version of the operating parameter table, a test sample set is generated according to the established procedure to verify whether the expected optimization benefit level is achieved. The parameter adjustment range involved in this step needs to be determined based on the effective operating range obtained from the analysis of historical big data, and fine-tuning should be performed near the center point (near the optimal solution) as much as possible. For example, if the initial setting is that the uniformly distributed thickness of the new coating should be adjusted within 2-10 nanometers, the initial value should be selected at around 6nm before the first experimental printing and increase / decrease according to the effect.

[0150] Finally, the comparison between the actual output quality E and the set benchmark index T determines whether the above changes can be solidified into permanent specifications to promote efficiency and maintain consistent product performance. Only when the measured results are better than or remain within the allowable error tolerance (i.e., E≥0.95×T or Δ≤1%), the new strategy is allowed to be saved in the production system. Otherwise, the previous process configuration should be returned to continue running or another round of feedback learning process should be carried out to obtain a more accurate answer. At the same time, no matter what the final decision is, the details of this round of optimization need to be recorded and archived for future reference in similar occasions. In this hypothetical example, assuming that the lower limit T of the expected performance is set to 97%, if the actual quality score of the finished product measured after the guidance of the above method is 98%, it not only meets the release standard but also has a certain buffer surplus. Therefore, it can be approved to add this round of iterative solutions to the database for long-term application, so as to realize the intelligent upgrade of process parameter control while ensuring that the overall quality stability is not affected.

[0151] Specifically, the key parameters of the dynamic adjustment of the present invention include the specific steps of printing speed, inkjet pressure and nozzle opening size: monitoring the ambient temperature Tenv and comparing it with the target operating temperature Topt, when Tenv≠Topt, adjusting the ambient temperature to make it consistent with the target operating temperature; adjusting the inkjet pressure of the printer until the measured ink drop volume matches the theoretical ideal value; observing and recording whether the ink diffusion condition on the paper exceeds the allowable limit L, and based on these observations, correcting the inkjet rate R until the ink diffusion meets the requirements; performing a cross-validation process on the final value of each printing parameter to ensure their consistency and reach a stable state in mass production. The specific steps are described as follows:

[0152] Monitor the ambient temperature Tenv and compare it with the target operating temperature Topt. This is because during the digital printing process, changes in ambient temperature may cause changes in liquid properties such as viscosity, which will directly or indirectly affect the ink jetting quality and printing results. The target operating temperature Topt represents the ideal operating state, under which the machine can achieve optimal printing performance. If the actual ambient temperature Tenv measured deviates from Topt, corresponding measures must be taken to maintain good output quality. Specifically for this step, in a certain application, Tenv may vary due to climate differences in different regions. For example, if Tenv is higher than Topt by a certain value, it may affect the accuracy of ink jetting; at this time, the room temperature should be adjusted to restore to the optimal ambient temperature as much as possible to provide stable environmental conditions for the following steps.

[0153] Adjust the inkjet pressure of the printer until the measured droplet volume matches the theoretical ideal value. This means that according to the detected pressure conditions, the system will give feedback to ensure that the actual amount of ink sprayed (i.e. the size of each droplet) meets the desired specifications, thereby maintaining consistent print density and improving the reproduction accuracy of the pattern. For example, due to the different effects of the internal temperature of the nozzle and the external environment, the volume of ink sprayed under the same pressure conditions has changed. At this time, the control system needs to adjust the size of each droplet emitted by fine-tuning the ink pressure value until the actual test data coincides with the pre-defined standard parameters.

[0154] The subsequent operation is to spray test strips on actual paper and check whether the ink has obvious diffusion, that is, it exceeds the process tolerance range (allowable limit L), and adjust the inkjet flow rate based on this detection information until the image edge is clear enough. Assuming that in a test batch, some pattern edges become too blurred or spotted, it is likely caused by too fast spraying speed. Therefore, the system needs to slow down R (here is the number of droplets sprayed per unit time or the speed of the nozzle movement) until the pattern meets the expected clarity and edge smoothness requirements again.

[0155] Finally, each parameter needs to be verified (commonly known as Cross Check) to ensure that the selected configuration can adapt to the changes in the actual production environment and can continuously maintain high-quality standard output. For example, after setting a set of TPR, the technician will check whether there is a significant deviation in the color reproduction effect and uniformity on each sample after printing a large number of copies on multiple machines of the same model or continuously. Only after confirmation can this set of parameters be used in large-scale production to ensure long-term stability and reliability. This calibration process is necessary. It is not just a simple adjustment of a few numbers to get the expected effect. Only after repeated iterative optimization can it be ensured that the equipment meets the technical indicators required by the user, while avoiding unnecessary risks caused by frequent modifications of control variables.

[0156] Specifically, the verification steps of whether the ink on paper meets the quality requirements of the present invention are composed of the following: the automated equipment measures the actual inkjet volume v and the ink penetration depth D; if v falls within the pre-established ideal range and the penetration depth conforms to the established rules, then enters the next stage; determines whether the condition (v belongs to [v_lower, v_upper], and D≤limit_depth) is true; when all the above checkpoints are marked as having passed successfully, the system determines that the current printing configuration is valid and the quality assurance meets the standards, and this configuration is confirmed as the optimal setting scheme.

[0157] The first step of this process is to use automated equipment to measure the actual inkjet volume v on the paper and the penetration depth D of the ink in the paper. In this process, a specially designed instrument is used to accurately measure the amount of each drop of ink, and the maximum thickness reached by the ink from the time it contacts the paper surface to the specified time is recorded, ensuring the objectivity and accuracy of data acquisition. The advantage of automated measurement is that it can quickly and continuously collect a large amount of data, reduce data deviations caused by human interference, and ensure the consistency of test results.

[0158] If the actual measured v value is within the preset ideal range and the D value meets the specified penetration control specifications, the next stage of inspection will be entered. The ideal inkjet volume usually depends on many factors such as the type of printing material, the target image quality and the printing cost, and may be different in different application scenarios. For example, in situations where high-end image quality is required, the setting of v needs to consider color saturation, printing speed and avoiding overflow caused by excessive ink droplets; while in office printing, more attention is paid to the economy of ink use, so the ideal range may be different. For the penetration rule, if the printed material is relatively soft, such as paper towels or drawing paper with specific absorption properties, the maximum allowable penetration depth should be reduced to avoid wrinkling or ink penetration on the paper surface; for thick and smooth cardboard materials, the allowable penetration limit can be relatively high.

[0159] The judgment condition is whether the inkjet volume v obtained by automatic measurement is within the predetermined ideal range of v_lower to v_upper (v_lower≤v≤v_upper), and the ink penetration depth does not exceed the specified limit (D≤limit_depth). Only when both conditions are met can it be judged as passed. The v_upper defined here refers to the upper limit of ink volume that can ensure good visual effects without excessive consumption, and v_lower refers to the lower limit to ensure sufficient display without light color distortion. The choice of limit_depth is intended to prevent excessive penetration from destroying the beauty of the page or affecting the clarity of the text on the other side. The specific value will be customized according to the actual standards of the specific application.

[0160] When all quality inspection indicators are verified as qualified by the system, it means that the current settings have performed well in the test process and have met the expected requirements in production. At this time, the current inkjet volume v and penetration limit D value combination is determined to be reasonable and feasible, and can effectively meet the customer's quality needs. Therefore, this set of inkjet and penetration matching strategies can be determined as the optimal solution to be promoted to actual operation in the production environment or subsequent batch tests. For example, in a specific embodiment: if the best parameter combination is found to be v = 0.75μL / per point of injection and the maximum penetration limit D ≤ 300 microns for a glossy paper printing experiment for making brochures, then this set of numerical configurations will be recorded as a basic reference for subsequent large-scale production.

[0161] Specifically, the process of the present invention after checking that the printing quality meets the requirements and finally confirming the optimal setting scheme is as follows: the actual measured inkjet point diffusion SD must be within the allowable error range δ, and whether to make further adjustments is determined accordingly; assuming that in one embodiment, we are performing printing performance adjustment on a digital printer using an intelligent control system, the first step is to measure the inkjet diffusion on the printed sample and check it against the allowed standards. If the measurement result (such as the inkjet point diameter or width obtained by calculation or instrument detection) shows that the diffusion SD exceeds the pre-defined maximum deviation max_spread, it is necessary to return to adjust parameters such as the print head voltage or paper preheating until the SD falls within the expected range (i.e., within the δ range). This δ represents the allowable deviation range specified by the manufacturer or required by the customer. It depends on the specific application scenario and the required resolution fineness, and is usually a positive or negative error value.

[0162] Next, assume that after the adjustment in the previous step, the current condition has reached the state of SD ≤ max_spread of the inkjet point and meets our quality requirements, which means that we can directly enter the subsequent verification stage without further changes.

[0163] The subsequent operation logic includes a quantitative analysis of the color uniformity Y of the finished product after printing to determine whether it meets the predetermined standard. This indicator is designed to monitor a series of attributes from the consistency of blackness or color distribution to the overall appearance. Y should float within the range of Y_min to Y_max, which is an effect threshold acceptable to customers and achievable by the system, to avoid color gradation interruption or other quality problems caused by uneven deposition of pigment particles, ensuring satisfactory results from any perspective.

[0164] Specifically, in the actual application environment, it may be operated like this: if you want to produce a series of works that need to be highly realistic to restore the original colors, the colors on each page must maintain excellent consistency and stability. Therefore, we will set strict Y_min and Y_max boundaries, such as 88%-120%, to ensure that all colors can be correctly rendered within this ideal ratio.

[0165] The final conclusion is based on the results of the previous links. When the nozzle spread rate and color reproduction quality reach the set goals at the same time, that is, (SD≤max_spread AND Y∈[Y_min,Y_max]), the operator can lock the various successful operation configuration information obtained in this experiment into the parameter control system inside the printer, so that it will become the benchmark solution for the same series of tasks or similar tasks in the future. The benefit of this approach is that it not only greatly improves production efficiency, but also ensures a high degree of consistency between each output product and the ability to reuse best practices.

[0166] The above is an overview of the working mechanism of the whole process and a description of the application example. The purpose of these settings is to enable the equipment to quickly adapt and adjust to the best printing results when facing different types of materials, colors and details. The selection of such a formula is also a comprehensive consideration of the best balance between actual industrial production efficiency and users' expectations for high-quality prints.

[0167] Specifically, the specific steps of constructing a dynamic model through machine learning based on the state parameter information set of the present invention include: the first step is to extract key features associated with quality from the state parameter information set. The key features here refer to process control variables that have a significant impact on the quality of the output product. For example, in digital printing equipment, the features that may be involved include temperature fluctuation amplitude, humidity level, paper thickness, and ink viscosity. These parameters play a decisive role in printing quality such as clarity or color consistency.

[0168] Next, a feature selection algorithm is applied to remove non-important features and obtain a simplified feature set. The purpose of feature selection is to remove unnecessary or uninfluential features, thereby increasing the speed of model training and avoiding unnecessary complexity or overfitting problems. Common feature selection algorithms include principal component analysis (PCA), chi-square test, and mutual information method. In one embodiment, we use mutual information as an evaluation criterion to select factors that can best represent the performance indicators of printed products.

[0169] Subsequently, an initial quality prediction model is trained based on the simplified feature set obtained, that is, a set of quality classifiers or regression models with good parameter configuration are trained using the status data in the historical records and the corresponding finished product feedback as input and output. This can be a decision tree support vector machine or even a deep neural network.

[0170] Finally, the model update step with conditional judgment formula is included to continuously improve the training process. When it is detected that the prediction performance of the current model on the actual test data (such as error rate, accuracy and other metrics) is less than a specific threshold T, the re-learning process is automatically initiated to generate a new and more effective parameter matrix to replace the original prediction mechanism version and continue to run until it reaches an acceptable performance level or no longer improves. The basis for setting this threshold is usually a reasonable estimate of the historical error level distribution statistics, and the optimal value is a trade-off between ensuring timely corrections while not being too frequent and causing computational overhead inflation.

[0171] For example, in the printing management system, T=80%, that is, when the prediction accuracy is lower than this level for many consecutive times, it means that the old version is no longer suitable, so the iterative improvement process is activated and the new architecture M after adjusting the hyperparameters is reloaded to the latest version N to ensure that it always matches the actual processing situation closely and the prediction is closer to the actual quality situation. This formula is set to maintain the long-term reliable and stable service of the system and reduce the possibility of false detection and missed judgment.

[0172] Specifically, the specific steps of the step of training the initial quality detection prediction model based on the simplified feature set of the present invention include: the step first includes selecting a specific machine learning algorithm and initializing the algorithm parameter P0, where the specific machine learning algorithm can be selected according to the type of problem to be solved, such as support vector machine (SVM), random forest (RandomForest), gradient boosting decision tree (Gradient Boosting Decision Tree, GBDT) or other applicable models, and the algorithm parameter P0 usually involves the initial hyperparameters of the machine learning model, which determine the initial structure or complexity of the model. For example, in one embodiment, GBDT is selected as the model, and the initialization hyperparameters include the maximum depth of the tree, the minimum number of sample splits, etc., and these initialization parameters can be set according to prior knowledge.

[0173] Secondly, the simplified feature set is divided into a training subset C and a validation subset D using the cross-validation technique CV. The training subset mentioned here is used to build the model, and the validation subset is a test set for the established preliminary model. The model parameters are adjusted by comparing the difference between the model prediction results and the actual results. For example, in actual applications, 80% of the data may be used for training, and the remaining 20% ​​is reserved for validation, which can fully utilize data resources and prevent overfitting problems.

[0174] Next, the machine learning model is trained on the training subset C to obtain a preliminary prediction model M. At this stage, the system will try to understand the pattern relationship between the input data consisting of feature vectors and labels. Specifically, the system finds a set of weights or model parameters through iterative learning to minimize the training error and obtain the preliminary model M. For example, when using random forest, the model will be obtained through the learning of decision trees.

[0175] Finally, the conditional formula is applied: the performance B (such as prediction accuracy) of the validation subset D must be greater than or equal to the preset standard S (here B represents the model score, and S can be regarded as a benchmark for the minimum acceptable accuracy or precision). For example, if the accuracy threshold S = 0.95 is set, model B can only be considered to meet the requirements if it reaches this accuracy. If the threshold is not reached (that is, the model score B on the validation subset D is less than S), the condition will be judged to be True, and some parameters Pi in the model training process will be adjusted and retrained. Such iterations will continue until the performance of the prediction model is satisfactory (that is, the accuracy reaches S or above), or the maximum number of iterations allowed is reached to prevent falling into an infinite loop or excessive consumption of computing resources. The significance of the above formula is to ensure that the obtained preliminary quality detection model can meet the performance requirements of actual use by setting a threshold S. This cyclical process is aimed at optimizing the initial parameters, improving the training quality, and ensuring that the model generalization ability is strong enough.

[0176] Specifically, the specific steps of determining whether to retrain according to specific formula conditions during the model update process of the present invention include: when the intelligent digital printing management method based on data processing of the present invention is implemented, before determining whether the model needs to be retrained, setting a verification data set E for evaluation as the first step; the verification data set E here should include a certain amount of historical data or current data fragments to verify the effectiveness of the model's predictive performance. The amount of data can be set according to the actual operation scenario, and generally should cover the changes in different scenarios so that the test is universal and persuasive. The amount of data in E can range from hundreds to tens of thousands of records. Specifically, in the intelligent digital printing management scenario, it can be the printing quality data of several batches of products.

[0177] Based on the E data set, the calculation operation of the actual performance index Rn of the latest version of the existing model (called model N) will be performed as step 2. This process mainly simulates the given E and statistically calculates the performance of model N on it based on the running results, such as accuracy or error value. In this step, if the model is predicting digital product defect detection, Rn can represent the model detection accuracy, and the value is usually between 0 and 1. The higher the value, the better the effect. It is assumed that the model accuracy reaches a historical peak of 1, but in general, the optimal value will be close to but not equal to 1.

[0178] Next, the accuracy standard threshold value T pre-set and optimized in the third step is used to perform a quantitative comparison with the actual indicator value Rn obtained in step two: Step three is to determine if Rn is lower than the threshold value T, which triggers the need for model retraining. This is actually a measurement mechanism for the effectiveness of the model. The selection of threshold value T needs to be adjusted according to the application scenario and the expected effect target. In the above example, if the model evaluation standard is expected to achieve high accuracy, it may be set close to 0.98 or even 0.99. In some cases where low-level deviations are allowed, it can be lowered to 0.8 or lower to ensure the appropriate frequency of system updates. Excessive requirements may cause unnecessary training resource consumption. Through such a set of formula judgment mechanisms, the automatic monitoring model of the data printing process in a dynamically changing environment can be adjusted in a timely manner to ensure that the production quality and efficiency remain above the preset standards. For example, when the quality control of digital products predicted by the model finds that the quality judgment accuracy of the latest model for the product batch has dropped to the standard line of not meeting 0.9, the next round of training cycle can be automatically started to prepare for the update and replacement of the current version.

[0179] Specifically, the present invention performs the following steps when the model needs to be updated: reset the hyperparameter configuration Hp; select a new sample F to add to the original training data; repeat the machine learning steps based on the enhanced training data set to generate an upgraded prediction model; set the iteration termination condition G: if G is False, that is, Rm>=T, then stop the iteration, otherwise readjust the hyperparameters and repeat the entire learning cycle to improve Rm until the end criterion G is met or the maximum learning iteration upper limit Limit is exceeded.

[0180] When the system detects that the prediction accuracy is insufficient or other indicators deviate from the target threshold and the existing machine learning model needs to be updated to improve its performance, the hyperparameter configuration Hp will be reset. Resetting the hyperparameters here means re-optimizing the hyperparameter settings involved in different machine learning algorithms in order to find a better set of parameters. Specifically, Hp refers to non-training parameters such as learning rate and batch size that are directly related to the training effect of a specific model. They have a decisive influence on the final modeling quality. In the update phase, the optimization algorithm is used to find the best hyperparameter combination that can achieve better training results.

[0181] Next, the system will select new representative samples F based on the existing historical samples to expand the existing database. For example, in an application scenario about predicting the life management of ink cartridges in inkjet printers, new samples may come from data records generated by new devices or new operating environments, including but not limited to the speed of ink consumption in different time periods, changes in printer working status under different ambient temperatures, etc. This expansion method can improve the versatility and accuracy of the model, because more diverse data can cover more comprehensive factors to improve the prediction results.

[0182] After collecting and integrating the new training set, the training process begins to recreate or improve the existing machine learning model. At this stage, various types of algorithms may be applied, such as convolutional neural networks (CNNs) for image recognition tasks or long short-term memory units (LSTM) for time series data analysis. The system uses these newly expanded samples as training materials and repeatedly trains the model based on the previously reset hyperparameter configuration and evaluates and optimizes it until a satisfactory level is reached.

[0183] Finally, set the end condition of the iteration. Here, the so-called Rm generally refers to one of the performance evaluation criteria of the model (such as the mean square error MSE, etc.), and T is a given standard threshold; G is a judgment condition used to determine when the learning process should be terminated (that is, the number of iterations or the degree of improvement will no longer continue). Specifically, if G is detected to be False, it means that the current Rm is greater than or equal to a set target level T, which means that the model has met the expected requirements to a certain extent and further training activities can be terminated; however, if the condition is not met, it means that there is still a need for optimization and it is necessary to return to the first step and start another training round by modifying the hyperparameter Hp. At the same time, in order to avoid unnecessary waste of calculations, it is also necessary to set a maximum upper limit of iteration Limit. For example, in one embodiment, the system fixes the Limit to 500 times, which means that even if the model has not reached the ideal state, the training should be automatically interrupted after so many repetitions to prevent waste of system resources.

[0184] The whole process is to make the machine approach the ideal prediction performance through the learning process. It optimizes the output through continuous feedback adjustment so that the final output is closer to the user's needs or the actual situation. In this way, we ensure that the model in IPD can always maintain the most efficient and accurate working state to support complex digital printing management workflows.

[0185] Specifically, the specific steps of the algorithm parameter adjustment process of the present invention are: first, define the adjustable parameter space V of the algorithm and its range. This step refers to setting a specific set to include all possible input variables of the algorithm to be adjusted and their possible value ranges. Specifically, if an edge detection algorithm used in image processing tasks is considered, then the adjustable parameters may be the scale of the operator and the maximum number of iterations allowed when applying this edge detection operator. Secondly, determine the objective function J to evaluate the model performance under different parameter settings. At this stage, the designer needs to set a mathematical expression to quantify the impact of different input configurations on the expected output effect. In this image recognition example, the possible objective function is to minimize the image classification error rate (such as the accuracy rate J) so that the number of correct recognitions of the model in the prediction test set is close to the optimal level. Furthermore, based on a certain search strategy such as grid search Grid, multiple rounds of experiments are performed from V sampling point X to solve the maximization / minimization J corresponding to the best point Z. This step requires selecting and implementing an enumeration method to traverse the entire or part of the pre-defined candidate set space V, and perform multiple training-validation cycles to find the one or several combinations that can make the previously formulated scoring rule obtain the most ideal value. Assume that a fixed step length is used to exhaustively traverse a series of initial values ​​with different weights in an image segmentation task. The conditions are optimized to find the initialization state that makes the cross entropy error function (i.e., J, which serves as the objective equation here) decrease the fastest. This step will generate a sequence of corresponding performance record points X, and select an optimal solution from them and mark it as Z; finally, the initial parameter setting is updated to the parameter Pz according to the optimal point until the optimal solution of J is reached and Rn reaches or exceeds the predefined accuracy target T, which means that when the optimal set of parameters obtained in the above iterative exploration process has allowed the system performance indicators to stably meet the expected quality standards of the project, it can be fixed to replace some default control variables set arbitrarily at the beginning, where Rn represents the result of the nth round of experiments, and T is a qualified bottom line or threshold value specified in advance; still in the scenario described in the previous paragraph, if after a series of experimental iterations, it is found that a certain set of initialization methods can effectively make the subsequent training convergence speed faster, and after N rounds of independent review, it is verified that the final result can indeed be not inferior to any other combination in the confidence region, then it is natural to consider directly using this mechanism as a benchmark reference solution or further improving it.

[0186] For these formulas and steps, V is usually a multi-dimensional space representing that each adjustment parameter has its own value range; the objective function J defines a criterion based on which we evaluate the performance of the current system configuration; the search process, such as Grid search, is a mechanical method used to exhaustively enumerate all potential feasible solutions within a given parameter set and select the point that contributes most to the target optimization; and in the update stage, the best practices found are reflected in the original control system to ensure that the future operating quality is stable at a high and satisfactory level; the predefined accuracy target T represents a qualified requirement for the output of the entire automated workflow to ensure that the system can reach the acceptance level of the business department when it is delivered.

[0187] Specifically, the specific parameter range and candidate set of the sampling point X configured for searching in the present invention should include but not be limited to the following types of operation modes: initialize a set of initial point sets Xo, and add random noise N to these initial values ​​so that the distribution of X covers all possible exploration areas rather than just around the local minimum; for each trial, use a certain rule such as random perturbation or heuristic search method based on the feedback information of the previous step to guide how to move to the new position Xi+1 to try to escape the current local extreme point; perform loop traversal until all allowed attempts L are exhausted or the desired model convergence degree is reached; after each adjustment, save the corresponding historical optimal parameter set Yi and the corresponding quality score Qi as the basic reference material and basis for the next round of selection of more efficient path iteration.

[0188] In the first operation step, a set of starting points is generated, which is called Xo. This set represents multiple starting points for the initial search of the algorithm; then random noise N is added to each point on Xo to ensure that all areas of the parameter space can be explored rather than just those positions that are most likely to become local optimization solutions. Specifically, in one embodiment, if the parameter set is a two-dimensional vector (a one-dimensional array with a length of 2), the point can be set to be initially at the center position, that is, the zero vector [0,0], and then random noise such as [0.2,-0.45] can be added to it to obtain the initial sample point. This method of adding random noise enables subsequent iterations to more effectively find the global optimum rather than being limited to the area near the starting point;

[0189] For each trial, the location of the sample is updated according to the information obtained in the previous step and in accordance with certain specific strategies. These strategies usually include processes such as random perturbations and heuristic calculations. For example, when performing iterative updates, we can randomly extract a certain amplitude direction from a normal distribution or other probability distribution to move the point set. This rule can adjust the step size very flexibly, and can even self-optimize to adapt to different situations during the experiment through learning;

[0190] The subsequent stage is to iterate until all the preset number of attempts L are completed or until the model stability standard within the expected level is met. Specifically, we may define L = 100 to indicate that a maximum of 100 rounds of optimization can be performed, or pre-set a certain convergence standard such as the change between two rounds is less than 0.01%. After this threshold is met, regardless of whether all the iterations have been completed, the operation will be terminated and the final result will be considered to have been achieved;

[0191] After each adjustment to the point set, the best performing parameter set in each change should be recorded as the historical optimal parameter set Yi and the corresponding excellent indicator, i.e., the score or cost function calculation result, as Qi. This not only prevents regression to a low-quality state, but also allows the selection of a new iteration starting point in the future to avoid duplication of work based on historical information, thereby improving the speed and accuracy of the overall optimization process.

[0192] The above process is a more general parameter optimization process, through which the goal of finding the best solution for a given problem can be achieved. Although no specific numerical formula is given, the operation of each step is clear and can be adjusted accordingly according to the actual situation. The setting of parameters such as the specific distribution form of random perturbations or the maximum number of iterations allowed requires further design and adjustment based on the specific problems faced and available resources.

[0193] The intelligent digital printing management method based on data processing of the present invention comprises: firstly, collecting various raw data generated during the operation of the printing equipment, which may contain noise or unstructured forms; secondly, applying data cleaning and preprocessing technology to clean and organize the raw data, generating a more orderly and standard format state parameter information set, laying a foundation for subsequent data analysis; then using the preprocessed state parameters as training materials, with the help of machine learning, a dynamic model with intelligent characteristics is established, which can not only detect but also predict printing quality problems, and this model fully considers external environmental conditions such as humidity, temperature, etc. The prediction model can be used to analyze the influence of factors such as the degree of printing, as well as the characteristics of raw materials such as paper quality on the final printing effect, so as to provide comprehensive and detailed analysis results; finally, under the guidance of the prediction model, timely changes and fine-tuning of the setting parameters of the printing machine in operation can be achieved to ensure that each link can obtain timely and accurate monitoring feedback and can respond immediately to correct it to achieve the optimal working mode. This realizes an intelligent management process in which the system automatically runs a series of processes from information collection to decision-making to implementation of corrections. This greatly improves the efficiency and effect of quickly capturing and solving potential quality risks in the production process, and effectively controls and improves the product yield.

[0194] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0195] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0196] Any process or method description in the flow chart or otherwise described herein can be understood to represent a module, fragment or portion of a code including one or more executable instructions for implementing the steps of a specific logical function or process. And the scope of the preferred embodiment of the present application includes other implementations, in which the functions may not be performed in the order shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved.

[0197] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor or other system that can fetch instructions from an instruction execution system, apparatus or device and execute instructions), or used in combination with these instruction execution systems, apparatuses or devices.

[0198] It should be understood that the various parts of the present application can be implemented with hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented with software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above embodiment method can be completed by instructing the relevant hardware through a program, which can be stored in a computer-readable storage medium, and when the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0199] In addition, each functional unit in each embodiment of the present application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium can be a read-only memory, a disk or an optical disk, etc.

[0200] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An intelligent digital printing management method based on data processing, characterized in that: The method comprises the following steps: Step 1: Receive personalized order information and convert it into a data structure in a preset format; Step 2: Use big data analysis technology to efficiently process and sort the converted order data; Step 3: Formulate an optimal printing production plan based on the preprocessing results; Step 4: Execute the printing production plan and monitor the efficiency and quality of the entire process; Step 5: Adjust the production plan based on the data feedback collected during the production process to ensure consistency between data processing and production.

2. The method according to claim 1, characterized in that Step one includes the following steps: Receive the order description file in JSON format from the client; Parse the JSON file to extract order key fields; Use the rule engine to select the corresponding order data conversion template based on product type; Match personalized customized fields with conversion templates to form a standardized data structure; The matched standardized data is verified and stored in the database queue for processing.

3. The method according to claim 1, characterized in that: Step 2 includes the following steps: A program that performs batch import of data into a data warehouse for data structures in a preset format; Desensitize and standardize data to eliminate noise and improve the quality of subsequent analysis; Use stream computing technology for online processing and instant update of order sorting; Apply machine learning algorithms to predict and prioritize the best delivery time for each order; Output the optimization sorting results and recommended printing resource allocation plan to the system management layer.

4. The method according to claim 1, characterized in that Step three includes the following steps: Allocate resources to each task order based on the sorting results; Predict the time window required to complete each task order based on historical data statistical models; Use heuristic search methods to construct the shortest path problem to find the fastest task sequence combination; The final execution schedule is determined by comprehensively considering the actual availability of the production line and the material preparation status; Automatically schedule printers and other production equipment to execute planned tasks in a specified order.

5. The method according to claim 1, characterized in that Step 4 includes: Smart printers automatically start and execute order processing tasks according to the pre-arranged task list; Install sensors at every node of the processing process to collect data to monitor quality parameters; Use closed-loop control systems to dynamically compare expected and measured results and adjust key process parameters in real time; Arrange quality inspection robots to regularly extract completed samples and compare them with design documents to evaluate the pass rate and record any problems; If a fault alarm occurs, the machine will be shut down immediately and a warning SMS will be sent to technicians via the cloud service to repair the fault in a timely manner.

6. The method according to claim 5, characterized in that Step five includes: The sensor automatically acquires the raw data generated by the printing equipment during the printing process; Processing the raw data using a data cleaning and preprocessing algorithm to form a state parameter information set that can be used for analysis; Based on the state parameter information set, a dynamic model for detecting and predicting printing quality problems is constructed through machine learning, and the model is used to monitor and adjust the printing press setting parameters in real time; adjusting printing parameters according to feedback provided by the model to achieve online closed-loop optimization; Among them, the dynamic model for detecting and predicting printing quality problems constructed by machine learning based on the state parameter information set also takes into account the feature extraction of the impact of environmental factors and raw material properties on printing quality.

7. The method according to claim 6, characterized in that The process of constructing a dynamic model based on the state parameter information set includes: Extract the printing environment temperature and humidity data as input of environmental factors; Extract the printing material characteristic data as input of raw material attributes, and the material characteristic data includes paper thickness and glossiness; Set a conditional judgment logic. If the current environmental humidity is higher than the preset humidity threshold H, then execute the next process of adjusting the printing press setting parameters based on the state parameter information set; Establish a mapping relationship model M between state parameters and quality using a predefined machine learning algorithm.

8. The method according to claim 7, characterized in that The process of adjusting the printing press setting parameters based on the state parameter information set includes: Use the model M to predict the printing quality score Q under a given combination of state parameters; Judge whether the predicted quality score Q is lower than the predetermined minimum quality standard QS; If Q < QS, then adjust the key printing parameters according to the model suggestions; Repeat the calculation step of the quality score Q after each parameter adjustment until Q ≥ QS to stop the parameter adjustment loop.

9. The method according to claim 8, characterized in that The basis for determining that the model suggests adjusting the key printing parameters is: Compare whether there is a significant difference between the quality prediction Q under the current printing parameters and the standard quality score QS; If a significant difference is detected and it is determined that the current paper characteristics change exceeds the preset allowable range, then dynamically calculate the adaptation parameter C according to the change of material characteristics; Perform a small-scale trial printing using the adjusted parameters and further verify the adjustment effect E using the model; If the verification result shows that the effect E is improved or not lower than the expected quality level T, then update the machine parameter setting to this adjustment value and record the current optimization information.

10. The method according to claim 9, characterized in that The key parameters for dynamic adjustment include printing speed, inkjet pressure, and nozzle opening size, and specifically include the steps: Monitor the environmental temperature Tenv and compare it with the target operating temperature Topt. When Tenv ≠ Topt, adjust the environmental temperature to make it consistent with the target operating temperature; Adjust the inkjet pressure of the printing press until the measured ink droplet volume matches the theoretical ideal value; Observe and record whether the ink diffusion condition on the paper surface exceeds the allowable limit L, and correct the inkjet rate R based on these observations until the ink diffusion meets the requirements; Perform a cross-validation process on the finally determined numerical values of each printing parameter to ensure their consistency and reach a stable state in mass production.

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