Backlight module processing film flatness monitoring system based on machine vision

Abstract

The invention discloses a backlight module processing coating flatness monitoring system based on machine vision, relates to the technical field of processing coating monitoring, and solves the technical problems that in the prior art, the influence of a coating material cannot be analyzed, and the execution efficiency of processing coating is reduced. The method specifically comprises the steps that a film covering influence analysis unit analyzes the processing film covering influence of the backlight module, film covering material thickness data and film covering glue matching data are collected, and whether the film covering material and a glue matching material are influenced or not is deduced through data comparison; the processing and laminating influence of the backlight module is analyzed, whether flatness floating is influenced by a laminating material or not in the processing and laminating process is deduced by analyzing the influence of the laminating material, so that the laminating material is repaired and replaced in time, and the situation that the use efficiency of the laminating material is reduced due to the fact that the influence of flatness floating cannot be solved due to reworking is avoided. And the film coating processing progress of the backlight module is influenced.

Description

Technical Field

[0001] The present invention relates to the technical field of film processing monitoring, and in particular to a backlight module film processing flatness monitoring system based on machine vision. Background Art

[0002] The backlight module is a light-emitting component located behind the liquid crystal display (LCD). Its main function is to provide a uniform planar light source for the liquid crystal display. The flatness of the coating is a key quality indicator in the processing of the backlight module, which directly affects the optical performance and display effect of the backlight module.

[0003] However, in the prior art, it is not possible to monitor the backlight module coating in real time, so that abnormal prevention and control cannot be carried out in time, and the impact of the coating material cannot be analyzed, which reduces the execution efficiency of the coating processing. In addition, it is impossible to analyze the impact of the coating equipment, and the stability and efficiency of the coating execution cannot be guaranteed.

[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 solve the above-mentioned problems and to propose a backlight module processing coating flatness monitoring system based on machine vision.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A backlight module processing film flatness monitoring system based on machine vision includes a film processing detection platform, wherein the film processing detection platform is communicatively connected with a film flatness detection unit, a film influence analysis unit, an equipment factor analysis unit, and a process influence analysis unit; The coating flatness detection unit monitors the flatness of the backlight module, collects the coating flatness information, and calculates the flatness detection coefficient, and infers whether the coating flatness detection is normal through data comparison; The coating impact analysis unit analyzes the coating impact of the backlight module, collects coating material thickness data and coating glue matching data, and infers whether there is any impact between the coating material and the matching glue material through data comparison; The equipment factor analysis unit analyzes the influence of the laminating equipment on the laminating process of the backlight module, collects the equipment supply pressure information and the laminating supply information, and infers whether the laminating equipment is operating abnormally through information analysis; The process impact analysis unit performs an impact analysis on the lamination method of the backlight module, collects process environment parameters and process speed parameters, and infers whether there is an impact on the lamination process based on parameter comparison. As a preferred embodiment of the present invention, the coating flatness information includes the maximum span value of the reciprocating floating of the horizontal value of the film surface corresponding to the coating area, the consecutive number of coatings in which the flatness fluctuation occurs continuously at the same position of the coating area corresponding to the single film sticking specification, and the increased span of the number of abnormal flatness positions corresponding to the rework stage when the film is reworked.

[0007] As a preferred embodiment of the present invention, if the flatness detection coefficient of the backlight module processing coating does not exceed the flatness detection coefficient threshold, a flatness detection normal signal is generated; if the flatness detection coefficient of the backlight module processing coating exceeds the flatness detection coefficient threshold, a flatness detection abnormal signal is generated.

[0008] As a preferred embodiment of the present invention, the coating material thickness data and the coating glue matching data are respectively the material thickness deviation value at any adjacent position in the coating material and the thickness time ratio corresponding to the non-floating duration of the coating material stress deviation span when the corresponding adjacent positions are bonded, and the flow area rising span deviation value when the coating material is used in combination with glue when the corresponding glue is in different coating bonding positions.

[0009] As a preferred embodiment of the present invention, if the thickness data of the coating material exceeds the thickness-to-time ratio threshold, or the coating glue matching data exceeds the area rising span deviation threshold, a material impact signal is generated; if the thickness data of the coating material does not exceed the thickness-to-time ratio threshold, and the coating glue matching data does not exceed the area rising span deviation threshold, a material normal signal is generated.

[0010] As a preferred embodiment of the present invention, the equipment pressure supply information and the bonding supply information are respectively the coating pressure deviation value of the coating equipment corresponding to any coating area during the coating process of the backlight module, the pressure control duration corresponding to the coating position after the corresponding pressure deviation value is generated, the corresponding pressure-to-time ratio, and the continuous floating frequency rising span of the coating flatness corresponding to any coating position area on the surface of the bonding platform during the coating process of the backlight module.

[0011] As a preferred embodiment of the present invention, if the equipment pressure supply information exceeds the pressure-to-time ratio threshold, or the lamination supply information exceeds the frequency rise span threshold, a lamination equipment impact signal is generated; if the equipment pressure supply information does not exceed the pressure-to-time ratio threshold, and the lamination supply information does not exceed the frequency rise span threshold, a lamination equipment qualified signal is generated.

[0012] As a preferred embodiment of the present invention, the equipment pressure supply information and the bonding supply information are respectively the span speed ratio corresponding to the floating span of the real-time bonding temperature value rise in the current lamination process of the backlight module and the reduction in the curing speed of the glue in the bonding area, and the maximum deviation value of the bonding speed corresponding to different floating values ​​of the lamination flatness in the current lamination process of the backlight module.

[0013] As a preferred embodiment of the present invention, if the process environment parameter exceeds the span speed ratio threshold, or the process speed parameter exceeds the speed maximum deviation threshold, a process adjustment signal is generated.

[0014] As a preferred embodiment of the present invention, if the process environment parameter does not exceed the span speed ratio threshold, and the process speed parameter does not exceed the speed maximum deviation threshold, a process qualified signal is generated.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. In the present invention, the flatness of the film processing of the backlight module is monitored, and the execution efficiency of the current film processing is inferred to meet the actual demand through the monitoring of the film flatness of the backlight module, so as to adjust the film processing according to the real-time execution efficiency, and also facilitate timely problem warning to further promote the avoidance efficiency of the film processing influence, thereby improving the film processing execution efficiency of the backlight module; The impact of the coating processing on the backlight module is analyzed. By analyzing the impact of the coating material, it is inferred whether the flatness fluctuation during the coating process is affected by the coating material, so as to repair and replace the coating material in time to avoid rework that still cannot solve the impact of flatness fluctuation, reduce the utilization efficiency of the coating material, and affect the coating processing progress of the backlight module.

[0016] 2. In the present invention, the lamination equipment impact analysis is performed during the lamination process of the backlight module. The lamination equipment impact analysis is used to infer whether there is equipment impact in the current lamination process, so as to avoid the decrease in lamination flatness caused by abnormal operation of the lamination equipment during the lamination process, thereby causing the film pasting efficiency of the lamination area to decrease and increase the probability of rework. The lamination process can be detected by the lamination equipment impact analysis, and the operating efficiency of the corresponding lamination equipment at any real time can be improved, so as to facilitate accurate tracing when the flatness is abnormal; An impact analysis is conducted on the lamination method of the backlight module. An impact analysis is conducted on the lamination process during the lamination process to infer whether there is any lamination impact in the lamination process. This reduces the stability of the lamination flatness and reduces the lamination processing efficiency of the backlight module. The lamination process impact analysis enables timely lamination regulation to ensure the lamination efficiency of the lamination process. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0018] Figure 1 It is a principle block diagram of the present invention; Figure 2 It is a schematic diagram of the structure of the device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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.

[0020] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0021] Embodiment 1 See also Figure 1 As shown, a backlight module processing coating flatness monitoring system based on machine vision includes a coating processing detection platform, wherein the coating processing detection platform is communicatively connected with a coating flatness detection unit, a coating influence analysis unit, an equipment factor analysis unit, and a process influence analysis unit; The film processing detection platform generates a film flatness detection signal and sends the film flatness detection signal to the film flatness detection unit. After receiving the film flatness detection signal, the film flatness detection unit monitors the film flatness of the backlight module. Through the film flatness monitoring of the backlight module, it is inferred whether the current film processing execution efficiency meets the actual demand, so as to adjust the film processing according to the real-time execution efficiency status, and also facilitate timely problem warning to further promote the avoidance efficiency of the film processing influence, thereby improving the film processing execution efficiency of the backlight module; The maximum span value of the reciprocating floating of the horizontal value of the film surface corresponding to the film area processed by the backlight module is obtained, and the maximum span value of the reciprocating floating of the horizontal value of the film surface corresponding to the film area processed by the backlight module is marked as ZDK; Obtain the number of times the flatness of the same position of the film area of ​​the film area processed by the backlight module corresponds to the film area of ​​the single film sticking specification, and mark the number of times the flatness of the same position of the film area of ​​the film area processed by the backlight module corresponds to the film area of ​​the single film sticking specification as FMC, wherein the film sticking specification is expressed as the area, size and other parameters of the single film sticking when the backlight module is stuck, and in this application, the flatness is measured by the concave area or the protruding area; Obtain the increase span of the number of abnormal flatness positions in the rework stage when the film is reworked during the lamination process of the backlight module, and mark the increase span of the number of abnormal flatness positions in the rework stage when the film is reworked during the lamination process of the backlight module as SLZ; The above collected data are uniformly marked as coating flatness information, and substituted into the formula to obtain the flatness detection coefficient of the backlight module coating, where the formula is: , where PJ is the flatness detection coefficient of the backlight module processing coating, fwa1, fwa2 and fwa3 are preset proportional coefficients, e is a natural constant, and LK is an error correction factor, which is 1.23; Compare the flatness detection coefficient of the backlight module processing coating with the flatness detection coefficient threshold: If the flatness detection coefficient of the backlight module coating does not exceed the flatness detection coefficient threshold, it is inferred that the flatness detection of the current backlight module coating is normal, and a flatness detection normal signal is generated and sent to the coating processing detection platform; If the flatness detection coefficient of the backlight module processing coating exceeds the flatness detection coefficient threshold, it is inferred that the current backlight module processing coating flatness detection is abnormal, and a flatness detection abnormality signal is generated and sent to the coating processing detection platform. After receiving the flatness detection abnormality signal, the coating processing detection platform analyzes the processing process of the backlight module. The lamination processing detection platform generates a lamination impact analysis signal and sends the lamination impact analysis signal to the lamination impact analysis unit. After receiving the lamination impact analysis signal, the lamination impact analysis unit analyzes the lamination impact of the backlight module. By analyzing the impact of the lamination material, it is inferred whether the flatness fluctuation during the lamination processing is affected by the lamination material, so as to repair and replace the lamination material in time to avoid rework and still fail to solve the impact of flatness fluctuation, which reduces the use efficiency of the lamination material and affects the lamination processing progress of the backlight module. Obtain the material thickness deviation value of any adjacent position in the coating material used for coating processing and the thickness time ratio corresponding to the non-floating duration of the stress deviation span of the coating material when the corresponding adjacent positions are bonded, and mark the material thickness deviation value of any adjacent position in the coating material used for coating processing and the thickness time ratio corresponding to the non-floating duration of the stress deviation span of the coating material when the corresponding adjacent positions are bonded as coating material thickness data; Obtain the flow area increase span deviation value when the corresponding glue is at different film laminating positions when the laminating material is used in combination with glue during the laminating process, and mark the flow area increase span deviation value when the corresponding glue is at different film laminating positions when the laminating material is used in combination with glue during the laminating process as laminating glue matching data; Compare the coating material thickness data and coating glue matching data with the thickness-to-duration ratio threshold and the area rise span deviation threshold respectively: If the thickness data of the coating material exceeds the thickness-to-time ratio threshold, or the coating glue matching data exceeds the area rise span deviation threshold, it is inferred that there is an influence between the coating material and the matching glue material during the coating stage of the backlight module processing, and a material influence signal is generated and sent to the coating processing detection platform. After receiving the material influence signal, the coating processing detection platform performs a surface inspection of the coating material during the coating process, and at the same time, performs a matching analysis on the type of coating material used and the matching glue of the coating material, and detects whether there is a deviation in the circulation speed of the matching glue corresponding to any part of the current type of coating material. If yes, re-match it, otherwise, continue to use it; If the thickness data of the coating material does not exceed the thickness-to-duration ratio threshold, and the coating glue matching data does not exceed the area rise span deviation threshold, it is inferred that the coating material and the matching glue material have no influence in the coating stage of the backlight module processing, and a normal material signal is generated and sent to the coating processing detection platform; At the same time, an equipment factor analysis signal is generated and sent to the equipment factor analysis unit. After receiving the equipment factor analysis signal, the equipment factor analysis unit performs a lamination equipment impact analysis on the lamination process of the backlight module. Through the lamination equipment impact analysis, it is inferred whether there is equipment impact in the current lamination process, so as to avoid the lamination flatness reduction caused by abnormal operation of the lamination equipment during the lamination process, thereby causing the lamination efficiency of the lamination area to decrease and increase the probability of rework. While detecting the lamination process through the lamination equipment impact analysis, the operating efficiency of the corresponding lamination equipment at any real time can be improved, which is convenient for accurate tracing when the flatness is abnormal; Obtain the lamination pressure deviation value of the lamination device corresponding to any lamination area during the lamination process of the backlight module, and the pressure control duration corresponding to the pressure time ratio of the corresponding lamination position after the corresponding pressure deviation value is generated, wherein the pressure control is represented by increasing the pressure of the current position in the subsequent film-sticking repetition execution stage when the pressure at the current position is reduced; and mark the lamination pressure deviation value of the lamination device corresponding to any lamination area during the lamination process of the backlight module, and the pressure control duration corresponding to the pressure time ratio of the corresponding lamination position after the corresponding pressure deviation value is generated as the equipment pressure supply information; Obtain the rising span of the continuous floating frequency of the film flatness corresponding to any film-laminating position area on the surface of the laminating table during the laminating process of the backlight module, and mark the rising span of the continuous floating frequency of the film flatness corresponding to any film-laminating position area on the surface of the laminating table during the laminating process of the backlight module as laminating supply information; And compare the equipment supply pressure information and the fitting supply information with the pressure-duration ratio threshold and the frequency rise span threshold respectively: If the equipment pressure supply information exceeds the pressure-to-duration ratio threshold, or the lamination supply information exceeds the frequency rise span threshold, it is inferred that the lamination equipment is operating abnormally during the lamination process of the backlight module, and a lamination equipment impact signal is generated and sent to the lamination processing detection platform. After receiving the lamination equipment impact signal, the lamination processing detection platform controls the operation of the lamination processing equipment of the backlight module and adjusts the pressure supply, that is, when the supply pressure deviates, the pressure at the corresponding position is supplemented, and the lamination flatness is controlled during lamination to avoid the presence of foreign matter; If the equipment supply pressure information does not exceed the pressure-to-duration ratio threshold, and the lamination supply information does not exceed the frequency rise span threshold, it is inferred that the lamination equipment operates normally during the backlight module lamination process, and a lamination equipment qualified signal is generated and sent to the lamination process detection platform; At the same time, a process impact analysis signal is generated and sent to the process impact analysis unit. After receiving the process impact analysis signal, the process impact analysis unit performs an impact analysis on the lamination method of the backlight module. By performing an impact analysis on the lamination process during the lamination process, it is inferred whether there is a lamination impact in the lamination process, which reduces the stability of the lamination flatness and reduces the lamination processing efficiency of the backlight module. Through the lamination process impact analysis, the lamination control can be carried out in time to ensure the lamination efficiency of the lamination process. Obtain the corresponding span speed ratio of the rising floating span of the real-time laminating temperature value in the current laminating process of the backlight module and the curing speed reduction amount of the adhesive in the laminating area, and mark the corresponding span speed ratio of the rising floating span of the real-time laminating temperature value in the current laminating process of the backlight module and the curing speed reduction amount of the adhesive in the laminating area as a process environment parameter; The maximum deviation value of the lamination speed corresponding to different floating values ​​of the lamination flatness in the current lamination process of the backlight module is obtained, and the maximum deviation value of the lamination speed corresponding to different floating values ​​of the lamination flatness in the current lamination process of the backlight module is marked as a process speed parameter; And the process environment parameters and process speed parameters are compared with the span speed ratio threshold and the speed maximum deviation threshold respectively: If the process environment parameter exceeds the span speed ratio threshold, or the process speed parameter exceeds the speed maximum deviation threshold, it is inferred that the lamination process of the backlight module is affected, and a process adjustment signal is generated and sent to the lamination processing detection platform. After receiving the process adjustment signal, the lamination processing detection platform adjusts the current lamination process of the backlight module, that is, controls the lamination temperature, and keeps the real-time lamination speed constant and adjusts the speed according to the fluctuation of the lamination flatness; If the process environment parameter does not exceed the span speed ratio threshold, and the process speed parameter does not exceed the maximum speed deviation threshold, it is inferred that the lamination process of the backlight module is normal, and a process qualification signal is generated and sent to the lamination processing detection platform.

[0022] Embodiment 2 This embodiment is a method for monitoring the flatness of a backlight module coating based on machine vision, comprising the following steps: S1. Monitor the flatness of the film processed on the backlight module, collect the flatness information of the film, and calculate the flatness detection coefficient, and infer whether the flatness detection of the processed film is normal through data comparison; S2. Analyze the influence of the processing coating on the backlight module, collect the coating material thickness data and coating glue matching data, and infer whether there is any influence between the coating material and the matching glue material through data comparison; S3. Analyze the impact of laminating equipment on the laminating process of the backlight module, collect equipment supply pressure information and laminating supply information, and infer whether the laminating equipment is operating abnormally through information analysis; S4. Conduct an impact analysis on the lamination method of the backlight module, collect process environment parameters and process speed parameters, and infer whether there is an impact on the lamination process based on parameter comparison.

[0023] Embodiment 3 Reference Figure 2 The embodiment of the present invention further provides a computer device, including: a memory 302 and a processor 301 and a computer program 303 stored in the memory 302. When the computer program 303 is executed on the processor 301, a backlight module processing coating flatness monitoring method based on machine vision as described in any one of the above methods is implemented.

[0024] The computer device may be a desktop computer, a notebook, a PDA, a cloud server or other computing device. The computer device may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will appreciate that Figure 2 It is only an example of a computer device and does not constitute a limitation of the computer device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0025] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0026] In some embodiments, the memory 302 may be an internal storage unit of the computer device, such as a hard disk or memory of the computer device. In other embodiments, the memory 302 may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device. Further, the memory 302 may also include both an internal storage unit of the computer device and an external storage device. The memory 302 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 302 may also be used to temporarily store data that has been output or is to be output.

[0027] Embodiment 4 An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for monitoring the flatness of a backlight module coating based on machine vision as described in any one of the above methods is implemented.

[0028] In this embodiment, if the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, USB flash drive, mobile hard disk, disk or optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0029] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0030] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0031] In the embodiments disclosed in the present application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0032] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0033] The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by technicians in this field according to actual conditions; The working principle of the present invention is as follows: when in use, the coating flatness detection unit monitors the flatness of the coating processed by the backlight module, collects the coating flatness information, and obtains the flatness detection coefficient according to the calculation, and infers whether the coating flatness detection is normal through data comparison; the coating influence analysis unit analyzes the coating influence of the backlight module, collects the coating material thickness data and the coating glue matching data, and infers whether the coating material and the matching glue material have an influence through data comparison; The equipment factor analysis unit performs an impact analysis on the lamination equipment during the lamination process of the backlight module, collects the equipment pressure supply information and the bonding supply information, and infers whether the lamination equipment is operating abnormally through information analysis; the process impact analysis unit performs an impact analysis on the lamination method of the backlight module, collects the process environment parameters and the process speed parameters, and infers whether there is an impact on the lamination process based on parameter comparison.

[0034] 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 content 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 those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The backlight module processing coating flatness monitoring system based on machine vision is characterized by: It includes a film coating processing detection platform, wherein the film coating processing detection platform is communicatively connected with a film coating flatness detection unit, a film coating influence analysis unit, an equipment factor analysis unit and a process influence analysis unit; The coating flatness detection unit monitors the flatness of the backlight module, collects the coating flatness information, and calculates the flatness detection coefficient, and infers whether the coating flatness detection is normal through data comparison; The coating impact analysis unit analyzes the coating impact of the backlight module, collects coating material thickness data and coating glue matching data, and infers whether there is any impact between the coating material and the matching glue material through data comparison; The equipment factor analysis unit analyzes the influence of the laminating equipment on the laminating process of the backlight module, collects the equipment supply pressure information and the laminating supply information, and infers whether the laminating equipment is operating abnormally through information analysis; The process impact analysis unit performs an impact analysis on the lamination method of the backlight module, collects process environment parameters and process speed parameters, and infers whether there is an impact on the lamination process based on parameter comparison.

2. The machine vision-based backlight module processing coating flatness monitoring system according to claim 1, characterized in that: The coating flatness information includes the maximum span value of the reciprocating floating of the horizontal value of the coating area corresponding to the film surface, the consecutive number of coatings with continuous flatness fluctuations at the same position in the coating area corresponding to the single film sticking specification, and the increased span of the number of abnormal flatness positions in the rework stage when the film is reworked.

3. The machine vision-based backlight module processing coating flatness monitoring system according to claim 2, characterized in that: If the flatness detection coefficient of the backlight module processing coating does not exceed the flatness detection coefficient threshold, a flatness detection normal signal is generated; If the flatness detection coefficient of the backlight module processing coating exceeds the flatness detection coefficient threshold, a flatness detection abnormality signal is generated.

4. The machine vision-based backlight module processing coating flatness monitoring system according to claim 1, characterized in that: The coating material thickness data and coating glue matching data are respectively the material thickness deviation value at any adjacent position in the coating material and the thickness time ratio of the non-floating duration of the coating material stress deviation span when the corresponding adjacent positions are bonded, and the flow area rising span deviation value when the coating material is matched with glue and the corresponding glue is in different coating bonding positions.

5. The machine vision-based backlight module processing coating flatness monitoring system according to claim 4, characterized in that: If the thickness data of the coating material exceeds the thickness-to-time ratio threshold, or the coating glue matching data exceeds the area rising span deviation threshold, a material impact signal is generated; if the thickness data of the coating material does not exceed the thickness-to-time ratio threshold, and the coating glue matching data does not exceed the area rising span deviation threshold, a material normal signal is generated.

6. The machine vision-based backlight module processing coating flatness monitoring system according to claim 1, characterized in that: The equipment pressure supply information and the bonding supply information are respectively the lamination pressure deviation value of the lamination equipment corresponding to any lamination area during the lamination process of the backlight module, the pressure control duration corresponding to the lamination position after the corresponding pressure deviation value is generated, the corresponding pressure-to-time ratio, and the continuous floating frequency rising span of the lamination flatness corresponding to any lamination position area on the surface of the bonding platform during the lamination process of the backlight module.

7. The machine vision-based backlight module processing coating flatness monitoring system according to claim 6, characterized in that: If the equipment pressure supply information exceeds the pressure-to-time ratio threshold, or the lamination supply information exceeds the frequency rise span threshold, a lamination equipment impact signal is generated; if the equipment pressure supply information does not exceed the pressure-to-time ratio threshold, and the lamination supply information does not exceed the frequency rise span threshold, a lamination equipment qualified signal is generated.

8. The machine vision-based backlight module processing coating flatness monitoring system according to claim 1, characterized in that: The equipment supply pressure information and the bonding supply information are respectively the span speed ratio corresponding to the floating span of the real-time bonding temperature value increase in the current lamination process of the backlight module and the reduction in the curing speed of the glue in the bonding area, and the maximum deviation value of the bonding speed corresponding to different floating values ​​of the lamination flatness in the current lamination process of the backlight module.

9. The machine vision-based backlight module processing coating flatness monitoring system according to claim 8, characterized in that: If the process environment parameter exceeds the span speed ratio threshold, or the process speed parameter exceeds the speed maximum deviation threshold, a process adjustment signal is generated.

10. The machine vision-based backlight module processing coating flatness monitoring system according to claim 8, characterized in that: If the process environment parameter does not exceed the span speed ratio threshold, and the process speed parameter does not exceed the speed maximum deviation threshold, a process qualified signal is generated.

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