Intelligent correction method, system and equipment for high-precision alignment detection and storage medium

Abstract

The invention provides an intelligent correction method, system and device for high-precision alignment detection and a storage medium, and belongs to the field of product alignment assembly. Comprising the steps of obtaining a product assembly precision detection data sequence; on the basis of quantitative data in the product assembly precision detection data sequence, obtaining a characteristic value describing a data change trend; determining correction data according to the size of the feature value; performing alignment correction on the next product assembly based on the correction data; and updating the product assembly precision detection data sequence and the correction data according to the product assembly precision detection data after each alignment correction so as to realize the correction of each product alignment detection. According to the method, the product assembly precision detection data sequence is obtained, the feature values are obtained according to the quantized data, the correction data are determined and used for positioning correction of subsequent product assembly, the production cost is reduced, and the high-precision assembly requirement of a novel screen panel is met.

Description

Technical Field

[0001] The present invention belongs to the technical field of product alignment and assembly, and specifically relates to an intelligent correction method, system, device and storage medium for high-precision alignment detection. Background Art

[0002] In the field of FPD (Flat Panel Display) assembly, with the booming consumer electronics market, consumers' demands for display effects, size, thinness, etc. of screen panels are constantly changing, prompting manufacturers to continuously launch new models of screen panels. The assembly accuracy of screen panels is crucial to their display quality, stability and overall performance. For example, the fitting accuracy of the panel and the backlight module will affect the display uniformity, and the alignment accuracy between the components will affect the appearance and electrical connection stability.

[0003] The current panel display assembly equipment has a progressive tolerance problem. Initially, the equipment progressive tolerance is within an acceptable range, such as 0.05mm, which can meet general precision requirements. However, with the long-term use of the equipment, the progressive tolerance gradually increases due to factors such as mechanical wear, temperature changes, and vibration. This increases the alignment deviation between the panel and the backlight module, resulting in uneven display, dark areas or bright spots; the distance deviation between the edge of the screen display panel and the inner side of the backlight affects the appearance and sealing, and cannot meet the requirements of new panel displays for high-precision assembly, reducing product yield and increasing production costs. Summary of the invention

[0004] In view of this, an object of the present invention is to provide an intelligent correction method, system, device and storage medium for high-precision alignment detection to solve the progressive tolerance problem existing in existing panel display assembly equipment.

[0005] In order to achieve the above object, the technical solution provided by the present invention is as follows: In a first aspect, the present invention provides an intelligent correction method for high-precision alignment detection, comprising the following steps: Acquire a series of product assembly precision test data to obtain a product assembly precision test data sequence; the precision test data is quantitative data for quantifying the alignment between products; Based on the quantitative data in the product assembly accuracy detection data sequence, obtain the characteristic value describing the data change trend; Determine the correction data according to the size of the characteristic value; Perform alignment correction for the next product assembly based on the correction data; The product assembly accuracy detection data sequence and the correction data are updated according to the product assembly accuracy detection data after each alignment correction to realize the correction of each product alignment detection.

[0006] Furthermore, the correction data is determined according to the magnitude of the characteristic value, including: Set the maximum correction threshold; If the characteristic value is less than the maximum correction threshold, the characteristic value is used as the correction data for the next correction; If the characteristic value is not less than the maximum correction threshold, the maximum correction threshold is used as the correction data for the next correction.

[0007] Furthermore, determining the correction data according to the magnitude of the characteristic value also includes: A plurality of adjacent numerical intervals are set; the numerical intervals at least include a non-correction interval, a correction interval and a warning interval that are adjacent in sequence, and the maximum correction threshold is in the correction interval; If the characteristic value is in the non-correction interval, no correction will be performed in the next alignment; If the characteristic value is within the correction interval, the correction data is determined based on the maximum correction threshold; If the characteristic value is in the warning interval, an alarm is issued based on the number of times the characteristic value in the warning interval occurs.

[0008] Furthermore, based on the quantitative data in the product assembly accuracy detection data sequence, characteristic values ​​describing the data change trend are obtained, including: Calculate the mean of the quantitative data in the product assembly accuracy detection data sequence; Calculate the deviation between the mean and the preset standard value; The deviation value is used as a characteristic value to describe the data change trend of quantitative data.

[0009] Furthermore, if the product to be assembled is a screen panel, the quantitative data at least includes: The first distance data is the distance value from the edge of the AA area to the outer edge of the backlight module measured at least three corners of the screen panel along the horizontal direction and the vertical direction in the plane coordinate system; The second distance data is the distance values ​​from the edge of the screen panel to the inner edge of the backlight module measured at least at three corners of the screen panel along the horizontal direction and the vertical direction in the plane coordinate system; Angle data, that is, the offset angle of the screen panel in the horizontal and vertical directions in the plane coordinate system.

[0010] Furthermore, the characteristic value is used as the correction data for the next correction, including: For the first distance data, calculate the distance deviation data in the same direction at the same angle to obtain at least 3 groups of first deviation values ​​in the horizontal direction and at least 3 groups of second deviation values ​​in the vertical direction; For the second distance data, calculate the distance deviation data in the same direction at the same angle to obtain at least 3 sets of third deviation values ​​in the horizontal direction and at least 3 sets of fourth deviation values ​​in the vertical direction; For the angle data, calculate the angle deviation data in the same direction at the same angle to obtain at least 3 sets of fifth deviation values ​​in the horizontal direction and at least 3 sets of sixth deviation values ​​in the vertical direction; For the first deviation value, the second deviation value, the third deviation value, the fourth deviation value, the fifth deviation value, and the sixth deviation value, the minimum value of the distance deviation data and the angle deviation data in the horizontal direction and the vertical direction are taken as the correction data in the corresponding direction, and the correction direction is opposite to the minimum value.

[0011] Furthermore, both the first distance data and the second distance data satisfy corresponding distance control requirements, and the range of the distance control requirements is the center value ± the allowable error value.

[0012] In a second aspect, the present invention provides an intelligent correction system for high-precision alignment detection, comprising: The data acquisition module is used to obtain a series of product assembly precision detection data to obtain a product assembly precision detection data sequence; the precision detection data is quantitative data for quantifying the alignment between products; The first calculation module is used to obtain characteristic values ​​describing the data change trend based on the quantitative data in the product assembly accuracy detection data sequence; The second calculation module is used to determine the correction data according to the size of the characteristic value; The intelligent correction module is used to perform alignment correction for the next product assembly based on the correction data; it is also used to update the product assembly accuracy detection data sequence and the correction data according to the product assembly accuracy detection data after each alignment correction, so as to realize the correction of each product alignment detection.

[0013] Accordingly, the present invention provides a computer device, the device comprising a processor and a memory: The memory is used to store the computer program and send the instructions of the computer program to the processor; The processor executes the intelligent correction method for high-precision alignment detection according to the instructions of the computer program.

[0014] Accordingly, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the intelligent correction method for high-precision alignment detection as in the first aspect is implemented.

[0015] In summary, the present invention provides an intelligent correction method, system, device and storage medium for high-precision alignment detection, including obtaining a series of precision detection data of product assembly to obtain a product assembly precision detection data sequence; the precision detection data is quantitative data for quantifying the alignment between products; based on the quantitative data in the product assembly precision detection data sequence, a characteristic value describing the data change trend is obtained; the correction data is determined according to the size of the characteristic value; the next product assembly is corrected based on the correction data; the product assembly precision detection data sequence and the correction data are updated according to the product assembly precision detection data after each alignment correction, so as to realize the correction of each product alignment detection. The present invention obtains a product assembly precision detection data sequence, obtains characteristic values ​​according to the quantitative data and determines the correction data, which is used for the positioning correction of subsequent product assembly, effectively overcomes the problem of increased progressive tolerance caused by mechanical wear, temperature change, vibration, etc. of the current screen panel assembly equipment, avoids the display and appearance sealing problems caused by the alignment deviation and edge distance deviation between the panel and the backlight module, greatly improves the product yield, reduces production costs, and meets the high-precision assembly requirements of new screen panels. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a flow chart of an intelligent correction method for high-precision alignment detection; Figure 2 This is a schematic diagram of the screen panel assembly alignment; Figure 3 It is the assembly accuracy detection and automatic correction flow chart; Figure 4 This is the trend change diagram of alignment accuracy; Figure 5 This is a schematic diagram for correction judgment; Figure 6 A schematic diagram of a computer device. DETAILED DESCRIPTION

[0018] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions 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 embodiments described below 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.

[0019] See also Figure 1 This embodiment provides an intelligent correction method for high-precision alignment detection, comprising the following steps: S1: Obtain a series of product assembly precision detection data to obtain a product assembly precision detection data sequence; the precision detection data is quantitative data for quantifying the alignment conditions between products.

[0020] The accuracy test data of product assembly refers to the result data obtained by evaluating the assembly accuracy through specific testing methods after the product assembly process is completed. These result data can reflect whether the actual alignment between the various components of the product meets the design requirements.

[0021] Quantitative data is the data that expresses the alignment between products in a specific numerical form. Quantification is to facilitate subsequent data analysis and processing. For example, millimeters are used to express the distance deviation between two parts, and angles are used to express the degree of inclination of parts.

[0022] The product assembly accuracy test data sequence is a data set that arranges a series of product assembly accuracy test data in a certain order (such as time sequence, product number sequence, etc.). This sequence can reflect the changes in product assembly accuracy over time or other factors, and provide a basis for subsequent analysis of data change trends.

[0023] S2: Based on the quantitative data in the product assembly accuracy detection data sequence, obtain the characteristic value describing the data change trend.

[0024] Characteristic values ​​are values ​​extracted from quantitative data that can represent the overall trend and characteristics of the data. For example, by calculating the mean of the quantitative data of product assembly accuracy over a period of time, we can understand the average level of assembly accuracy during this period; by calculating the slope of the data, we can determine whether the accuracy is increasing or decreasing.

[0025] S3: Determine correction data according to the magnitude of the characteristic value.

[0026] The correction data is a numerical value determined for adjustment in order to correct the deviations that occur during the product assembly process and to achieve higher accuracy in product assembly.

[0027] S4: Perform alignment correction for the next product assembly based on the correction data.

[0028] During the product assembly process, the position, angle, etc. of each component of the product are adjusted according to the correction data so that they can be accurately aligned and assembled together.

[0029] S5: updating the product assembly accuracy detection data sequence and the correction data according to the product assembly accuracy detection data after each alignment correction, so as to realize the correction of each product alignment detection.

[0030] Each newly obtained product assembly accuracy test data is added to the original product assembly accuracy test data sequence, replacing the old data or expanding the data sequence. At the same time, the feature value is recalculated based on the new data, and the correction data is adjusted accordingly. This ensures that the system always makes corrections based on the latest product alignment accuracy, achieving continuous optimization of product assembly accuracy.

[0031] This embodiment provides an intelligent correction method for high-precision alignment detection, which not only considers the quantitative data of product assembly accuracy, but also intelligently determines the correction data by analyzing the data change trend, thereby realizing dynamic adjustment and optimization of assembly accuracy. In addition, the method ensures the accuracy and effectiveness of the correction by continuously updating the product assembly accuracy detection data sequence and the correction data, effectively solving the problem of decreased accuracy caused by progressive tolerance during the assembly of the screen panel.

[0032] The intelligent correction method provided in this embodiment can be applied to screen panel assembly, and can also be applied to other products that require precise alignment correction. Figure 2 , Figure 2 The positional relationship and related spacing measurement of PCB (printed circuit board), panel and BLU (backlight unit) in screen panel assembly are shown. Among them, the AA area, i.e. the effective area of ​​the screen display, is located in the middle. A1, A2, B1, B2, C1, C2, D1 and D2 are the distance measurement data at the four corners of the screen panel. These distances include the distance from the edge of the AA area to the BLU Outline and the distance from the Panel cross mark to the BLU Inline. These data are used as quantitative data for alignment detection, so that alignment correction operations can be implemented.

[0033] See also Figure 3 , Figure 3The screen panel assembly accuracy detection and automatic correction process is shown. First, the BLU alignment and panel alignment are performed, and then the alignment data is calculated. The BLU carrier is corrected according to the correction data, and then the assembly is combined. Then the assembly accuracy is tested. If the judgment result is OK, the product is discharged; if the judgment result is NG, the device alarms when there are problems for 3 consecutive pieces or 10 pieces in total. At the same time, the detection data is collected to determine the assembly accuracy trend and make corrections accordingly. The correction data is fed back to the alignment data calculation link (Assy equipment), forming a closed loop to continuously optimize the assembly accuracy.

[0034] See also Figure 4 , Figure 4 It shows the change of product accuracy with product sequence during product assembly. The correction data is determined and calculated based on the trend of the test results of the set number of products. After correction, the set number of test results are taken for trend determination. Figure 4 As shown, although the results on the curve are within the range, the overall trend of the results is upward. At this time, correction is needed to make all results fluctuate around the center value.

[0035] The following is based on Figure 2-4 , some other embodiments of the present invention are introduced.

[0036] In one embodiment, the correction data is determined according to the magnitude of the characteristic value, including: S31: Set the maximum correction threshold.

[0037] S32: If the characteristic value is less than the maximum correction threshold, the characteristic value is used as correction data for the next correction.

[0038] S33: If the characteristic value is not less than the maximum correction threshold, the maximum correction threshold is used as correction data for the next correction.

[0039] This embodiment provides a strategy for determining correction data based on feature values ​​to ensure the rationality and effectiveness of the correction. By setting the maximum correction threshold, the system can flexibly respond to different degrees of assembly accuracy deviation. When the feature value is small, that is, the deviation is within an acceptable range, the system directly uses the feature value as the correction data to achieve fine-tuning optimization. When the feature value is large, indicating that the deviation exceeds a certain range, the system uses the preset maximum correction threshold for correction to prevent new problems caused by excessive adjustment. This strategy not only ensures the timeliness of the correction, but also avoids additional errors introduced by excessive intervention, thereby ensuring the stability and reliability of product assembly.

[0040] In a further embodiment, determining the correction data according to the magnitude of the characteristic value further includes: S34: setting a number of adjacent numerical intervals; the numerical intervals at least include a non-correction interval, a correction interval and a warning interval that are adjacent in sequence, and the maximum correction threshold is in the correction interval; S35: If the characteristic value is in the non-correction interval, no correction is performed in the next alignment; S36: If the characteristic value is within the correction interval, determining correction data based on the maximum correction threshold; S37: If the characteristic value is in the warning interval, an alarm is issued based on the number of times the characteristic value in the warning interval appears.

[0041] This embodiment provides a correction judgment mechanism to enhance the flexibility and response speed of the system. By setting multiple adjacent numerical intervals, the system can adopt different operation strategies according to the interval in which the characteristic value is located. The non-correction interval is used to identify those situations where the deviation is extremely small and no correction is required, thereby avoiding unnecessary adjustment operations and improving production efficiency. The correction interval is for situations where the deviation is within an acceptable range but requires fine-tuning. The system will determine the appropriate correction data based on the maximum correction threshold. The warning interval is for situations where the deviation exceeds the preset range and may cause quality problems. The system will issue an alarm based on the number of times the characteristic value appears in the warning interval, reminding the operator to intervene in time for inspection and processing to prevent the problem from further expanding. This strategy of interval management enables the system to make more intelligent and efficient responses according to different situations, further improving the accuracy and stability of product assembly.

[0042] See also Figure 5 , Figure 5 The correction judgment diagram based on the above embodiment is shown. The characteristic value is ΔD, the upper limit of the non-correction interval is set to α; the lower limit of the correction interval is α, and its upper limit is set to β, and the maximum correction threshold μ is located within the correction interval; the lower limit of the warning interval is β. Then the correction judgment is as follows: 1) If ΔD<α, no correction is made; 2) If α≤ΔD<β, use the ΔD value for correction; 3) If ΔD>β, use the maximum correction threshold μ for correction.

[0043] In one embodiment, based on the quantitative data in the product assembly accuracy detection data sequence, a characteristic value describing the data change trend is obtained, including: S21: Calculate the mean of the quantitative data in the product assembly accuracy detection data sequence; S22: Calculate the deviation between the mean and the preset standard value; S23: The deviation value is used as a characteristic value to describe the data change trend of the quantitative data.

[0044] This embodiment provides a method for determining characteristic values, which can intuitively reflect the overall level and changing trend of product assembly accuracy by calculating the mean of quantitative data and its deviation from the preset standard value. The mean, as the average level of quantitative data, can summarize the overall accuracy of product assembly over a period of time. The deviation between the mean and the preset standard value reveals the gap between the actual assembly accuracy and the design requirements, providing an important reference for subsequent correction operations. This method is not only simple to calculate, but also can accurately capture the changing trend of the data, providing strong support for subsequent intelligent correction.

[0045] Please refer again Figure 2 ,by Figure 2 Taking the screen panel assembly shown in the figure as an example, assuming that the product assembly accuracy detection data sequence collects the alignment detection data of N adjacent products, in the X and Y directions, according to the distance data measured at the four corners of the screen panel, the distance data sequences obtained are {X1(1), X1(2), ..., X1(N)}, ..., {X4(1), X4(2), ..., X4(N)}; {Y1(1), Y1(2), ..., Y1(N)}, ..., {Y4(2), ..., Y4(N)}; and the angle data sequence {θ x (1), θ x (2), ..., θ x (N)} and {θ y (1), θ y (2), ..., θ y (N)}.

[0046] The characteristic value is calculated as follows:

[0047]

[0048]

[0049] Substituting the characteristic values ​​calculated above into the correction judgment, the corresponding correction data can be obtained.

[0050] In one embodiment, if the product to be assembled is a screen panel, the quantitative data at least includes: The first distance data is the distance value from the edge of the AA area to the outer edge of the backlight module measured at least three corners of the screen panel along the horizontal direction and the vertical direction in the plane coordinate system; The second distance data is the distance values ​​from the edge of the screen panel to the inner edge of the backlight module measured at least at three corners of the screen panel along the horizontal direction and the vertical direction in the plane coordinate system; Angle data, that is, the offset angle of the screen panel in the horizontal and vertical directions in the plane coordinate system.

[0051] In this embodiment, the first distance data and the second distance data respectively reflect the distance between the edge of the AA area of ​​the screen panel and the outer edge of the backlight module, and the distance between the edge of the screen panel and the inner edge of the backlight module. These data are important bases for evaluating the alignment accuracy of the screen panel and the backlight module. By measuring these distances, the alignment of the screen panel and the backlight module at each corner can be intuitively understood, so as to determine whether there is a deviation.

[0052] The angle data reveals the horizontal and vertical offset angles of the screen panel, which is an important indicator for evaluating whether the screen panel is tilted or rotated during assembly. The angle data can be determined by the distance data of the three angles. The angle data can accurately determine the offset degree of the screen panel and provide an accurate reference for subsequent alignment correction operations.

[0053] When implementing the present invention, the measurement points and directions of the quantitative data can be flexibly selected according to the specific assembly requirements and precision requirements of the screen panel to ensure the accuracy and effectiveness of the alignment detection. At the same time, through in-depth analysis and processing of the quantitative data, the correction strategy can be further optimized to improve the accuracy and stability of product assembly and meet the needs of high-precision alignment detection.

[0054] In a further embodiment, the characteristic value is used as correction data for the next correction, including: For the first distance data, calculate the distance deviation data in the same direction at the same angle to obtain at least 3 groups of first deviation values ​​in the horizontal direction and at least 3 groups of second deviation values ​​in the vertical direction; For the second distance data, calculate the distance deviation data in the same direction at the same angle to obtain at least 3 sets of third deviation values ​​in the horizontal direction and at least 3 sets of fourth deviation values ​​in the vertical direction; For the angle data, calculate the angle deviation data in the same direction at the same angle to obtain at least 3 sets of fifth deviation values ​​in the horizontal direction and at least 3 sets of sixth deviation values ​​in the vertical direction; For each group of deviation values ​​obtained (i.e., the first deviation value and the second deviation value, the third deviation value and the fourth deviation value, and the fifth deviation value and the sixth deviation value), the minimum value of the distance deviation data and the angle deviation data in the horizontal direction and the vertical direction is taken as the correction data in the corresponding direction, and the correction direction is opposite to the minimum value.

[0055] Taking the characteristic values ​​calculated above as an example, the corresponding correction data is as follows: X correction amount = minimum value (ΔX1|ΔX2|ΔX3|ΔX4), the correction direction is opposite to the minimum value; Y correction amount = minimum value (ΔY1|ΔY2|ΔY3|ΔY4), the correction direction is opposite to the minimum value; θ correction amount = minimum value (Δθ x |Δθ y ), the correction direction is opposite to the minimum value.

[0056] In one embodiment, both the first distance data and the second distance data meet corresponding distance control requirements, and the range of the distance control requirements is the center value ± the allowable error value.

[0057] For example, the distance from the edge of the AA area to the BLU Outline is controlled within the range of the center value (different products have different center values) ±0.3mm; the distance from the panel cross mark to the BLU Inline is controlled within the range of the center value (different products have different center values) ±0.15mm.

[0058] In addition, for the above-mentioned intelligent correction method, if the detection result is the distance of a series of points, then the final detection result is the minimum value in this series of values, and this value is involved in the algorithm calculation; the detection equipment requires high structural stability, and the overall jitter does not exceed 3 pixels from the visual point of view. If it exceeds 3 pixels, the device alarm mechanism stability needs to be improved; (Specific operation steps: static repeated photography T times, under normal circumstances, the static accuracy error of the equipment is <1 pixel, if it is >3 pixels, it is considered that the equipment stability needs to be improved) When there is a slight change in the edge of the product and it is mistakenly grasped or cannot be grasped, it will not participate in the algorithm calculation, and other problems such as visual edge grasping or mechanism need to be checked.

[0059] The embodiment of the present invention further provides an intelligent correction system for high-precision alignment detection, comprising: The data acquisition module is used to obtain a series of product assembly precision detection data to obtain a product assembly precision detection data sequence; the precision detection data is quantitative data for quantifying the alignment between products; The first calculation module is used to obtain characteristic values ​​describing the data change trend based on the quantitative data in the product assembly accuracy detection data sequence; The second calculation module is used to determine the correction data according to the size of the characteristic value; The intelligent correction module is used to perform alignment correction for the next product assembly based on the correction data; it is also used to update the product assembly accuracy detection data sequence and the correction data according to the product assembly accuracy detection data after each alignment correction, so as to realize the correction of each product alignment detection.

[0060] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0061] Reference Figure 6 An embodiment of the present invention further provides a computer device, comprising: a memory and a processor and a computer program stored in the memory. When the computer program is executed on the processor, an intelligent correction method for high-precision alignment detection as described in any one of the above methods is implemented.

[0062] 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 and a memory. Those skilled in the art will understand that Figure 6 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.

[0063] The processor may be a central processing unit (CPU), 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.

[0064] In some embodiments, the memory 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 may also be an external storage device of the computer device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device. Further, the memory may include both an internal storage unit and an external storage device of the computer device. The memory 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 may also be used to temporarily store data that has been output or is to be output.

[0065] 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 intelligent correction method for high-precision alignment detection as described in any one of the above methods is implemented.

[0066] 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 various 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, Random Access 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent correction method for high-precision alignment detection, characterized in that: The steps include: Acquire a series of product assembly precision detection data to obtain a product assembly precision detection data sequence; the precision detection data is quantitative data for quantifying the alignment conditions between products; Based on the quantitative data in the product assembly accuracy detection data sequence, obtaining a characteristic value describing a data change trend; Determine the correction data according to the magnitude of the characteristic value; Performing alignment correction for the next product assembly based on the correction data; The product assembly accuracy detection data sequence and the correction data are updated according to the product assembly accuracy detection data after each alignment correction, so as to realize the correction of each product alignment detection.

2. The intelligent correction method for high-precision alignment detection according to claim 1, characterized in that: Determining correction data according to the magnitude of the characteristic value includes: Set the maximum correction threshold; If the characteristic value is less than the maximum correction threshold, the characteristic value is used as the correction data for the next correction; If the characteristic value is not less than the maximum correction threshold, the maximum correction threshold is used as the correction data for the next correction.

3. The intelligent correction method for high-precision alignment detection according to claim 2, characterized in that: Determining the correction data according to the magnitude of the characteristic value also includes: A plurality of adjacent numerical intervals are set; the numerical intervals at least include a non-correction interval, a correction interval and a warning interval that are adjacent in sequence, and the maximum correction threshold is within the correction interval; If the characteristic value is in the non-correction interval, no correction is performed in the next alignment; If the characteristic value is within the correction interval, determining the correction data based on the maximum correction threshold; If the characteristic value is in the warning interval, an alarm is issued based on the number of times the characteristic value in the warning interval occurs.

4. The intelligent correction method for high-precision alignment detection according to claim 2, characterized in that: Based on the quantitative data in the product assembly accuracy detection data sequence, a characteristic value describing a data change trend is obtained, including: Calculating the mean of the quantitative data in the product assembly accuracy detection data sequence; Calculate the deviation between the mean and a preset standard value; The deviation value is used as the characteristic value describing the data change trend of the quantitative data.

5. The intelligent correction method for high-precision alignment detection according to claim 4, characterized in that: If the product to be assembled is a screen panel, the quantitative data at least includes: The first distance data is the distance value from the edge of the AA area to the outer edge of the backlight module measured at least three corners of the screen panel along the horizontal direction and the vertical direction in the plane coordinate system; The second distance data is the distance values ​​from the edge of the screen panel to the inner edge of the backlight module measured at least at three corners of the screen panel along the horizontal direction and the vertical direction in the plane coordinate system; Angle data, that is, the offset angle of the screen panel in the horizontal and vertical directions in the plane coordinate system.

6. The intelligent correction method for high-precision alignment detection according to claim 5, characterized in that: Using the characteristic value as the correction data for the next correction includes: For the first distance data, calculate the distance deviation data in the same direction at the same angle to obtain at least 3 groups of first deviation values ​​in the horizontal direction and at least 3 groups of second deviation values ​​in the vertical direction; For the second distance data, calculate the distance deviation data in the same direction at the same angle to obtain at least 3 sets of third deviation values ​​in the horizontal direction and at least 3 sets of fourth deviation values ​​in the vertical direction; For the angle data, calculate the angle deviation data in the same direction at the same angle to obtain at least 3 groups of fifth deviation values ​​in the horizontal direction and at least 3 groups of sixth deviation values ​​in the vertical direction; For the first deviation value and the second deviation value, the third deviation value and the fourth deviation value, and the fifth deviation value and the sixth deviation value obtained, the minimum value of the distance deviation data and the angle deviation data in the horizontal direction and the vertical direction are taken as the correction data in the corresponding direction, and the correction direction is opposite to the minimum value.

7. The intelligent correction method for high-precision alignment detection according to claim 5, characterized in that: Both the first distance data and the second distance data meet corresponding distance control requirements, and the range of the distance control requirements is the center value ± the allowable error value.

8. An intelligent correction system for high-precision alignment detection, characterized in that: include: A data acquisition module is used to obtain a series of product assembly precision detection data to obtain a product assembly precision detection data sequence; The precision detection data is quantitative data of the alignment between quantitative products; A first calculation module, configured to obtain a characteristic value describing a data change trend based on the quantitative data in the product assembly accuracy detection data sequence; A second calculation module, used for determining correction data according to the magnitude of the characteristic value; The intelligent correction module is used to perform alignment correction for the next product assembly based on the correction data; it is also used to update the product assembly accuracy detection data sequence and the correction data according to the product assembly accuracy detection data after each alignment correction, so as to realize the correction of each product alignment detection.

9. A computer device, characterized in that: The device comprises a processor and a memory: The memory is used to store a computer program and send instructions of the computer program to the processor; The processor executes the intelligent correction method for high-precision alignment detection according to any one of claims 1 to 7 according to the instructions of the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the intelligent correction method for high-precision alignment detection according to any one of claims 1 to 7 is implemented.

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