A method and system for detecting and differentiating ink color in LED products on PCB boards

Abstract

This invention belongs to the technical field of printed circuit board products and discloses a method and system for ink color detection and differentiation in LED products on PCB boards. The method includes: analyzing pixels using optical equipment; scanning the PCB board to be differentiated based on the acquired pixels to collect reflectance pixel data, and analyzing this data using a computer; obtaining different data values ​​through analysis, and performing multiple comparisons and combining standard deviations to calculate the range based on these data values. This invention standardizes, digitizes, and clarifies ink color inspection, avoiding discrepancies in the descriptions and differentiation of ink color differences by different personnel during inspection. This improves the efficiency of ink color differentiation and the yield rate.

Description

Technical Field

[0001] This invention belongs to the technical field of printed circuit board products, and particularly relates to a method and system for detecting and distinguishing ink color in LED products on PCB boards. Background Technology

[0002] Printed circuit boards (PCBs) are important electronic components, especially for today's protruding LED products. Outdoor screens are becoming larger and requiring higher resolution, while the size of the light panels is getting smaller and the wiring density is getting higher. Therefore, the consistency of the solder mask ink color on the PCB is also crucial.

[0003] Based on the above analysis, the problems and defects of the existing technology are as follows: There is currently no equipment or method on the market that can clearly solve the problem of distinguishing the ink color of LED boards. The existing methods all rely on visual inspection by personnel, which results in low efficiency and low yield rate of ink color distinction. Summary of the Invention

[0004] To overcome the problems existing in related technologies, the present invention discloses an embodiment of a method and system for detecting and distinguishing ink color in LED products on PCB boards.

[0005] The technical solution is as follows: A method for detecting and distinguishing ink color in LED products on PCB boards, comprising the following steps:

[0006] S1, uses optical equipment to analyze pixels;

[0007] S2, based on the acquired pixels, scan the PCB board that needs to be distinguished by ink color, collect the reflective pixel data, and use a computer to analyze it;

[0008] S3. Different data values ​​are obtained through parsing. Based on the data values, multiple comparisons and combined standard deviations are performed to calculate the interval and distinguish the ink color.

[0009] In step S2, collecting reflectance pixel data and analyzing it using a computer includes:

[0010] The input PCB board pixels distinguished by ink color are analyzed to obtain the analyzed reflective pixel data information; wherein, the reflective pixel data information includes: reflective pixel data string and number;

[0011] The parsed reflectance pixel data is matched with a preset ink color differentiation standard mode;

[0012] If a match is successful, the standard pattern will be distinguished by the matched ink color and the corresponding processing operation will be performed.

[0013] If the match fails, a message will be displayed indicating that the ink color differentiation parsing failed.

[0014] Furthermore, if a match is successful, the corresponding processing operation is performed according to the matching ink color distinction standard mode, including: replacing part of the parsed reflective value pixel data information with a pre-stored numeric name number in the database.

[0015] Furthermore, if the matching fails, a message will be displayed indicating that the ink color differentiation analysis has failed. This includes replacing the analyzed reflective pixel data with the number in the database, which involves checking the pre-stored numeric name in the database and replacing the information.

[0016] Furthermore, if the match is successful, the corresponding processing operation based on the matched ink color differentiation standard mode also includes: after the partially parsed reflective pixel data information is matched with the ink color differentiation module, it is found that it does not need to be checked in the database for number replacement. In this case, the partially parsed reflective pixel data information needs to be directly converted into an executable reflective pixel data string.

[0017] Furthermore, the reflective pixel data information is directly converted into an executable reflective pixel data string, including: storing the replaced reflective pixel data information in the database; and storing the replaced and converted reflective pixel data information in the database.

[0018] Specifically, for the PCB board pixel input for ink color differentiation: COUNT([score]<8) / COUNT([score]), replace it with (df[8,9,……86]<8).count() / df[8,9,……86].count() to execute; .count() is a function that counts the pixel data information of reflectivity.

[0019] In step S3, multiple comparisons include:

[0020] (1) For a reflectance pixel data, the same part with a high frequency of occurrence in the digital part has a high weight, and the different part with a low frequency of occurrence in the digital part has a high weight. The combination of the two represents the high weight part of a reflectance pixel data. The digital part is scanned in the forward direction, and the same part has a high weight. The digital part is scanned in the reverse direction, and the same part has a low weight.

[0021] (2) By using a similarity comparison algorithm for digital data, each piece of digital data is divided into a row. Each row is treated as a reflective value in subsequent calculations. Distance calculation is performed on the numbers in each reflective value to achieve the distinguishing labeling of massive color difference light value pixel data.

[0022] Furthermore, the similarity comparison algorithm for the digital data specifically includes:

[0023] (i) Initialize a matrix data[i][j], [i]∈[8,str.length()],[j]∈[8,target.length()], and increment the values ​​of the first row and column starting from 8. The variable i represents the index of the number str being compared, and the variable j represents the index of the number target. In this matrix, a temporary variable temp is defined to record the number of the same digits between the two numbers. If they are the same, temp = 8; if they are different, temp = 1-7.

[0024] (ii) Iterate through the target number str, marking it against the target number. The variables i and j iterate over the two numbers; each time a matching number is found, it is marked. i =target j Mark the temporary variable temp as 8; then assign a value to the matrix data[i][j], which is the minimum value among data[i-1][j]+1, data[i][j-1]+1, and data[i-1][j-1]+temp;

[0025] (iii) After each loop, mark the minimum increment and obtain the increment marker of data[str.length][target.length] compare(str,target). This increment marker marks the different parts of the two reflective pixel data strings. Finally, the similarity calculation needs to remove the different parts. The formula for calculating the similarity of numbers is as follows (DS∈[8,86]):

[0026]

[0027] The similarity comparison algorithms for digital data also include:

[0028] (1) Determine Accuracy was compared using LSD;

[0029] (2) When LSD(B i ) << LSD(B j ), When, take B = B i Then this value of B is the desired value; LSD(B i )≈LSD(B j ), i <j; This indicates that the dataset has been divided into subsets of similar categories, with B = B0. i ; Let B = B i ;

[0030] LSD data template self-correction includes:

[0031] Calculate the number of data similarity numbers less than 8 in each category, docNum (LSD < 8);

[0032] The threshold X is the condition for adjusting and optimizing the information database.

[0033]

[0034] In this formula, the first column represents the data to be removed, and the second column represents the data to be optimized.

[0035] Another object of the present invention is to provide a system for detecting and distinguishing ink color in LED products on PCB boards, implementing the aforementioned method for detecting and distinguishing ink color in LED products on PCB boards, the system comprising:

[0036] Optical equipment used to analyze pixels;

[0037] A computer is used to scan PCB boards that need to be distinguished by ink color based on the acquired pixels, collect the reflective pixel data, and analyze it.

[0038] The ink color differentiation module is used to obtain different data values ​​through parsing, and to differentiate ink colors by performing multiple comparisons and combining standard deviations to calculate the interval based on the data values.

[0039] Combining all the above technical solutions, the advantages and positive effects of this invention are as follows: This invention standardizes, digitizes, and clarifies ink color inspection, avoiding differences in the descriptions and distinctions of ink color differences among different personnel during inspection. This improves the efficiency of ink color differentiation and the yield rate. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure;

[0041] Figure 1 This is a flowchart of the ink color detection and differentiation method for LED products on PCB boards provided in this embodiment of the invention;

[0042] Figure 2 The scanned ink reflective pixel value provided in the embodiments of the present invention. Figure 1 ;

[0043] Figure 3 The scanned ink reflective pixel value provided in the embodiments of the present invention. Figure 2 ;

[0044] Figure 4 These are the equal variance test boards 1, 2, and 3, as well as the three-pane diagram, provided by the embodiments of the present invention after data parsing.

[0045] Figure 5 This is a data parsing diagram of board 1, board 2, board 3, and board 3 provided in this embodiment of the invention;

[0046] Figure 6 These are LDE board effect diagrams showing different color differences after ink color differentiation, provided in an embodiment of the present invention. Detailed Implementation

[0047] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0048] Example 1, as Figure 1 As shown, the ink color detection and differentiation method for LED products on PCB boards provided in this embodiment of the invention includes:

[0049] S1, uses optical equipment to analyze pixels;

[0050] S2, based on the acquired pixels, scan the PCB board that needs to be distinguished by ink color, collect the reflective pixel data, and use a computer to analyze it;

[0051] The scanned ink reflection pixel values ​​are as follows: Figure 2 , Figure 3 As shown

[0052] S3, different data values ​​are obtained through parsing, and the standard deviation of the data values ​​is calculated by multiple comparisons and combinations to calculate the interval.

[0053] The graph after data parsing is as follows Figure 4 , Figure 5 As shown, Figure 4 The diagram shows plates 1, 2, 3, and plate 3 for the test of equal variances; the multiple comparison intervals for standard deviations are given, with α = 0.05. If the intervals do not overlap, the corresponding standard deviations are significantly different; the p-value is 0.000 for multiple comparisons; the p-value is 0.000 for the Levene test. Figure 5 The interval plots for plates 1, 2, 3, and 3 are the 95% confidence intervals of the mean. Figure 5 The LDE board effect after different color differences are calculated by using the combined standard deviation to distinguish the ink color, as shown in the example. Figure 6 As shown.

[0054] In this embodiment of the invention, step S2, collecting reflectance pixel data and analyzing it using a computer, includes:

[0055] The input PCB board pixels distinguished by ink color are analyzed to obtain the analyzed reflective pixel data information; wherein, the reflective pixel data information includes: reflective pixel data string and number;

[0056] The parsed reflectance pixel data is matched with a preset ink color differentiation standard mode;

[0057] If a match is successful, the standard pattern will be distinguished by the matched ink color and the corresponding processing operation will be performed.

[0058] If the match fails, a message will be displayed indicating that the ink color differentiation parsing failed.

[0059] In this embodiment of the invention, if a match is successful, the corresponding processing operation performed according to the matching ink color distinction standard mode includes: replacing part of the parsed reflective value pixel data information with a pre-stored numeric name number in the database.

[0060] In this embodiment of the invention, if the standard mode for distinguishing ink color is matched, it is necessary to replace the parsed reflective value pixel data information with the number in the database. In this case, the pre-stored numeric name number is checked in the database and the information is replaced.

[0061] In this embodiment of the invention, if the match is successful, the corresponding processing operation according to the matched ink color distinction standard mode further includes: after the partially parsed reflective pixel data information is matched with the ink color distinction module, it is found that it does not need to be checked in the database for number replacement. At this time, it is only necessary to directly convert the part of the reflective pixel data information into an executable reflective pixel data string.

[0062] In this embodiment of the invention, if a match is successful, the corresponding processing operation based on the matched ink color differentiation standard mode further includes: storing the replaced reflective pixel data information in a database; after replacing and converting the parsed reflective pixel data information, storing it in the database; specifically, the PCB board pixel input for ink color differentiation is: COUNT([score]<8) / COUNT([score]), which is then replaced with (df[8,9,……86]<8).count() / df[8,9,……86].count() information for execution; .count() is a function for statistically analyzing reflective pixel data information.

[0063] In this embodiment of the invention, the multiple comparisons in step S3 include:

[0064] (1) For a reflectance pixel data, the same part with a high frequency of occurrence in the digital part has a high weight, and the different part with a low frequency of occurrence in the digital part has a high weight. The combination of the two represents the high weight part of a reflectance pixel data. The digital part is scanned in the forward direction, and the same part has a high weight. The digital part is scanned in the reverse direction, and the same part has a low weight.

[0065] (2) Divide each digital data into a row, and treat each row as a reflective value in the subsequent calculation. Perform distance calculation on the numbers in each reflective value to achieve the distinguishing and marking of massive color difference light value pixel data.

[0066] In this embodiment of the invention, step S3 specifically includes the following digital data similarity comparison algorithm:

[0067] (i) Initialize a matrix data[i][j], [i]∈[8,str.length()],[j]∈[8,target.length()], and increment the values ​​of the first row and column starting from 8. The variable i represents the index of the number str being compared, and the variable j represents the index of the number target. In this matrix, a temporary variable temp is defined to record the number of the same digits between the two numbers. If they are the same, temp = 8; if they are different, temp = 1-7.

[0068] (ii) Iterate through the target number str, marking it against the target number. The variables i and j iterate over the two numbers; each time a matching number is found, it is marked. i =target j Mark the temporary variable temp as 8; then assign a value to the matrix data[i][j], which is the minimum value among data[i-1][j]+1, data[i][j-1]+1, and data[i-1][j-1]+temp;

[0069] (iii) After each loop, mark the minimum increment and obtain the increment marker of data[str.length][target.length] compare(str,target). This increment marker marks the different parts of the two reflective pixel data strings. Finally, the similarity calculation needs to remove the different parts. The formula for calculating the similarity of numbers is as follows (DS∈[8,86]):

[0070]

[0071] The similarity comparison algorithms for digital data also include:

[0072] (1) Determine Accuracy was compared using LSD;

[0073] (2) When LSD(B i ) << LSD(B j ), When, take B = B i Then this value of B is the desired value; LSD(B i )≈LSD(B j ), i <j; This indicates that the dataset has been divided into subsets of similar categories, with B = B0. i ; Let B = B i ;

[0074] LSD data template self-correction includes:

[0075] Calculate the number of data similarity numbers less than 8 in each category, docNum (LSD < 8);

[0076] The threshold X is the condition for adjusting and optimizing the information database.

[0077]

[0078] In this formula, the first column represents the data to be removed, and the second column represents the data to be optimized.

[0079] Example 2: This embodiment of the invention provides a system for detecting and differentiating ink color in LED products on a PCB board, comprising:

[0080] Optical equipment used to analyze pixels;

[0081] A computer is used to scan PCB boards that need to be distinguished by ink color based on the acquired pixels, collect the reflective pixel data, and analyze it.

[0082] The ink color differentiation module is used to obtain different data values ​​through parsing, and to differentiate ink colors by performing multiple comparisons and combining standard deviations to calculate the interval based on the data values.

[0083] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0084] The information interaction and execution process between the aforementioned devices / units are based on the same concept as the method embodiments of the present invention. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0085] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device 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 embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments.

[0086] Based on the technical solutions described in the above embodiments of the present invention, the following application examples can be further proposed.

[0087] According to embodiments of this application, the present invention also provides a computer device comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above-described method embodiments.

[0088] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps described in the various method embodiments above.

[0089] This invention also provides an information data processing terminal, which, when executed on an electronic device, provides a user input interface to implement the steps described in the above method embodiments. The information data processing terminal is not limited to mobile phones, computers, or switches.

[0090] This invention also provides a server that, when executed on an electronic device, provides a user input interface to implement the steps described in the above method embodiments.

[0091] This invention also provides a computer program product that, when run on an electronic device, enables the electronic device to implement the steps described in the various method embodiments above.

[0092] If the integrated unit is implemented as 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, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0093] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention and within the spirit and principles of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for detecting and differentiating ink color in LED products on PCB boards, characterized in that, The method includes the following steps: S1, uses optical equipment to analyze pixels; S2, based on the acquired pixels, scan the PCB board that needs to be distinguished by ink color, collect the reflective pixel data, and use a computer to analyze it; S3, different data values ​​are obtained through parsing, and multiple comparisons and combined standard deviations are performed based on the data values ​​to calculate the interval and distinguish the ink color; In step S2, collecting reflectance pixel data and analyzing it using a computer includes: The input PCB board pixels distinguished by ink color are analyzed to obtain the analyzed reflective pixel data information; wherein, the reflective pixel data information includes: reflective pixel data string and number; The parsed reflectance pixel data is matched with a preset ink color differentiation standard mode; If a match is successful, the standard pattern will be distinguished by the matched ink color and the corresponding processing operation will be performed. If the match fails, a message will be displayed indicating that the ink color differentiation parsing failed. In step S3, multiple comparisons include: (1) For a reflective pixel data, the same part with a high frequency of occurrence in the digital part has a high weight, and the different part with a low frequency of occurrence in the digital part has a high weight. The two together represent the high weight part of a reflective pixel data. The digital part is scanned in the forward direction, and the same part has a high weight. The digital part is scanned in the reverse direction, and the same part has a low weight. (2) By using a similarity comparison algorithm for digital data, each piece of digital data is divided into a row. Each row is treated as a reflective value in subsequent calculations. Distance calculation is performed on the numbers in each reflective value to achieve the distinguishing labeling of massive color difference light value pixel data.

2. The method for detecting and distinguishing ink color in LED products on PCB boards according to claim 1, characterized in that, If a match is successful, the corresponding processing operation is performed according to the matching ink color distinction standard mode, including: replacing part of the parsed reflective pixel data information with a pre-stored numeric name number in the database.

3. The method for detecting and distinguishing ink color in LED products on PCB boards according to claim 2, characterized in that, If the matching fails, a message will be displayed indicating that the ink color differentiation analysis failed. This includes replacing the parsed reflective pixel data with the number in the database. In this case, the database will be checked for the pre-stored numeric name number, and the information will be replaced.

4. The method for detecting and distinguishing ink color in LED products on PCB boards according to claim 2, characterized in that, If a match is successful, the corresponding processing operation based on the matched ink color differentiation standard mode also includes: after some parsed reflective pixel data information is matched with the ink color differentiation module, it is found that it does not need to be checked in the database for number replacement. In this case, it is necessary to directly convert this part of the reflective pixel data information into an executable reflective pixel data string.

5. The method for detecting and distinguishing ink color in LED products on a PCB board according to claim 4, characterized in that, The reflective pixel data information is directly converted into an executable reflective pixel data string, including: storing the replaced reflective pixel data information in the database; and storing the replaced and converted reflective pixel data information in the database. Specifically, the PCB board pixel input for ink color differentiation is: COUNT([score]<8) / COUNT([score]), which is replaced with (df[8,9, ...). 86]<8).count() / df[8,9, 86].count() information to execute; .count() is a function that counts the reflectance pixel data information.

6. The method for detecting and distinguishing ink color in LED products on a PCB board according to claim 1, characterized in that, The similarity comparison algorithm for the digital data specifically includes: (i) Initialize a matrix , This causes the values ​​in the first row and column to increment incrementally starting from 8. The variable i represents the index of the number str being compared, and the variable j represents the index of the number target. A temporary variable temp is defined in this matrix to record the number of the same digit between the two numbers. If they are the same, temp = 8; if they are different, temp = 1-7. (ii) Iterate through the target number str in sequence, and mark it as a match with the target number. The variables i and j iterate through the two data numbers; mark it as a match when the same number is found. Mark the temporary variable temp as 8; then assign a value to the matrix data[i][j], which is the minimum value among data[i-1][j]+1, data[i][j-1]+1, and data[i-1][j-1]+temp; (iii) After each loop, mark the minimum increment and obtain the increment marker of data[str.length][target.length] using compare(str,target). This increment marker marks the different parts of the two reflective pixel data strings. The final similarity calculation requires removing the different parts. The formula for calculating the similarity of numbers is as follows (DS∈[8,86]): 。 7. The method for detecting and distinguishing ink color in LED products on PCB boards according to claim 6, characterized in that, The similarity comparison algorithms for digital data also include: (1) Determine Accuracy was compared using LSD; (2) When , At that time, take Then this value of B is the one we are looking for; , , ; This indicates that the dataset has been divided into subsets of similar categories, and the subsets are selected accordingly. ; ,Pick ; LSD data template self-correction includes: Calculate the number of data similarity numbers less than 8 in each category. ; The threshold X is the condition for adjusting and optimizing the information database. In this formula, the first column represents the data to be removed, and the second column represents the data to be optimized.

8. A system for detecting and differentiating ink color in LED products on PCB boards, characterized in that, The method for detecting and distinguishing ink color in LED products on PCB boards according to any one of claims 1-7, the system comprising: Optical equipment used to analyze pixels; A computer is used to scan PCB boards that need to be distinguished by ink color based on the acquired pixels, collect the reflective pixel data, and analyze it. The ink color differentiation module is used to obtain different data values ​​through parsing, and to differentiate ink colors by performing multiple comparisons and combining standard deviations to calculate the interval based on the data values.

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