Film coating machine operation intelligent control system based on data analysis

Abstract

The invention belongs to the technical field of film coating machine management and control, and particularly relates to a film coating machine operation intelligent control system based on data analysis, which comprises a coating operation monitoring module, a coating drying and curing analysis module, an intelligent control module, a film coating machine operation quality decision module and a film coating machine supervision end, the coating operation monitoring module is used for monitoring the coating process of the film coating machine and analyzing the coating performance, and the coating drying and curing analysis module is used for monitoring and analyzing the drying and curing process, so that the coating operation performance and the drying and curing performance can be accurately evaluated, and early warning can be performed in time; the efficient stability of the operation process of the film coating machine and the quality of processed products are guaranteed, the quality of all processed base materials is inspected one by one and accurately fed back through the film coating machine operation quality decision-making module, corresponding optimization and adjustment measures are taken in time, the processing quality of the film coating machine is further guaranteed, and the working efficiency of the film coating machine is improved. And the workload and the working difficulty of workers are remarkably reduced, and the intelligent and automatic levels are high.

Description

Technical Field

[0001] The present invention relates to the technical field of film coating machine control and management, and in particular to an intelligent control system for film coating machine operation based on data analysis. Background Art

[0002] Coating machines are common equipment in industrial production. They use specific processes and technologies to evenly coat the coating on the surface of the substrate, and after drying and curing, the coating forms a continuous film layer with a certain thickness and performance. They are widely used in printing, decoration, lithium battery manufacturing and other fields.

[0003] At present, when controlling the operation of the coating machine, the coating machine generally operates according to the set control program, which makes it difficult to effectively monitor and automatically adjust the coating process and drying and curing process of the coating machine, and it is difficult to reasonably analyze the processing quality and operation control performance of the coating machine and issue timely warnings. It is impossible to make corresponding optimization and adjustment measures in time, which is not conducive to ensuring the efficient and stable operation of the coating machine and improving product quality.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent control system for the operation of a coating machine based on data analysis, which solves the problem that the prior art is difficult to effectively monitor and automatically adaptively regulate the coating process and drying and curing process of the coating machine, and is difficult to reasonably analyze and timely warn the processing quality and operation control performance of the coating machine, which is not conducive to ensuring the efficient and stable operation of the coating machine and improving product quality.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The intelligent control system for the operation of the coating machine based on data analysis includes a coating operation monitoring module, a coating drying and curing analysis module, an intelligent control module, a coating machine operation quality decision module and a coating machine supervision terminal; when the coating machine coats the coating on the surface of the substrate, the coating operation monitoring module monitors the coating process of the coating machine, analyzes the coating performance and generates a coating qualified signal or a coating optimization signal, and sends the coating optimization signal to the intelligent control module;

[0008] After coating, the coating machine dries and solidifies the coating on the substrate, and the coating drying and solidification analysis module monitors and analyzes the drying and solidification process, thereby generating a drying qualified signal or a drying optimization signal, and sending the drying optimization signal to the intelligent control module;

[0009] When the intelligent control module receives the coating optimization signal or the drying optimization signal, it generates the corresponding control instruction and adaptively adjusts the coating machine based on the control instruction; the coating machine operation quality decision module performs quality inspection on the substrate processed by the coating machine, marks the corresponding substrate as a high-quality object or a low-quality object through analysis, and sends the marking information of the corresponding substrate to the coating machine supervision end.

[0010] Furthermore, the specific operation process of the coating operation monitoring module is as follows:

[0011] The coating speed of the coating head is collected and marked as the coating speed value, and the pressure applied by the coating head to the substrate during coating is collected and marked as the coating pressure inspection value. The coating speed value and the coating pressure inspection value are numerically compared with the preset coating speed value range and the preset coating pressure inspection value range respectively. If the coating speed value or the coating pressure inspection value is not within the corresponding preset range, a coating optimization signal is generated. If both the coating speed value and the coating pressure inspection value are within the corresponding preset range, a coating qualified signal is generated.

[0012] Furthermore, the specific analysis process of the coating drying and curing analysis module includes:

[0013] The operating space for drying and curing the substrate coating in the coating machine is obtained and marked as the monitoring space, a number of detection points are set in the monitoring space, the hot air temperature and hot air speed at the corresponding detection points are collected and marked as the hot air temperature analysis value and the hot air speed analysis value, respectively, the hot air temperature analysis values ​​of all the detection points in the monitoring space are calculated by variance to obtain the hot air temperature distribution value, and the hot air speed analysis values ​​of all the detection points in the monitoring space are calculated by variance to obtain the hot air speed distribution value;

[0014] The hot air temperature distribution value and the hot air speed distribution value are numerically compared with the preset hot air temperature distribution threshold and the preset hot air speed distribution threshold, respectively. If the hot air temperature distribution value or the hot air speed distribution value exceeds the corresponding preset threshold, a drying optimization signal is generated.

[0015] Furthermore, if both the hot air temperature distribution value and the hot air speed distribution value do not exceed the corresponding preset threshold value, the deviation value of the hot air temperature distribution value compared to the preset standard hot air temperature value is marked as a hot air temperature anomaly value, and the deviation value of the hot air speed distribution value compared to the preset standard hot air speed value is marked as a hot air speed anomaly value;

[0016] The hot air performance value is obtained by numerically calculating the hot air temperature difference value and the hot air speed difference value, and the hot air performance value is numerically compared with the preset hot air performance threshold value. If the hot air performance value exceeds the preset hot air performance threshold value, the corresponding detection point is marked as a thermally inferior point; the number of thermally inferior points in the monitoring space is obtained and marked as a thermally inferior number detection value, and the thermally inferior number detection value is numerically compared with the preset thermally inferior number detection threshold value. If the thermally inferior number detection value exceeds the preset thermally inferior number detection threshold value, a drying optimization signal is generated;

[0017] If the thermal inferiority detection value does not exceed the preset thermal inferiority detection threshold, the hot air performance values ​​of all detection points in the monitoring space are averaged to obtain the spatial analysis value, and the hot air performance value with the largest value in the monitoring space is marked as the hot air amplitude difference value; the drying and curing non-optimal coefficient is obtained by numerically calculating the thermal inferiority detection value, the spatial analysis value and the hot air amplitude difference value, and the drying and curing non-optimal coefficient is numerically compared with the preset drying and curing non-optimal coefficient threshold value; if the drying and curing non-optimal coefficient exceeds the preset drying and curing non-optimal coefficient threshold value, a drying optimization signal is generated; if the drying and curing non-optimal coefficient does not exceed the preset drying and curing non-optimal coefficient threshold value, a drying qualified signal is generated.

[0018] Furthermore, the specific analysis process of the coating machine operation quality decision module is as follows:

[0019] An image of the surface of the substrate after drying and curing is collected, and cracks and bubbles on the surface are identified based on the image of the surface of the substrate. If cracks and bubbles are present, the corresponding substrate is marked as a poor quality object;

[0020] If there are no cracks or bubbles, the coating thickness data of several positions on the surface of the corresponding substrate are collected, the variance of the coating thickness data at all positions is calculated to obtain the thickness uniformity value, and the mean of the coating thickness data at all positions is calculated to obtain the thickness performance value, and the thickness uniformity value and the thickness performance value are numerically compared with the preset thickness uniformity threshold and the preset thickness performance value range respectively. If the thickness uniformity value exceeds the preset thickness uniformity threshold or the thickness performance value is not within the preset thickness performance value range, the corresponding substrate is marked as a poor quality object;

[0021] If the thickness uniformity value does not exceed the preset thickness uniformity threshold and the thickness performance value is within the preset thickness performance value range, the gloss data of the corresponding substrate surface is collected and the deviation value from the preset standard gloss is marked as the gloss detection value, the color difference data of the corresponding substrate surface is collected and marked as the color difference detection value, and the thickness performance value is calculated and the difference between the median value of the preset thickness performance value range is calculated and the absolute value is taken to obtain the thickness segregation value;

[0022] The substrate quality decision value is obtained by numerically calculating the gloss detection value, the color difference detection value and the thickness segregation value, and the substrate quality decision value is numerically compared with the preset substrate quality decision threshold. If the substrate quality decision value exceeds the preset substrate quality decision threshold, the corresponding substrate is marked as a poor quality object; if the substrate quality decision value does not exceed the preset substrate quality decision threshold, the corresponding substrate is marked as a high quality object.

[0023] Furthermore, the coating machine supervision end is communicated with the operation control evaluation module, and the operation control evaluation module is used to set an interval period with a duration of L1. When the duration reaches L1, the operation control performance of the coating machine in the interval period is analyzed, and an operation control abnormal signal or an operation control qualified signal is generated through the analysis, and the operation control abnormal signal or the operation control qualified signal is sent to the coating machine supervision end. When the coating machine supervision end receives the operation control abnormal signal, a corresponding early warning is issued.

[0024] Furthermore, the specific analysis process of the operation control and assessment module is as follows:

[0025] The number of inferior objects processed by the coating machine during the interval period is obtained and the ratio thereof is calculated with the total number of processed substrates to obtain the inferior quality detection value, and the inferior quality detection value is numerically compared with the preset inferior quality detection threshold. If the inferior quality detection value exceeds the preset inferior quality detection threshold, an operation control abnormal signal is generated;

[0026] If the inferior quality detection value does not exceed the preset inferior quality detection threshold, the number of times the coating optimization signal is generated and the number of times the drying optimization signal is generated in the interval period are summed to obtain the optimization warning value, and the number of times the interval between the time when the intelligent control module generates the corresponding control instruction and the time when the corresponding regulation is completed exceeds the corresponding preset interval time threshold is marked as the optimization control abnormality value;

[0027] The operation control analysis value is obtained by numerically calculating the inferior inspection value, the optimized warning value and the optimized control abnormality value, and the operation control analysis value is numerically compared with the preset operation control analysis threshold. If the operation control analysis value exceeds the preset operation control analysis threshold, an operation control abnormal signal is generated; if the operation control analysis value does not exceed the preset operation control analysis threshold, a qualified operation control signal is generated.

[0028] Furthermore, the operation control and evaluation module is communicatively connected to the paint detection and analysis module, and the operation control and evaluation module sends the operation control qualified signal to the paint detection and analysis module. When the paint detection and analysis module receives the operation control qualified signal, it detects and analyzes the paint used in the coating machine to determine the quality status of the paint, and generates a paint abnormality signal or a paint qualified signal based on this, and sends the paint abnormality signal or the paint qualified signal to the coating machine monitoring end. When the coating machine monitoring end receives the paint abnormality signal, it issues a corresponding warning.

[0029] Furthermore, the specific analysis process of the coating detection and analysis module is as follows:

[0030] The viscosity, solid matter content, density, temperature and pH value of the coating in the coating machine are collected, and the viscosity is calculated by difference with the median of the preset suitable viscosity range and the absolute value is taken to obtain the coating viscosity measurement value, the solid matter content is calculated by difference with the median of the preset suitable solid matter content range and the absolute value is taken to obtain the coating solid concentration value, the density is calculated by difference with the median of the preset suitable density range and the absolute value is taken to obtain the coating density inspection value, the temperature is calculated by difference with the median of the preset suitable temperature range and the absolute value is taken to obtain the coating temperature value, and the pH value is calculated by difference with the median of the preset suitable pH value range and the absolute value is taken to obtain the coating acidity and alkalinity value;

[0031] The coating quality evaluation value is obtained by numerically calculating the coating viscosity measurement value, coating solid concentration value, coating close inspection value, coating temperature value and coating pH value, and the coating quality evaluation value is numerically compared with the preset coating quality evaluation threshold. If the coating quality evaluation value exceeds the preset coating quality evaluation threshold, a coating abnormality signal is generated; if the coating quality evaluation value does not exceed the preset coating quality evaluation threshold, a coating qualified signal is generated.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. In the present invention, the coating process of the coating machine is monitored and the coating performance is analyzed by the coating operation monitoring module, and the coating drying and curing analysis module is used to monitor and analyze the drying and curing process, which can accurately evaluate the coating operation performance and the drying and curing performance and timely warn, ensure the high efficiency and stability of the coating machine operation process and the quality of the processed products, and the coating machine operation quality decision module is used to perform quality inspections on all processed substrates one by one and provide accurate feedback, so as to make corresponding optimization and adjustment measures in time to ensure the subsequent processing quality and significantly reduce the workload and difficulty of the staff;

[0034] 2. In the present invention, the operation control performance of the coating machine within the interval period is analyzed through the operation control evaluation module. When the operation control qualified signal is generated, the paint used in the coating machine is detected and analyzed through the paint detection and analysis module to determine the paint quality status. When the operation control abnormal signal or the paint abnormal signal is generated, a corresponding early warning is issued and the coating machine is suspended to remind the supervisor to take corresponding improvement measures, so as to ensure the subsequent safe, stable and efficient operation of the coating machine, further improve the operation effect of the coating machine and the quality of the processed products, and have a high degree of intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0036] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0037] Figure 2 This is a system block diagram of Embodiment 2 and Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Embodiment 1: Figure 1 As shown, the intelligent control system for coating machine operation based on data analysis proposed in the present invention includes a coating operation monitoring module, a coating drying and curing analysis module, an intelligent control module, a coating machine operation quality decision module and a coating machine supervision terminal;

[0040] When the coating machine applies the coating to the surface of the substrate, the coating operation monitoring module monitors the coating process of the coating machine, analyzes the coating performance and generates a coating qualified signal or a coating optimization signal, and sends the coating optimization signal to the intelligent control module, which can reasonably analyze the coating operation performance and issue a timely warning, so as to quickly realize the regulation of the coating operation process and ensure the high efficiency and stability of the coating process; the specific operation process of the coating operation monitoring module is as follows:

[0041] The coating speed of the coating head is collected and marked as a coating speed value, and the pressure applied by the coating head to the substrate during coating is collected and marked as a coating pressure detection value, and the coating speed value and the coating pressure detection value are numerically compared with a preset coating speed value range and a preset coating pressure detection value range respectively;

[0042] If the coating speed value or the coating pressure inspection value is not within the corresponding preset range, it indicates that the current coating operation performance of the coating machine is poor, and a coating optimization signal is generated. If the coating speed value and the coating pressure inspection value are both within the corresponding preset range, it indicates that the current coating operation performance of the coating machine is good, and a coating qualified signal is generated.

[0043] After coating, the coating machine dries and cures the coating on the substrate. The coating drying and curing analysis module monitors and analyzes the drying and curing process, generates a drying qualified signal or a drying optimization signal, and sends the drying optimization signal to the intelligent control module, which can reasonably analyze the drying and curing operation performance and issue a timely warning, so as to quickly realize the regulation of the drying and curing operation process and ensure that the drying and curing process is carried out efficiently and stably. The specific analysis process of the coating drying and curing analysis module is as follows:

[0044] The operating space for drying and curing the substrate coating in the coating machine is obtained and marked as the monitoring space, a number of detection points are set in the monitoring space, the hot air temperature and hot air speed at the corresponding detection points are collected and marked as the hot air temperature analysis value and the hot air speed analysis value, respectively, the hot air temperature analysis values ​​of all the detection points in the monitoring space are calculated by variance to obtain the hot air temperature distribution value, and the hot air speed analysis values ​​of all the detection points in the monitoring space are calculated by variance to obtain the hot air speed distribution value;

[0045] The hot air temperature distribution value and the hot air speed distribution value are numerically compared with the preset hot air temperature distribution threshold and the preset hot air speed distribution threshold respectively. If the hot air temperature distribution value or the hot air speed distribution value exceeds the corresponding preset threshold, indicating that uneven drying is likely to occur in the monitored space, a drying optimization signal is generated.

[0046] If the hot air temperature distribution value and the hot air speed distribution value do not exceed the corresponding preset threshold value, indicating that uneven drying is not likely to occur in the monitoring space, the deviation value of the hot air temperature distribution value compared to the preset standard hot air temperature value is marked as a hot air temperature anomaly value, and the deviation value of the hot air speed distribution value compared to the preset standard hot air speed value is marked as a hot air speed anomaly value;

[0047] The hot air temperature difference RS and the hot air speed difference RL are numerically calculated by the formula RX=a1*RS+a2*RL to obtain the hot air performance value RX; wherein a1 and a2 are preset weight coefficients with values ​​greater than zero, and the larger the value of the hot air performance value RX is, the less the drying and curing performance of the corresponding test point meets the requirements;

[0048] The hot air performance value is numerically compared with the preset hot air performance threshold. If the hot air performance value exceeds the preset hot air performance threshold, it indicates that the drying and curing performance of the corresponding detection point does not meet the requirements, and the corresponding detection point is marked as a thermally inferior point; the number of thermally inferior points in the monitoring space is obtained and marked as a thermally inferior number detection value, and the thermally inferior number detection value is numerically compared with the preset thermally inferior number detection threshold. If the thermally inferior number detection value exceeds the preset thermally inferior number detection threshold, a drying optimization signal is generated;

[0049] If the thermal deterioration detection value does not exceed the preset thermal deterioration detection threshold, the thermal air performance values ​​of all detection points in the monitoring space are averaged to obtain the spatial analysis value, and the thermal air performance value with the largest value in the monitoring space is marked as the thermal air amplitude difference value;

[0050] The drying and curing non-optimal coefficient HX is obtained by numerically calculating the thermal inferiority value HS, the spatial analysis value HY and the hot air amplitude difference HF through the formula HX=tu1*HS+(tu2*HY+tu3*HF) / 2; wherein tu1, tu2 and tu3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the drying and curing non-optimal coefficient HX, the worse the drying and curing operation performance of the coating machine is overall;

[0051] The drying and curing non-optimal coefficient HX is numerically compared with the preset drying and curing non-optimal coefficient threshold. If the drying and curing non-optimal coefficient HX exceeds the preset drying and curing non-optimal coefficient threshold, it indicates that the drying and curing operation performance of the coating machine is generally poor, and a drying optimization signal is generated; if the drying and curing non-optimal coefficient HX does not exceed the preset drying and curing non-optimal coefficient threshold, it indicates that the drying and curing operation performance of the coating machine is generally good, and a drying qualified signal is generated.

[0052] When the intelligent control module receives the coating optimization signal or the drying optimization signal, it generates the corresponding control instruction, and adaptively adjusts the coating machine based on the control instruction, so as to realize the effective monitoring and control of the operation process of the coating machine, significantly reduce the workload and work difficulty of the staff, improve the automation and intelligence level of the coating machine, and help to ensure its operation effect and the quality of the processed products.

[0053] The coating machine operation quality decision module performs quality inspection on the substrates processed by the coating machine, marks the corresponding substrates as high-quality objects or low-quality objects through analysis, and sends the marking information of the corresponding substrates to the coating machine supervision end. It can perform quality inspection on all processed substrates and provide accurate feedback, which is conducive to the staff to grasp the processing effect of each substrate in detail and make corresponding optimization and adjustment measures in time to ensure the subsequent processing quality; the specific analysis process of the coating machine operation quality decision module is as follows:

[0054] An image of the surface of the substrate after drying and curing is collected, and cracks and bubbles on the surface are identified based on the image of the surface of the substrate. If cracks and bubbles exist on the surface of the substrate, it indicates that the surface quality is poor, and the corresponding substrate is marked as a poor-quality object;

[0055] If there are no cracks or bubbles, the coating thickness data at several locations on the corresponding substrate surface are collected by the coating thickness gauge, the coating thickness data at all locations are calculated for variance to obtain the thickness uniformity value, and the coating thickness data at all locations are calculated for mean to obtain the thickness performance value;

[0056] The thickness uniformity value and the thickness performance value are numerically compared with the preset thickness uniformity threshold and the preset thickness performance value range, respectively. If the thickness uniformity value exceeds the preset thickness uniformity threshold or the thickness performance value is not within the preset thickness performance value range, it indicates that the coating thickness condition on the substrate surface is poor, and the corresponding substrate is marked as a poor quality object;

[0057] If the thickness uniformity value does not exceed the preset thickness uniformity threshold value and the thickness performance value is within the preset thickness performance value range, the gloss data of the corresponding substrate surface is collected by the gloss meter and the deviation value between the gloss data and the preset standard gloss is marked as the gloss detection value, and the color difference data of the corresponding substrate surface is collected by the colorimeter and marked as the color difference detection value, and the thickness performance value and the median value of the preset thickness performance value range are calculated and the absolute value is taken to obtain the thickness segregation value;

[0058] By formula The glossiness detection value QL, the color difference detection value QP and the thickness segregation value QW are numerically calculated to obtain the substrate quality decision value TL; wherein re1, re2 and re3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the substrate quality decision value TL is, the worse the processing quality condition of the corresponding substrate is in general;

[0059] The substrate quality decision value TL is numerically compared with the preset substrate quality decision threshold. If the substrate quality decision value TL exceeds the preset substrate quality decision threshold, it indicates that the processing quality condition of the corresponding substrate is generally poor, and the corresponding substrate is marked as a low-quality object; if the substrate quality decision value TL does not exceed the preset substrate quality decision threshold, it indicates that the processing quality condition of the corresponding substrate is generally good, and the corresponding substrate is marked as a high-quality object.

[0060] Embodiment 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the film coating machine supervision end is connected to the operation control evaluation module in communication, and the operation control evaluation module is used to set an interval period of time L1. When the time reaches L1, preferably, L1 is twenty minutes; the operation control performance of the film coating machine in the interval period is analyzed, and an operation control abnormal signal or an operation control qualified signal is generated through the analysis;

[0061] And send the abnormal operation control signal or the qualified operation control signal to the coating machine supervision end. When the coating machine supervision end receives the abnormal operation control signal, it will issue a corresponding warning to suspend the operation of the coating machine and remind the supervisor to investigate and analyze the cause and make reasonable optimization measures, so as to ensure the subsequent safe, stable and efficient operation of the coating machine, with a high degree of intelligence. The specific analysis process of the operation control evaluation module is as follows:

[0062] The number of inferior objects processed by the coating machine during the interval period is obtained and the ratio thereof is calculated with the total number of processed substrates to obtain the inferior quality detection value, and the inferior quality detection value is numerically compared with the preset inferior quality detection threshold. If the inferior quality detection value exceeds the preset inferior quality detection threshold, it indicates that the quality of the products processed during the interval period is generally poor, and an operation control abnormality signal is generated;

[0063] If the inferior quality detection value does not exceed the preset inferior quality detection threshold, the number of times the coating optimization signal is generated and the number of times the drying optimization signal is generated in the interval period are summed to obtain the optimization warning value, and the number of times the interval between the time when the intelligent control module generates the corresponding control instruction and the time when the corresponding regulation is completed exceeds the corresponding preset interval time threshold is marked as the optimization control abnormality value;

[0064] The operation and control analysis value YL is obtained by numerically calculating the inferior quality inspection value NY, the optimized warning value XF and the optimized control value WP through the formula YL=uy1*NY+(uy2*XF+uy3*WP) / 2; wherein, uy1, uy2 and uy3 are preset proportional coefficients, uy1>uy3>uy2>0.236; and the larger the value of the operation and control analysis value YL, the worse the overall operation and control performance of the coating machine during the interval period;

[0065] The operation and control analysis value YL is numerically compared with the preset operation and control analysis threshold. If the operation and control analysis value YL exceeds the preset operation and control analysis threshold, it indicates that the operation and control performance of the coating machine in the interval period is generally poor, and an operation and control abnormality signal is generated; if the operation and control analysis value YL does not exceed the preset operation and control analysis threshold, it indicates that the operation and control performance of the coating machine in the interval period is generally good, and an operation and control qualified signal is generated.

[0066] Embodiment 3: Figure 2 As shown, the difference between this embodiment and the first and second embodiments is that the operation control evaluation module is communicatively connected to the paint detection and analysis module, and the operation control evaluation module sends the operation control qualified signal to the paint detection and analysis module. When the paint detection and analysis module receives the operation control qualified signal, it detects and analyzes the paint used in the coating machine to determine the paint quality status, and generates a paint abnormality signal or a paint qualified signal accordingly;

[0067] The coating abnormality signal or coating qualified signal is sent to the coating machine monitoring end. When the coating machine monitoring end receives the coating abnormality signal, it issues a corresponding warning to suspend the coating machine and remind the supervisor to check the coating in time and take corresponding improvement measures (such as replacing the coating) to ensure the quality of the coating used, thereby improving the operation effect of the coating machine and the quality of the processed products. The specific analysis process of the coating detection and analysis module is as follows:

[0068] The viscosity, solid content (referring to the proportion of solid components in the coating), density, temperature and pH value of the coating in the coating machine are collected, and the viscosity is calculated by difference with the median of the preset suitable viscosity range and the absolute value is taken to obtain the coating viscosity measurement value, and the solid content is calculated by difference with the median of the preset suitable solid content range and the absolute value is taken to obtain the coating solid concentration value;

[0069] The density is calculated by difference with the median of the preset suitable density range and the absolute value is taken to obtain the coating density inspection value, the temperature is calculated by difference with the median of the preset suitable temperature range and the absolute value is taken to obtain the coating temperature condition value, and the pH value is calculated by difference with the median of the preset suitable pH range and the absolute value is taken to obtain the coating pH value;

[0070] The coating viscosity measurement value ZW, the coating solid concentration value ZS, the coating density inspection value ZY, the coating temperature value ZM and the coating pH value ZF are numerically calculated by the formula ZP=(c1*ZW+c2*ZS+c3*ZY+c4*ZM+c5*ZF) / 5 to obtain the coating quality evaluation value ZP; wherein c1, c2, c3, c4 and c5 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the coating quality evaluation value ZP is, the worse the overall quality of the coating in the coating machine is;

[0071] The paint quality evaluation value ZP is numerically compared with the preset paint quality evaluation threshold. If the paint quality evaluation value exceeds the preset paint quality evaluation threshold, it indicates that the quality condition of the paint in the coating machine is generally poor, and a paint abnormality signal is generated; if the paint quality evaluation value ZP does not exceed the preset paint quality evaluation threshold, it indicates that the quality condition of the paint in the coating machine is generally good, and a paint qualified signal is generated.

[0072] The working principle of the present invention is as follows: when in use, the coating process of the coating machine is monitored and the coating performance is analyzed through the coating operation monitoring module, and the drying and curing analysis module monitors and analyzes the drying and curing process, which can accurately evaluate the coating operation performance and the drying and curing performance and give timely warnings, so as to quickly realize the regulation of the coating machine operation process, ensure the efficient stability of the coating machine operation process and the quality of the processed products, and the coating machine operation quality decision module will perform quality inspection on the substrate processed by the coating machine, so as to realize quality inspection and accurate feedback on all processed substrates, which is conducive to detailed grasp of the processing effect for each substrate and timely making corresponding optimization and adjustment measures, ensuring the subsequent processing quality, significantly reducing the workload and work difficulty of the staff, and having a high level of intelligence and automation.

[0073] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. The intelligent control system for coating machine operation based on data analysis is characterized by: It includes a coating operation monitoring module, a coating drying and curing analysis module, an intelligent control module, a coating machine operation quality decision module and a coating machine supervision terminal; when the coating machine coats the coating on the surface of the substrate, the coating operation monitoring module monitors the coating process of the coating machine, analyzes the coating performance and generates a coating qualified signal or a coating optimization signal, and sends the coating optimization signal to the intelligent control module; After coating, the coating machine dries and solidifies the coating on the substrate, and the coating drying and solidification analysis module monitors and analyzes the drying and solidification process, thereby generating a drying qualified signal or a drying optimization signal, and sending the drying optimization signal to the intelligent control module; When the intelligent control module receives the coating optimization signal or the drying optimization signal, it generates corresponding control instructions and adaptively regulates the coating machine based on the control instructions; The coating machine operation quality decision module performs quality inspection on the substrates processed by the coating machine, marks the corresponding substrates as high-quality objects or low-quality objects through analysis, and sends the marking information of the corresponding substrates to the coating machine supervision end.

2. The intelligent control system for coating machine operation based on data analysis according to claim 1 is characterized in that: The specific operation process of the coating operation monitoring module is as follows: The coating speed of the coating head is collected and marked as the coating speed value, and the pressure applied by the coating head to the substrate during coating is collected and marked as the coating pressure inspection value. If the coating speed value or the coating pressure inspection value is not within the corresponding preset range, a coating optimization signal is generated, otherwise a coating qualified signal is generated.

3. The intelligent control system for coating machine operation based on data analysis according to claim 1 is characterized in that: The specific analysis process of the coating drying and curing analysis module includes: The operating space for drying and curing the substrate coating in the coating machine is obtained and marked as the monitoring space, and the variance of the hot air temperature analysis values ​​of all the detection points in the monitoring space is calculated to obtain the hot air temperature distribution value, and the variance of the hot air speed analysis values ​​of all the detection points in the monitoring space is calculated to obtain the hot air speed distribution value; if the hot air temperature distribution value or the hot air speed distribution value exceeds the corresponding preset threshold value, a drying optimization signal is generated.

4. The intelligent control system for coating machine operation based on data analysis according to claim 3 is characterized in that: If the hot air temperature distribution value and the hot air speed distribution value do not exceed the corresponding preset thresholds, the thermal inferiority detection value is numerically compared with the preset thermal inferiority detection threshold. If the thermal inferiority detection value exceeds the preset thermal inferiority detection threshold, a drying optimization signal is generated. If the thermal poor detection value does not exceed the preset thermal poor detection threshold, the drying and curing non-optimal coefficient is numerically compared with the preset drying and curing non-optimal coefficient threshold. If the drying and curing non-optimal coefficient exceeds the preset drying and curing non-optimal coefficient threshold, a drying optimization signal is generated; otherwise, a drying qualified signal is generated.

5. The intelligent control system for coating machine operation based on data analysis according to claim 1 is characterized in that: The specific analysis process of the coating machine operation quality decision module is as follows: Based on the surface image of the substrate, cracks and bubbles on the surface are identified. If cracks and bubbles exist, the corresponding substrate is marked as a poor quality object; if cracks and bubbles do not exist, the thickness uniformity value and the thickness performance value are numerically compared with the preset thickness uniformity threshold and the preset thickness performance value range respectively. If the thickness uniformity value exceeds the preset thickness uniformity threshold or the thickness performance value is not within the preset thickness performance value range, the corresponding substrate is marked as a poor quality object; If the thickness uniformity value does not exceed the preset thickness uniformity threshold and the thickness performance value is within the preset thickness performance value range, the substrate quality decision value is numerically compared with the preset substrate quality decision threshold. If the substrate quality decision value exceeds the preset substrate quality decision threshold, the corresponding substrate is marked as a poor quality object; if the substrate quality decision value does not exceed the preset substrate quality decision threshold, the corresponding substrate is marked as a high quality object.

6. The intelligent control system for coating machine operation based on data analysis according to claim 1 is characterized in that: The film coating machine monitoring end is communicated with the operation control evaluation module. The operation control evaluation module is used to set an interval period with a duration of L1. When the duration reaches L1, the operation control performance of the film coating machine during the interval period is analyzed, and an operation control abnormal signal or an operation control qualified signal is generated through the analysis.

7. The intelligent control system for coating machine operation based on data analysis according to claim 6 is characterized in that: The specific analysis process of the operation control and assessment module is as follows: If the poor quality inspection value exceeds the preset poor quality inspection threshold, an operation control abnormal signal is generated; if the poor quality inspection value does not exceed the preset poor quality inspection threshold, the operation control analysis value is numerically compared with the preset operation control analysis threshold. If the operation control analysis value exceeds the preset operation control analysis threshold, an operation control abnormal signal is generated; if the operation control analysis value does not exceed the preset operation control analysis threshold, an operation control qualified signal is generated.

8. The intelligent control system for coating machine operation based on data analysis according to claim 6 is characterized in that: The operation control and evaluation module is communicatively connected to the paint detection and analysis module. The operation control and evaluation module sends the operation control qualified signal to the paint detection and analysis module. When the paint detection and analysis module receives the operation control qualified signal, it detects and analyzes the paint used in the coating machine. If the paint quality evaluation value exceeds the preset paint quality evaluation threshold, a paint abnormality signal is generated; otherwise, a paint qualified signal is generated.

Images