Accurate printing method of rotary press

Abstract

The invention provides an accurate printing method of a rotary press, and relates to the technical field of data processing.The method comprises the steps that printing data are obtained, a thickness and tension relation curve is constructed through the gradient fitting principle, and deformation curve data are obtained; based on the mapping relation between the material deformation quantity and the imprinting response, the displacement change of each printing point position is calculated, and an error vector sequence is generated; a roller compensation proportion parameter is generated by combining different roller load structure differences, so that a roller phase reference instruction is adjusted, and a static fine adjustment vector set is obtained; constructing a dynamic adjustment parameter set for roller control synchronous adjustment by combining the transmission characteristics of the roller and servo system parameters, and generating a control scheduling signal flow through a time discrete mode; according to the control scheduling signal flow, the relative position between the rollers is finely adjusted in real time, so that the image-text edge positioning error is kept within a preset tolerance range; the autonomy and accuracy of the printing machine are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a precise printing method for a rotary printing press. Background Art

[0002] In the prior art, a rotary printing press mainly combines a graphic area with a plate cylinder, transfers ink to the plate through an ink roller, and then transfers the graphics to the surface of the substrate through pressure to achieve continuous printing. To improve printing accuracy, precision gear transmission and electronic shaft control technologies are usually used to achieve synchronous movement between cylinders. In addition, some systems also introduce tension control and automatic registration devices to reduce problems such as color difference and graphic ghosting. However, such systems generally rely on physical hardware coordination and preset control logic, lacking the ability to adaptively adjust dynamic errors during the printing process.

[0003] In the printing scenario of high-gloss detail images, such as the printing of high-end color labels, the existing printing control methods are difficult to cope with the problem of the accumulation of graphic edge errors. Specifically, when there are slight fluctuations in the thickness of the substrate, the current synchronous control cannot accurately identify the micro-displacement differences caused by such fluctuations, resulting in slight ghosting at the graphic boundaries. Although such errors are difficult to detect by the naked eye, in scenarios that require high-precision overprinting, such as the printing of security anti-counterfeiting labels, they will significantly affect the quality of the finished product and are not easy to correct in a timely manner at the initial stage of printing, restricting the scope of application of the existing technology. Summary of the Invention

[0004] The purpose of the present invention is to provide a precise printing method for a rotary printing press, aiming to solve the problems mentioned in the background art.

[0005] To solve the above technical problems, the technical solution of the present invention is as follows:

[0006] A precise printing method for a rotary printing press, the method comprising:

[0007] Obtain printing data of the substrate, and construct a thickness-tension relationship curve using the gradient fitting principle to obtain deformation curve data;

[0008] According to the deformation curve data, based on the mapping relationship between the material deformation amount and the imprint response, calculate the displacement change of each printing point to generate an error vector sequence;

[0009] According to the error vector sequence, combined with the structural differences of different cylinder loads, generate cylinder compensation ratio parameters, and adjust the cylinder phase reference command accordingly to obtain a static fine-tuning vector set;

[0010] According to the static fine-tuning vector set, combined with the transmission characteristics of the cylinder and the servo system parameters, construct a dynamic adjustment parameter set for synchronous adjustment of cylinder control, and generate a control scheduling signal stream through time discretization;

[0011] According to the control scheduling signal flow, the relative positions between the rollers are finely adjusted in real time to keep the graphic edge positioning error within a preset tolerance range. The rollers include a plate cylinder, an impression cylinder and an inking cylinder.

[0012] Preferably, according to the printing data, a thickness-tension relationship curve is constructed using the gradient fitting principle to obtain deformation curve data, including:

[0013] The thickness sampling values in the printing data are segmented by intervals to construct corresponding tension distribution intervals, forming a set of data pairs;

[0014] According to the set of data pairs, the tension change rate is calculated respectively within each thickness sub-interval, and a corresponding local tension fitting function is constructed using the piecewise linear interpolation principle;

[0015] The local tension fitting functions are spliced in the order of the thickness intervals to form a continuously differentiable tension response function, and the first derivative change is extracted based on this function to obtain a response rate curve for describing the micro-variation of tension with thickness;

[0016] The response rate curve is normalized and mapped in the coordinate system of the running track of the printed material to obtain the deformation curve data.

[0017] Preferably, according to the deformation curve data, based on the mapping relationship between the material deformation amount and the impression response, the displacement change of each printing point is calculated to generate an error vector sequence, including:

[0018] According to the deformation curve data, the tension gradient values at multiple equally spaced position points are extracted to form a tension change array;

[0019] The tension change array is input into a preset material impression response mapping function, and the displacement deformation values at the corresponding position points are output;

[0020] The difference between the deformation value of each position point and the target displacement deformation value is calculated to obtain the edge error estimation value at this position point;

[0021] All the edge error estimation values are summarized in the spatial order of the printing points to construct an error vector sequence.

[0022] Preferably, according to the edge error prediction value, combined with the differences in the load structures of different rollers, a roller compensation ratio parameter is generated to adjust the roller phase reference instruction, obtaining a set of static fine-tuning vectors, including:

[0023] Obtain the structural attributes and load inertia parameters of each roller, classify them according to the preset structural types, assign a number to each roller, and establish a physical attribute mapping relationship for characterizing the roller response differences, forming a set of roller difference parameters with number indexes;

[0024] According to the error vector sequence, combined with the drum difference parameter set, and using the principle of differential normalization mapping, calculate the compensation ratio parameter for each drum;

[0025] Decompose the error vector sequence according to the drum type, and perform weighted correction in combination with the compensation ratio parameter to construct a static fine-tuning vector set.

[0026] Preferably, according to the static fine-tuning vector set, combined with the transmission characteristics of the drum and the servo system parameters, construct a dynamic adjustment parameter set for synchronous adjustment of drum control, and generate a control scheduling signal flow through time discretization, including:

[0027] According to the fine-tuning target values of each drum in the printing section in the static fine-tuning vector set, combined with the transmission characteristic parameters and servo response performance parameters of the drum, use the drum dynamic execution modeling principle to establish a drum execution time delay model;

[0028] According to the drum execution time delay model, extract the start response time, inertial adjustment rate, and stable holding accuracy of each drum within the control cycle to construct a drum control execution characteristic set;

[0029] According to the control execution characteristic set, segment and match the fine-tuning target values in the static fine-tuning vector set along the time axis to generate a control node sequence indexed by the double keys of drum number and time index. Each control node includes a drum identifier, an execution time point, and a fine-tuning target value;

[0030] Organize the control node sequence in the order of the execution time points and encode it in the drum control instruction format to generate a control scheduling signal flow.

[0031] Preferably, splice the local linear function family in the order of thickness intervals to form a continuously differentiable tension response function, and extract the first derivative change based on this function to obtain a response rate curve for describing the micro-variation of tension with thickness, including:

[0032] Perform continuity correction processing on the interval boundaries of each local tension fitting function to form the spliced tension response function;

[0033] According to the tension response function, extract the first derivative value at each thickness point to construct a tension response derivative sequence;

[0034] Map the tension response derivative sequence to the running trajectory coordinate system of the substrate to form a response rate curve.

[0035] Preferably, according to the error vector sequence, combined with the drum difference parameter set, and using the principle of differential normalization mapping, calculate the compensation ratio parameter for each drum, including:

[0036] Construct a normalized reference value for the drum characteristics based on the structural attribute parameters and load inertia parameters included in the drum difference parameter set.

[0037] Perform drum grouping and corresponding processing on each error data in the error vector sequence, and calculate the compensation factor of the drum by using the linear proportional scaling method according to the normalized reference value of each drum.

[0038] Take the compensation factor as the proportional coefficient and output the compensation ratio parameter corresponding to each drum.

[0039] Preferably, decompose the error vector sequence according to the drum type, and perform weighted correction in combination with the compensation ratio parameter to construct a static fine-tuning vector set, including:

[0040] According to the spatial position relationship of each printing point in the error vector sequence, map the edge error estimation value to the corresponding drum control section to form an error subset divided by drum type.

[0041] For the edge error estimation value in each error subset, perform item-by-item weighted adjustment by using the compensation ratio parameter of the corresponding drum to construct a drum-level fine-tuning data vector.

[0042] Integrate all drum-level fine-tuning data vectors according to the drum number to form a static fine-tuning vector set.

[0043] Preferably, according to the fine-tuning target value of each drum in the static fine-tuning vector set, combine the transmission characteristic parameters and servo response performance parameters of the drum, and use the drum dynamic execution modeling principle to establish a drum execution time delay model, including:

[0044] Obtain the no-load response time parameter of each drum in the no-load state and construct a basic response delay model.

[0045] According to the transmission structure parameters of the drum, extract the inertia load characteristic indexes including the drum mass distribution, drive radius and transmission chain reduction ratio to form a structural inertia matrix.

[0046] Collect the control performance parameters of the servo control system, including the open-loop gain, speed integral time constant and target positioning accuracy, and establish a drum closed-loop control response curve.

[0047] According to the structural inertia matrix and the drum closed-loop control response curve, construct a multi-factor joint model for fitting the dynamic behavior of the drum under different fine-tuning target values, and output the drum execution time delay model.

[0048] Preferably, according to the control execution characteristic set, the fine-tuning target values in the static fine-tuning vector set are segmented and matched along the time axis to generate a control node sequence indexed by the double keys of the drum number and the time index. Each control node includes a drum identifier, an execution time point, and a fine-tuning target value, including:

[0049] According to the start-up response time and control period of each drum in the control execution characteristic set, determine the time window interval that can be allocated for each drum within the target execution period;

[0050] Based on the distribution position of the fine-tuning target value of each drum in the static fine-tuning vector set, adopt the time-axis linear equal division strategy or the error gradient weighting strategy, and segment and map the target value to each executable time window interval according to time segments to obtain the drum mapping result data;

[0051] For the drum mapping result data, perform double indexing according to the time window interval number and the drum number, construct a control node sequence, and assign a drum identifier, an execution time point, and the corresponding fine-tuning target value to each node;

[0052] Sort the time points in the control node sequence in chronological order to form a structured scheduling node set.

[0053] The above solution of the present invention has at least the following beneficial effects:

[0054] By introducing a fine-tuning control mechanism driven by an error vector into the traditional rotary printing press control system, this method realizes the dynamic recognition of the microscopic deformation state of the printing substrate surface and the synchronous adjustment of the drums, significantly improving the graphic edge positioning accuracy and the system's adaptive compensation ability, and overcoming the limitation of the prior art in being difficult to respond to dynamic error changes in high-precision overprinting scenarios.

[0055] Compared with the traditional method of synchronizing drums relying on mechanical structures and static control logics, by constructing a thickness-tension relationship model and further forming deformation curve data, this method can deduce the microscopic deformation trend during the material operation process using the existing sampling data without the need to additionally increase the physical sensor load. This trend modeling not only provides a finer-grained material state perception ability than static tension control but also has the characteristic of predicting the error trend before graphic imprinting.

[0056] In terms of the processing control strategy, this method no longer uses a unified and fixed synchronous reference signal, but generates a drum compensation ratio parameter based on the error vector sequence and the drum structure difference, and adjusts the phase reference instruction of the drum accordingly. This strategy effectively breaks through the position cumulative error problem caused by "controlling different response systems with the same parameters", enabling the system to have personalized control capabilities at the drum level, and is particularly suitable for equipment environments with inconsistent structures and large assembly error tolerances.

[0057] Furthermore, by constructing a static fine-tuning vector set and generating a control scheduling signal flow, this method realizes an end-to-end data-driven closed-loop control process from error perception to fine-tuning execution. The scheduling signals are organized in a time-discrete manner, enabling the control between cylinders to no longer depend on a fixed cycle, but rather dynamically allocate control windows based on the material state and cylinder capabilities, achieving coordinated adjustment under the three-dimensional constraints of "space-time-structure". This mechanism has good adjustment flexibility and scalability, and can maintain stable alignment of graphic edges even under high-speed operation conditions.

[0058] Through the method of the present invention, even in the case of slight fluctuations in the thickness of the substrate or offset of the imprint position due to tension disturbances, real-time compensation can still be completed through error quantification modeling and dynamic control strategies, avoiding phenomena such as blurring, ghosting, or overprinting of graphic boundaries. This solution is particularly applicable to printing fields with strict requirements such as high-end labels, security anti-counterfeiting, and color positioning. While ensuring the operation efficiency of high-speed production lines, it improves the consistency of finished product quality, reduces the scrap rate, and lays a foundation for realizing an intelligent printing control system for multi-cylinder coordinated adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a flowchart of a precise printing method for a rotary printing press provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0061] As Figure 1 shown, an embodiment of the present invention proposes a precise printing method for a rotary printing press, and the method includes:

[0062] S100. Obtain substrate data, where the substrate data includes thickness sampling values and tension change values of the substrate in multiple printing cycles, and is used to characterize the physical form state of the substrate surface;

[0063] S200. According to the substrate data, construct a thickness-tension relationship curve using the gradient fitting principle to obtain deformation curve data, where the deformation curve data is used to reflect the microscopic deformation trend per unit length of the substrate surface;

[0064] S300. According to the deformation curve data, based on the mapping relationship between the material deformation amount and the imprint response, calculate the displacement change at each printing point to generate an error vector sequence;

[0065] S400. Generate a drum compensation ratio parameter based on the error vector sequence and in combination with the differences in the load structures of different drums, and use this to adjust the drum phase reference command to obtain a set of static fine-tuning vectors.

[0066] S500. Based on the set of static fine-tuning vectors and in combination with the transmission characteristics of the drum and the servo system parameters, construct a set of dynamic adjustment parameters for synchronous adjustment of drum control, and generate a control scheduling signal flow through time discretization.

[0067] S600. According to the control scheduling signal flow, fine-tune the relative positions between the drums in real time so that the graphic edge positioning error is kept within a preset tolerance range, where the drums include a plate cylinder, an impression cylinder, and an inking cylinder.

[0068] In an embodiment of the present invention, this method extracts and processes the thickness sampling values and tension change values of the substrate in multiple printing cycles by obtaining the printing data, thereby constructing a quantitative expression basis for the physical morphological state of the substrate surface. In a specific implementation, through a periodic sampling mechanism, it is possible to capture thickness fluctuations and tension disturbances caused by differences in material batches, transportation deformation, or environmental temperature and humidity changes, etc., providing a true and reliable data basis for subsequent modeling and control. Compared with traditional methods that only rely on fixed material specifications or one-time detections, this solution provides higher timeliness and state perception capabilities.

[0069] In the data modeling stage, the gradient fitting principle is introduced to construct a thickness-tension relationship curve, and on this basis, deformation curve data is formed. The introduction of the deformation curve enables the digital expression of the microscopic deformation trend within a unit length range of the substrate surface, which is particularly suitable for micro-scale deviation problems such as stretching, shrinking, and edge warping caused by the continuous transmission of the substrate in a rotary printing scenario. This modeling mechanism not only improves the foresight of error prediction but also lays a logical foundation for subsequent dynamic compensation.

[0070] Using the deformation curve data and in combination with the physical strain-stress mapping law of the material, further complete the calculation of displacement changes at the printing point level. Different from the previous control method that solely relies on sensor feedback, this solution provides a global view of the point-level error distribution at the prediction level, which helps to deploy compensation measures in advance at the control layer. The generated error vector sequence retains the spatial order information and error amplitude change characteristics of each point, providing a data basis for subsequent error classification and compensation parameter matching according to the drum dimension.

[0071] At the execution level, in combination with the differences in the load structures of the drum bodies, by modeling the structural parameters and transmission inertia, the response differences between the drums are extracted. On this basis, the drum compensation ratio parameters are designed to achieve the equalization calibration of the response capabilities between different drums, effectively avoiding the overprint offset problem caused by the out-of-sync of multiple drums or inconsistent reaction amplitudes in the traditional solution. The compensated static fine-tuning vector set has clear drum identifiers and target adjustment values, supporting subsequent segmented scheduling of the displacement targets.

[0072] Finally, in combination with the drum transmission characteristics and the servo system response performance, by establishing a set of dynamic adjustment parameters, the displacement control is shifted from static offset to dynamic scheduling. By constructing the control scheduling signal flow in a time-discrete manner, the real-time fine-tuning control of the relative positions between the drums is realized. Without increasing the complexity of the hardware structure, this solution realizes the continuous adjustment control of the graphic edge error, can significantly improve the edge alignment accuracy of printed products, and is especially suitable for scenarios such as high-precision label printing and security anti-counterfeiting printing, enhancing the finished product consistency and quality stability while maintaining high production speed.

[0073] Among them, the printing substrate data is obtained, and the printing substrate data includes the thickness sampling values and the tension change values of the printing substrate in multiple printing cycles, specifically including:

[0074] In order to accurately reflect the dynamic physical state of the printing substrate during continuous printing, it is necessary to perform periodic sampling on the thickness and tension changes during its operation to construct the input data basis. The thickness sampling values can be obtained by a non-contact thickness sensing device arranged at the feeding channel or the front section of printing. This sensing device has high-frequency sampling ability and can measure the thickness change of the printing substrate surface at fixed intervals, such as at intervals of 0.5 ms to 2 ms.

[0075] The tension change values can be collected in real time by arranging tension sensors on the delivery reel, guide roller or tension control roller. Such tension data will fluctuate with factors such as printing speed, material ductility, and curling state during the actual process. Therefore, it is necessary to regularly record the tension values at the corresponding time points or position points through the control system and perform paired synchronous processing with the thickness data.

[0076] The above data collection should cover multiple complete printing cycles, that is, the whole process from the printing of a graphic unit of the printing substrate to its return to the starting position again. By collecting and structuring these multi-cycle data, a printing substrate data set can be formed, and the subsequent steps are based on this set as the input basis for modeling and compensation control.

[0077] In a preferred embodiment of the present invention, according to the printing substrate data, the gradient fitting principle is used to construct the thickness-tension relationship curve to obtain the deformation curve data, including:

[0078] Segment the thickness sampling values in the printing data into intervals, construct the corresponding tension distribution intervals, and form a set of data pairs;

[0079] According to the set of data pairs, calculate the tension change rate in each thickness sub-interval respectively, and construct the corresponding local tension fitting function using the principle of piecewise linear interpolation;

[0080] Stitch the local tension fitting functions in the order of thickness intervals to form a continuously differentiable tension response function, and extract the first derivative change on the basis of this function to obtain a response rate curve for describing the micro-variation of tension with thickness;

[0081] Perform a normalized mapping of the response rate curve in the coordinate system of the running trajectory of the substrate to obtain the deformation curve data.

[0082] In the embodiments of the present invention, by segmenting the thickness sampling values in the printing data into intervals and constructing a set of data pairs in combination with the tension change values, the method realizes the spatial division of the thickness-tension coupling relationship, making the subsequent modeling have higher resolution and pertinence. Each data pair represents the tension response of the substrate under specific thickness conditions, which helps to eliminate local abnormal errors caused by global averaging in the overall fitting process.

[0083] In the modeling method, the principle of piecewise linear interpolation is introduced to construct a family of local tension fitting functions. While ensuring that the calculation intensity of the model is controllable, the flexibility and adjustability of the model are retained. Each local function corresponds to a thickness sub-interval, enabling the model to fully adapt to the non-linear response characteristics of tension in different thickness sections, and avoiding common boundary jumps or overfitting problems in traditional overall polynomial fitting. By sequentially stitching the function family and introducing continuity correction measures at the stitching nodes, it can be ensured that the final tension response function is continuously differentiable in the sense of the first derivative, thus having physical rationality and engineering interpretability.

[0084] Furthermore, by extracting the first derivative change value of the tension response function, the response sensitivity of tension to thickness change, that is, the tension gradient information, can be obtained. These information can be used for the construction of subsequent deformation trends and are also important reference bases for evaluating the regional stability of the substrate. By mapping the derivative change sequence to the coordinate system of the running trajectory of the substrate, the function originally with thickness as the independent variable is converted into a deformation response description method with spatial position as the reference, realizing the bridging between physical characteristics and control instructions, and facilitating the control system to deploy compensation strategies according to the running path.

[0085] Among them, segmenting the thickness sampling values in the printing data into intervals, constructing the corresponding tension distribution intervals, and forming a set of data pairs specifically include:

[0086] In order to extract structured data that can be used for function fitting from large-scale continuous sampling data, this method performs intervalization on the thickness sampling values, that is, divides them into several non-overlapping sub-intervals according to a preset thickness range. For example, if the thickness sampling value range is between 30 microns and 50 microns, it can be divided into 4 to 8 sub-intervals, and each interval covers a thickness value of about 5 microns.

[0087] Subsequently, within each thickness sub-interval, the corresponding tension sampling values are extracted, that is, those tension data points that are recorded simultaneously or nearly simultaneously with this thickness value in the dataset. These data pairs constitute a one-to-one thickness-tension mapping relationship, called the "data pair set". Each group of data pairs can be regarded as the typical response result of the material under stress at a certain thickness, providing a real physical basis for subsequent tension modeling and curve construction.

[0088] This processing method can effectively avoid the problem of error diffusion caused by forced association of non-associated data, and at the same time can extract the data distribution law in small-scale sections, making subsequent local fitting more accurate and engineering adaptable.

[0089] Among them, according to the data pair set, the tension change rate is calculated separately within each thickness sub-interval, and the corresponding local tension fitting function is constructed using the principle of piecewise linear interpolation, specifically including:

[0090] In specific operations, for the data pair set in each thickness sub-interval, it can be sorted in ascending order of thickness, and based on the ratio of the tension difference to the thickness difference between adjacent data points, the rate of change of tension with thickness within this sub-interval is calculated, that is, the tension change rate.

[0091] Since the change trends of data in different sub-intervals may not be consistent, this method calculates the local slope of each sub-interval separately, and based on this slope, constructs a linear interpolation function, which can be used to estimate the tension value at any thickness value within this interval and has the characteristics of continuity and fast calculation.

[0092] The local linear functions in multiple sub-intervals together constitute a set of "local tension fitting functions". This function family can cover the entire thickness change range and has the advantages of local adjustability and overall continuity. Compared with traditional single polynomial fitting or global fitting methods, it is more suitable for dealing with non-linear responses and local abnormal data.

[0093] Among them, the response rate curve is normalized and mapped on the running trajectory coordinate system of the substrate to obtain the deformation curve data, specifically including:

[0094] After the construction of the tension response function, the trend of its first derivative can reflect the sensitivity of the tension to small changes in thickness, namely the so-called "response rate curve". Substantially, this curve describes the severity of the internal tension change of the material when the material thickness changes slightly, and can be regarded as a warning signal for the impending deformation of the material.

[0095] However, the response rate curve was initially expressed with the thickness value as the independent variable and cannot directly reflect the deformation trend of the material on the spatial path. Therefore, this method introduces the way of "normalized mapping", mapping the thickness sampling points corresponding to each derivative value to the position coordinates in the running track of the substrate through the recorded timestamp or printing distance, usually expressed in "millimeters" or "web feed length".

[0096] Through this coordinate transformation, the independent variable of the response rate curve is changed from "thickness" to "running distance" or "spatial position", thus forming "deformation curve data". This deformation curve can intuitively reflect the deformation risk or trend of each small segment of the material in the actual running path of the substrate and becomes an important input for calculating the displacement change of each printing point position subsequently.

[0097] The normalized mapping process not only retains the physical meaning of the microscopic tension response of the material but also realizes precise positioning in space, enabling the system to have a clear point-to-point correlation when calculating errors and scheduling compensation, greatly improving the accuracy and practical implementability of the control strategy.

[0098] In a preferred embodiment of the present invention, according to the deformation curve data, based on the mapping relationship between the material deformation amount and the imprinting response, the displacement change of each printing point position is calculated to generate an error vector sequence, including:

[0099] According to the deformation curve data, the tension gradient values at multiple equally spaced position points are extracted to form a tension change array;

[0100] The tension change array is input into a preset material imprinting response mapping function, and the displacement deformation values at the corresponding position points are output;

[0101] The difference between the deformation value of each position point and the target displacement deformation value is calculated to obtain the edge error estimation value of this position point, and the target displacement deformation value is used to describe the target displacement deformation value of the ideal imprinting position in the case of no material deformation, and the ideal imprinting position is generated by the preset graphic design layout parameters;

[0102] All the edge error estimation values are summarized in the spatial order of the printing point positions to construct an error vector sequence.

[0103] In the embodiment of the present invention, according to the deformation curve data, a tension change array is formed by extracting the tension gradient values at equally spaced position points, so as to obtain the microscopic stress distribution state of the printing substrate within the entire running trajectory range. This array does not rely on local extreme points, but establishes a benchmark in a global traversal manner, having higher stability and coverage ability, effectively avoiding the problem of overall error estimation deviation caused by a local anomaly.

[0104] Subsequently, the tension change array is input into a preset material imprint response mapping function, which is established based on historical data and experimental models and is used to characterize the imprint displacement change law of the material under the action of a specific tension gradient. Through this mapping process, the microscopic strain data that cannot be directly measured can be converted into a computable displacement deformation result, realizing the mapping connection between physical properties and control variables.

[0105] By calculating the difference with the target displacement deformation value, the edge error estimation value of each position point is obtained. This difference represents the spatial deviation between the actual printing offset and the theoretical ideal position under the current material deformation condition. The target displacement deformation value is preset and generated by the layout parameters, with traceability and uniqueness, effectively ensuring the standard unity of error estimation.

[0106] Finally, all the edge error estimation values are summarized in the spatial order of the printing points to construct an error vector sequence. This error vector not only retains the amplitude information of the error, but also carries the spatial distribution pattern, providing a necessary basis for section division for subsequent control compensation at the drum level. Through the vectorized organizational structure, it can be efficiently stored and quickly indexed in the hardware system, enhancing the response efficiency and stability of the overall control system. This structural form also lays a foundation for data parallelism in the collaborative control between multiple drums and is the input support source for key links such as subsequent drum parameter matching, compensation value weighting, and node generation.

[0107] Among them, according to the deformation curve data, extracting the tension gradient values of multiple equally spaced position points to form a tension change array specifically includes:

[0108] Before performing the printing error prediction, first, the key numerical information reflecting the dynamic change trend of tension needs to be extracted from the deformation curve data. In this method, the deformation curve data is obtained by performing a normalized mapping on the running trajectory coordinate system of the printing substrate after modeling the thickness-tension change trend in the previous step. Its vertical axis can represent the tension change rate, and the horizontal axis is the linear position coordinate of the printing substrate along the paper feeding direction.

[0109] To ensure structural balance and computational efficiency during the data processing, this implementation adopts an equidistant sampling strategy. Within the spatial range of the entire deformation curve, a set of position points are selected as sampling nodes at fixed length intervals. The equidistant method makes the subsequent data matrix have a unified structure, facilitating vectorized parallel processing in the system.

[0110] For each selected position point, the corresponding longitudinal data value on the deformation curve is extracted, that is, the tension change rate value at this point, which can be regarded as the intensity of the tension response inside the material caused by thickness perturbation at this point position. After arranging these tension gradient values in the order of position, a tension change array is formed. This array can be understood as the tension gradient characteristic distribution of the substrate in the spatial dimension and is the basic data structure for identifying potential position offset trends.

[0111] Among them, the tension change array is input into a preset material imprint response mapping function, and the displacement deformation value of the corresponding position point is output, specifically including:

[0112] Since different materials have different deformation responses under the same tension perturbation, in order to more accurately reflect the material deformation behavior, this method introduces a material imprint response mapping function as a bridge between the tension gradient value and the displacement deformation value.

[0113] This mapping function is preset and loaded during the system initialization stage according to the material category of the selected substrate (such as coated paper, PET film, thermal paper, etc.). The function form can be a set of section response laws obtained by fitting experimental data. The function internally records the imprint displacement response data corresponding to a specific tension change gradient, such as the unit area compression length caused by the increase of unit tension gradient or the edge movement displacement caused by strain, etc.

[0114] The tension change array is input into this mapping function point by point, and each tension gradient value correspondingly outputs the micro-displacement value that a real position point is expected to occur during the printing process. This micro-displacement value represents the offset of the imprint position of this point relative to the normal state under the current tension state and constitutes the "displacement deformation value" of this point.

[0115] The output displacement deformation value retains the spatial sequence attribute and corresponds one by one with the tension change array, forming a micro-displacement prediction sequence covering the entire printing graphic path, which is the direct basis for realizing edge error estimation.

[0116] Among them, the difference between the deformation value of each position point and the target displacement deformation value is calculated to obtain the edge error estimation value of this position point. The target displacement deformation value is used to describe the set of ideal imprint position coordinate points in the case of no material deformation. The set of ideal imprint position coordinate points is generated by the preset graphic design layout parameters, specifically including:

[0117] After calculating the displacement deformation values, in order to evaluate the possible edge errors that may actually occur, it is necessary to compare the predicted deformation positions with the design reference positions. The "target displacement deformation values" used in this method are an ideal set of embossing position coordinate points generated based on the design parameters of the printed document and the drum arrangement rules, with fixed spacing and arrangement order.

[0118] This set of coordinate points is automatically generated by the system when receiving graphic layout information. It depends on parameters such as the preset graphic length, printing format width, and length of the graphic repeating unit to form a set of continuous spatial point coordinates, representing the standard position of the drum embossing graphics when the material has not undergone any deformation. Its advantages are clear data sources, traceable arrangements, and complete consistency with the plate pitch.

[0119] By performing a difference operation on the corresponding points between the aforementioned displacement deformation values and the target displacement deformation values, the estimated edge error value for each position point can be obtained. The direction of the difference is "predicted position minus ideal position", and the positive and negative signs are retained to identify whether the embossed graphics are ahead or behind, and whether the error is caused by stretching or shrinking.

[0120] The acquisition of the estimated edge error value enables the system to know in advance the offset trend of the graphic boundary before actual printing and to have the ability to judge whether it enters the graphic tolerance range, which is the basis for formulating subsequent dynamic compensation strategies.

[0121] Among them, all the estimated edge error values are summarized in the spatial order of the printing points to construct an error vector sequence, specifically including:

[0122] This step organizes the estimated edge error values of each position point into a data structure with spatial order, that is, an error vector sequence. This vector uses the spatial order as the main index and records the estimated error values of each position point from the starting embossing position to the ending embossing position of the substrate.

[0123] When constructing the error vector sequence, the original order of the tension sampling points or thickness sampling points is maintained, ensuring that this structure not only has error amplitude information but also completely carries the spatial distribution characteristics of the error along the material running path. The system can directly perform regional analysis, drum section matching, and error accumulation trend prediction based on this vector during the scheduling stage.

[0124] This error vector is the basic input structure for multiple control links such as subsequent drum compensation parameter generation, drum response difference matching, and control node segmented scheduling. Compared with traditional single-point error values, statistical extreme values of this section, or moving averages, the error vector constructed by this method has the advantages of high data density, strong response timeliness, and expandable structure, and is the core data carrier for realizing dynamic precise control of the entire printing path.

[0125] In a preferred embodiment of the present invention, according to the predicted value of the edge error, combined with the structural differences of different drum load structures, a drum compensation ratio parameter is generated to adjust the drum phase reference command, and a static fine-tuning vector set is obtained, including:

[0126] Obtain the structural attributes and load inertia parameters of each drum, classify them according to the preset structural types, assign a number to each drum, establish a physical property mapping relationship for characterizing the drum response differences, and form a drum difference parameter set with number indexes;

[0127] According to the error vector sequence, combined with the drum difference parameter set, using the principle of differential normalization mapping, calculate the compensation ratio parameter of each drum;

[0128] Decompose the error vector sequence according to the drum type, and perform weighted correction in combination with the compensation ratio parameter to construct a static fine-tuning vector set.

[0129] In the embodiment of the present invention, through the correlation processing between the error vector sequence and the drum load structure differences, the quantitative modeling of the differential control ability of the multi-drum system is realized. Specifically, the structural attributes and load inertia parameters of the drum start from the mechanical body structure and motion response angles, covering the core physical factors that cause control lag or inconsistent response amplitudes in actual use. By classifying according to the preset structural types, the instability brought by the interference of artificial experience in the modeling process is effectively avoided, making the drum modeling more general and standardized.

[0130] The construction of the drum physical property mapping relationship maps the structural indexes of each drum to the normalized dimension, forming a drum difference parameter set, enabling the system to implement a unified error compensation logic under different drum response conditions. Previous solutions often regarded the drum control as a homogeneous response, resulting in problems such as misalignment, body vibration, or imprint overlap caused by drum inertia differences in high-precision scenarios. However, in this embodiment, by introducing the difference parameter set, a clear and data-driven calculation basis is provided for the subsequent formulation of the compensation ratio.

[0131] The principle of differential normalization mapping not only normalizes the modeling of drum differences, but also directly maps the error vector and the drum response ability through a mathematical function. This method avoids the risk of over-compensation or reverse in the high-error state. Especially in the multi-drum combined structure, there may be significant differences in aspects such as the diameter, drive source type, and transmission chain path of different drums. At this time, using a unified compensation parameter will lead to system imbalance, while this solution realizes dynamic matching and personalized correction by calculating the compensation ratio parameter of each drum separately.

[0132] Finally, by decomposing the error vector sequence in the drum dimension and applying respective compensation ratio parameters for weighted correction, not only is the adaptability to error sources effectively improved, but also the control precision of the coordination between drums is enhanced. The output static fine-tuning vector set has characteristics such as accuracy, grading, and traceability, and is a key bridge structure connecting error prediction and dynamic control scheduling, which helps to promote the system to evolve towards the closed-loop fine control goal of higher resolution and lower error.

[0133] Among them, obtain the structural attributes and load inertia parameters of each drum, classify them according to the preset structural type, assign a number to each drum, establish a physical attribute mapping relationship for characterizing the response differences of the drums, and form a drum difference parameter set with number indexes, specifically including:

[0134] Before implementing the dynamic fine-tuning of the multi-drum system, it is necessary to first identify and model the response ability differences between the drums due to structural configuration and installation differences. This step is to construct a parameter set reflecting the response ability differences of the drums by extracting the key physical parameters of each drum, which serves as the basis for subsequent compensation ratio calculation and fine-tuning scheduling.

[0135] First of all, the extraction of structural attributes usually includes the following types of parameters: the diameter, wall thickness, material (such as steel, aluminum or composite material), drive mode (independent servo or coaxial synchronization), and the type of coupling or transmission chain of the drum. These parameters can be directly obtained from the equipment technical manual, design drawings or the initialization configuration of the control system.

[0136] Secondly, the load inertia parameter mainly refers to the inertia torque required for the drum to rotate around its central axis. This parameter is not only affected by the mass distribution of the drum, but also closely related to its installation position and drive mode. In the case where it cannot be directly measured, the structural simplification assumption and empirical coefficient estimation method can also be used to approximately calculate it through "mass × radius squared × coefficient".

[0137] After combining the parameters in the above multiple physical dimensions and classifying them according to the preset structural type (such as light hollow drum, heavy solid drum, drum with gear pair, etc.), the drum physical attribute mapping relationship can be formed. This mapping relationship can be in the form of rule expression, table or section function, and is used to quickly judge the speed, stability and adjustment sensitivity of its control response on the premise of knowing the structural characteristics of the drum.

[0138] Finally, by uniformly sorting out the above mapping results of each drum, a "drum difference parameter set" is formed. Each drum has a set of label data in this parameter set for describing its control response differences, and the subsequent control system will apply the corresponding intensity and response rhythm fine-tuning strategies according to these labels to achieve differential control.

[0139] In a preferred embodiment of the present invention, according to the static fine-tuning vector set, combined with the transmission characteristics of the cylinder and the servo system parameters, a dynamic adjustment parameter set for synchronous adjustment of cylinder control is constructed, and a control scheduling signal stream is generated in a time-discrete manner, including:

[0140] According to the fine-tuning target values of each cylinder in the printing section in the static fine-tuning vector set, combined with the transmission characteristic parameters and servo response performance parameters of the cylinder, using the cylinder dynamic execution modeling principle, a cylinder execution time-delay model is established;

[0141] According to the cylinder execution time-delay model, the start response time, inertial adjustment rate, and stable holding accuracy of each cylinder within the control period are extracted, and a cylinder control execution characteristic set is constructed;

[0142] According to the control execution characteristic set, the fine-tuning target values in the static fine-tuning vector set are segmented and matched according to the time axis, and a control node sequence with the cylinder number and time index as the double-key index is generated. Each control node includes a cylinder identifier, an execution time point, and a fine-tuning target value;

[0143] The control node sequence is organized in the order of the execution time points and encoded in the cylinder control instruction format to generate a control scheduling signal stream.

[0144] In the embodiment of the present invention, by introducing a coupled modeling method of the cylinder transmission characteristics and the servo system parameters, a dynamic adjustment parameter set for cylinder control is constructed, and combined with the target values in the static fine-tuning vector set, a fine-tuning control system with time-dimensional scheduling ability is formed. Different from the traditional passive mechanism that only performs compensation control based on position feedback, this embodiment adopts a strategy combining active modeling and pre-adjustment scheduling, realizing feed-forward prediction and timing allocation of control actions during the printing process.

[0145] The cylinder dynamic execution modeling principle comprehensively considers the structural attributes of the cylinder and the response performance of the control system. For example, a large structural inertia may lead to slow startup, and servo response delay may cause a lag in achieving the target. By establishing a cylinder execution time-delay model, these physical process parameters are parameterized, further improving the control accuracy and robustness. This model can not only be used to estimate the actual displacement response time but also be used as a scheduling constraint condition in the control system to avoid time drift between cylinders.

[0146] The construction of the control execution characteristic set enables the system to allocate reasonable control periods, start response times, and fine-tuning holding characteristics to each cylinder, and has the ability to carry out control scheduling according to the personalized parameters of the cylinder. Combined with the fine-tuning target values in the static fine-tuning vector set, the system can be orderly segmented based on the preset time axis, enabling the originally discrete and unorganized fine-tuning values to be constructed into a control node sequence with a dual-index structure of time and cylinder.

[0147] Controlling the generation of the node sequence is a key step in realizing the fine-tuning action advancing in time. The node structure has a triple definition of "drum identifier + time index + fine-tuning target value", with clear scheduling logic and programmability. After being sorted, this sequence forms a control scheduling signal flow, which is further input into the drum control system to achieve closed-loop drive, ensuring high-precision graphic and text edge alignment even under high-speed printing conditions.

[0148] This structured scheduling mechanism effectively avoids problems such as control conflicts, lag accumulation, and cycle mismatch in multi-drum systems, significantly improving the overprint accuracy and production stability, and is particularly suitable for fine printing scenarios with extremely high requirements such as multi-color, variable graphics and texts, and anti-counterfeiting codes.

[0149] Among them, according to the drum execution time-delay model, the start response time, inertia adjustment rate, and stable holding accuracy of each drum within the control cycle are extracted to construct a set of drum control execution characteristics, specifically including:

[0150] During the dynamic adjustment process, the control system not only needs to know "how the drum should be adjusted", but also needs to clarify "at what time and at what rate each drum can be adjusted". This requires the system to extract the control behavior characteristics of each drum. This step is to output three key characteristics with engineering control significance based on the drum execution time-delay model: start response time, inertia adjustment rate, and stable holding accuracy.

[0151] The "start response time" refers to the delay time between the system sending a fine-tuning instruction and the drum starting to show an actual position offset, which is usually jointly determined by factors such as motor response inertia, electronic dead zone, and backlash compensation. In actual engineering, this parameter can be obtained through internal recording of the servo system or through experimental scanning.

[0152] The "inertia adjustment rate" describes the average displacement change amount that the drum can achieve per unit time under the action of a continuous control signal, reflecting its adjustment speed. This rate is jointly affected by the drum moment of inertia, motor output capacity, and control bandwidth. The extraction of the rate helps to determine whether each section of the control instruction exceeds the drum response ability.

[0153] The "stable holding accuracy" refers to the position fluctuation range when the drum maintains the target displacement state. This parameter represents whether the system can maintain accurate output without causing secondary offset due to factors such as vibration, recoil, and rebound. It is usually obtained by statistically analyzing the feedback data of the position encoder and is reflected in the system tolerance strategy.

[0154] Organize and file the above three characteristic parameters according to the drum number to form a drum control execution characteristic set. This characteristic set can be used as the basis for the scheduler to allocate the adjustment rhythm, beat and fine-tuning amplitude. It is the key data bridge between the physical model and the control strategy, ensuring the "reachability" and "real-time" of the scheduling.

[0155] Among them, organize the control node sequence in the order of execution time points and encode it in the drum control instruction format to generate a control scheduling signal stream, specifically including:

[0156] After completing the error modeling, parameter matching and timing allocation, the control system needs to output a set of instruction streams that can be recognized by the actuator in a standard structure to ensure that the control actions are accurately implemented in chronological order. This step realizes the structural conversion from the "control node sequence" to the "signal stream", enabling the system to gradually drive the drum to complete fine-tuning according to the predetermined rhythm.

[0157] The control node sequence is a set composed of the fine-tuning target value and the corresponding execution time point of each drum. Each node contains three items: drum number, execution time point and target displacement. Since each node may come from multiple drums and multiple time periods, these nodes need to be first unified into a time axis and globally sorted according to the time point to form an ordered execution sequence.

[0158] Subsequently, encode each control node in the control instruction format compatible with the drum control system. The encoding structure can be set as a three-segment format of "timestamp - target number - target value". Among them, the timestamp can be the relative time number within the system control period, the target number is the drum identifier, and the target value is the required position offset. The encoding format should be preset to a standard length for easy reading and caching by the controller frame by frame.

[0159] The generation of the control scheduling signal stream is to form a continuous instruction sequence from the above encoded nodes, which can be pre-loaded before the start of the control cycle or dynamically updated during operation. The signal stream is finally sent by the main control unit to the drive controllers of each drum and triggers the corresponding fine-tuning actions at the actual time points.

[0160] This encoding method ensures the timing correctness, object uniqueness and action accuracy of the control execution, can avoid problems such as control overlap, signal conflict or adjustment time mismatch, significantly improves the execution stability of the control system and the coordination between drums, and is the core execution link for finally realizing the high-precision register control closed loop.

[0161] In a preferred embodiment of the present invention, splice the local linear function family in the order of thickness intervals to form a continuously differentiable tension response function, and extract the first derivative change on the basis of this function to obtain a response rate curve for describing the micro-variation of tension with thickness, including:

[0162] Perform continuity correction processing on each local tension fitting function at the interval boundary to form a spliced ​​tension response function;

[0163] According to the tension response function, the first-order derivative value at each thickness point is extracted to construct a tension response derivative sequence;

[0164] The tension response derivative sequence is mapped to the running trajectory coordinate system of the substrate to form a response rate curve.

[0165] In the embodiment of the present invention, the construction of the tension response function is not only the splicing of local tension fitting functions, but also a detailed processing at the level of structural continuity control and derivative information extraction. In the specific implementation, the local function family has a good fitting ability within the thickness sub-interval, but when the interval is spliced, if the boundary continuity is not corrected, it is very easy to introduce problems such as response function mutation, interpolation jitter or numerical instability. To this end, this method uses a derivative smoothing connection algorithm at the node to ensure that the tension response function remains continuous in the sense of the first-order derivative.

[0166] After the response function is constructed, the numerical differentiation method is further used to extract the first-order derivative at each thickness point to form a tension response derivative sequence. This sequence reflects the sensitivity of tension to small changes in thickness and is an important basis for identifying material inhomogeneity. In traditional methods, tension control mostly relies on average values ​​or segment extreme values ​​and lacks the ability to adapt to detailed perturbations. The introduction of the derivative sequence significantly enhances the system's perception resolution of local deformation.

[0167] Subsequently, the derivative sequence is mapped to the running trajectory of the substrate according to the thickness position, so that the tension response transitions from the material thickness dimension to the spatial dimension, realizing the running path binding of the model. This mapping mechanism has a good engineering implementation basis and can be embedded in the path control module in the main control system of the printing press, and linked with the position encoder in real time, making the response rate curve an important basis for subsequent error prediction. Compared with the traditional single-point tension compensation method, this implementation provides a continuity control basis for the entire trajectory, with higher adaptability and response accuracy.

[0168] In a preferred embodiment of the present invention, according to the error vector sequence, combined with the roller difference parameter set, the difference normalization mapping principle is adopted to calculate the compensation ratio parameter of each roller, including:

[0169] According to the structural property parameters and load inertia parameters contained in the roller difference parameter set, a normalized reference value of the roller characteristic is constructed;

[0170] Each error data in the error vector sequence is processed by roller grouping, and the compensation factor of the roller is calculated by linear scaling method according to the normalized reference value of each roller; , is the compensation factor for the th roller, used to adjust the gain control of the error vector. is the structural mass of the th roller. is the load moment of inertia of the th roller. is the standard reference value of the roller mass, set during system initialization. is the standard reference value of the roller moment of inertia, set during system initialization. is the number of printing points controlled by the th roller. is the set of printing point indices corresponding to the th roller. is the displacement deformation value of the th position point. is the target displacement deformation value of the th position point under ideal conditions (no material disturbance, theoretical uniform tension distribution). is the geometric distance of the transmission path for the th roller to control the th position point;

[0171] Taking the compensation factor as the proportional coefficient, the compensation ratio parameter corresponding to each roller is output.

[0172] In the embodiment of the present invention, by combining the error vector sequence with the roller difference parameter set and adopting the principle of difference normalization mapping, a compensation ratio parameter generation mechanism for a multi-roller system is constructed. This mechanism shows a high degree of adaptability when dealing with significant differences in the response characteristics of rollers in printing equipment, and is particularly suitable for complex systems where multiple rollers work together but there are physical differences such as transmission structure, mass inertia, or installation angle.

[0173] First, through the roller difference parameter set, the structural attributes and inertial characteristics of each roller are normalized and modeled to construct a normalized reference value for roller characteristics. This processing method effectively avoids the scale error and unit inconsistency problems that may be caused by directly performing linear operations on roller physical quantities, enabling the subsequent generation of compensation factors to have a unified reference benchmark. The normalization process not only facilitates parameter comparison but also provides data support for the normalization compensation strategy in the subsequent control system.

[0174] Secondly, when performing the drum grouping process on the error data, the system can automatically identify the error segments corresponding to each drum control area in the error vector sequence, thereby achieving precise binding between the error data and the drum objects. Traditional systems mostly adopt a unified error standard or manually set compensation coefficients, and cannot make dynamic adjustments for the response differences of different drums. However, this method realizes the on-demand weighted correction of each group of error data through the compensation factors calculated based on the drum parameters.

[0175] Using the linear proportional scaling method to calculate the compensation factor helps to maintain the operational stability of the control system under complex compensation logics. Especially when the variation range of the displacement error amplitude is relatively large, linear scaling can not only prevent system oscillations caused by over-compensation but also avoid insufficient response to minor errors, thereby improving the sensitivity and stability of system regulation.

[0176] Finally, the output result of the compensation ratio parameter, as an important input for the drum-level control strategy, provides a high-resolution, personalized, and computable basic basis for the construction of subsequent static fine-tuning vectors. This method not only improves the error adaptability of the system but also enhances the drum independence of the control strategy, providing a theoretical and engineering implementation basis for multi-channel and differential drum printing systems.

[0177] In a preferred embodiment of the present invention, the error vector sequence is decomposed according to the drum type and weighted correction is performed in combination with the compensation ratio parameter to construct a set of static fine-tuning vectors, including:

[0178] According to the spatial position relationship of each printing point in the error vector sequence, the estimated edge error value is mapped to the corresponding drum control section to form an error subset divided by drum type;

[0179] For the estimated edge error value in each error subset, item-by-item weighted adjustment is performed using the compensation ratio parameter of the corresponding drum to construct a drum-level fine-tuning data vector;

[0180] All the drum-level fine-tuning data vectors are integrated according to the drum numbers to form a set of static fine-tuning vectors.

[0181] In the embodiment of the present invention, through the structured processing of the error vector sequence and drum-level compensation correction, a closed-loop data processing path from error decomposition to the generation of static fine-tuning vectors is constructed, significantly improving the hierarchical accuracy of system error compensation and the usability of compensation vectors.

[0182] First, by identifying the spatial position relationships of each printing point in the error vector sequence, the estimated edge error values are accurately mapped to their respective drum control sections. This mapping strategy takes into account the physical continuity between the points in the printing path, enabling the error subset division to have a logical basis for the actual drum control boundaries and eliminating control anomalies caused by repeated or omitted error attribution.

[0183] Applying the compensation ratio parameter to the estimated edge error values in the error subset for item-by-item weighting is an important step in achieving the drum differential control ability in this method. The compensation ratio parameter for each drum is derived from the modeling results of the drum physical response differences, so it has adaptability to individual differences. The weighting operation is completed in the form of vector multiplication or matrix operation in the control system, which can amplify the response to small errors and suppress the callback of large errors while maintaining the calculation efficiency, demonstrating a high dynamic range adjustment ability.

[0184] The construction of the drum-level fine-tuning data vector indicates that the error data has been transformed from a spatial distribution to a control command. This vector set retains the sequential relationship of the errors within the drum control unit, enabling subsequent time-segment scheduling to be carried out based on accurate displacement target values. At the same time, the vector structure facilitates data compression, caching, and segment calling, which helps to improve the response speed and redundancy tolerance of the control system in high-speed printing scenarios.

[0185] Finally, by integrating the fine-tuning data vectors of each drum according to their numbers to form a static fine-tuning vector set, the input basis for generating subsequent dynamic adjustment parameters and scheduling signals is constructed. Compared with the traditional method of using fixed ratio correction or section mean compensation, this method is more controllable, predictable, and iterative. It is an important intermediate step in achieving high-precision alignment of graphic and text edges and is particularly suitable for error compensation tasks in complex scenarios such as multi-drum coordination, continuous printing, and variable data output.

[0186] Among them, integrating all the drum-level fine-tuning data vectors according to the drum numbers to form a static fine-tuning vector set specifically includes:

[0187] After completing the error vector decomposition and compensation ratio parameter weighting, this method has generated a set of drum-level fine-tuning data vectors for each drum. Each set of vectors contains the fine-tuning target values of the corresponding printing points in the control area of the drum, with spatial order and amplitude value information, and is an independent compensation sequence generated based on the actual load characteristics of the drum.

[0188] To facilitate the subsequent unified invocation and scheduling of execution strategies, it is necessary to number and integrate these fine-tuning vectors scattered in each drum data block to form a complete "static fine-tuning vector set". In this integration process, the "drum number" is used as the main index label, each group of fine-tuning vectors is packaged and marked, and they are uniformly stored in a structured set.

[0189] The set structure can be designed as a three-element structure of "number - position - fine-tuning value". Each record contains the drum identity, the position index within its control section, and the corresponding fine-tuning displacement value. This set has drum differentiability in data logic and control spatiality in physical meaning, and it is an intermediate data structure connecting the upper and lower levels.

[0190] This set can be directly called by multiple modules such as subsequent drum execution modeling, control parameter generation, and timing scheduling. It is an important bridge structure for the conversion from error compensation to execution control. At the same time, since the data structures of each drum in the set are the same, it is convenient for vector processing and matrix operations, enabling efficient software implementation and hardware scheduling.

[0191] In a preferred embodiment of the present invention, according to the fine-tuning target values of each drum in the static fine-tuning vector set, combined with the transmission characteristic parameters and servo response performance parameters of the drum, the drum execution delay model is established by using the drum dynamic execution modeling principle, including:

[0192] Obtain the no-load response time parameters of each drum in the no-load state and construct a basic response delay model;

[0193] According to the transmission structure parameters of the drum, extract the inertial load characteristic indexes including the drum mass distribution, drive radius, and transmission chain reduction ratio to form a structural inertia matrix;

[0194] Collect the control performance parameters of the servo control system, including the open-loop gain, speed integral time constant, and target positioning accuracy, and establish a drum closed-loop control response curve;

[0195] According to the structural inertia matrix and the drum closed-loop control response curve, construct a multi-factor joint model for fitting the execution dynamic behavior of the drum under different fine-tuning target values, and output the drum execution delay model.

[0196] In the embodiment of the present invention, by establishing the drum execution delay model, the problems of inconsistent execution responses, adjustment lags, and unstable instruction execution accuracy among different drums in the traditional multi-drum system are solved. Due to differences in structural attributes, drive chain parameters, and load inertia among drums, even under the same control command, there are still significant differences in the actual response times and displacement results of each drum. This heterogeneous response is a key obstacle to achieving high-precision synchronous control of multi-drums.

[0197] To address such issues, before obtaining the drum control target, this method first constructs a basic dynamic characteristic model of the drum. First, the response time of the drum in the no-load state is extracted to construct a basic response delay model, which reflects the minimum response time delay of the drum under interference-free conditions and serves as a benchmark reference for subsequent dynamic modeling. Compared with the traditional method of setting response parameters only using the maximum speed or acceleration, this method provides a more accurate and measurable response evaluation mechanism.

[0198] Subsequently, through in-depth analysis of the drum structure parameters, including factors such as mass distribution, drive radius, and transmission chain reduction ratio, a structural inertia matrix is constructed to accurately evaluate the inertial load of the drum during startup and adjustment. Structural inertia is a decisive factor affecting the control response speed, especially in printing machines with large drum diameters, large masses, or complex transmission chain structures, where its impact is more prominent.

[0199] In terms of the servo system, by collecting control performance parameters such as open-loop gain, speed integration time constant, and target positioning accuracy, a closed-loop response curve of the servo control system is established. This response curve can describe the amplitude change and time delay characteristics between the input command and the output action of the control system and is an essential part of the drum dynamic behavior analysis.

[0200] By jointly modeling the structural inertia matrix and the closed-loop response curve, the constructed drum execution time delay model can be used to predict the response time and adjustment characteristics required for each fine-tuning target value during the execution of each drum, thus providing a clear basis for subsequent scheduling decisions, synchronization adjustments, and control instruction accuracy matching. This model not only improves the preset accuracy of the scheduling strategy but also greatly reduces the probability of misalignment of the graphic and text edges caused by response deviations and is the key foundation for realizing a high-precision drum control system driven by physical behavior.

[0201] Among them, obtaining the no-load response time parameters of each drum in the no-load state and constructing a basic response delay model specifically includes:

[0202] During the actual operation of the drum, its control response characteristics are significantly affected by the load. However, to establish a stable and reliable response model, it is necessary to first strip the load interference and measure the minimum response delay of the equipment under ideal conditions, which is defined as the no-load response time.

[0203] In specific implementation, the no-load response time can be measured in the following way: disconnect the actual contact load between the drum and the material or plate, and while keeping the power supply and the servo system connected, send a unit fine-tuning command to the drum through the control system. The system records the difference between the time point when the command is issued and the time point when the drum starts to rotate in real-time through a high-resolution position encoder, which is the no-load response time.

[0204] After multiple drums repeat this process, a set of basic delay time parameters can be obtained to form an "unloaded response time schedule". The delay schedule reflects the minimum response speed of the drum in a state without inertial resistance, constituting a basic response delay model, which provides a time benchmark for subsequent addition of inertial characteristic correction and construction of a complete dynamic model.

[0205] This model can be used to evaluate the response delay differences brought about by different drive structures of drums, and at the same time provide limiting conditions for the scheduler to arrange the earliest executable time, avoiding problems such as execution lag or frame loss caused by the control signal arriving prematurely.

[0206] Among them, according to the transmission structure parameters of the drum, inertial load characteristic indicators including the mass distribution of the drum, the drive radius, and the reduction ratio of the transmission chain are extracted to form a structural inertia matrix, specifically including:

[0207] The purpose of this step is to quantify the inertial load characteristics of each drum, thereby providing an important basis for setting the adjustment speed and control strategy of the drum. The inertia of the drum not only depends on its own mass, but is also affected by factors such as its mass distribution, rotation radius, and drive connection structure.

[0208] During the implementation process, the following parameters can be used as inputs:

[0209] Drum mass distribution: Through simplified modeling, the drum can be regarded as a hollow cylinder or a solid cylinder, and its mass and the position of the center of mass of the mass distribution can be estimated according to its outer diameter, length, and material density;

[0210] Drive radius: It refers to the actual radius at which the driving force acts on the drum. If it is a gear transmission structure, the pitch circle radius of the acting tooth is taken;

[0211] Transmission chain reduction ratio: That is, the mechanical speed ratio between the drive motor and the drum, which can be measured through a transmission gear set or a belt pulley system.

[0212] Combining the above multiple parameters and integrating them into a set of inertia coefficients for modeling through a preset inertia influence weight model (for example, drums with a large diameter or a high reduction ratio have a greater impact on the adjustment response speed). After organizing the multi-drum parameters by number, a "structural inertia matrix" is constructed, which characterizes the inertial load capacity faced by each drum during the control response process.

[0213] The structural inertia matrix serves as an inertia weighting term in the drum execution model, determining the path delay and response amplitude from the generation of the control signal to its actual conversion into a displacement response, which is crucial for subsequent construction of a time delay compensation mechanism.

[0214] Among them, collect the control performance parameters of the servo control system, including the open-loop gain, speed integral time constant, and target positioning accuracy, and establish a closed-loop control response curve of the drum, specifically including:

[0215] As an actuator, the response of the drum not only depends on its structural characteristics but is also restricted by the electronic control performance of the servo system. To more comprehensively characterize the response ability of the drum, it is necessary to obtain the dynamic performance parameters of the controller during the execution process and establish its closed-loop response behavior curve.

[0216] Specifically, the following three types of parameters are crucial for evaluating the performance of the servo system:

[0217] Open-loop gain: Measures the linear response strength of the change in the output voltage of the control signal to the change in the angular velocity of the drum. The larger the open-loop gain, the faster the control response, but the system may be less stable;

[0218] Velocity integral time constant: Represents the proportion of time required for the system to reach the target speed after receiving the speed adjustment command and determines the system's ability to make a smooth transition;

[0219] Target positioning accuracy: Is the standard deviation value of the actual position deviation of the drum after executing the control command and represents the system's anti-interference ability and stable control ability.

[0220] After measuring the above parameters through the built-in diagnostic module of the controller or external sensors, use the corresponding parameters of each drum to plot its "closed-loop response curve". The horizontal axis of this curve is time, and the vertical axis is the displacement or speed response, showing the complete behavioral process of how the system goes from rest to reaching the target fine-tuning position.

[0221] Among them, based on the structural inertia matrix and the closed-loop control response curve of the drum, a multi-factor joint model is constructed to fit the dynamic behavior of the drum under different fine-tuning target values, and a drum execution delay model is output, specifically including:

[0222] In this step, the system fuses the structural inertia matrix and the closed-loop control response curve obtained above to construct a set of joint models reflecting the execution delay and behavioral characteristics of the drum, which are used to predict the actual response ability of the drum under different target values.

[0223] The construction method is as follows: Correlate the inertia parameters of each drum with the response characteristics of its control system one by one, and establish a mapping relationship according to the adjustment amplitude (i.e., the size of the fine-tuning target value). When the adjustment amplitude is small, the system can quickly reach the target without multi-segment control; while when the adjustment amplitude is large, the system needs to go through multiple stages of acceleration, stability, and calibration, and its response time increases non-linearly with the amplitude.

[0224] Therefore, the system uses the target fine-tuning value as the input variable, and under the dual constraints of inertia influence and response ability, calculates the "shortest reachable time" and "actual response curve" corresponding to each target value, and finally generates a multi-dimensional response model that covers four elements: drum number, adjustment amplitude, response time, and steady-state deviation.

[0225] The output result is the "drum execution time delay model", which provides a constraint basis of "given adjustment value - acceptable time delay range" for the control scheduler. This model is the key control basis for realizing discrete time scheduling and avoiding drum out-of-step or overshoot in adjustment, ensuring that the entire system maintains graphic and text positioning consistency and rhythm synchronization under high-precision and high-speed operation.

[0226] In a preferred embodiment of the present invention, according to the control execution characteristic set, the fine-tuning target values in the static fine-tuning vector set are segmented and matched according to the time axis to generate a control node sequence indexed by the drum number and time index. Each control node includes a drum identifier, an execution time point, and a fine-tuning target value, including:

[0227] According to the start-up response time and control period of each drum in the control execution characteristic set, determine the time window interval that can be allocated for each drum within the target execution period;

[0228] Based on the distribution position of the fine-tuning target value of each drum in the static fine-tuning vector set, adopt a time-axis linear equal division strategy or an error gradient weighting strategy to segment and map the target value to each executable time window interval according to time segments to obtain drum mapping result data;

[0229] For the drum mapping result data, perform double indexing according to the time window interval number and the drum number to construct a control node sequence, and assign a drum identifier, an execution time point, and the corresponding fine-tuning target value to each node;

[0230] Sort the time points in the control node sequence in chronological order to form a structured scheduling node set.

[0231] In the embodiment of the present invention, on the basis of completing the drum execution time delay modeling, this method further realizes the high-precision generation of the control node sequence, constructs a scheduling structure with double indexing for the time domain and the drum domain, and provides structured data support for the execution of timing instructions of the drum control system.

[0232] This embodiment first uses the start-up response time and control period parameters included in the drum control execution characteristic set to determine the executable time window interval of each drum within the current control period. This step makes the scheduling of the drum no longer an equal-frequency synchronization mode, but arranges the timing according to the personalized control response characteristics, thereby improving the system tolerance and control accuracy.

[0233] According to the distribution position of the fine-tuning target value of each drum in the static fine-tuning vector set in the running trajectory, adopt the time-axis linear equal division strategy or the error gradient weighting strategy to split the continuous target value into multiple time periods for refined matching to their respective time windows. Linear equal division ensures the basic synchronization, while error weighting can make the high-error sections receive more intensive adjustment coverage, thus improving the adjustment efficiency and local correction ability.

[0234] The mapped result data of each drum are organized with double indexing according to the time window number and the drum number. The generated control node sequence not only records the drum identity and the execution time point, but also accurately carries the fine-tuning target value required during the current time period, forming a scheduling data set with the characteristics of strong binding between timeliness and displacement target.

[0235] Finally, by globally sorting all node sequences according to the execution time, a structured scheduling node set is generated, which not only ensures the continuity of the control rhythm, but also can be efficiently called, scheduled and traced back in the system scheduler, improving the organization efficiency and execution predictability of control commands.

[0236] Compared with the traditional control strategy based on fixed-period polling, this method realizes refined orchestration in both the spatial and temporal dimensions, providing a key technical path for high-precision collaborative adjustment in multi-drum systems, and is especially suitable for complex printing scenarios that require multi-segment fine-tuning, drum linkage and limited time windows.

[0237] Among them, according to the start response time and control period of each drum in the control execution characteristic set, determine the time window interval that can be allocated for each drum within the target execution period, specifically including:

[0238] In the drum synchronization control system, in order to avoid time conflicts or response delay mismatches of control instructions, it is necessary to preset a reasonable control time period for each drum so that it has sufficient and predictable execution space within the actual working cycle. This method analyzes the constructed "control execution characteristic set" to extract the response boundary information of each drum and clarify its "executable time window".

[0239] Specifically, the control execution characteristic set includes the start response time of each drum, that is, the earliest time point when the drum can respond after the system issues an adjustment instruction; at the same time, it also includes the control period information, that is, the time length limit of each complete control adjustment process (issuing, executing, stabilizing).

[0240] In this step, the start response time of the drum is first read, and this value is used as the earliest schedulable start time point in the current control cycle. Then, by subtracting the time required for the inertial execution of the drum (which can be estimated by the drum execution delay model) from the total system control cycle, the actual control time interval that can be allocated to the drum in a complete control cycle is determined. For example, if the system cycle is 100 milliseconds and the start response time of a certain drum is 20 milliseconds, its maximum executable interval ranges from the 20th millisecond to the end of the system cycle, which is 80 milliseconds.

[0241] The role of this time window is to limit the drum adjustment action to be executed only within the "section allowed by its response ability", preventing the scheduler from allocating time periods when the drum cannot start or has been unable to complete stably, thus avoiding problems such as "frame loss" or "frame stringing" of execution instructions and ensuring the feasibility of the adjustment action.

[0242] Finally, the time window interval of each drum will be stored in the scheduler in the form of start time, end time, and cycle label, providing boundary limits for the time period allocation of the next fine-tuning target value.

[0243] Among them, based on the distribution position of the fine-tuning target value of each drum in the static fine-tuning vector set, the time-axis linear equal division strategy or the error gradient weighting strategy is adopted, and the target value is segmented and mapped to each executable time window interval according to time segments to obtain the drum mapping result data, specifically including:

[0244] After determining the time window of the drum, the system needs to further schedule and allocate the adjustment task of the drum, that is, the fine-tuning target value sequence, to be executed within this time period. To achieve this mapping, this method uses the drum number as an index to extract the fine-tuning target value sequence required for the current drum from the "static fine-tuning vector set", and constructs a scheduling mapping strategy based on the distribution characteristics of this sequence in the printing path.

[0245] Two mapping strategies are provided in this step for the system to select according to the actual accuracy requirements or error patterns:

[0246] Linear equal division strategy:

[0247] It is applicable to the situation where the error distribution is relatively uniform and the adjustment action does not have obvious local mutations. The system evenly divides the time window interval of the drum into several time segments according to the number of target values. For example, if the length of a time window is 60 milliseconds and the number of target values is 6, each fine-tuning target value can be allocated an execution segment of 10 milliseconds. This strategy ensures the isochronism of the control rhythm and is applicable to high-speed and stable operation scenarios.

[0248] Error gradient weighting strategy:

[0249] Applicable to scenarios where errors are concentrated in local areas and the deviation values ​​of some points are much larger than those of other areas. The system first analyzes the fine-tuning target value, calculates the amplitude gradient of each value relative to its adjacent values, and weightedly allocates time segments according to the gradient size. Position points with large error amplitudes or drastic changes will receive a larger time quota to allow the system to gradually approach the target and avoid drum jumping or offset oscillations due to "sudden adjustments".

[0250] After the execution of the above two strategies, the system will form a set of "drum mapping result data", each record of which indicates: the drum number, the start and end points of the execution time segment corresponding to each value in the target value sequence, and the specific fine-tuning instructions for the value. This data result set will be used for subsequent control node sequence construction and signal flow encoding, and is the basic mapping structure for implementing the time-driven drum fine-tuning strategy.

[0251] Through this mapping, the drum adjustment action is no longer centrally triggered or uniformly compressed, but based on the error characteristics and system dynamic capabilities, it realizes on-demand allocation, precise matching, and rhythm-controllable adjustment control logic, greatly enhancing the system's scheduling flexibility and response accuracy in scenarios with complex graphics, small errors, and structural heterogeneity.

[0252] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A precise printing method for a rotary printing press, characterized in that, The method includes: Obtaining printing data, and constructing a thickness-tension relationship curve using the gradient fitting principle to obtain deformation curve data; According to the deformation curve data, based on the mapping relationship between the material deformation amount and the imprinting response, calculating the displacement change of each printing point to generate an error vector sequence; According to the error vector sequence, combining the differences in the load structures of different cylinders, generating a cylinder compensation ratio parameter, and adjusting the cylinder phase reference command with this to obtain a static fine-tuning vector set; According to the static fine-tuning vector set, combining the transmission characteristics of the cylinder and the servo system parameters, constructing a dynamic adjustment parameter set for synchronous adjustment of cylinder control, and generating a control scheduling signal stream through time discretization; According to the control scheduling signal stream, fine-tuning the relative positions between the cylinders in real time so that the graphic edge positioning error is kept within a preset tolerance range, where the cylinders include a plate cylinder, an impression cylinder, and an inking cylinder.

2. The precise printing method of a rotary printing press according to claim 1, characterized in that, Constructing a thickness-tension relationship curve using the gradient fitting principle to obtain deformation curve data, including: Performing interval segmentation on the thickness sampling values in the printing data, constructing corresponding tension distribution intervals, and forming a data pair set; According to the data pair set, calculating the tension change rate in each thickness sub-interval respectively, and constructing a corresponding local tension fitting function using the piecewise linear interpolation principle; Splicing the local tension fitting functions in the order of the thickness intervals to form a continuously differentiable tension response function, and extracting the first derivative change on the basis of this function to obtain a response rate curve for describing the micro-variation of tension with thickness; Performing a normalized mapping of the response rate curve in the coordinate system of the running trajectory of the substrate to obtain the deformation curve data.

3. The precise printing method of a rotary printing press according to claim 2, characterized in that, According to the deformation curve data, based on the mapping relationship between the material deformation amount and the imprinting response, calculating the displacement change of each printing point to generate an error vector sequence, including: According to the deformation curve data, extracting the tension gradient values of multiple equally spaced position points to form a tension change array; Inputting the tension change array into a preset material imprinting response mapping function to output the displacement deformation values of the corresponding position points; Calculating the difference between the deformation value of each position point and the target displacement deformation value to obtain the edge error estimation value of this position point; Summarizing all the edge error estimation values in the spatial order of the printing points to construct an error vector sequence.

4. The precise printing method of a rotary printing press according to claim 3, characterized in that, According to the edge error prediction value, combining the differences in the load structures of different cylinders, generating a cylinder compensation ratio parameter, and adjusting the cylinder phase reference command with this to obtain a static fine-tuning vector set, including: Obtaining the structural attributes and load inertia parameters of each cylinder, classifying them according to the preset structural types, setting a number for each cylinder, and establishing a physical attribute mapping relationship for characterizing the response differences of the cylinders to form a cylinder difference parameter set with number indexes; According to the error vector sequence, combining the cylinder difference parameter set, and using the difference normalization mapping principle to calculate the compensation ratio parameter of each cylinder; Decomposing the error vector sequence according to the cylinder types, and performing weighted correction in combination with the compensation ratio parameters to construct a static fine-tuning vector set.

5. The precision printing method of a rotary printing press according to claim 4, characterized in that Based on the set of static fine-tuning vectors, combined with the driving characteristics of the cylinders and the servo system parameters, construct a set of dynamic adjustment parameters for the synchronous adjustment of cylinder control, and generate a control scheduling signal flow through time discretization, including: Based on the fine-tuning target values of each cylinder in the printing section in the set of static fine-tuning vectors, combined with the driving characteristic parameters and servo response performance parameters of the cylinders, use the cylinder dynamic execution modeling principle to establish a cylinder execution time-delay model; According to the cylinder execution time-delay model, extract the start response time, inertia adjustment rate, and stable holding accuracy of each cylinder within the control period, and construct a cylinder control execution characteristic set; According to the control execution characteristic set, segment and match the fine-tuning target values in the set of static fine-tuning vectors along the time axis, and generate a control node sequence with the cylinder number and time index as the double-key index. Each control node includes a cylinder identifier, an execution time point, and a fine-tuning target value; Organize the control node sequence in the order of the execution time points and encode it in the cylinder control instruction format to generate a control scheduling signal flow.

6. The precise printing method of a rotary printing press according to claim 2, characterized in that, Splice the local linear function families in the order of the thickness intervals to form a continuously differentiable tension response function, and extract the first derivative change based on this function to obtain a response rate curve for describing the micro-variation of tension with thickness, including: Perform continuity correction processing on the boundaries of each local tension fitting function to form the spliced tension response function; According to the tension response function, extract the first derivative value at each thickness point to construct a tension response derivative sequence; Map the tension response derivative sequence to the running trajectory coordinate system of the substrate to form a response rate curve.

7. The precise printing method of a rotary printing press according to claim 4, characterized in that, According to the error vector sequence, combined with the cylinder difference parameter set, use the difference normalization mapping principle to calculate the compensation ratio parameter of each cylinder, including: Based on the structural attribute parameters and load inertia parameters included in the cylinder difference parameter set, construct a cylinder characteristic normalization reference value; Perform cylinder grouping and corresponding processing on each error data in the error vector sequence, and calculate the compensation factor of the cylinder using the linear proportional scaling method based on the normalization reference value of each cylinder; Take the compensation factor as the proportional coefficient and output the compensation ratio parameter corresponding to each cylinder.

8. The precise printing method of a rotary printing press according to claim 7, characterized in that, Decompose the error vector sequence according to the cylinder type and perform weighted correction combined with the compensation ratio parameter to construct a set of static fine-tuning vectors, including: According to the spatial position relationship of each printing point in the error vector sequence, map the estimated edge error value to the corresponding cylinder control section to form an error subset divided by cylinder type; For the estimated edge error value in each error subset, perform item-by-item weighted adjustment using the compensation ratio parameter of the corresponding cylinder to construct a cylinder-level fine-tuning data vector; Integrate all cylinder-level fine-tuning data vectors according to the cylinder number to form a set of static fine-tuning vectors.

9. The precise printing method of a rotary printing press according to claim 5, characterized in that, Based on the fine-tuning target values of each cylinder in the printing section in the set of static fine-tuning vectors, combined with the driving characteristic parameters and servo response performance parameters of the cylinders, use the cylinder dynamic execution modeling principle to establish a cylinder execution time-delay model, including: Obtain the no-load response time parameter of each cylinder in the no-load state and construct a basic response delay model; Extract the inertial load characteristic indexes including the drum mass distribution, driving radius and transmission chain reduction ratio according to the transmission structure parameters of the drum, and form a structural inertia matrix; Collect the control performance parameters of the servo control system, including open-loop gain, speed integral time constant and target positioning accuracy, and establish a closed-loop control response curve of the drum; According to the structural inertia matrix and the closed-loop control response curve of the drum, construct a multi-factor joint model for fitting the dynamic behavior of the drum under different fine-tuning target values, and output the drum execution delay model.

10. The precise printing method of a rotary printing press according to claim 9, characterized in that, According to the control execution characteristic set, segment and match the fine-tuning target values in the static fine-tuning vector set along the time axis, and generate a control node sequence indexed by the double keys of the drum number and time index. Each control node includes the drum identifier, execution time point and fine-tuning target value, including: Determine the time window interval that can be allocated for each drum within the target execution cycle according to the start-up response time and control period of each drum in the control execution characteristic set; Based on the distribution position of the fine-tuning target value of each drum in the static fine-tuning vector set, adopt the time-axis linear equal division strategy or the error gradient weighting strategy, and segment and map the target value to each executable time window interval according to time segments to obtain the drum mapping result data; For the drum mapping result data, perform double indexing according to the time window interval number and the drum number, construct a control node sequence, and assign a drum identifier, execution time point and corresponding fine-tuning target value to each node; Sort the time points in the control node sequence in chronological order to form a structured scheduling node set.

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