Quality data acquisition and abnormal early warning method and system for printed matter production line

Abstract

The application discloses a quality data acquisition and abnormal early warning method and system for a printed product production line, relates to the field of production process control, and comprises the following steps: monitoring and acquiring the ink jet abnormal characteristic parameters of each nozzle in a monitoring period; obtaining pressure abnormal nozzles through statistics; inserting an interval frame micro-burst air to clean the pressure abnormal nozzles; and performing micro-adjustment optimization on the ink jet parameters of the pressure abnormal nozzles. The application improves the accuracy and real-time performance of abnormal detection through accurate identification of nozzle pressure abnormalities. The application sets a lack of jet compensation and micro-amplitude adjustment of ink jet parameters to ensure stable printing quality. The application implements targeted fine adjustment on the structure abnormal nozzle to ensure stable operation of the equipment and prolong the service life of the nozzle, and significantly improves the quality control level and intelligent management capability of the printed product production line.

Description

Technical Field

[0001] This invention relates to the field of production process control, specifically to a method and system for quality data acquisition and anomaly early warning in printing production lines. Background Technology

[0002] A printing production line refers to a complete continuous and automated operating system used for the mass production of printed materials (such as books, packaging, labels, and advertising materials). It integrates multiple processes and equipment links, from graphic input and image processing to printing execution, quality control, and post-processing. In existing technologies, monitoring components such as inkjet pressure sensors and ink flow meters are installed to track the ink supply pressure and instantaneous flow rate of the printhead, analyzing whether changes exceed normal ranges to determine if nozzle blockage or leakage exists. However, this method is an indirect judgment method, with limitations in accuracy and precise positioning.

[0003] Existing technologies, such as the invention patent with announcement number CN113589772B, are intelligent operation control and management systems for packaging bag graphic printing production lines. These systems include a paper packaging bag surface defect identification module at the production line entrance, an environmental parameter detection module for the production line conveying area, a management database, an operation control module, and a printing management terminal for the production line printing area. The system automatically identifies and processes surface defects of paper packaging bags at the entrance of the paper packaging bag graphic printing production line, detects and processes environmental parameters in the conveying area, and flexibly and automatically controls the printing coating templates of the corresponding printing machines in the printing area.

[0004] Existing technology, such as the invention patent with announcement number CN112034805B, is an intelligent printing production management system for corrugated boxes, including: an intelligent printing production management controller, a printing production management server, an intelligent printing production management system installed in the intelligent printing production management controller, a printing content and size detector, an intelligent water meter for measuring the actual water consumption of the printing press, an intelligent electricity meter for measuring the actual electricity consumption of the printing press, an ink weighing sensor for measuring the ink consumption of each printing unit of the printing press, an LCD display for displaying the control interface of the intelligent printing production management system, an LED screen for centrally displaying real-time production data of each printing press in the workshop, LED alarm lights for maintenance reminders and over-order reminders, and a button control panel for intuitive tactile operation by operators.

[0005] As can be seen from the above, existing technologies in the field of printing production process control often focus on the identification of surface defects at the inlet end and the automatic adjustment of coating templates. Although they integrate energy consumption and material monitoring modules such as smart water meters, electricity meters, and ink / water weighing, they do not collect or control data on the inkjet quality of the printed pattern itself. In practical applications, due to factors such as minor blockage of the nozzle micro-orifices and unstable pressure, inkjet abnormalities are easily caused, such as ink droplet deviation, broken lines, splattering, color imbalance, or white streaks, which seriously affect the quality stability of the printed product. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method and system for quality data acquisition and anomaly early warning in printing production lines. To achieve the above objectives, this invention utilizes the following technical solution: a method for quality data acquisition and anomaly early warning in printing production lines, comprising:

[0007] For each inkjet action that occurs on the printing production line, the time-series data of inkjet rebound behavior within the monitoring period is acquired, and the inkjet anomaly characterization parameters of each nozzle within the monitoring period are calculated, including the rebound angle fluctuation rate, the amount of change in decay rate, and the probability of sudden droplet splitting.

[0008] Extract the printhead pressure anomaly judgment conditions corresponding to the inkjet anomaly characterization parameters, match the inkjet anomaly characterization parameters of each nozzle with the corresponding printhead pressure anomaly judgment conditions, and statistically identify the nozzles with pressure anomalies.

[0009] Send a cleaning control command to the nozzle controller for nozzles with abnormal pressure, and insert interval frame micro-explosion gas deblocking pulses to clean the nozzles with abnormal pressure.

[0010] After cleaning the nozzle with abnormal pressure, the inkjet parameters of the nozzle with abnormal pressure are fine-tuned and optimized.

[0011] In addition, a quality data acquisition and anomaly early warning system for printing production lines is also provided, including:

[0012] The monitoring module is used to acquire time-series data of inkjet rebound behavior during each inkjet action on the printing production line within the monitoring period, and to calculate the inkjet anomaly characterization parameters of each nozzle within the monitoring period, including rebound angle fluctuation rate, decay rate change and sudden droplet splitting probability.

[0013] The anomaly identification module is used to extract the printhead pressure anomaly judgment conditions corresponding to the inkjet anomaly characterization parameters, match the inkjet anomaly characterization parameters of each nozzle with the corresponding printhead pressure anomaly judgment conditions, and statistically identify the nozzles with pressure anomalies.

[0014] The cleaning module is used to send cleaning control commands to the nozzle controller for nozzles with abnormal pressure, and inserts interval frames of micro-explosive gas deblocking pulses to clean the nozzles with abnormal pressure.

[0015] The optimization module is used to fine-tune and optimize the inkjet parameters of nozzles with abnormal pressure after cleaning them.

[0016] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects:

[0017] (1) This invention provides a method for quality data acquisition and anomaly early warning in a printing production line. Through the acquisition and analysis of multi-dimensional inkjet rebound behavior time-series data, it covers key parameters such as droplet impact rebound angle, initial rebound velocity, and droplet splitting number. Edge detection and particle tracking algorithms are used to fit the main rebound trajectory, acquiring the dynamic characteristics of inkjet rebound in real time. Based on this data, anomaly characterization parameters such as rebound angle fluctuation rate, decay rate change, and sudden droplet splitting probability are calculated, accurately reflecting the directional stability, dynamic consistency, and droplet quality changes during the inkjet process, thereby effectively identifying nozzles with abnormal pressure. This multi-parameter comprehensive evaluation method significantly improves the accuracy and reliability of inkjet anomaly detection.

[0018] (2) This invention addresses the issue of missing print caused by nozzles with abnormal pressure. By pre-reading the image buffer and utilizing interpolated pixels and the flight parameters of neighboring nozzles, the pulse offset time of the missing print compensation nozzle is accurately calculated and written into the control parameters, achieving dynamic pulse timing compensation. Simultaneously, considering various environmental characteristics such as ink viscosity, paper ink absorption speed, and ambient temperature and humidity, the power of the missing print compensation nozzle is slightly adjusted to ensure stable printing quality. This multi-dimensional dynamic compensation and fine-tuning strategy effectively improves printing quality and the adaptability of the equipment.

[0019] (3) This invention performs secondary rebound behavior data acquisition and anomaly determination on the inkjet parameters of the cleaned nozzles to distinguish between normal nozzles and nozzles with abnormal structural pressure. For normal nozzles, the inkjet parameters are fine-tuned to ensure adaptability to different printing tasks; for nozzles with abnormal structural pressure, the inkjet parameter fine-tuning mapping factor is corrected after collecting structural parameters and performing weighted fitting, and targeted anomaly fine-tuning is implemented. This hierarchical optimization strategy ensures stable equipment operation and improves printing quality and nozzle lifespan.

[0020] Of course, any product implementing this invention does not necessarily need to achieve all of the above advantages at the same time. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0022] Figure 2This is a schematic diagram of the system modules of the present invention.

[0023] Figure 3 This is a schematic diagram of the logic flow of the present invention.

[0024] Figure 4 This is a diagram of the inkjet equipment control interface of the printing production management system involved in this embodiment of the invention.

[0025] Figure 5 This is a continuation of the diagram showing the control interface of the inkjet equipment in the printing production management system involved in the embodiments of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Please see Figure 1 As shown, this embodiment of the invention provides a method for quality data collection and anomaly early warning in a printing production line, specifically including:

[0028] like Figure 3 The diagram shown is a logical flow diagram involved in the embodiment of the present invention, used to illustrate the logical flow of the technical solution in the embodiment of the present invention, highlighting the data flow and control dependency between different functional modules.

[0029] For each inkjet action that occurs on the printing production line, the time-series data of inkjet rebound behavior within the monitoring period is acquired, and the inkjet anomaly characterization parameters of each nozzle within the monitoring period are calculated, including the rebound angle fluctuation rate, the amount of change in decay rate, and the probability of sudden droplet splitting.

[0030] The specific process for acquiring time-series data on inkjet bounce behavior during the monitoring period is as follows:

[0031] Inkjet bounce behavior data includes droplet impact bounce angle, initial bounce velocity, and droplet splitting number. The main bounce trajectory curve is fitted using edge detection and particle tracking algorithms. The specific acquisition process includes:

[0032] The inkjet process is captured frame-by-frame by a high-speed camera, forming a series of high temporal resolution image frames. For each frame, edge detection algorithms (such as Canny edge detection or the Sobel operator) are used to identify the ink droplet edges, extract the outer contour of the ink droplet, and count the number of ink droplet splits. Particle tracking algorithms (such as multi-target tracking based on Kalman filtering or MeanShift algorithm) are introduced to continuously track the changing path of the center coordinates of the ink droplet or its split droplets across multiple frames. This path constitutes the trajectory of the ink droplet's flight and rebound. After tracking is completed, the angle between the ink droplet impact point and the initial rebound direction is calculated, thus extracting the ink droplet impact and rebound angle; simultaneously, based on the displacement of the ink droplet's centroid between adjacent frames and the imaging time interval, the displacement velocity of the ink droplet's rebound segment is calculated, extracting its initial rebound velocity. The trajectory points of multiple ink droplets are interpolated and fitted (such as quadratic spline fitting, parabolic fitting, or Bézier curve fitting) to obtain the main rebound trajectory curve. This curve represents the trend line of the overall rebound behavior of the ink droplet after impacting the inkjet medium surface.

[0033] During the monitoring period, multiple inkjet rebound behavior data of each nozzle are recorded to form continuous time-series data, including ink droplet impact rebound angle sequence, rebound initial velocity sequence and ink droplet splitting number sequence, which are then integrated into inkjet rebound behavior time-series data.

[0034] The specific process for calculating the inkjet anomaly characterization parameters for each nozzle during the monitoring period is as follows:

[0035] The parameters characterizing inkjet anomalies include bounce angle fluctuation rate, decay rate change, and sudden droplet splitting probability. The specific calculation process is as follows:

[0036] The standard deviation of the droplet impact rebound angle sequence of each nozzle within the monitoring period is calculated to be the rebound angle fluctuation rate of each nozzle. The rebound angle fluctuation rate is used to characterize the stability of the ink jet direction.

[0037] The initial rebound velocity sequence of each nozzle is obtained, and combined with the final velocity data corresponding to each initial velocity in the sequence, the velocity decay of each inkjet during the monitoring period is calculated. Then, the decay rate change of the nozzle is obtained by averaging the decay difference between adjacent inkjet actions. The decay rate change is used to reflect the consistency of inkjet power output.

[0038] Extract the droplet splitting threshold from the database, count the number of inkjet events where the number of droplet splitting exceeds the threshold within the monitoring period, and use the proportion of such inkjet events to the total number of inkjet events as the probability of sudden droplet splitting.

[0039] Extract the printhead pressure anomaly judgment conditions corresponding to the inkjet anomaly characterization parameters, match the inkjet anomaly characterization parameters of each nozzle with the corresponding printhead pressure anomaly judgment conditions, and statistically identify the nozzles with pressure anomalies.

[0040] The specific conditions for determining printhead pressure anomalies corresponding to the inkjet anomaly characterization parameters include:

[0041] If the rebound angle fluctuation rate of a nozzle during the monitoring period is greater than the preset rebound angle fluctuation rate threshold, the nozzle is recorded as a pressure abnormal nozzle.

[0042] If the change in the decay rate of a nozzle during the monitoring period is greater than the preset threshold for the change in decay rate, then the nozzle is recorded as a nozzle with abnormal pressure.

[0043] If the probability of sudden droplet splitting in a nozzle during the monitoring period is greater than the preset threshold for sudden droplet splitting probability, then the nozzle is recorded as a nozzle with abnormal pressure.

[0044] The nozzle with abnormal pressure is sent to the production management terminal with an alert. If the number of abnormal alerts for a nozzle exceeds the alert threshold, the nozzle is marked as a high-risk nozzle and a maintenance request is sent to the production management terminal.

[0045] like Figure 4 The diagram shows the inkjet equipment control interface of the printing production management system involved in this embodiment of the invention. It includes panels displaying the current number of abnormal inkjet prints and the number of high-risk inkjet prints, as well as an inkjet data monitoring section and an abnormal nozzle list section. This is used by production management personnel to monitor the inkjet equipment in real time.

[0046] If the number of abnormal warnings for a nozzle does not exceed the warning threshold, a cleaning control command for the nozzle with abnormal pressure is sent to the nozzle controller, and a micro-explosion gas deblocking pulse is inserted at interval frames to clean the nozzle with abnormal pressure.

[0047] When a nozzle with abnormal pressure enters the inkjet output shutdown preparation stage, the printhead controller sends a temporary shutdown command to the nozzle with abnormal pressure, temporarily interrupting the ink droplet pulse signal generation unit of the nozzle with abnormal pressure and locking the ink solenoid valve output.

[0048] Obtain the timing sequence of the current printing task, select the frame interval as the cleaning insertion point, inject a pressure impact signal into the nozzle with abnormal pressure, and clean the nozzle with abnormal pressure.

[0049] Figure 5 This is a continuation of the inkjet equipment control interface diagram of the printing production management system involved in the embodiments of the present invention, including a cleaning status monitoring module and a cleaning control module, which are used by production management personnel to control nozzles with abnormal pressure.

[0050] Cleaning nozzles with abnormal pressure also includes adjusting nozzles that compensate for insufficient spray, specifically including:

[0051] Before the image data enters the inkjet scheduling module, the cached data of the current image is pre-read. After identifying the pixel area responsible for the nozzle with abnormal pressure, the interpolated pixel generated by the image block ejected by the adjacent printhead is inserted at the image coordinates corresponding to the nozzle with abnormal pressure. In this embodiment of the invention, the interpolation method adopts linear interpolation, and the approximate color value and gray value are calculated from the image output of the left and right nozzles to fill the missing area.

[0052] Simultaneously, the nozzle distance between the nozzle with abnormal pressure and its adjacent nozzles is acquired. This nozzle distance is a fixed parameter in the printhead structure design, obtained from a scheduling parameter library. Adjacent nozzles are designated as missing-printing compensation nozzles. The average droplet velocity of the missing-printing compensation nozzles within the monitoring period is extracted. Specifically, during the ink ejection process of the missing-printing compensation nozzles, a high-speed camera captures the trajectory of ink droplets from the nozzle to the printed material. Image processing techniques are used to extract continuous droplet positions, and the flight velocity sequence is calculated based on droplet displacement and time frame number. Based on interpolated pixels, nozzle distance, and average droplet velocity, the pulse offset time of the missing-printing compensation nozzles is analyzed. The specific analysis process includes:

[0053] To obtain the interpolated pixel coordinates, it should be noted that in this embodiment of the invention, the inkjet processing inlet is taken as the origin. Since the interpolated pixels are calculated from the images of the left and right nozzles, the interpolated pixel coordinates p(x) are... a ,y a ), y a The coordinates of the missing spray compensation nozzle's position z( ,y i y in ) i Similarly, based on the interpolated pixel coordinates and the nozzle distance, the required spatial offset of the nozzle for missing spray compensation is obtained. ,in, The required spatial offset for nozzles that compensate for missing spray. The x-coordinate of the interpolated pixel. Nozzle distance, This is the sequence number of the nozzle for missing spray compensation.

[0054] Based on the required spatial offset of the missing-spray compensation nozzle and the average velocity of ink droplets, the pulse offset time of the missing-spray compensation nozzle is obtained by dividing the two using the physical formula of velocity-time displacement.

[0055] The printhead controller writes the pulse offset time into the inkjet pulse control parameters of the missing-spray compensation nozzle to constrain the missing-spray compensation nozzle. The constraint process includes:

[0056] The starting time of the inkjet drive pulse is modified based on the pulse offset time to control the spatial offset of the ink droplet landing point.

[0057] Based on the quality tolerance of the current printing task, implement slight power compensation control for the missing print nozzles, specifically including:

[0058] The quality tolerance of a current printing task refers to the maximum acceptable range or deviation limit of image output in terms of color, edge sharpness, and texture continuity within a specific printing task. This is a quantifiable, task-driven control standard, primarily used to guide whether fine compensation is required during the printing process, whether printhead error-tolerant output is allowed, and the adjustment space for various parameters.

[0059] Based on the ink viscosity, paper ink absorption rate, and ambient temperature and humidity characteristics used in the current printing task, ink viscosity refers to the internal resistance of ink flow, affecting the formation speed of ink droplets and their diffusion after falling, and is usually measured in millipascals per second. Ink viscosity is measured in real time using a miniature rotational viscometer built into the inkjet equipment. Paper ink absorption rate refers to the ability of paper to absorb ink per unit time, expressed in grams per square meter per second, and a preset absorption rate value is retrieved from a material database according to the paper type. Ambient temperature and humidity characteristics reflect the temperature and humidity conditions in the production environment, which directly affect the ink evaporation rate, printhead condition, and paper moisture content, thus affecting the trajectory of ink droplets and printing quality. Temperature and humidity data are periodically collected by sensors installed at key nodes in the production line and input into a preset temperature and humidity ratio-ambient temperature and humidity characteristic value mapping set in the database for mapping and matching to obtain the ambient temperature and humidity characteristic values.

[0060] Simultaneously, the pre-stored ink viscosity verification values, paper ink absorption speed verification values, and environmental temperature and humidity characteristic verification values ​​in the database are extracted, compared one by one, and then weighted and fitted to obtain the maximum ink volume change index factor, which specifically includes:

[0061] ;

[0062] Where Maxink is the maximum ink volume change index factor, IV is the ink viscosity, PIAR is the paper ink absorption speed, THF is the ambient temperature and humidity characteristic value, IV0 is the ink viscosity check value, PIAR0 is the paper ink absorption speed check value, THF0 is the ambient temperature and humidity characteristic check value, α1 is the ink viscosity weighting factor, α2 is the paper ink absorption speed weighting factor, and α3 is the ambient temperature and humidity characteristic value weighting factor.

[0063] It should be noted that the ink viscosity weighting factor, paper ink absorption speed weighting factor, and environmental temperature and humidity characteristic value weighting factor are weighting coefficients used to measure the importance of these three parameters in the comprehensive calculation of the maximum ink volume change indicative factor. Their function is to weight the influence of each parameter, enabling the calculation results to more accurately reflect the contribution of each factor to ink volume adjustment in the actual printing environment. The setting of these weighting factors ensures that key parameters receive higher weights in the comprehensive judgment, thus making the adjustment strategy more targeted and effective. The ink viscosity weighting factor, paper ink absorption speed weighting factor, and environmental temperature and humidity characteristic value weighting factor are obtained based on statistical analysis and empirical research of historical printing data. The impact of changes in ink viscosity, paper ink absorption speed, and environmental temperature and humidity on the ink volume adjustment effect is quantitatively evaluated in a large number of printing tasks. Specific methods include using machine learning models to identify the correlation between each parameter and ink volume changes, thereby determining their reasonable weight allocation.

[0064] It's also worth noting that there's a correlation between ink viscosity, paper ink absorption rate, and ambient temperature and humidity. Ink viscosity is significantly affected by ambient temperature and humidity; higher temperatures generally reduce ink viscosity, making ink droplets easier to eject and spread. Humidity, on the other hand, affects the ink's drying rate and surface tension. Paper ink absorption rate is not only related to the paper material but also influenced by ambient humidity; high humidity increases the water content of paper fibers, potentially reducing the absorption rate, and vice versa. Changes in ink viscosity affect the speed at which ink droplets spread and penetrate the paper surface, while the paper's ink absorption rate determines how quickly ink droplets are absorbed and fixed. Both factors together influence the ink droplet drying time and diffusion range.

[0065] Input the maximum ink volume change referential factor into the pre-stored maximum ink volume change referential factor-maximum ink volume change mapping set in the database, and perform mapping matching to obtain the maximum ink volume change of the missing spray compensation nozzle.

[0066] Based on the maximum ink volume change of the missing ink compensation nozzle, the pulse width of the missing ink compensation nozzle is slightly increased. The pulse width is the duration of the drive signal for ink droplet formation. According to the maximum ink volume change of the missing ink compensation nozzle, the allowable range of pulse width increase is determined to ensure that the increase in ink volume does not exceed a preset safety threshold, thereby avoiding over-spraying or color distortion. The printhead controller converts the maximum ink volume change of the missing ink compensation nozzle into a specific pulse width extension time, usually in microseconds. By adjusting the duration of the pulse signal in the drive circuit, the ink droplet formation time is slightly prolonged, resulting in larger or more ink droplets.

[0067] After cleaning the nozzle with abnormal pressure, the inkjet parameters of the nozzle with abnormal pressure are fine-tuned and optimized.

[0068] Acquire time-series data of inkjet rebound behavior of the nozzle with abnormal pressure during the first monitoring cycle after cleaning. Recalculate the inkjet anomaly characterization parameters of the nozzle with abnormal pressure. Then, match these parameters with the corresponding printhead pressure anomaly judgment conditions to obtain the post-cleaning test results of the pressure anomaly unit, including:

[0069] If none of the inkjet abnormality characterization parameters of the nozzle with abnormal pressure meet the corresponding printhead pressure abnormality judgment conditions, then the nozzle with abnormal pressure is recorded as a normal nozzle, and the corresponding normal inkjet parameters are finely adjusted.

[0070] Perform corresponding minor adjustments to the normal inkjet parameters. The specific processing conditions are as follows:

[0071] Obtain the task feature parameters of the current printing task, including image and text complexity and color gamut density, and extract the maximum ink volume change index factor of the current printing task again.

[0072] Image and text complexity refers to the richness of detail and structural complexity of graphic and text elements in an image or document to be printed, reflecting the level of detail and frequency of change in the printed content. Color gamut density indicates the richness and range of colors in an image, specifically involving the types, saturation, and breadth of color distribution, affecting the amount of ink used and the difficulty of ink mixing during printing. Image and text complexity is generally obtained through digital image processing analysis of the printed image, such as calculating edge density, texture features, and pixel change rate, or using image segmentation and statistical methods to assess the number and distribution complexity of graphic elements. OCR technology can also be used to identify text density and layout structure. Color gamut density, on the other hand, is obtained by extracting color space data (such as RGB or CMYK channels) from the image, analyzing the distribution range and frequency of colors, and calculating indicators such as color histograms, mean saturation, and color coverage area to quantify color richness. Both can be directly analyzed from digital files before printing.

[0073] A set of reference values ​​for task feature parameters is extracted from the database, including reference values ​​for image and text complexity and color gamut density. The task feature parameters and their corresponding reference values ​​are compared one-to-one and then weighted for fitting. Simultaneously, the maximum ink volume change factor is introduced for fusion processing to obtain the inkjet parameter fine-tuning mapping factor for the current printing task. Specifically, this includes:

[0074] ;

[0075] Wherein, PM is the inkjet parameter fine-tuning mapping factor for the current printing task, LDC is the image complexity, CCR is the color gamut density, Maxink is the maximum ink volume change index factor, LDC0 is the image complexity reference value, CCR0 is the color gamut density reference value, β1 is the image complexity weighting factor, β2 is the color gamut density weighting factor, and β3 is the maximum ink volume change index factor weighting factor.

[0076] It should be noted that the weighting factors of image and text complexity, color gamut density, and maximum ink volume change referential factors are used to assess the impact of different parameters on the final inkjet adjustment decision. They are obtained by statistical analysis of historical printing data, based on the correlation between a large number of task characteristic parameters and printing quality results, to quantitatively evaluate and identify the sensitivity and influence of each parameter when adjusting the inkjet volume.

[0077] It's also important to note that there's a close correlation between image complexity, color gamut density, and the maximum ink volume variation factor, all of which jointly influence ink volume adjustment strategies during printing. Image complexity reflects the richness of detail and structural complexity of the printed content. Higher complexity means more subtle textures and edges in the image, requiring more precise ink control and typically necessitating finer ink volume adjustments to ensure image quality. Color gamut density represents the richness and gradation of colors in an image. A wider color gamut and richer colors mean more precise ink jetting is needed for color reproduction, while also placing higher demands on the size and uniformity of ink droplet distribution. The maximum ink volume variation factor, calculated by considering image complexity and color gamut density, along with actual environmental parameters (such as ink viscosity, paper ink absorption rate, and temperature and humidity), represents the maximum allowable ink volume adjustment range. Image complexity and color gamut density influence the intensity of ink volume adjustment needs, while the maximum ink volume variation factor quantifies the specific limits of these needs under current physical conditions.

[0078] The inkjet parameter fine-tuning mapping factor is input into the pre-stored mapping set of inkjet parameter fine-tuning mapping factor-inkjet adjustment parameter in the database for mapping and matching to obtain the inkjet adjustment parameters of the normal nozzle, including the pulse width inkjet fine-tuning value, the inkjet frequency inkjet fine-tuning value, and the pressure compensation value inkjet fine-tuning value. The printhead controller writes the inkjet adjustment parameters into the normal nozzle for application.

[0079] The printhead controller writes inkjet adjustment parameters into the normal nozzles and applies them, typically through digital control signals. The specific process includes: the printhead controller generating corresponding inkjet parameter instructions based on the inkjet adjustment parameters, including pulse width, inkjet frequency, and pressure compensation value, all encoded numerically. The printhead controller sends these adjustment parameters to the control unit of the nozzle drive circuit via an internal communication bus (such as SPI, I2C, or a dedicated driver interface). Upon receiving the instructions, the drive unit adjusts the pulse width of the drive signal to fine-tune the droplet formation time; adjusts the inkjet frequency parameters to control the nozzle's ejection rhythm; and adjusts the pressure output of the solenoid valve or piezoelectric actuator according to the pressure compensation value to ensure the stability of droplet dynamics.

[0080] If any inkjet abnormality characteristic parameter of the nozzle with abnormal pressure meets the corresponding printhead pressure abnormality judgment condition, then the nozzle with abnormal pressure is recorded as a structural pressure abnormality nozzle, and the corresponding abnormal inkjet parameters are finely adjusted.

[0081] Perform corresponding minor adjustments to the abnormal inkjet parameters. The specific handling conditions are as follows:

[0082] The operating temperature of the nozzle with structural pressure abnormality is collected from the nozzle temperature sensor, and the nozzle impedance and drive response delay of the nozzle with structural pressure abnormality are also obtained.

[0083] Operating temperature, nozzle impedance, and drive response delay of nozzles with structural pressure anomalies are key parameters for evaluating the hardware status and performance of nozzles, used to determine whether there are structural faults or performance degradation in the nozzle.

[0084] Operating temperature refers to the actual temperature of the nozzle components during operation. Temperatures that are too high or too low can affect the physical properties of the ink and its jetting performance, leading to inkjet instability or printhead damage. This temperature is typically collected in real-time by a temperature sensor integrated into the nozzle. The sensor converts temperature changes into electrical signals using a thermistor, which are then read and analyzed by the controller.

[0085] Nozzle impedance reflects the electrical characteristics of the nozzle drive circuit, including resistive and inductive components. Abnormal impedance indicates an internal nozzle malfunction. Nozzle impedance is periodically detected by a measurement circuit built into the nozzle control system, obtained by exciting an AC signal and measuring the response.

[0086] Drive response delay refers to the time delay between the issuance of a control signal and the actual completion of the inkjet action by the nozzle. This delay directly affects the accuracy and synchronization of inkjet printing. It is calculated by comparing the timestamps of the control signal and the inkjet action, and is typically achieved using a high-speed sensor.

[0087] Standard values ​​for nozzle operating temperature, nozzle impedance, and drive response delay threshold are extracted from the database. The operating temperature, nozzle impedance, and drive response delay of the nozzle with structural pressure anomalies are compared one-to-one with the standard values ​​for these parameters, and then weighted and fitted to obtain the structural anomaly characterization values ​​for the nozzle. Specifically, these values ​​include:

[0088] ;

[0089] Wherein, JG represents the structural anomaly characterization value of the nozzle with structural pressure anomaly, and T represents the operating temperature of the nozzle with structural pressure anomaly. γ1 is the nozzle impedance of the nozzle with abnormal structural pressure, tl is the drive response delay of the nozzle with abnormal structural pressure, T0 is the standard value of nozzle operating temperature, r0 is the standard value of nozzle impedance, tl0 is the drive response delay threshold, γ1 is the weighting factor of operating temperature, γ2 is the weighting factor of nozzle impedance, and γ3 is the weighting factor of drive response delay.

[0090] It should be noted that the operating temperature weighting factor, nozzle impedance weighting factor, and drive response delay weighting factor are all coefficients used to assign weights to the importance of each hardware status parameter in nozzle structure anomaly assessment. Their core function is to adjust the influence of each parameter on the overall anomaly judgment and adjustment decision by weighting. The acquisition process is based on historical fault data analysis. By statistically analyzing the performance of different parameters in nozzle fault cases and their actual impact on printing quality, regression analysis is used to quantitatively determine the weight of each parameter.

[0091] There is a close correlation between the nozzle's operating temperature, nozzle impedance, and drive response delay, all of which reflect the nozzle's hardware condition and operational performance. Changes in operating temperature directly affect the physical properties of the internal electronic components and ink. Excessively high or low temperatures can lead to changes in circuit impedance; for example, increased temperature may cause increased resistance or circuit aging, thus affecting the stability of the nozzle impedance value. Simultaneously, abnormal changes in nozzle impedance can also affect the transmission efficiency and response speed of the drive signal, leading to increased drive response delay, which in turn affects the accuracy and synchronization of inkjet printing. In other words, temperature fluctuations can trigger impedance anomalies, which in turn can cause changes in drive response delay. These three factors interact and collectively affect the nozzle's structural integrity and inkjet quality.

[0092] The structural anomaly characterization value of the nozzle with abnormal structural pressure is input into the pre-stored mapping set of structural anomaly characterization value - inkjet parameter fine-tuning mapping factor correction value in the database for mapping matching to obtain the inkjet parameter fine-tuning mapping factor correction value of the nozzle with abnormal structural pressure. The inkjet parameter fine-tuning mapping factor correction value of the nozzle with abnormal structural pressure is then multiplied and corrected with the inkjet parameter fine-tuning mapping factor of the current printing task to obtain the corrected inkjet parameter fine-tuning mapping factor of the nozzle with abnormal structural pressure.

[0093] The corrected inkjet parameter fine-tuning mapping factor of the nozzle with abnormal structural pressure is input into the mapping set of inkjet parameter fine-tuning mapping factor - abnormal inkjet adjustment parameter to obtain the abnormal inkjet adjustment parameters of the nozzle with abnormal structural pressure. These parameters include the inkjet fine-tuning values ​​of abnormal pulse width, abnormal inkjet frequency, and abnormal pressure compensation. The printhead controller writes the abnormal inkjet adjustment parameters into the nozzle with abnormal structural pressure for application.

[0094] The printhead controller writes inkjet adjustment parameters into the abnormal nozzle and applies them, which is achieved through digital control signals.

[0095] In this embodiment, as Figure 2 As shown, the present invention provides a quality data acquisition and anomaly early warning system for a printing production line, comprising:

[0096] The monitoring module is used to acquire time-series data of inkjet rebound behavior during each inkjet action on the printing production line within the monitoring period, and to calculate the inkjet anomaly characterization parameters of each nozzle within the monitoring period, including rebound angle fluctuation rate, decay rate change and sudden droplet splitting probability.

[0097] The anomaly identification module is used to extract the printhead pressure anomaly judgment conditions corresponding to the inkjet anomaly characterization parameters, match the inkjet anomaly characterization parameters of each nozzle with the corresponding printhead pressure anomaly judgment conditions, and statistically identify the nozzles with pressure anomalies.

[0098] The cleaning module is used to send cleaning control commands to the nozzle controller for nozzles with abnormal pressure, and inserts interval frames of micro-explosive gas deblocking pulses to clean the nozzles with abnormal pressure.

[0099] The optimization module is used to fine-tune and optimize the inkjet parameters of nozzles with abnormal pressure after cleaning them.

[0100] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0101] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to specific implementation methods. Clearly, many modifications and variations can be made based on the content of this specification. The selection and detailed description of these embodiments in this specification are intended to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. Any modifications or variations that do not deviate from the structure of the invention or exceed the scope defined by the invention should fall within the protection scope of the invention.

Claims

1. A method for quality data collection and anomaly early warning in a printing production line, characterized in that, include: When each inkjet action occurs on the printing production line, acquire the time-series data of inkjet rebound behavior within the monitoring period, and calculate the inkjet anomaly characterization parameters of each nozzle within the monitoring period, including rebound angle fluctuation rate, decay rate change and sudden droplet splitting probability. Extract the printhead pressure anomaly judgment conditions corresponding to the inkjet anomaly characterization parameters, match the inkjet anomaly characterization parameters of each nozzle with the corresponding printhead pressure anomaly judgment conditions, and statistically obtain the nozzles with pressure anomalies. Send a cleaning control command to the nozzle controller for nozzles with abnormal pressure, and insert interval frame micro-explosion deblocking pulses to clean the nozzles with abnormal pressure. After cleaning the nozzle with abnormal pressure, the inkjet parameters of the nozzle with abnormal pressure are fine-tuned and optimized. The specific process for acquiring the time-series data of inkjet rebound behavior within the monitoring period is as follows: The inkjet rebound behavior data includes the droplet impact rebound angle, initial rebound velocity, and number of droplet splits. The main rebound trajectory curve is fitted by edge detection and particle tracking algorithms. During the monitoring period, multiple inkjet rebound behavior data of each nozzle are recorded to form continuous time-series data, including ink droplet impact rebound angle sequence, rebound initial velocity sequence and ink droplet splitting number sequence, which are then integrated into inkjet rebound behavior time-series data. The specific process for calculating the inkjet anomaly characterization parameters of each nozzle during the monitoring period is as follows: The parameters characterizing inkjet anomalies include bounce angle fluctuation rate, decay rate change, and sudden droplet splitting probability. The specific calculation process is as follows: The standard deviation of the droplet impact rebound angle sequence of each nozzle within the monitoring period is calculated to be the rebound angle volatility of each nozzle. The rebound angle volatility is used to characterize the stability of the ink jet direction. The initial rebound velocity sequence of each nozzle is obtained, and combined with the final velocity data corresponding to each initial velocity in the sequence, the velocity decay of each inkjet during the monitoring period is calculated. Then, the decay rate change of the nozzle is obtained by averaging the decay difference between adjacent inkjet actions. The decay rate change is used to reflect the consistency of inkjet power output. Extract the droplet splitting number threshold from the database, count the number of inkjet behaviors that exceed the droplet splitting number threshold within the monitoring period, and use the proportion of such inkjet behaviors to the total number of inkjet behaviors as the probability of sudden droplet splitting. The extraction of inkjet anomaly characterization parameters and their corresponding printhead pressure anomaly determination conditions involves matching the inkjet anomaly characterization parameters of each nozzle with their corresponding printhead pressure anomaly determination conditions. Specifically, this includes: The specific conditions for determining printhead pressure anomalies corresponding to the inkjet anomaly characterization parameters include: If the rebound angle fluctuation rate of a nozzle during the monitoring period is greater than the preset rebound angle fluctuation rate threshold, the nozzle is recorded as a pressure abnormal nozzle. If the change in the decay rate of a nozzle during the monitoring period is greater than the preset threshold for the change in decay rate, then the nozzle is recorded as a nozzle with abnormal pressure. If the probability of sudden droplet splitting in a nozzle during the monitoring period is greater than the preset threshold for sudden droplet splitting probability, then the nozzle is recorded as a nozzle with abnormal pressure. The nozzle with abnormal pressure is sent to the production management terminal with an alert. If the number of abnormal alerts for a nozzle exceeds the alert threshold, the nozzle is marked as a high-risk nozzle and a maintenance request is sent to the production management terminal.

2. The method for quality data collection and anomaly early warning in a printing production line according to claim 1, characterized in that: The step of sending a cleaning control command to the nozzle controller for the nozzle with abnormal pressure, and inserting interval frame micro-explosion deblocking pulses to clean the nozzle with abnormal pressure, specifically includes: When a nozzle with abnormal pressure enters the inkjet output shutdown preparation stage, the printhead controller sends a temporary shutdown command to the nozzle with abnormal pressure, temporarily interrupting the ink droplet pulse signal generation unit of the nozzle with abnormal pressure and locking the output of the ink solenoid valve. Obtain the timing sequence of the current printing task, select the frame interval as the cleaning insertion point, inject a pressure impact signal into the nozzle with abnormal pressure, and clean the nozzle with abnormal pressure.

3. The method for quality data collection and anomaly early warning in a printing production line according to claim 2, characterized in that: The cleaning of the nozzle with abnormal pressure also includes adjusting the nozzle for missing spray compensation, specifically including: Before the image data enters the inkjet scheduling module, the cached data of the current image is pre-read. After identifying the pixel area responsible for the nozzle with abnormal pressure, the interpolated pixel generated by the image block ejected by the adjacent printhead is inserted at the image coordinates corresponding to the nozzle with abnormal pressure. Simultaneously, the nozzle distance between the nozzle with abnormal pressure and the adjacent nozzle is obtained, and the adjacent nozzle is recorded as the missing nozzle. The average droplet flight speed of the missing nozzle during the monitoring period is extracted. Based on the interpolated pixel, nozzle distance and average droplet flight speed, the pulse offset time of the missing nozzle is analyzed. The printhead controller writes the pulse offset time into the inkjet pulse control parameters of the missing nozzle to constrain the missing nozzle. Based on the quality tolerance of the current printing task, implement slight power compensation control for the missing print nozzles, specifically including: Based on the ink viscosity, paper ink absorption speed, and ambient temperature and humidity characteristics of the current printing task, the ink viscosity verification value, paper ink absorption speed verification value, and ambient temperature and humidity characteristic verification value pre-stored in the database are extracted and compared one by one. After weighted fitting, the maximum ink volume change index factor is obtained. The maximum ink volume change index factor is input into the mapping set of maximum ink volume change index factor-maximum ink volume change amount pre-stored in the database for mapping matching to obtain the maximum ink volume change amount of the missing spray compensation nozzle. Based on the maximum ink volume change of the missing ink compensation nozzle, the pulse width of the missing ink compensation nozzle is slightly increased.

4. The method for quality data collection and anomaly early warning in a printing production line according to claim 1, characterized in that: After cleaning the nozzle with abnormal pressure, the inkjet parameters of the nozzle with abnormal pressure are fine-tuned and optimized, specifically including: Acquire time-series data of inkjet rebound behavior of the nozzle with abnormal pressure during the first monitoring cycle after cleaning. Recalculate the inkjet anomaly characterization parameters of the nozzle with abnormal pressure. Then, match these parameters with the corresponding printhead pressure anomaly judgment conditions to obtain the post-cleaning test results of the pressure anomaly unit, including: If none of the inkjet abnormality characterization parameters of the nozzle with abnormal pressure meet the corresponding printhead pressure abnormality judgment conditions, then the nozzle with abnormal pressure is recorded as a normal nozzle, and the corresponding normal inkjet parameters are finely adjusted. If any inkjet abnormality characteristic parameter of the nozzle with abnormal pressure meets the corresponding printhead pressure abnormality judgment condition, then the nozzle with abnormal pressure is recorded as a structural pressure abnormality nozzle, and the corresponding abnormal inkjet parameters are finely adjusted.

5. The method for quality data collection and anomaly early warning in a printing production line according to claim 4, characterized in that: The specific processing conditions for making corresponding minor adjustments to the normal inkjet parameters are as follows: Obtain the task feature parameters of the current printing task, including image and text complexity and color gamut density, and extract the maximum ink volume change factor of the current printing task again. The database extracts a set of reference values ​​for task feature parameters, including reference values ​​for image and text complexity and reference values ​​for color gamut density. The task feature parameters and their corresponding reference values ​​are compared one by one and then weighted and fitted. At the same time, the maximum ink volume change index factor is introduced for fusion processing to obtain the inkjet parameter fine-tuning mapping factor for the current printing task. The inkjet parameter fine-tuning mapping factor is input into the pre-stored mapping set of inkjet parameter fine-tuning mapping factor-inkjet adjustment parameter in the database for mapping and matching to obtain the inkjet adjustment parameters of the normal nozzle, including the pulse width inkjet fine-tuning value, the inkjet frequency inkjet fine-tuning value, and the pressure compensation value inkjet fine-tuning value. The printhead controller writes the inkjet adjustment parameters into the normal nozzle for application.

6. The method for quality data collection and anomaly early warning in a printing production line according to claim 4, characterized in that: The specific conditions for performing fine-tuning of the corresponding abnormal inkjet parameters are as follows: The operating temperature of the nozzle with abnormal structural pressure is collected from the nozzle temperature sensor, and the nozzle impedance and drive response delay of the nozzle with abnormal structural pressure are also obtained. The standard values ​​of nozzle operating temperature, nozzle impedance, and drive response delay threshold are extracted from the database. The operating temperature, nozzle impedance, and drive response delay of the nozzle with abnormal structural pressure are compared one by one with the standard values ​​of nozzle operating temperature, nozzle impedance, and drive response delay threshold, and then weighted fitting is performed to obtain the structural anomaly characterization value of the nozzle with abnormal structural pressure. The structural anomaly characterization value of the nozzle with abnormal structural pressure is input into the pre-stored mapping set of structural anomaly characterization value-inkjet parameter fine adjustment mapping factor correction value in the database for mapping matching to obtain the inkjet parameter fine adjustment mapping factor correction value of the nozzle with abnormal structural pressure. The inkjet parameter fine adjustment mapping factor correction value of the nozzle with abnormal structural pressure is then corrected with the inkjet parameter fine adjustment mapping factor of the current printing task to obtain the corrected inkjet parameter fine adjustment mapping factor of the nozzle with abnormal structural pressure. The corrected inkjet parameter fine-tuning mapping factor of the nozzle with abnormal structural pressure is input into the mapping set of inkjet parameter fine-tuning mapping factor - abnormal inkjet adjustment parameter to obtain the abnormal inkjet adjustment parameters of the nozzle with abnormal structural pressure. These parameters include the inkjet fine-tuning values ​​of abnormal pulse width, abnormal inkjet frequency, and abnormal pressure compensation. The printhead controller writes the abnormal inkjet adjustment parameters into the nozzle with abnormal structural pressure for application.

7. A quality data acquisition and anomaly early warning system for a printing production line, the system being used to implement the quality data acquisition and anomaly early warning method for a printing production line as described in any one of claims 1 to 6, characterized in that, The system includes: The monitoring module is used to acquire the time-series data of inkjet rebound behavior during each inkjet action on the printing production line within the monitoring period, and to calculate the inkjet anomaly characterization parameters of each nozzle within the monitoring period, including the rebound angle fluctuation rate, the amount of decay rate change and the probability of sudden ink droplet splitting. The anomaly identification module is used to extract the printhead pressure anomaly judgment conditions corresponding to the inkjet anomaly characterization parameters, match the inkjet anomaly characterization parameters of each nozzle with the corresponding printhead pressure anomaly judgment conditions, and statistically identify the nozzles with pressure anomalies. The cleaning module is used to send cleaning control commands to the nozzle controller for nozzles with abnormal pressure, and inserts interval frame micro-explosion deblocking pulses to clean the nozzles with abnormal pressure. The optimization module is used to fine-tune and optimize the inkjet parameters of nozzles with abnormal pressure after cleaning them.

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