An intelligent control method for lateral expansion and contraction register error of a paper intaglio printing machine and related equipment

Abstract

The application discloses a paper intaglio printing machine transverse expansion and contraction register error intelligent control method and related equipment, belongs to the technical field of paper intaglio printing machine electronic shaft control system, and through collecting printing process parameters and environmental humidity and temperature, calculating a theoretical oven temperature and a fan air speed, assigning the theoretical oven temperature and the fan air speed to corresponding oven setting values for printing, collecting transverse expansion and contraction register errors generated during printing, establishing an RBF neural network control model between the transverse expansion and contraction register errors and the oven temperature and the back fan air speed, outputting compensation amounts of the oven temperature and the back oven air speed, and accumulating the control compensation amounts to the theoretical oven temperature and the back oven air speed, the transverse expansion and contraction register error intelligent control is realized, the problem that direct modeling control of the transverse expansion and contraction register error is difficult is solved, and high-precision control of the transverse expansion and contraction register error of the water-based ink paper intaglio printing machine is realized.

Description

Technical Field

[0001] The invention belongs to the technical field of electronic axis control systems of paper gravure printing machines, and in particular relates to an intelligent control method for transverse expansion and contraction registration errors of paper gravure printing machines and related equipment. Background Art

[0002] The printing and packaging industry is developing rapidly. As a core piece of packaging and printing equipment, water-based ink gravure printing presses offer numerous advantages, including thick ink layers, lifelike images, clear layers, vibrant colors, stable processes, and high print run time. Compared to solvent-based inks, water-based inks contain fewer organic solvents, causing less environmental pollution and making them more suitable for the green development of the packaging and printing industry. However, paper printed with water-based inks absorbs water from the ink. If this absorbed water fails to completely evaporate during the drying process, transverse registration errors can easily occur. Traditional transverse registration error control relies entirely on the operator's experience to subjectively adjust the oven temperature and fan speed, resulting in poor control effectiveness, low efficiency, and insufficient timeliness. Furthermore, the complex coupling mechanism between transverse registration error and the oven temperature and fan speed makes direct mechanism model control difficult, making existing registration error control strategies incapable of achieving precise control. Summary of the Invention

[0003] The present invention provides an intelligent control method for the transverse expansion and contraction registration error of a paper gravure printing machine and related equipment, which solves the problem of complex coupling between the transverse expansion and contraction registration error and oven parameters and difficulty in direct modeling control.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] An intelligent control method for transverse expansion and contraction registration error of a paper gravure printing machine, comprising:

[0006] Collect printing process parameters and ambient humidity temperature, and calculate theoretical oven temperature and fan speed parameters based on these parameters;

[0007] Assign theoretical oven temperature and fan speed to the corresponding oven parameter printing, and collect the lateral expansion and contraction registration error during the printing process;

[0008] Establish an RBF neural network control model between the lateral expansion and contraction registration error and printing parameters, and output the compensation value of oven temperature and reverse oven wind speed based on the RBF neural network control model;

[0009] The oven temperature and reverse oven wind speed compensation are added to the theoretical oven temperature and fan speed to obtain the final oven operating parameters, realizing intelligent control of lateral expansion and contraction registration errors.

[0010] Preferably, the collected printing process parameters include: printing speed, printing plate area, paper width, ink amount, water content in ink, paper water absorption rate and oven nozzle diameter.

[0011] Preferably, the steps of calculating the theoretical oven temperature and fan speed parameters according to the printing process parameters and the ambient humidity temperature are as follows:

[0012]

[0013] Among them, T L is the oven temperature, V1 is the front oven wind speed, V L2 is the wind speed of the reverse oven, h fg is the latent heat of vaporization of water, A s is the ink printing area, M s is the molar mass of water in the ink, P s is the saturated vapor pressure of the ink, R u is the universal gas constant, ρ A is the hot air density, c A is the specific heat capacity of hot air, T s is the ink temperature during constant drying, λ A is the thermal conductivity of hot air, c A is the specific heat capacity of hot air, D A is the diffusion coefficient of water vapor in hot air, L is the characteristic length, and μ is the temperature T L The dynamic viscosity of air under the condition of α, β and δ are empirical parameters, B is the nozzle diameter, is the convective heat transfer coefficient, K ct is the convection mass transfer coefficient of the reverse oven.

[0014] Preferably, when collecting the lateral expansion and contraction registration error during the printing process, when the lateral expansion and contraction registration error e>±0.1mm, adjust the oven temperature and the reverse oven wind speed to achieve lateral expansion and contraction registration error control

[0015] Preferably, establishing the RBF neural network control model between the lateral expansion and contraction registration error and the printing parameters further includes training a generative adversarial network to enhance the printing data set based on the collected historical printing process parameters, the lateral expansion and contraction registration error, and random noise, which is divided into the following steps:

[0016] The generator is fixed, and the real printing history data and the generated sample data are used as the input of the discriminator, and the discriminator is optimized to maximize its accuracy;

[0017] A fixed discriminator is used to input random noise into the generator to generate sample data, and the discriminator is used to determine whether the sample data is true or false. The generator weight is updated until the accuracy of the generator is maximized.

[0018] Repeat the above steps to complete the training of the generative adversarial network and expand the printing data set.

[0019] Preferably, an RBF neural network control model between the lateral expansion and contraction registration error and the printing parameters is established, and the specific establishment steps are as follows:

[0020] Determine the input, output, and network structure of the neural network. The input layer contains four nodes, representing the lateral expansion and contraction registration error, printing speed, ink loading, and paper water absorption. The output layer contains two nodes, representing the oven temperature compensation and the reverse oven fan speed. The RBF neural network model structure is determined to be 4-10-2.

[0021] The activation function from the input layer to the hidden layer is the Gaussian radial basis function, and its expression is:

[0022]

[0023] Where X is the input vector of the neural network, C k is the center vector corresponding to the kth neuron in the hidden layer, D k is the width vector of the k-th neuron in the hidden layer;

[0024] The linear output function is used from the hidden layer to the output layer, and its expression is:

[0025]

[0026] Where j is the total number of neurons in the hidden layer, w k is the neuron connection weight from the hidden layer to the output layer, and y is the output result of the neural network.

[0027] Preferably, collecting the lateral expansion and contraction registration error during the printing process further includes data denoising, abnormal data cleaning and data set division.

[0028] An intelligent control system for transverse expansion and contraction registration error of a paper gravure printing machine, comprising:

[0029] Data acquisition module: used to collect printing process parameters and ambient humidity temperature, and calculate theoretical oven temperature and fan speed parameters based on the printing process parameters and ambient humidity temperature;

[0030] Assignment module: used to assign theoretical oven temperature and fan speed to corresponding oven parameter printing, and collect lateral expansion and contraction registration errors during the printing process;

[0031] Model building module: used to establish the RBF neural network control model between the lateral expansion and contraction registration error and the printing parameters, and output the compensation amount of the oven temperature and the reverse oven wind speed based on the RBF neural network control model;

[0032] Parameter acquisition module: used to add the oven temperature and reverse oven wind speed compensation to the theoretical oven temperature and fan wind speed to obtain the final oven operating parameters and realize intelligent control of lateral expansion and contraction registration error.

[0033] A computer device comprises a memory, a processor and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the steps of an intelligent control method for transverse expansion and contraction registration error of a paper gravure printing machine are realized.

[0034] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of an intelligent control method for transverse expansion and contraction registration errors of a paper gravure printing machine.

[0035] Compared with the prior art, the present invention has the following beneficial effects: the present invention provides an intelligent control method for the lateral expansion and contraction registration error of a paper gravure printing machine, which collects printing process parameters and ambient humidity temperature, calculates theoretical oven temperature and fan speed, assigns the theoretical oven temperature and fan speed to corresponding oven setting values ​​for printing, collects the lateral expansion and contraction registration error generated by printing, establishes an RBF neural network control model between the lateral expansion and contraction registration error and the oven temperature and the reverse fan speed, outputs compensation amounts for the oven temperature and the reverse oven speed, accumulates the control compensation amounts to the theoretical oven temperature and the reverse oven speed, and realizes intelligent control of the lateral expansion and contraction registration error, solves the problem of difficulty in direct modeling and control of the lateral expansion and contraction registration error, and realizes high-precision control of the lateral expansion and contraction registration error of a water-based ink paper gravure printing machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of an intelligent control method for transverse expansion and contraction registration error of a paper gravure printing machine according to the present invention;

[0037] Figure 2 This is a detailed flow chart of an intelligent control method for transverse expansion and contraction registration error of a paper gravure printing machine according to the present invention;

[0038] Figure 3 This is a block diagram of the lateral expansion and contraction registration error control based on the mechanism model and data-driven method of the present invention;

[0039] Figure 4 RBF neural network structure diagram of the lateral expansion and contraction registration error of the present invention;

[0040] Figure 5 This is a block diagram of an intelligent control system for the transverse expansion and contraction registration error of a paper gravure printing machine according to the present invention. DETAILED DESCRIPTION

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0042] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0043] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0044] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0045] like Figure 1 As shown, the present invention provides an intelligent control method for the transverse expansion and contraction registration error of a paper gravure printing machine, comprising:

[0046] S101 collects printing process parameters and ambient humidity temperature, and calculates theoretical oven temperature and fan speed parameters based on these parameters;

[0047] S102 assigns the theoretical oven temperature and fan speed to the corresponding oven parameter printing, and collects the lateral expansion and contraction registration error during the printing process;

[0048] S103 establishes an RBF neural network control model between the lateral expansion and contraction registration error and the printing parameters, and outputs compensation values ​​for the oven temperature and the reverse oven wind speed based on the RBF neural network control model;

[0049] S104 adds the oven temperature and the reverse oven wind speed compensation to the theoretical oven temperature and fan wind speed to obtain the final oven operating parameters, thereby realizing intelligent control of the lateral expansion and contraction registration error.

[0050] Detailed steps as follows Figure 2 As shown, specifically:

[0051] Step 1: Collect printing process parameters and ambient humidity temperature, and calculate theoretical oven temperature and fan speed parameters;

[0052] After water-based ink is transferred to the paper surface during printing, some of the water is absorbed by the paper, while the remaining water remains on the surface. Assuming the front and back ovens are separated by the paper and do not affect each other, the front oven dries the water in the ink, while the back oven dries the water in the paper. The theoretical oven temperature and fan speed required to evaporate the water from both the paper and the ink are calculated and used as the initial values ​​for the oven parameters.

[0053] The collected printing process parameters specifically include: printing speed, printing plate area, paper format, ink amount, water content in ink, paper water absorption rate, and oven nozzle diameter.

[0054] Furthermore, the oven temperature T L , front oven wind speed V1, back oven wind speed V L2 The specific calculation expression is:

[0055]

[0056] Where h fg is the latent heat of vaporization of water, A s is the ink printing area, M s is the molar mass of water in the ink, P s is the saturated vapor pressure of the ink, R u is the universal gas constant, ρ A is the hot air density, c A is the specific heat capacity of hot air, T s is the ink temperature during constant drying, λ A is the thermal conductivity of hot air, c A is the specific heat capacity of hot air, D A is the diffusion coefficient of water vapor in hot air, L is the characteristic length, and μ is the temperature T L The dynamic viscosity of air under the condition of α, β and δ are empirical parameters with the range of values ​​of [0.20, 0.40], [0.50, 0.60] and [0.20, 0.30] respectively. B is the nozzle diameter. is the convective heat transfer coefficient, K ct is the convection mass transfer coefficient of the reverse oven, and the specific expression is:

[0057]

[0058] Where A is the paper area, M w is the molar mass of water, ΔH v is the enthalpy of water vapor evaporating from the paper, ΔH s is the adsorption heat of water in paper, P p is the saturated vapor pressure in paper, P tot is the saturated vapor pressure in the environment.

[0059] Step 2: Calculate the theoretical oven temperature T L , oven wind speed V1, V2 is assigned to the corresponding parameters of oven pre-registration for printing, and the lateral expansion and contraction registration error during the printing process is collected;

[0060] Step 3: Establish an RBF neural network control model between the lateral expansion and contraction registration error and printing parameters, and output the compensation amount of the oven temperature and the reverse oven wind speed;

[0061] The paper passes through the middle of the oven and is dried by hot air from the front and back ovens. The transverse expansion and contraction registration control system controls the transverse expansion and contraction registration error by adjusting the oven temperature and the back air inlet speed. The coupling mechanism between the transverse expansion and contraction registration error and the oven parameters is complex, and direct mechanism model control is impossible. We propose to integrate RBF neural network data drive to achieve precise control of transverse expansion and contraction registration error. The control process is as follows: Figure 3 shown.

[0062] Currently, the printing industry has low levels of equipment intelligence and insufficient historical operating data. To address the issues of small sample sizes for lateral expansion and contraction registration errors and poor training results for neural network prediction models, we chose to use a generative adversarial network to expand historical printing data. The training process for the generative adversarial network is as follows:

[0063] The generator G is fixed, and the real printing history data and the generated sample data are used as the input of the discriminator D, and the discriminator D is optimized to maximize its accuracy;

[0064] Fixed discriminator D, input random noise to the generator G to generate sample data G(z), use discriminator D to determine its authenticity, and update the generator weight until the accuracy of generator G is maximized;

[0065] Repeat the above steps to complete the training of the generative adversarial network and obtain the trained adversarial network.

[0066] The loss function of the adversarial generative network is as follows:

[0067]

[0068] Where, Loos G Represents the loss function of the generator, Loos D represents the loss function of the discriminator, x represents each group of printing process parameters and lateral expansion and contraction registration error, z represents random noise, P x is the distribution of historical printing data x, P z is the distribution of random noise z, Represents all noise z according to its probability distribution P z Calculate the expectation, Represents all real printing data according to their probability distribution Px The expected value is calculated, G(z) represents the data generated by the generator, D(x) represents the authenticity score of the discriminator for the input sample x, and D(G(z)) represents the authenticity score of the discriminator for the generated data G(z).

[0069] The objective function of the adversarial generative network is as follows:

[0070]

[0071] Where V(G,D) is the value function of GAN.

[0072] Determine the specific structure of the RBF neural network model as follows:

[0073] Determine the input, output, and network structure of the neural network. The input layer contains four nodes, namely, the horizontal expansion and contraction registration error, printing speed, ink loading, and paper water absorption; the output layer contains two nodes, namely, the oven temperature compensation ΔT and the reverse oven fan speed ΔV. The RBF neural network model structure is determined to be 4-10-2, such as Figure 4 shown.

[0074] The activation function from the input layer to the hidden layer is the Gaussian radial basis function, and its expression is:

[0075]

[0076] Where X is the input vector of the neural network, C k is the center vector corresponding to the kth neuron in the hidden layer, D k is the width vector of the k-th neuron in the hidden layer.

[0077] The linear output function is used from the hidden layer to the output layer, and its expression is:

[0078]

[0079] Where j is the total number of neurons in the hidden layer, w k is the neuron connection weight from the hidden layer to the output layer, and y is the output result of the neural network.

[0080] Step 4: Add the compensation output by the network model to the theoretical oven temperature and fan speed calculation values ​​to obtain the final oven operating parameters and realize intelligent control of lateral expansion and contraction registration error.

[0081] Oven temperature T:

[0082] T=T L +ΔT (7)

[0083] Rear oven wind speed V2:

[0084] V2=V L2 +ΔV (8)

[0085] like Figure 5 As shown, the present invention provides an intelligent control system for transverse expansion and contraction registration error of a paper gravure printing machine, comprising:

[0086] Data acquisition module: used to collect printing process parameters and ambient humidity temperature, and calculate theoretical oven temperature and fan speed parameters based on the printing process parameters and ambient humidity temperature;

[0087] Assignment module: used to assign theoretical oven temperature and fan speed to corresponding oven parameter printing, and collect lateral expansion and contraction registration errors during the printing process;

[0088] Model building module: used to establish the RBF neural network control model between the lateral expansion and contraction registration error and the printing parameters, and output the compensation amount of the oven temperature and the reverse oven wind speed based on the RBF neural network control model;

[0089] Parameter acquisition module: used to add the oven temperature and reverse oven wind speed compensation to the theoretical oven temperature and fan wind speed to obtain the final oven operating parameters and realize intelligent control of lateral expansion and contraction registration error.

[0090] An embodiment of the present invention provides a terminal device. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of each of the aforementioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of each module / unit in each of the aforementioned device embodiments are implemented.

[0091] The computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to accomplish the present invention.

[0092] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0093] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0094] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0095] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

Claims

1. An intelligent control method for transverse expansion and contraction registration error of a paper gravure printing machine, characterized in that: include: Collect printing process parameters and ambient humidity temperature, and calculate theoretical oven temperature and fan speed based on these parameters; Assign theoretical oven temperature and fan speed to corresponding oven parameters for printing, and collect lateral expansion and contraction registration errors during the printing process; Establishing an RBF neural network control model between the lateral expansion and contraction registration error and printing parameters, and outputting compensation values ​​for the oven temperature and the reverse oven wind speed based on the RBF neural network control model. The printing parameters are printing speed, ink loading, and paper water absorption. The oven temperature and reverse oven wind speed compensation are added to the theoretical oven temperature and fan speed to obtain the final oven operating parameters, thus realizing intelligent control of lateral expansion and contraction registration error. The printing process parameters include: printing speed, printing plate area, paper width, ink amount, water content in ink, paper water absorption rate and oven nozzle diameter; The specific steps for calculating the theoretical oven temperature and fan speed based on the printing process parameters and ambient humidity temperature are as follows: in, T L is the oven temperature, V 1 is the front oven wind speed, V L2 is the wind speed of the reverse oven, h fg is the latent heat of vaporization of water, A s is the ink printing area, M s is the molar mass of water in the ink, P s is the saturated vapor pressure of the ink, R u is the universal gas constant, ρ A is the hot air density, T s is the ink temperature during constant drying, λ A is the thermal conductivity of hot air, c A is the specific heat capacity of hot air, D A is the diffusion coefficient of water vapor in hot air, L is the characteristic length, μ Temperature T L The dynamic viscosity of air under α 、 β and δ is an empirical parameter, B is the nozzle diameter, is the convective heat transfer coefficient, K ct is the convection mass transfer coefficient of the reverse oven.

2. The intelligent control method for transverse expansion and contraction registration error of a paper gravure printing machine according to claim 1, characterized in that: When collecting the lateral expansion and contraction registration error during the printing process, e When the error is >±0.1mm, adjust the oven temperature and the wind speed of the reverse oven to achieve lateral expansion and contraction registration error control.

3. The intelligent control method for transverse expansion and contraction registration error of a paper gravure printing machine according to claim 1, characterized in that: Establishing the RBF neural network control model between the lateral scaling registration error and printing parameters also includes training a generative adversarial network to enhance the printing dataset based on the collected historical printing process parameters, lateral scaling registration error, and random noise. The steps are as follows: Fixed the generator, used real printing history data and sample data generated by inputting random noise into the generator as the discriminator input, and optimized the discriminator to maximize its accuracy; A fixed discriminator is used to input random noise into the generator to generate sample data, and the discriminator is used to determine whether the sample data is true or false. The generator weight is updated until the accuracy of the generator is maximized. Repeat the above steps to complete the training of the generative adversarial network and expand the printing data set.

4. The intelligent control method for transverse expansion and contraction registration error of a paper gravure printing machine according to claim 3, characterized in that: Establish an RBF neural network control model between the lateral expansion and contraction registration error and printing parameters. The specific steps are as follows: Determine the input, output, and network structure of the neural network. The input layer contains four nodes, representing the lateral expansion and contraction registration error, printing speed, ink loading, and paper water absorption rate. The output layer contains two nodes, representing the oven temperature compensation and the reverse oven fan speed compensation. The RBF neural network model structure is determined to be 4-10-2. The activation function from the input layer to the hidden layer is the Gaussian radial basis function, and its expression is: Where, X is the input vector of the neural network, C k The hidden layer k The center vector corresponding to the neuron is D k The hidden layer k The width vector of neurons; The linear output function is used from the hidden layer to the output layer, and its expression is: Where, j is the total number of hidden layer neurons, w k is the neuron connection weight from the hidden layer to the output layer, y is the output of the neural network.

5. The intelligent control method for transverse expansion and contraction registration error of a paper gravure printing machine according to claim 1, characterized in that: The collection of lateral expansion and contraction registration errors during the printing process also includes data denoising, abnormal data cleaning and data set division.

6. An intelligent control system for transverse expansion and contraction registration error of a paper gravure printing machine, characterized in that: include: Data acquisition module: used to collect printing process parameters and ambient humidity temperature, and calculate theoretical oven temperature and fan speed based on these parameters; Assignment module: used to assign theoretical oven temperature and fan speed to corresponding oven parameters for printing, and collect lateral expansion and contraction registration errors during the printing process; Model building module: used to establish an RBF neural network control model between the lateral expansion and contraction registration error and printing parameters, and output the compensation amount of the oven temperature and the reverse oven wind speed based on the RBF neural network control model. The printing parameters are printing speed, ink amount and paper water absorption rate; Parameter acquisition module: used to add the oven temperature and reverse oven wind speed compensation to the theoretical oven temperature and fan wind speed to obtain the final oven operating parameters and realize intelligent control of lateral expansion and contraction registration error; Printing process parameters include: printing speed, printing plate area, paper format, ink amount, water content in ink, paper water absorption rate and oven nozzle diameter; In the data acquisition module, the theoretical oven temperature and fan speed are calculated based on the printing process parameters and the ambient humidity temperature as follows: in, T L is the oven temperature, V 1 is the front oven wind speed, V L2 is the wind speed of the reverse oven, h fg is the latent heat of vaporization of water, A s is the ink printing area, M s is the molar mass of water in the ink, P s is the saturated vapor pressure of the ink, R u is the universal gas constant, ρ A is the hot air density, T s is the ink temperature during constant drying, λ A is the thermal conductivity of hot air, c A is the specific heat capacity of hot air, D A is the diffusion coefficient of water vapor in hot air, L is the characteristic length, μ Temperature T L The dynamic viscosity of air under α 、 β and δ is an empirical parameter, B is the nozzle diameter, is the convective heat transfer coefficient, K ct is the convection mass transfer coefficient of the reverse oven.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for intelligently controlling the transverse expansion and contraction registration error of a paper gravure printing machine according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the intelligent control method for transverse expansion and contraction registration error of a paper gravure printing machine according to any one of claims 1 to 5 are implemented.

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