An online coating data remote acquisition and analysis method and device for a thermal paper production line

Abstract

The present application relates to the technical field of production control, in particular to a kind of online coating data remote acquisition and analysis method and device of heat-sensitive paper production line.The method analyzes heat-sensitive paper image and each information in coating time on time sequence.Analyze the thickness change and shape change between adjacent time, which can effectively quantify the thickness change significance of each thickness area. Quantify the first coating unevenness at each time. Analyzing the linear correlation between the first coating unevenness and the ambient humidity can evaluate the influence of humidity in the current environment, obtain the second coating unevenness, obtain the influence weight of each parameter data on the doctor blade, and adjust the real-time parameter data based on the adjustment amount. The present application quantifies the real unevenness caused by the wear of the doctor blade by analyzing the thickness change and other characteristics in the coating process of heat-sensitive paper on time sequence in real time, combined with the influence of ambient humidity on coating thickness, and then adjusts the doctor blade parameters to avoid further wear of the doctor blade in time.

Description

Technical Field

[0001] This invention relates to the field of production control technology, specifically to a method and apparatus for remote acquisition and analysis of online coating data in a thermal paper production line. Background Technology

[0002] Thermal paper is a special coated paper that generates images directly through heat. Its core principle is to use heat to trigger a color reaction in the chemical substances within the coating, recording information without the need for traditional inks or toners. In the production process, raw materials are ground into a fine powder and mixed into a water-based coating. This coating is then evenly applied to the base paper using methods such as roller coating, spraying, or scraping. Each layer is applied in multiple coats, and each coat is dried and cured to ensure adhesion. The final product is then rewound and cut before being used in receipts, labels, medical records, and other fields.

[0003] In the production of thermal paper, the doctor blade is a core component of the coating process, and its wear directly affects the uniformity of the coating and the thermal color development performance. The doctor blade height is usually designed to be fixed, which cannot dynamically adapt to changes in working conditions after wear, leading to problems such as uneven printing pressure distribution and coating thickness fluctuations, which in turn cause quality defects such as blurred color development and missed coating.

[0004] Therefore, existing technologies determine whether there is uneven thickness based on the thickness distribution on the thermal paper coating, thereby determining whether the doctor blade is worn. However, existing technologies only judge uneven thickness based on the thickness at a preset detection time, failing to consider the real-time nature of the coating process. This makes it impossible to effectively assess doctor blade wear and adjust parameters in a timely manner to prevent further wear. Furthermore, after the thermal paper coating process is completed, it needs to be dried. Accurately controlling the humidity of the environment during this process is difficult. Excessive humidity can cause the substrate to absorb moisture and expand, affecting coating adhesion. This uncontrollable factor directly affects the final coating thickness, leading to misjudgments of doctor blade wear. Summary of the Invention

[0005] To address the technical problems of existing technologies that only analyze the surface thickness of thermal paper at a fixed time and ignore the influence of environmental humidity on the coating material of thermal paper, resulting in inaccurate judgment of thickness non-uniformity and thus the inability to effectively control parameters to prevent further wear of the doctor blade, the present invention aims to provide a method and device for remote acquisition and analysis of online coating data in thermal paper production lines. The specific technical solution adopted is as follows:

[0006] This invention proposes a method for remote acquisition and analysis of online coating data in a thermal paper production line, the method comprising:

[0007] Real-time acquisition of parameter data, thermal paper images, surface thickness of thermal paper at various locations, and ambient humidity during the scraper's operation at each moment;

[0008] The thermal paper image is divided into various thickness regions based on the surface thickness at various locations of the thermal paper; the thickness regions between adjacent time points are matched; the thickness difference and shape difference between the matched thickness regions at adjacent time points are used to obtain the thickness change significance of each thickness region at each time point; the first coating non-uniformity at each time point is obtained based on the change in the thickness change significance and thickness change of different thickness regions at each time point.

[0009] A linear correlation between ambient humidity and first coating nonuniformity over time is obtained, and a second coating nonuniformity is obtained based on the linear correlation and the first coating nonuniformity.

[0010] If the second coating non-uniformity meets the preset control conditions, the influence weight of each parameter data on the doctor blade is obtained, the adjustment amount is obtained according to the influence weight and the preset adjustment step size, and the real-time parameter data is adjusted based on the adjustment amount.

[0011] Furthermore, the thickness region division method includes:

[0012] Based on the surface thickness at each location, the DBSCAN clustering algorithm is used to cluster the data, and the respective regions of the clusters on the thermal paper are taken as the thickness regions.

[0013] Furthermore, the method for obtaining the shape difference includes:

[0014] Choose any time as the target time, and choose any thickness region at the target time as the target thickness region. The set of thickness regions that match the target thickness region at adjacent times constitutes the comparison region of the target thickness region. In the scanning direction of the thickness detection sensor, obtain the width difference between the comparison region and the target thickness region. Based on the width difference and the number of regions in the set of thickness regions that match at adjacent times, obtain the shape difference.

[0015] Furthermore, the method for obtaining the significance of the thickness change includes:

[0016] After negatively correlated mapping and normalization of the shape difference, it is multiplied by the thickness difference to obtain the significance of the thickness change.

[0017] Furthermore, the method for obtaining the first coating non-uniformity includes:

[0018] For each thickness region, the thickness variation significance is multiplied by the average thickness of the region to obtain the thickness coating characteristics; at each time step, the thickness coating characteristic differences between all thickness regions are obtained, and the sum of all thickness coating characteristic differences is taken as the first coating non-uniformity at each time step.

[0019] Furthermore, the method for obtaining the second coating non-uniformity includes:

[0020] Using the first coating non-uniformity as the ordinate and the ambient humidity at the corresponding time as the abscissa, a coordinate system is constructed and curve fitting is performed to obtain the tangent slope on the fitted curve at each time. The tangent slope is negatively correlated and normalized to obtain the non-environmental influence weight. The product of the non-environmental influence weight and the first coating non-uniformity is used as the second coating non-uniformity.

[0021] Furthermore, the method for obtaining the influence weights includes:

[0022] The second coating non-uniformity curve over time is obtained for each value when each parameter data is used as a variable using the controlled variable method. The wear rate for each value is obtained based on the slope of the tangent line on the curve. The parameter data sequence used as a variable is aligned with the wear rate sequence, and the Pearson correlation coefficient is calculated. The Pearson correlation coefficient is used as the influence weight.

[0023] Furthermore, the method for obtaining the adjustment amount includes:

[0024] The product of the influence weight and the preset adjustment step size is used as the control quantity.

[0025] Further, adjusting the real-time parameter data based on the adjustment amount includes:

[0026] The difference between the real-time parameter data and the controlled amount is used as the parameter data after control.

[0027] The present invention also proposes an online coating data remote acquisition and analysis device for a thermal paper production line. The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the steps of the online coating data remote acquisition and analysis method for a thermal paper production line.

[0028] The present invention has the following beneficial effects:

[0029] This invention analyzes thermal paper images and coating information over time. For each moment in the thermal paper image, the significance of thickness changes in each thickness region can be effectively quantified by analyzing thickness and shape changes between adjacent moments. This allows for the quantification of the first coating non-uniformity at each moment. Further analysis of the influence of ambient humidity reveals that the linear correlation between the first coating non-uniformity and ambient humidity assesses the impact of humidity in the current environment. A greater environmental influence suggests that the thickness non-uniformity is less likely to be caused by doctor blade wear, thus allowing for the acquisition of a second coating non-uniformity to characterize the non-uniformity truly caused by doctor blade wear. A larger second coating non-uniformity indicates more significant doctor blade wear, necessitating timely adjustment of doctor blade parameters to prevent further wear. Therefore, the influence weight of each parameter on the doctor blade can be obtained, and real-time parameter data can be adjusted based on the adjustment amount. This invention, by analyzing the thickness changes and other characteristics during the thermal paper coating process in real time, combined with the influence of ambient humidity on coating thickness, quantifies the true non-uniformity characterized by doctor blade wear, thereby adjusting doctor blade parameters to promptly prevent further wear. Attached Figure Description

[0030] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 This is a flowchart of an online coating data remote acquisition and analysis process for a thermal paper production line, provided in one embodiment of the present invention.

[0032] Figure 2 This is a schematic diagram of thermal paper scanning provided in one embodiment of the present invention. Detailed Implementation

[0033] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method and apparatus for remote acquisition and analysis of online coating data for a thermal paper production line according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0035] The following description, in conjunction with the accompanying drawings, details the specific scheme of the online coating data remote acquisition and analysis method and apparatus for a thermal paper production line provided by the present invention.

[0036] Please see Figure 1 The diagram illustrates a flowchart of a remote acquisition and analysis method for online coating data in a thermal paper production line, according to an embodiment of the present invention. The method includes:

[0037] Step S1: Real-time acquisition of parameter data, thermal paper images, surface thickness of thermal paper at various locations, and ambient humidity during the use of the scraper at each moment.

[0038] During the coating process of thermal paper, different degrees of doctor blade wear will have different effects. In the initial wear stage, the blade edge is slightly dulled, and the coating thickness increases slightly. At this stage, it is not easy to judge the wear by directly measuring the thickness at a fixed time. In the intermediate wear stage, the blade edge is uneven, and the coating shows streaks or uneven thickness, affecting the color uniformity of the thermal paper. The coating thickness unevenness is more significant at this stage and is relatively easy to detect. However, at this time, significant blade wear has already occurred, and the doctor blade should be replaced. In the final wear stage, the blade edge is severely damaged, the coating is incomplete, the coating is broken, or even the doctor blade breaks, causing a shutdown. Therefore, in order to ensure the effective operation of the coating process and extend the service life of the doctor blade, this embodiment of the invention needs to effectively identify the thickness unevenness caused by coating in the initial wear stage, and then adjust the doctor blade parameters in time to avoid further wear. Therefore, this embodiment of the invention acquires the parameter data of the doctor blade during use, the thermal paper image, the surface thickness of the thermal paper at various locations, and the ambient humidity in real time at each moment, and analyzes and processes the data in subsequent processes.

[0039] In this embodiment of the invention, a laser sensor can be used to identify the thickness at various locations in a thermal paper image. For example... Figure 2 As shown, it illustrates a thermal paper scanning schematic diagram provided by an embodiment of the present invention. Since the width D of the thermal paper is fixed and the scanning width d of the sensor is also fixed, the scanning direction of the sensor is set to be perpendicular to the moving direction of the thermal paper to perform longitudinal scanning. By continuously traversing the scan, the surface thickness at each scanning position can be obtained.

[0040] In this embodiment of the invention, the parameters of the doctor blade include at least the coating speed and the doctor blade pressure. These parameters are directly related to the service life of the doctor blade, so further wear of the doctor blade can be avoided by adjusting these parameters.

[0041] In this embodiment of the invention, the ambient humidity can be obtained by a humidity sensor installed in the production line environment. The thermal paper image can be obtained using a camera perpendicular to the thermal paper.

[0042] In this embodiment of the invention, each type of data is collected at a fixed sampling frequency, with one sample collected each time the scraper performs a leveling operation. Furthermore, since this embodiment primarily quantifies and evaluates features based on the variations and correlations of each data point, the obtained data can be normalized and standardized to eliminate the influence of dimensions during data processing. Specific techniques are well-known to those skilled in the art and will not be elaborated upon here.

[0043] In this embodiment of the invention, after collecting data from various dimensions, the data is stored in the device's memory and transmitted to the control terminal via the Internet of Things for data processing. After processing the data, the control terminal sends control commands back to the coating device.

[0044] Step S2: Divide the thermal paper image into different thickness regions based on the surface thickness of each location on the thermal paper; match the thickness regions between adjacent time points; obtain the thickness change significance of each thickness region at each time point based on the thickness difference and shape difference between the matched thickness regions at adjacent time points; obtain the first coating non-uniformity at each time point based on the change in the thickness change significance and thickness change of different thickness regions at each time point.

[0045] Since the surface thickness of each location on the thermal paper has been obtained in step S1 above, the surface thickness at the corresponding location can be mapped onto the thermal paper image, thereby dividing the thermal paper image into various thickness regions. One of these thickness regions is a region composed of consecutive locations with similar surface thicknesses. Because the coating process is sequential, the changes in each thickness region between adjacent time points can be analyzed, thereby quantifying the characteristics generated by the coating at each time point.

[0046] This invention matches thickness regions between adjacent time points. For matched thickness regions, a greater thickness difference indicates effective coating at a given time point, with a significant change in coating thickness. Conversely, differences in shape between matched regions indicate poor coating at a given time point, suggesting the scraper cannot effectively and evenly spread the coating. Therefore, this invention obtains the significance of thickness change for each thickness region at each time point based on the thickness difference and shape difference between matched thickness regions at adjacent time points. In other words, the significance of thickness change characterizes the scraper coating effect of each thickness region at the corresponding time point. Thus, by analyzing the changes in the significance of thickness change between different thickness regions in the same frame of thermal paper image and the thickness changes between different regions, the first coating non-uniformity at each time point can be obtained. Specifically, a greater change in the significance of thickness change and a greater thickness change indicate a poorer thermal paper coating effect at the current time point, resulting in a greater first coating non-uniformity.

[0047] Preferably, in this embodiment of the invention, the thickness region division method includes:

[0048] The surface thickness at each location is clustered using the DBSCAN clustering algorithm, with each region on the thermal paper representing a thickness region. The DBSCAN clustering algorithm is a well-known technique and will not be elaborated upon here. The clustering metric during the clustering process can directly utilize the surface thickness difference.

[0049] In one embodiment of the present invention, the preceding moment is selected for each adjacent moment; that is, for each moment, the thickness region is matched with the thermal paper image of the preceding moment. In the same coordinate system, if the thickness region of the preceding moment is located within the target thickness region of the current moment, then the thickness region of the preceding moment is the matching region of the target thickness region. After matching, the target thickness region may have multiple matching regions, thus forming a set of matching regions for the target thickness region. Each thickness region at each moment corresponds to a set of matching regions. It should be noted that, because the embodiment of the present invention selects each moment to match with the preceding moment, the corresponding thickness difference should be the difference between the average thickness of the target thickness region and the average thickness in the set of matching regions. That is, this difference retains its sign; a positive and larger thickness difference indicates that a more effective coating has been produced at the current moment.

[0050] Preferably, in this embodiment of the invention, the method for obtaining shape differences includes:

[0051] Choose any time as the target time, and choose any thickness region at the target time as the target thickness region. The set of thickness regions that match the target thickness region at adjacent times of the target time constitutes the comparison region of the target thickness region.

[0052] It should be noted that if there is regional splitting when the set of thickness regions is merged into the comparison region, it can be filled by interpolation. Since the matching in the embodiments of the present invention is all in the same coordinate system and at the same position, the matching regions in the set of thickness regions will not be too far apart.

[0053] In the scanning direction of the thickness detection sensor, the width difference between the comparison area and the target thickness area is obtained; based on the width difference and the number of areas in the set of thickness areas matched at adjacent time points, the shape difference is obtained. That is, in this embodiment of the invention, the width in the scanning direction is selected as reference data. This reference data is used to evaluate the shape difference between the two areas. The larger the width difference, the more significant the shape change after coating at the target time, indicating that the coating effect at the target time is not uniform compared to the previous time point. The larger the number of areas in the set of thickness areas, the more significant the regional inconsistency in the uneven coating of the scraper compared to adjacent time points. Therefore, the larger the width difference and the larger the number of areas in the set of thickness areas, the greater the shape difference. A larger shape difference indicates a poorer coating effect at the target time, and a smaller significant change in thickness.

[0054] In this embodiment of the invention, the Euclidean norm, obtained based on the width difference and the number of regions in the set of thickness regions matched at adjacent time points, is used as the shape difference. That is, the squares of the two data points are squared and then summed and the square root is taken.

[0055] Preferably, in this embodiment of the invention, since the significance of thickness change is used to characterize the coating effectiveness at each time point, the shape difference should be negatively correlated with the significance of thickness change, and the thickness difference should be positively correlated. Therefore, after negatively mapping and normalizing the shape difference, it is multiplied by the thickness difference to obtain the significance of thickness change.

[0056] In this embodiment of the invention, the negative number of the shape difference is used as the power of an exponential function with the natural constant as the base, and the output data of the exponential function is the result of negative correlation mapping and normalization.

[0057] Preferably, in this embodiment of the invention, the method for obtaining the first coating unevenness includes:

[0058] For each thickness region, the thickness variation significance and the average thickness of the region are multiplied to obtain the thickness coating feature. At each time step, the difference in thickness coating feature between all thickness regions is obtained, and the sum of all thickness coating feature differences is taken as the first coating non-uniformity at each time step. That is, in this embodiment of the invention, the thickness variation significance and the average thickness of the region are integrated and quantified by multiplication, and then the first coating non-uniformity can be obtained through the difference between thickness coating features. That is, the greater the difference in coating feature between regions, the more uneven the coating effect in the current thermal paper image.

[0059] It should be noted that, in other embodiments of the present invention, the variance of the thickness coating characteristics can also be used as the first coating non-uniformity, and the variance can be used to characterize the fluctuation and non-uniformity of the data.

[0060] Step S3: Obtain the linear correlation between ambient humidity and the first coating non-uniformity over time, and obtain the second coating non-uniformity based on the linear correlation and the first coating non-uniformity.

[0061] Excessive humidity can cause the substrate on the paper surface to absorb moisture and expand, affecting coating adhesion and consequently the thickness of the thermal paper, leading to misjudgments of the doctor blade wear. In other words, if the ambient humidity can affect the thickness, it indicates that the currently identified first coating unevenness is not solely caused by doctor blade wear; the influence of humidity needs to be eliminated before determining whether doctor blade wear has occurred. Therefore, this embodiment of the invention obtains a linear correlation between ambient humidity and the first coating unevenness over time. The stronger the correlation between the two dimensions, the more the thickness unevenness is affected by humidity, and the more necessary it is to adjust the first coating unevenness based on the linear correlation to obtain the second coating unevenness.

[0062] Preferably, in this embodiment of the invention, the method for obtaining the second coating non-uniformity includes:

[0063] Using the first coating non-uniformity as the ordinate and the ambient humidity at the corresponding time as the abscissa, a coordinate system is constructed and curve fitting is performed to obtain the tangent slope on the fitted curve at each time.

[0064] It should be noted that the least squares method is used to fit the curve in the embodiments of the present invention. The fitting curve here is the same as the curve of the parameter data below. Both are implemented by the least squares method. The specific content is a conventional method used by those skilled in the art and will not be described in detail here.

[0065] A larger tangent slope indicates a stronger linear correlation between the two dimensions. Therefore, the tangent slope is negatively correlated and normalized to obtain the non-environmental influence weight. That is, a larger non-environmental influence weight indicates a smaller impact of ambient humidity on thickness uniformity, suggesting that the first coating uniformity is mainly caused by doctor blade wear. Since the value range of the non-environmental influence weight is between 0 and 1, the product of the non-environmental influence weight and the first coating uniformity is taken as the second coating uniformity.

[0066] In this embodiment of the invention, the inverse of the tangent slope is used as the power of an exponential function with the natural constant as the base. The output value of the exponential function is the negative correlation mapping and normalized value. Those skilled in the art can also choose other negative correlation mapping and normalization methods, such as directly using the reciprocal of the tangent slope as the negative correlation mapping result, and then normalizing it using range standardization. Specific details are not limited or elaborated upon.

[0067] Step S4: If the second coating non-uniformity meets the preset control conditions, obtain the influence weight of each parameter data on the doctor blade, obtain the adjustment amount according to the influence weight and the preset adjustment step size, and adjust the real-time parameter data based on the adjustment amount.

[0068] Because the data processing in this embodiment of the invention is real-time, a second coating non-uniformity can be obtained at each moment. Therefore, the adjustment can be set as a threshold judgment condition. In this embodiment of the invention, the non-uniformity threshold is set to 0.3. If the normalized value of the second coating non-uniformity is greater than the non-uniformity threshold, it indicates that the non-uniformity of the thermal paper thickness is caused by the wear of the doctor blade, and the doctor blade parameters need to be adjusted in time. After adjustment, real-time detection can continue to achieve automatic optimization of parameter data and form a closed-loop control. It should be noted that if at a certain moment, after adjusting the doctor blade parameters by operation, each doctor blade parameter has been adjusted to the rated upper or lower limit, but the second non-uniformity is still greater than the set non-uniformity threshold, it indicates that the current doctor blade wear cycle cannot be extended by adjusting the process parameters. The machine should be automatically stopped to reduce material loss, and the shutdown information and related collected data should be transmitted to the remote control terminal through the Internet of Things to remind the staff to replace the doctor blade.

[0069] In this embodiment of the invention, the normalization method for the second non-uniformity adopts range standardization, which normalizes each data by statistically analyzing the maximum and minimum values ​​of the second non-uniformity. The specific method is a conventional technique for those skilled in the art and will not be described in detail here.

[0070] To effectively control the scraper parameters, it's first necessary to obtain the influence weight of each parameter on the scraper. A higher influence weight indicates that the parameter in that dimension requires more adjustment, as it has a greater impact on scraper wear. Combining this with a preset adjustment step size yields the adjustment amount. That is, the adjustment amount is fixed; by gradually adjusting the real-time parameter data based on this amount, automatic parameter optimization can be achieved.

[0071] Preferably, in this embodiment of the invention, the influence weight of each parameter data on the wear of the doctor blade can be determined experimentally using the controlled variable method. That is, one parameter data is selected as a variable, while other parameter data remain unchanged. In this embodiment, the variable value is gradually increased from small to large, and the curve of the second coating non-uniformity under each value is statistically analyzed over time. The slope of the tangent line on this curve represents the wear rate at the current value. By statistical analysis, a parameter data sequence composed of each variable value can be obtained, corresponding to a wear rate sequence. After aligning the two sequences, the Pearson correlation coefficient can be obtained. The Pearson correlation coefficient ranges from -1 to 1. The larger the absolute value of the correlation coefficient, the higher the correlation between the parameter data and the doctor blade wear. Its positive or negative sign indicates the trend of the influence of increasing or decreasing the current process parameter on the doctor blade wear. In this embodiment, the parameter data sequence is arranged from small to large. Therefore, if the Pearson correlation coefficient is positive and large, it indicates that the doctor blade wears faster as the parameter data increases; if it is negative and small, it indicates that the wear rate is slower as the parameter data increases. Therefore, the Pearson correlation coefficient can be used as the influence weight.

[0072] Furthermore, the preset adjustment step size can be obtained through the maximum adjustment amount specified in the process guidelines or the maximum adjustment amount preset in the process. That is, the preset adjustment step size is the maximum amount that can be adjusted in one adjustment.

[0073] Because the influence weight ranges from -1 to 1, the preset adjustment step size can be directly weighted by multiplication, and the product of the influence weight and the preset adjustment step size is used as the adjustment amount. The sign of the adjustment amount represents the adjustment direction; a positive number indicates that a larger parameter value is more likely to cause further wear, so the current parameter value needs to be adjusted negatively; a negative number indicates that a smaller parameter value is more likely to cause further wear, so the current parameter value needs to be adjusted positively. Therefore, in this embodiment of the invention, the difference between the real-time parameter data and the adjustment amount can be used as the adjusted parameter data.

[0074] It should be noted that the above description only uses one parameter as an example. The same method can be used to analyze and process each parameter. After obtaining the parameter data after adjustment, the control terminal can send control commands to the coating equipment to adjust the various parameters of the doctor blade.

[0075] In summary, this invention analyzes the thermal paper image and various information during coating in a time-series manner. By analyzing thickness and shape changes between adjacent time points, the significance of thickness changes in each thickness region can be effectively quantified. Furthermore, the first coating non-uniformity is quantified at each time point. By analyzing the linear correlation between the first coating non-uniformity and ambient humidity, the influence of humidity in the current environment can be assessed, obtaining the second coating non-uniformity. The influence weight of each parameter data on the doctor blade is obtained, and the real-time parameter data is adjusted based on the adjustment amount. This invention, by analyzing the characteristics such as thickness changes during the thermal paper coating process in real time, combined with the influence of ambient humidity on coating thickness, quantifies the true non-uniformity characterizing doctor blade wear, thereby adjusting doctor blade parameters and promptly preventing further doctor blade wear.

[0076] Based on the same inventive concept, the present invention also proposes an online coating data remote acquisition and analysis device for a thermal paper production line. The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the steps of the online coating data remote acquisition and analysis method for a thermal paper production line.

[0077] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0078] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for remote acquisition and analysis of online coating data in a thermal paper production line, characterized in that, The method includes: Real-time acquisition of parameter data, thermal paper images, surface thickness of thermal paper at various locations, and ambient humidity during the scraper's operation at each moment; The thermal paper image is divided into various thickness regions based on the surface thickness at various locations of the thermal paper; the thickness regions between adjacent time points are matched; the thickness difference and shape difference between the matched thickness regions at adjacent time points are used to obtain the thickness change significance of each thickness region at each time point; the first coating non-uniformity at each time point is obtained based on the change in the thickness change significance and thickness change of different thickness regions at each time point. Using the first coating non-uniformity as the ordinate and the ambient humidity at the corresponding time as the abscissa, a coordinate system is constructed and curve fitting is performed to obtain the tangent slope on the fitted curve at each time. The tangent slope is negatively correlated and normalized to obtain the non-environmental influence weight. The product of the non-environmental influence weight and the first coating non-uniformity is used as the second coating non-uniformity. If the second coating non-uniformity meets the preset control conditions, the influence weight of each parameter data on the doctor blade is obtained, the adjustment amount is obtained according to the influence weight and the preset adjustment step size, and the real-time parameter data is adjusted based on the adjustment amount.

2. The method for remote acquisition and analysis of online coating data in a thermal paper production line according to claim 1, characterized in that, The thickness region division method includes: Based on the surface thickness at each location, the DBSCAN clustering algorithm is used to cluster the data, and the respective regions of the clusters on the thermal paper are taken as the thickness regions.

3. The method for remote acquisition and analysis of online coating data in a thermal paper production line according to claim 1, characterized in that, The method for obtaining the shape difference includes: Choose any time as the target time, and choose any thickness region at the target time as the target thickness region. The set of thickness regions that match the target thickness region at adjacent times constitutes the comparison region of the target thickness region. In the scanning direction of the thickness detection sensor, obtain the width difference between the comparison region and the target thickness region. Based on the width difference and the number of regions in the set of thickness regions that match at adjacent times, obtain the shape difference.

4. The method for remote acquisition and analysis of online coating data in a thermal paper production line according to claim 1, characterized in that, The method for obtaining the significance of the thickness change includes: After negatively correlated mapping and normalization of the shape difference, it is multiplied by the thickness difference to obtain the significance of the thickness change.

5. The method for remote acquisition and analysis of online coating data in a thermal paper production line according to claim 1, characterized in that, The method for obtaining the first coating non-uniformity includes: For each thickness region, the thickness variation significance is multiplied by the average thickness of the region to obtain the thickness coating characteristics; at each time step, the thickness coating characteristic differences between all thickness regions are obtained, and the sum of all thickness coating characteristic differences is taken as the first coating non-uniformity at each time step.

6. The method for remote acquisition and analysis of online coating data in a thermal paper production line according to claim 1, characterized in that, The method for obtaining the influence weights includes: The second coating non-uniformity curve over time is obtained for each value when each parameter data is used as a variable using the controlled variable method. The wear rate for each value is obtained based on the slope of the tangent line on the curve. The parameter data sequence used as a variable is aligned with the wear rate sequence, and the Pearson correlation coefficient is calculated. The Pearson correlation coefficient is used as the influence weight.

7. The method for remote acquisition and analysis of online coating data in a thermal paper production line according to claim 6, characterized in that, The method for obtaining the adjustment amount includes: The product of the influence weight and the preset adjustment step size is used as the control quantity.

8. The method for remote acquisition and analysis of online coating data in a thermal paper production line according to claim 7, characterized in that, The adjustment of real-time parameter data based on the adjustment amount includes: The difference between the real-time parameter data and the controlled amount is used as the parameter data after control.

9. A remote acquisition and analysis device for online coating data of a thermal paper production line, the device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the online coating data remote acquisition and analysis method for a thermal paper production line as described in any one of claims 1-8.

Images