Ink-jet printing liquid drop point state evaluation method

Abstract

The invention belongs to the technical field of ink-jet printing, and particularly relates to an ink-jet printing liquid drop point state evaluation method which comprises the following steps: spot-checking drop point deviation of part of ink drops; solving the likelihood function value of the drop point deviation of each spot check ink drop under each group of drop point state data, and multiplying all the likelihood function values under the group of drop point state data by the joint prior probability corresponding to the group of data to obtain the posterior probability of each spot check ink drop under the condition of the group of data; each group of data comprises a mean value and a standard deviation of drop point deviations in different directions among all drop points during pre-printing; the prior probability corresponding to each group of data is obtained through pre-constructed joint prior distribution; multiplying the likelihood function of the drop point deviation of each spot check ink droplet under the drop point state data as the function independent variable, then multiplying by the prior distribution, and dividing the result by the sum of all posterior probabilities to obtain posterior distribution; and evaluating the drop point state according to the comparison of the posterior distribution and the prior distribution. The liquid drop point precision can be dynamically detected in real time.

Description

Technical Field

[0001] The present invention belongs to the technical field related to inkjet printing, and more specifically, relates to a method for evaluating the state of ink droplet landing points in inkjet printing. Background Art

[0002] Due to its characteristics of high precision, high efficiency and low cost, inkjet printing technology has been widely used in fields such as printed electronics and display manufacturing. It can accurately deposit organic light-emitting materials on a large-area substrate to form the pixel structure of OLED. However, the quality of inkjet printing highly depends on the accuracy of ink droplet landing points. Any slight deviation of the ink droplets during the printing process will directly affect the alignment and shape of the printed pattern, and may even lead to functional failure.

[0003] Traditional methods for detecting the accuracy of ink droplet landing points usually rely on offline full inspection. After printing is completed, the printed results are collected for comprehensive detection and statistical analysis to evaluate the accuracy of ink droplet landing points. Although this method can provide relatively accurate results, it has problems such as low detection efficiency and inability to provide real-time feedback, and it is difficult to meet the requirements of modern intelligent manufacturing for high precision and high real-time performance. In addition, during the inkjet printing process, the accuracy of ink droplet landing points is affected by various factors, such as the state of the nozzle holes, printing speed, environmental temperature and humidity, etc. The changes in these factors may cause dynamic fluctuations in the deviation of ink droplet landing points. Traditional fixed-threshold detection methods cannot effectively cope with this dynamic change, and are prone to false positives or false negatives.

[0004] Therefore, how to detect the accuracy of ink droplet landing points in real time and dynamically during the printing process, and adjust the printing parameters in a timely manner according to the detection results, has become a key technical requirement for improving the quality and production efficiency of inkjet printing. Summary of the Invention

[0005] In view of the above-mentioned defects or improvement requirements of the prior art, the present invention provides a method for evaluating the state of ink droplet landing points in inkjet printing, aiming to detect the accuracy of ink droplet landing points in real time and dynamically during the printing process.

[0006] To achieve the above object, according to one aspect of the present invention, there is provided a method for evaluating the state of ink droplet landing points in inkjet printing, including:

[0007] During the printing gap, randomly select and inspect some ink droplets to obtain the landing point deviation data of some ink droplets;

[0008] Solve the likelihood function value of the landing point deviation of each sampled ink droplet under each group of landing point state data, multiply the likelihood function values of all sampled ink droplets solved under this group of landing point state data by the joint prior probability corresponding to this group of landing point state data respectively to obtain the posterior probability corresponding to each sampled ink droplet under the condition of this group of landing point state data, and calculate the sum of all posterior probabilities corresponding to multiple groups of landing point state data; wherein, each group of landing point state data includes the mean and standard deviation of the landing point deviations in different directions between all landing points during pre-offline full-nozzle printing, and the multiple groups of landing point state data are pre-obtained by performing multiple full-nozzle printings offline; the joint prior probability corresponding to each group of landing point state data is obtained by substituting this group of landing point state data into the pre-constructed joint prior distribution;

[0009] Multiply the likelihood functions of the known landing point deviations of each sampled ink droplet under the landing point state data as the function independent variable, multiply the multiplication result by the joint prior distribution, and divide the final multiplication result by the sum to obtain the posterior distribution; calculate the deviation between the mean of the landing point deviations in each direction corresponding to the maximum posterior probability in the posterior distribution and the mean of the corresponding direction landing point deviations corresponding to the maximum prior probability in the joint prior distribution to complete the evaluation of the landing point state of the inkjet printing liquid droplet.

[0010] Furthermore, the construction method of each group of landing point state data is as follows:

[0011] Through full-nozzle printing of the test pattern, collect the arrayed ink droplet landing point images to extract the landing point deviation information of each ink droplet. After excluding abnormal landing points, obtain the full-inspection data of the ink droplet landing point deviation; calculate a group of landing point state data according to the full-inspection data of the ink droplet landing point deviation.

[0012] Furthermore, the construction method of the joint prior distribution is as follows:

[0013] According to multiple groups of landing point state data, and in combination with the tolerance range of the mean and standard deviation of the landing point deviation determined based on production requirements, construct the probability density function of each parameter in the landing point state data that conforms to the normal distribution; take the product of the probability density functions of various parameters as the joint prior distribution of the landing point state to complete the construction of the joint prior distribution.

[0014] Furthermore, the probability density function of each parameter that conforms to the normal distribution is:

[0015]

[0016] In the formula, and respectively represent μ , Δy , Δy , Δx , , Δx , Δx , μ Δy , σ Δx , σ Δy of the probability density function that conforms to the normal distribution, μ Δx, μ Δt are the means of the landing point deviations in the x and y directions respectively, and σ Δx , σ Δy are the standard deviations of the landing point deviations in the x and y directions respectively; μ μΔx , are the mean and standard deviation of μ Δx respectively, and μ μΔy , are the mean and standard deviation of μ Δy respectively, and μ σΔx , are the mean and standard deviation of σ Δx respectively, and μ σΔy , are the mean and standard deviation of σ Δy respectively.

[0017] Furthermore, the landing point deviation data is obtained by collecting the images of the landing points of the corresponding sampled ink drops and through image processing.

[0018] Furthermore, the likelihood function of the landing point deviation of the sampled ink drops under each group of landing point state data is:

[0019]

[0020] In the formula, p(Δ xi , Δ yi ∣μ Δx , μ Δy , σ Δx , σ Δy ) represents the likelihood function of the landing point deviation (Δ xi , Δ yi ) of the i-th sampled ink drop in the x and y directions under a group of landing point state data (μ Δx , μ Δy , σ Δx , σ Δy ), μ Δx , μ Δy are the means of the landing point deviations in the x and y directions respectively, and σ Δx , σ Δy are the standard deviations of the landing point deviations in the x and y directions respectively, and k represents the total number of sampled ink drops.

[0021] Furthermore, the posterior distribution is:

[0022]

[0023] In the formula, p(μ Δx , μ Δy , σ Δx , σ Δy ∣Δ x1 , Δy1 , Δ x2 , Δ y2 , …, Δ xk , Δ yk ) represents the posterior distribution, Δ x1 , Δ y1 , Δ x2 , Δ y2 , …, Δ xk , Δ yk respectively represent the landing point deviations of k sampled ink droplets in the x and y directions, μ Δx , μ Δy , σ Δx , σ Δy represents a set of landing point state data, μ Δx , μ Δy are respectively the means of the landing point deviations in the x and y directions, σ Δx , σ Δy are respectively the standard deviations of the landing point deviations in the x and y directions; p(Δ x1 , Δ y1 ∣μ Δx , μ Δy , σ Δx , σ Δy ), p(Δ x2 , Δ y2 ∣μ Δx , μ Δy , σ Δx , σ Δy ), …, p(Δ xk , Δ yk ∣μ Δx , μ Δy , σ Δx , σ Δy ) respectively represent the likelihood functions of the known landing point deviations of each sampled ink droplet under the landing point state data as the independent variable of the function; p(μ Δx , μ Δy , σ Δx , σ Δy ) represents the joint prior distribution.

[0024] According to another aspect of the present invention, an on-line regulation method for the landing point state of inkjet printing droplets is provided, and based on the landing point state of inkjet printing droplets determined by the above-mentioned inkjet printing droplet landing point state evaluation method, the printing parameters are adjusted on-line.

[0025] According to another aspect of the present invention, an on-line regulation system for the landing point state of inkjet printing droplets is provided, including an inkjet printing module, a vision module, a processing unit and a feedback control module;

[0026] The inkjet printing module is used to perform printing;

[0027] The visual module is used to collect images of the droplet landing points in an array.

[0028] The processing unit is used to execute the method for evaluating the state of the inkjet printing droplet landing point as described above.

[0029] The system control module is used to execute the online regulation method for the state of the inkjet printing droplet landing point as described above, automatically correcting the deviation of the droplet landing point to ensure the stability of the printing quality.

[0030] According to another aspect of the present invention, there is provided a computer-readable storage medium, which includes a stored computer program. When the computer program is run by a processor, it controls the device where the storage medium is located to execute the steps of the method as described above.

[0031] Generally speaking, compared with the prior art by the above technical solution conceived by the present invention, the technical solution provided by the present invention mainly has the following beneficial effects:

[0032] 1. The present invention proposes a method for evaluating the state of the inkjet printing droplet landing point. By pre-using the statistical inference method, establishing the prior distribution of the droplet landing point deviation, and combining the real-time sampling inspection data to update the posterior distribution, it effectively improves the prediction and optimization of the droplet landing point deviation. And through inference and probability update, not only can previous empirical data be considered, but also more accurate judgments can be made based on current data, reducing the detection error and improving the reliability of precision control. Therefore, the present invention combines historical data and real-time sampling inspection information through probability modeling, significantly reducing the detection frequency while ensuring the detection accuracy, with the characteristics of strong real-time performance and wide adaptability, and can effectively improve the stability of the printing quality and reduce the manual intervention cost. In addition, through online real-time detection, the present invention realizes continuous monitoring of the accuracy of the inkjet printing droplet landing point. Compared with the traditional offline detection method, it can timely feedback the droplet deviation during the printing process and correct the droplet deviation by dynamically adjusting the printing parameters (such as nozzle state, printing speed, temperature and humidity, etc.), avoiding the problem that the traditional method cannot respond in real time.

[0033] 2. Regarding the construction of the joint prior distribution, the present invention proposes to obtain the relational expressions according to the production requirements and the mean vector μ Δ and the standard deviation vector σ Δ of the droplet landing point deviation of the working nozzles currently collected: 3σ μΔx ≤a%μ μΔx ; 3σ μΔy ≤a%μ μΔy ; μ μΔx +3(3σ σΔx +μ μΔx )≤Δ max ; μ μΔy+3(3σ σΔy +μ μΔy )≤Δ max 。According to these relationships, the standard deviations σ μΔx , σ μΔy , σ σΔx , σ σΔy are determined for determining the prior probability distribution, which conforms to the actual production scenario and ensures accuracy.

[0034] 3. The present invention also proposes an on-line regulation method for the state of the inkjet printing droplet landing point. By realizing the on-line monitoring and feedback control of the droplet accuracy distribution, a closed-loop control system is formed. When a deviation is detected, the printing parameters can be immediately adjusted, avoiding the accumulation of printing errors and ensuring the accuracy of each round of printing. This closed-loop control can not only improve production efficiency but also maintain the consistency of quality in multi-batch production, especially suitable for high-precision and large-scale production requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a flowchart of a method for evaluating the state of the inkjet printing droplet landing point provided by an embodiment of the present invention;

[0036] Figure 2 is a schematic flow diagram of a method for evaluating the state of the inkjet printing droplet landing point provided by an embodiment of the present invention;

[0037] Figure 3 is a schematic structural diagram of a system for evaluating the state of the inkjet printing droplet landing point provided by an embodiment of the present invention;

[0038] Figure 4 is a schematic diagram of the full inspection stage in the method for evaluating the state of the inkjet printing droplet landing point provided by an embodiment of the present invention;

[0039] Figure 5 is a schematic diagram of the sampling inspection stage of the method for evaluating the state of the inkjet printing droplet landing point provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0041] Embodiment 1

[0042] A method for evaluating the state of the inkjet printing droplet landing point, as Figure 1 shown, includes:

[0043] During the printing gap, randomly sample some ink droplets to obtain the landing deviation data of some ink droplets;

[0044] Solve the likelihood function values of the landing deviations of each sampled ink droplet under each set of landing state data, and multiply the likelihood function values of all sampled ink droplets solved under this set of landing state data by the joint prior probability corresponding to this set of landing state data respectively to obtain the posterior probability corresponding to each sampled ink droplet under the condition of this set of landing state data, and calculate the sum of all posterior probabilities corresponding to multiple sets of landing state data; wherein, each set of landing state data includes the mean and standard deviation of the landing deviations in different directions between all landing points during pre-offline full-nozzle printing, and multiple sets of landing state data are pre-obtained by performing multiple full-nozzle printings offline; the joint prior probability corresponding to each set of landing state data is obtained by substituting this set of landing state data into the pre-constructed joint prior distribution;

[0045] Multiply the likelihood functions of the known landing deviations of each sampled ink droplet under the landing state data as the function independent variable, then multiply the multiplication result by the joint prior distribution, and divide the final multiplication result by the sum to obtain the posterior distribution; calculate the deviation between the mean of the landing deviations in each direction corresponding to the maximum posterior probability in the posterior distribution and the mean of the corresponding direction landing deviations corresponding to the maximum prior probability in the joint prior distribution to complete the evaluation of the landing state of the inkjet printing liquid droplets.

[0046] It can be said that implementing the method of this embodiment can be divided into an offline stage and an online stage:

[0047] Offline stage: Initial full-nozzle printing, print a preset test pattern on the substrate, and the test pattern includes a plurality of marked points arranged regularly; fully inspect the landing deviation of the liquid droplets. After printing, collect the arrayed liquid droplet landing pictures and extract the landing deviation information of each marked point; establish a prior distribution. According to the initial full-inspection data, obtain the mean vector μ Δ0 and the standard deviation vector σ Δ0 , and establish the prior distribution of the landing deviation;

[0048] Online stage: During normal operation, randomly sample the landing deviation of the liquid droplets to obtain a series of liquid droplet deviation accuracy data, and calculate the likelihood function values of the landing deviation of each sampled liquid droplet; according to the prior distribution and a series of likelihood function values obtained by sampling, construct the posterior distribution of the landing deviation of the liquid droplets; compare the parameter deviation between the posterior distribution and the prior distribution. If the deviation is within the preset threshold, determine that the result of this sampling inspection is normal; if the deviation exceeds the preset threshold, determine that the result of this sampling inspection is abnormal, and the printing parameters can be dynamically adjusted to correct the landing deviation.

[0049] Figure 2The figure shows a schematic flow diagram of the method for evaluating the droplet landing point state of the inkjet printing liquid provided in this embodiment. To better illustrate the present invention, a specific control system is now given to illustrate the control method of this embodiment.

[0050] Figure 3 It is an inkjet printing liquid droplet landing point state evaluation system corresponding to the method of this embodiment. As Figure 3 shown, the system includes an inkjet printing module, a vision module, a processing unit, and a system control module.

[0051] The inkjet printing module 1 includes a print head 11 and a moving substrate 12. The print head 11 is used to generate an array of droplets with a preset initial velocity and deposit them on the moving substrate 12; the moving substrate 12 performs array droplet printing through the system control module 4 according to the set substrate movement speed. The vision module 2 includes a light source 21 and a scanning camera 22, where the light source 21 is located in the droplet landing point observation area; the scanning camera 22 takes pictures and scans the printed array of droplets and feeds the collected pictures of the array of droplet landing points back to the processing unit 3. The processing unit is used to obtain the droplet landing point deviation information and execute the inference and anomaly determination algorithms. The system control module is used to, based on the inference and anomaly determination results, if the droplet landing point accuracy distribution of the nozzle is abnormal, adjust the nozzle state, printing speed, environmental temperature and humidity and other parameters of the inkjet printing device in real time, and automatically correct the deviation of the droplet landing point to ensure the stability of the printing quality.

[0052] Specifically, Figure 4 It is a schematic diagram of the full inspection stage in the method for evaluating the inkjet printing liquid droplet landing point state. Correspondingly, the method includes the following steps:

[0053] Before the formal printing work is carried out, a full array of ink droplet measurements are performed, and the landing point deviation data of each printed ink droplet is recorded, and at the same time, abnormal landing points are excluded.

[0054] Assume that the mean and standard deviation of the droplet landing point deviation in the x and y directions follow a normal distribution, and the mean μ Δx , μ Δy and the standard deviation σ x , σ y are independent of each other, then the mean vector μ Δ0 and the standard deviation vector σ Δ0 are calculated as follows:

[0055]

[0056] Among them, μ Δx0 and μ Δx0 respectively represent the mean of the landing point deviation of each droplet in the array of droplets in the x and y directions, and σ Δx0 and σ Δy0respectively represent the standard deviations of the landing point deviations of each droplet in the arrayed droplets in the x and y directions, n is the total number of droplets, Δ xi and Δ yi respectively represent the landing point deviations of the i-th droplet in the arrayed droplets in the x and y directions.

[0057] Preferably, the construction method of the joint prior distribution is as follows:

[0058] According to multiple sets of landing point state data, and in combination with the tolerance ranges of the mean and standard deviation of the landing point deviation determined based on production requirements, construct the probability density function of each parameter in the landing point state data that conforms to the normal distribution; take the product of the probability density functions of various parameters as the joint prior distribution of the landing point state, and complete the construction of the joint prior distribution.

[0059] Specifically, according to production requirements and the mean vector μ Δ0 of the landing point deviation of the droplets collected currently and the standard deviation vector σ Δ0 , respectively determine the standard deviations σ μΔx , σ μΔy , σ σΔx , σ σΔy that μ and σ follow the normal distribution, and the specific values are:

[0060] 3σ μΔx ≤a%μ μΔx ; 3σ μΔy ≤a%μ μΔy ;

[0061] μ μΔx +3(3σ σΔx +μ μΔx )≤Δ max ; μ μΔy +3(3σ σΔy +μ μΔy )≤Δ max ;

[0062] where a% represents the maximum allowable deviation of the mean in a single direction of the droplet landing point accuracy in actual production requirements, and Δ max represents the maximum allowable value of the droplet landing point deviation in a single direction in actual production requirements.

[0063] Furthermore, the true values of the said μ Δ0 and σ Δ0 vary with the working conditions, and the variation law conforms to the normal distribution. The elements in the given multiple sets of μ Δ0 and σ Δ0 take values within the following given ranges:

[0064] μ Δx ∈(μ μΔx -3σ μΔx ,μμΔx +3σ μΔx )

[0065] μ Δy ∈(μ μΔy -3σ μΔy ,μ μΔy +3σ μΔy )

[0066] σ Δx ∈(μ σΔx -3σ σΔx ,μ σΔx +3σ σΔx )

[0067] σ Δy ∈(μ σΔy -3σ σΔy ,μ σΔy +3σ σΔy )

[0068] Furthermore, the joint prior distribution can be expressed as:

[0069]

[0070]

[0071]

[0072] wherein, and respectively represent the probability density functions of the normal distribution for μ Δx , μ Δy , σ Δx , μ Δy . μ Δx , μ Δy are respectively the means of the landing point deviations in the x and y directions, and σ Δx , σ Δy are respectively the standard deviations of the landing point deviations in the x and y directions; μ μΔx , are respectively the mean and standard deviation of μ Δx , μ μΔy , are respectively the mean and standard deviation of μ Δy , μ σΔx , are respectively the mean and standard deviation of σ Δx , μ σΔy , are respectively the mean and standard deviation of σ Δy .

[0073] Preferably, the landing point deviation data is obtained by collecting the images of the landing points of the corresponding sampled ink droplets and through image processing.

[0074] Figure 5 It is a schematic diagram of the sampling stage of the ink droplet landing point state evaluation method provided by this embodiment. Correspondingly, the method includes the following steps:

[0075] During normal operation, randomly sample the landing point deviation of the droplets, where the total number of sampled droplets is k, and a series of droplet deviation accuracy data (Δ xi , Δ yi ) are obtained, where i = 1, 2, 3…, k.

[0076] Then, according to the deviation data of each sampled droplet, calculate the likelihood function of the landing point deviation of each sampled droplet. Optionally, the likelihood function is expressed as:

[0077]

[0078] In the formula, p(Δ xi , Δ yi ∣μ Δx , μ Δy , σ Δx , σ Δy ]>) represents the likelihood function of the landing point deviation (Δ xi , Δ yi ) of the sampled droplet under μ Δx , μ Δy , σ Δx , σ Δy .

[0079] After obtaining the prior distribution of the ink droplet landing point deviation and the likelihood function of each sampled ink droplet deviation, calculate, evaluate, and determine the state of the ink droplet landing point according to the following steps:

[0080] The prior distribution, likelihood function, and posterior distribution satisfy the following relationship:

[0081] p(μ Δx , μ Δy , σ Δx , σ Δy ∣Δ x1 , Δ y1 ) = p(Δ x1 , Δ y1 ∣μ Δx , μ Δy , σ Δx , σ Δy ) × p(μ Δx , μ Δy , σ Δx , σ Δy )

[0082] For the k observed samples (Δ xi , Δ yi ) randomly selected for inspection, where i = 1, 2, 3…, k, preferably, the posterior distribution is expressed as:

[0083]

[0084] In the formula, p(μ Δx , μ Δy , σ Δx , σ Δy ∣Δ x1 , Δ y1 , Δ x2 , Δ y2 , …, Δ xk , Δ yk ) represents the posterior distribution, p(Δ xi , Δ yi ∣μ Δx , μ Δy , σ Δx , σ Δy ) represents the likelihood function of the dropping point accuracy (Δ xi , Δ yi ) under μ Δx , μ Δy , σ Δx , σ Δu , where i = 1, 2, 3…, k; p(μ Δx , μ Δy , σ Δx , σ Δy ) represents the prior distribution, and p(Δ x1 , Δ y1 , Δ x2 , Δ y2 , …, Δ xk , Δ yk ) represents the sum of all posterior distribution probabilities, which is the normalization term. In the application scenario, it is not necessary to obtain the complete curve of the posterior distribution. Within the required accuracy range, the above formula can be discretized by sampling, and the maximum value of the posterior probability or the average value of the posterior can be used to estimate the true value.

[0085] Calculate the deviations d Δx % and d Δy % of the values μ Δx1 and μ Δy1 corresponding to the maximum posterior probability in the posterior distribution and the values μ Δx and μ Δy corresponding to the maximum prior probability in the prior distribution, that is, d Δx2 % = (μ Δy2 % = (μ x % and d y % = (μ x % = (μΔx1 -μ Δx2 ) / μ Δx2 、d y %=(μ Δy1 -μ Δy2 ) / μ y2 ; If d x % is greater than a% or d y % is greater than a%, the droplet landing point accuracy distribution of the nozzle is abnormal, otherwise the nozzle works normally and printing can continue. The a% represents the maximum allowable deviation of the average value of the droplet landing point accuracy in one direction in actual production requirements.

[0086] In the application of high-precision inkjet printing scenarios, it is required that the change in the average deviation of the ink droplet landing points ejected from the working nozzles does not exceed ±a%. Once the above requirements are not met, the landing point accuracy of the ink droplets ejected by the nozzle can no longer meet the basic production requirements, and the accuracy standard is restricted according to the printing target and application scenario.

[0087] Generally speaking, the method of this embodiment obtains the droplet landing point deviation data by initially printing a test pattern with all nozzles, and establishes a normal prior distribution including the mean vector and the standard deviation vector; randomly selects samples during the normal working stage, and fuses the sampled data with the prior distribution to calculate the posterior distribution; by comparing the parameter deviations between the posterior distribution and the prior distribution, the working state of the nozzle can be dynamically determined and parameter adjustment can be triggered. This embodiment fuses historical data and real-time sampling information through probability modeling, significantly reduces the detection frequency while ensuring the detection accuracy, has the characteristics of strong real-time performance and wide adaptability, and can effectively improve the stability of the printing quality and reduce the manual intervention cost.

[0088] Embodiment 2

[0089] An online control method for the state of ink droplet landing points in inkjet printing, based on the state of ink droplet landing points in inkjet printing determined by the above-mentioned ink droplet landing point state evaluation method in Embodiment 1, adjusts the printing parameters online.

[0090] The related technical solutions are the same as those in Embodiment 1 and will not be elaborated here.

[0091] Embodiment 3

[0092] An online control system for the state of ink droplet landing points in inkjet printing, including an inkjet printing module, a vision module, a processing unit, and a feedback control module;

[0093] The inkjet printing module is used to perform printing; the vision module is used to collect the images of the arrayed ink droplet landing points; the processing unit is used to execute the above-mentioned ink droplet landing point state evaluation method; the system control module is used to execute the above-mentioned online control method for the state of ink droplet landing points in inkjet printing, automatically correct the deviation of the ink droplet landing points to ensure the stability of the printing quality.

[0094] The related technical solutions are the same as above and will not be elaborated here.

[0095] Embodiment 4

[0096] This application also relates to a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0097] Specifically, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0098] The related technical solutions are the same as above and will not be elaborated here.

[0099] Embodiment 5

[0100] The embodiment of this application provides a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the steps of the method of the above embodiments of this application.

[0101] The related technical solutions are the same as above and will not be elaborated here.

[0102] Those skilled in the art can easily understand that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An evaluation method for the state of the droplet landing point of an inkjet printing liquid, characterized in that, Comprising: During the printing gap, randomly select and inspect some ink droplets to obtain the landing deviation data of the some ink droplets; Solve the likelihood function values of the landing deviations of each inspected ink droplet under each set of landing state data, and multiply the likelihood function values of all the inspected ink droplets solved under this set of landing state data by the joint prior probability corresponding to this set of landing state data respectively to obtain the posterior probability corresponding to each inspected ink droplet under the condition of this set of landing state data, and calculate the sum of all the posterior probabilities corresponding to multiple sets of landing state data; wherein, each set of landing state data includes the mean and standard deviation of the landing deviations in different directions between all the landing points during pre-offline full-nozzle printing, and the multiple sets of landing state data are pre-obtained by offline performing multiple full-nozzle printings; The joint prior probability corresponding to each set of landing state data is obtained by substituting this set of landing state data into the pre-constructed joint prior distribution; Multiply the likelihood functions of the known landing deviations of each inspected ink droplet under the landing state data as the function independent variable, multiply the multiplication result by the joint prior distribution, divide the final multiplication result by the sum to obtain the posterior distribution; calculate the deviation between the mean of the landing deviations in each direction corresponding to the maximum posterior probability in the posterior distribution and the mean of the corresponding direction landing deviations corresponding to the maximum prior probability in the joint prior distribution to complete the evaluation of the landing state of the inkjet printing liquid droplets.

2. The method for evaluating the state of the inkjet printing liquid drop point according to claim 1, wherein, The construction method of each set of landing state data is as follows: Through full-nozzle printing a test pattern, collect the arrayed ink droplet landing images to extract the landing deviation information of each ink droplet, and after excluding abnormal landing points, obtain the full-inspection data of the ink droplet landing deviations; calculate a set of landing state data according to the full-inspection data of the ink droplet landing deviations.

3. The method for evaluating the state of the inkjet printing liquid drop point according to claim 1, characterized in that, The construction method of the joint prior distribution is as follows: According to multiple sets of landing state data, and in combination with the tolerance range of the mean and standard deviation of the landing deviations determined based on production requirements, construct the probability density function of each parameter in the landing state data that conforms to the normal distribution; use the product between the probability density functions of various parameters as the joint prior distribution of the landing state to complete the construction of the joint prior distribution.

4. The method for evaluating the state of the inkjet printing liquid drop point according to claim 3, characterized in that, The probability density function of each parameter that conforms to the normal distribution is: In the formula, and respectively represent the probability density functions of the normal distribution of μ Δx , μ Δy , σ Δx , σ Δy . μ Δx , μ Δy are respectively the means of the landing point deviations in the x and y directions, and σ Δx , σ Δy are respectively the standard deviations of the landing point deviations in the x and y directions; μ μΔx , are respectively the mean and standard deviation of μ Δx , μ μΔy , are respectively the mean and standard deviation of μ Δy , μ σΔx , are respectively the mean and standard deviation of σ Δx , μ σΔy , are respectively the mean and standard deviation of σ Δy ; 3σ μΔx ≤a%μ μΔx ; 3σ μΔy ≤a%μ μΔy ; μ μΔx +3(3σ σΔx +μ μΔx )≤Δ max ; μ μΔy +3(3σ σΔy +μ μΔy )≤Δ max . a% is the maximum allowable deviation of the mean of the liquid drop landing point deviation in a single direction in the actual production requirements, and Δ max is the maximum allowable value of the liquid drop landing point deviation in a single direction in the actual production requirements.

5. The method for evaluating the state of the inkjet printing liquid drop point according to claim 1, characterized in that The landing deviation data is obtained through collecting the landing images of the corresponding inspected ink droplets and performing image processing.

6. The method for evaluating the state of the droplet landing point of an inkjet printing liquid according to claim 1, wherein The likelihood function of the landing deviation of the inspected ink droplet under each set of landing state data is: where p(Δ xi ,Δ yi ∣μ Δx ,μ Δu ,σ Δx ,σ Δy ) represents the likelihood function of the landing deviation (Δ xi ,Δ yi ) of the i-th sampled ink droplet in the x and y directions under a set of landing state data (μ Δx ,μ Δy ,σ Δx ,σ Δy ). μ Δx ,μ Δy are the means of the landing deviations in the x and y directions respectively, and σ Δx ,σ Δy are the standard deviations of the landing deviations in the x and y directions respectively. k represents the total number of sampled ink droplets.

7. The method for evaluating the state of the dripping point of an inkjet printing liquid according to claim 1, wherein The posterior distribution is: Wherein, p(μ Δx , μ Δy , σ Δx , σ Δy |Δ x1 , Δ y1 , Δ x2 , Δ y2 , …, Δ xk , Δ yk ) represents the posterior distribution, and Δ x1 , Δ y1 , Δ x2 , Δ y2 , …, Δ xk , Δ yk respectively represent the landing point deviations of k sampled ink droplets in the x and y directions, μ Δx , μ Δy , σ Δx , σ Δy represent a set of landing point state data, and μ Δx , μ Δy are respectively the means of the landing point deviations in the x and y directions, and σ Δx , σ Δy are respectively the standard deviations of the landing point deviations in the x and y directions; p(Δ x1 , Δ y1 |μ Δx , μ Δy , σ Δx , σ Δu ), p(Δ x2 , Δ y2 |μ Δx , μ Δy , σ Δx , σ Δy ), …, p(Δ xk , Δ yk |μ Δx , μ Δy , σ Δx , μ Δy ) respectively represent the likelihood functions of the known landing point deviations of each sampled ink droplet under the landing point state data as the independent variables of the function; p(μ Δx , μ Δy , σ Δx , σ Δy ) represents the joint prior distribution.

8. An on-line regulation method for the state of the droplet landing point of an inkjet printing liquid, characterized in that, Based on the landing state of the inkjet printing liquid droplets determined by an inkjet printing liquid droplet landing state evaluation method according to any one of claims 1 to 7, online adjust the printing parameters.

9. An on-line regulation system for the state of the dropping point of an inkjet printing liquid drop, characterized in that, Including an inkjet printing module, a vision module, a processing unit and a feedback control module; The inkjet printing module is used to perform printing; The vision module is used to collect the arrayed liquid droplet landing images; The processing unit is used to execute an inkjet printing liquid droplet landing state evaluation method according to any one of claims 1 to 7; The system control module is used to execute an inkjet printing liquid droplet landing state online regulation method according to claim 8, automatically correct the deviation of the liquid droplet landing points to ensure the stability of the printing quality.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein when the computer program is run by a processor, it controls the device where the storage medium is located to execute the steps of the method according to any one of claims 1 to 7 and / or the steps of the method according to claim 8.

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