High-speed motion high-recognition-rate PCB two-dimensional code manufacturing method and system

Abstract

The application relates to the field of PCB two-dimensional code technology, in particular to a high-speed motion high-recognition-rate PCB two-dimensional code manufacturing method and system. The method comprises the following steps: obtaining an initial feature package of an initial splicing plate, setting pre-processing parameters according to a preset pre-processing model and the initial feature package; obtaining a first splicing plate according to the pre-processing parameters, and setting a plurality of printing plate sub-components according to the first splicing plate; setting a first coding strategy according to a preset coding production model and all the printing plate sub-components, and setting operation sub-strategies of the printing plate sub-components. Through the pre-processing model, automatic selection of pickling, screen printing and ink can be realized, real-time feedback data of positioning blocks are collected, ink strategies are dynamically corrected, dynamic adaptation to different requirements is realized, and the manufacturing efficiency of the PCB two-dimensional code is improved. The positioning efficiency and positioning accuracy of the two-dimensional code position of each printing plate sub-component are improved by selecting an anchor printing plate to generate a positioning sub-strategy.

Description

Technical Field

[0001] This application relates to the field of PCB QR code technology, and in particular to a method and system for creating high-speed, high-recognition-rate PCB QR codes. Background Technology

[0002] In the PCB manufacturing and assembly process, QR codes serve as crucial traceability identifiers, and their production quality directly impacts production process control and product lifecycle management. Currently, the industry primarily employs methods such as text printing, laser engraving, and inkjet coding.

[0003] Among these technologies, inkjet printing is limited by ink droplet precision. When producing tiny QR codes smaller than 5mm x 5mm, the graphics are prone to blurring and distortion, leading to a sharp drop in readability. While inkjet printing is faster, the ink layer is easily corroded during subsequent chemical cleaning, electroplating, or high-temperature processes, causing damage to the markings and resulting in insufficient reliability. Laser engraving is currently the mainstream process, forming permanent markings by ablating surface ink or substrate. However, its engraving depth is significantly affected by material properties and laser parameters, often resulting in incomplete engraving or over-engraving that damages the substrate. Furthermore, this method requires stringent PCB surface preparation, necessitating the pre-laying of a flat white ink block and prohibiting operation on uneven areas such as conductors and vias, limiting the flexibility of marking placement. In addition, the high purchase and maintenance costs of laser engraving equipment are unaffordable for most PCB manufacturers, with only some SMT factories possessing such equipment, hindering the widespread application of this technology in the industry chain. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for manufacturing high-speed, high-recognition-rate PCB QR codes in order to solve the above-mentioned technical problems, thereby improving the manufacturing efficiency of PCB QR codes and reducing manufacturing and maintenance costs.

[0005] In some embodiments of this application, by adding a pre-processing model, automatic selection of pickling, screen printing and ink can be realized, and by collecting real-time feedback data from positioning blocks, the ink strategy can be dynamically corrected to achieve dynamic adaptation to different needs and improve the production efficiency of PCB QR codes.

[0006] In some embodiments of this application, by adding an encoding production model, encoding rules and conversion sub-models can be intelligently matched according to different board features (size, content), achieving continuous and non-duplicate QR codes for each printed circuit board within a single primary splicing board. Furthermore, by selecting an anchored printed board to generate a positioning sub-strategy, the positioning efficiency and accuracy of the QR code position for each printed board component are improved.

[0007] In some embodiments of this application, a method for creating high-speed, high-recognition-rate PCB QR codes is provided, including: Obtain the initial feature package of the initial splicing board, and set the preprocessing parameters according to the preset preprocessing model and the initial feature package; The first-level splicing board is obtained based on the preprocessing parameters, and multiple printed circuit board components are set based on the first-level splicing board. Based on the preset coding production model and all printed circuit board components, a primary coding strategy is set, and an operation sub-strategy is set for each printed circuit board component.

[0008] In some embodiments of this application, the preset preprocessing model includes: Several basic characteristic indicators were set based on historical production data; Multiple processing sub-scenes are generated based on all basic feature indicators; Establish a sequence A of sub-scenarios to be processed, A=(a1, a2, ..., ai, ..., an1), where ai is the i-th sub-scenarios to be processed; n1 is the number of sub-scenarios to be processed; Based on the sequence of sub-scenes A, ai is sequentially set as the target processing scene; Pre-processing strategies for generating target scenarios; The pre-processing strategies include: acid washing sub-strategy, screen printing sub-strategy, and ink sub-strategy; Generate the pre-processing strategies for each sub-scenario in sequence; Establish a pre-processing model based on all pre-processing strategies.

[0009] In some embodiments of this application, the setting of preprocessing parameters includes: Generate the initial feature package and the first-level similarity value between it and each processing sub-scene; Set the pre-processing sub-scenario corresponding to the maximum value among all first-level similarity values ​​as the pre-processing strategy to be executed; Set the first-level pickling instruction according to the pickling sub-strategy in the pre-execution strategy; Set the first-level screen printing instruction according to the screen printing sub-policy in the pre-execution policy; The ink sub-strategy in the pending pre-strategy is designated as the first-level ink strategy. Multiple positioning blocks are generated in the initial splicing board according to the first-level pickling instructions and the first-level screen printing instructions; Obtain feedback data from each positioning block to determine whether to correct the primary ink strategy.

[0010] In some embodiments of this application, the determination of whether to modify the primary ink strategy includes: Multiple preset evaluation metrics for blocks; Select the target block sequentially from all the positioning blocks and obtain the feedback data from the target block; The processing deviation value f for generating the target block; f=[ (si-s'i)]; Where θ1 is the number of monitoring indicators; si is the expected reference value of the i-th monitoring indicator in the target block generated based on feedback data; and s'i is the expected reference value of the i-th monitoring indicator set according to the pre-execution strategy. Preset processing deviation threshold F1; If f > F1, generate the first-level correction instruction for the target block; Check each positioning block in turn to see if it has generated a first-level correction command.

[0011] In some embodiments of this application, the preset coding production model includes: Multiple coding feature indicators are set based on historical production data; Multiple coding scenarios are constructed based on all coding feature indicators; Establish a sequence of encoded scenarios B, B=(b1,b2…bi…bn2), where bi is the i-th encoded scenario and n2 is the number of encoded scenarios; Based on the coding scenario sequence B, bi is sequentially set as the target coding scenario; Define the encoding sub-model for the target encoding scenario; The encoding sub-model includes: encoding rules and transformation sub-model; The coding sub-models for each coding scenario are set sequentially, and the coding production model is constructed based on all the coding sub-models.

[0012] In some embodiments of this application, the setting of the operation sub-strategies for each printed circuit board component includes: Establish a sequence W, W=(w1, w2…wi…wm) for the number of printing plate components, where wi is the i-th printing plate component and m is the number of printing plate components; Generate a coded feature package based on the number of printed plate components W, and set the anchor plate; Generate encoded feature packets and secondary similarity values ​​for each encoded scenario; The coding sub-strategy corresponding to the maximum value among all second-level similarity values ​​is set as the first-level coding strategy; QR code images for each printed circuit board component are generated based on the primary coding strategy; A positioning sub-strategy is generated based on the anchored printed plate; Based on the sequence of printed circuit board components W, wi is sequentially set as the target component; Set the operation sub-strategy for the target sub-component based on the positioning sub-strategy and the pre-execution strategy; Set the operation sub-strategies for each printing plate component in sequence.

[0013] In some embodiments of this application, the step of generating a positioning sub-strategy based on the anchored printed board includes: Set the positioning block of the anchored printing plate as the block to be exposed; Multiple initial optical points are set based on the QR code image of the anchored printed plate; Obtain the exposure feedback data of the anchored printing plate, and determine whether to generate a positioning correction command based on the exposure feedback data; A positioning sub-strategy is generated based on all initial optical points and positioning correction instructions.

[0014] In some embodiments of this application, a high-speed, high-recognition-rate PCB QR code manufacturing system is provided, comprising: The central control unit is used to set the pre-processing parameters for the initial splicing board; The central control unit is also used to set the operation sub-strategies for each printed circuit board component; The pre-processing unit is used to execute pre-processing parameters and generate a first-level splicing board based on the execution results; The exposure unit is used to execute the operation sub-strategies of each printed circuit board component; The central control unit includes: The first control module is used to obtain the initial feature package of the initial splicing board; The first control module is also used to set preprocessing parameters according to the preset preprocessing model and the initial feature package; The second control module is used to set multiple printed circuit board components according to the first-level splicing board; The third control module is used to set the primary coding strategy based on the preset coding production model and all printed circuit board components; The third control module is also used to set the operation sub-strategies for each printed circuit board component; In some embodiments of this application, the first control module is further configured to: Several basic characteristic indicators were set based on historical production data; Multiple processing sub-scenes are generated based on all basic feature indicators; Establish a sequence A of sub-scenarios to be processed, A=(a1, a2, ..., ai, ..., an1), where ai is the i-th sub-scenarios to be processed; n1 is the number of sub-scenarios to be processed; Based on the sequence of sub-scenes A, ai is sequentially set as the target processing scene; Pre-processing strategies for generating target scenarios; The pre-processing strategies include: acid washing sub-strategy, screen printing sub-strategy, and ink sub-strategy; Generate the pre-processing strategies for each sub-scenario in sequence; Establish a pre-processing model based on all pre-processing strategies.

[0015] In some embodiments of this application, the second control module is further configured to: Multiple coding feature indicators are set based on historical production data; Multiple coding scenarios are constructed based on all coding feature indicators; Establish a sequence of encoded scenarios B, B=(b1,b2…bi…bn2), where bi is the i-th encoded scenario and n2 is the number of encoded scenarios; Based on the coding scenario sequence B, bi is sequentially set as the target coding scenario; Define the encoding sub-model for the target encoding scenario; The encoding sub-model includes: encoding rules and transformation sub-model; Sequentially set up the coding sub-models for each coding scenario, and construct the coding production model based on all the coding sub-models; Establish a sequence W, W=(w1, w2…wi…wm) for the number of printing plate components, where wi is the i-th printing plate component and m is the number of printing plate components; Generate a coded feature package based on the number of printed plate components W, and set the anchor plate; Generate encoded feature packets and secondary similarity values ​​for each encoded scenario; The coding sub-strategy corresponding to the maximum value among all second-level similarity values ​​is set as the first-level coding strategy; QR code images for each printed circuit board component are generated based on the primary coding strategy; A positioning sub-strategy is generated based on the anchored printed plate; Based on the sequence of printed circuit board components W, wi is sequentially set as the target component; Set the operation sub-strategy for the target sub-component based on the positioning sub-strategy and the pre-execution strategy; Set the operation sub-strategies for each printing plate component in sequence.

[0016] Compared with existing technologies, the advantages of the high-speed motion, high-recognition-rate PCB QR code manufacturing method and system of this application are as follows: By adding a pre-processing model, automatic selection of pickling, screen printing, and ink can be achieved. By collecting real-time feedback data from positioning blocks, the ink strategy can be dynamically corrected to meet different needs and improve the production efficiency of PCB QR codes.

[0017] By adding a coding production model, coding rules and conversion sub-models can be intelligently matched according to different board characteristics (size, content), enabling continuous and non-duplicate QR codes for each printed circuit board within a single primary splicing board. Furthermore, by selecting an anchored printed board to generate a positioning sub-strategy, the positioning efficiency and accuracy of the QR code position for each printed circuit board component are improved. Attached Figure Description

[0018] Figure 1This is a flowchart illustrating a method for creating high-speed, high-recognition-rate PCB QR codes in a preferred embodiment of this application. Detailed Implementation

[0019] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.

[0020] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0021] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0022] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0023] like Figure 1 As shown in the preferred embodiment of this application, a method for manufacturing high-speed, high-recognition-rate PCB QR codes includes: S101: Obtain the initial feature package of the initial splicing board, and set the preprocessing parameters according to the preset preprocessing model and the initial feature package; S102: Obtain the first-level splicing board according to the preprocessing parameters, and set multiple printed circuit board components according to the first-level splicing board; S103: Set the primary coding strategy based on the preset coding production model and all printed circuit board components, and set the operation sub-strategy for each printed circuit board component.

[0024] Specifically, the initial splicing board refers to the splicing board after the printed circuit board has gone through normal production from raw material cutting to solder mask and text, and then undergone post-baking curing. The splicing board is composed of multiple sets of printed circuit boards assembled together.

[0025] Specifically, a first-level splicing board refers to a splicing board that has undergone initial pickling, QR code production positions on each printed circuit board using screen printing, and ink printing and pre-baking curing.

[0026] Specifically, multiple printed circuit board components are generated based on all the printed circuit boards in the primary splicing board, where each printed circuit board component represents a group of printed circuit boards.

[0027] Specifically, the pre-processing model includes: Several basic characteristic indicators were set based on historical production data; Multiple processing sub-scenes are generated based on all basic feature indicators; Establish a sequence A of sub-scenarios to be processed, A=(a1, a2, ..., ai, ..., an1), where ai is the i-th sub-scenarios to be processed; n1 is the number of sub-scenarios to be processed; Based on the sequence of sub-scenes A, ai is sequentially set as the target processing scene; Pre-processing strategies for generating target scenarios; The pre-processing strategies include: acid washing sub-strategy, screen printing sub-strategy, and ink sub-strategy; Generate the pre-processing strategies for each sub-scenario in sequence; Establish a pre-processing model based on all pre-processing strategies.

[0028] Specifically, historical production data refers to the data related to the historical PCB QR code production process.

[0029] Specifically, the basic characteristic indicators include user requirements (requirement of ink color), substrate material, type of solder resist ink, curing state, splicing board size and thickness ratio, surface line uniformity, panel oxidation degree, surface flatness, QR code accuracy requirements, and other production requirement parameters and splicing board structural parameters that affect the pre-processing parameters.

[0030] Specifically, historical production data is filtered and analyzed based on all basic characteristic indicators to generate multiple processing sub-scenarios. In any two processing sub-scenarios, the parameter values ​​corresponding to each technical characteristic indicator are not exactly the same.

[0031] Specifically, based on the parameter values ​​corresponding to each basic characteristic indicator in the current processing sub-scenario, relevant data from historical production data (i.e., data consistent with the current processing sub-scenario) is filtered. Through optimization analysis of this relevant data, an acid washing sub-strategy (i.e., the concentration of dilute sulfuric acid in the cleaning solution, cleaning temperature, and duration), a screen printing sub-strategy (i.e., the selection of screen printing specifications (36T, 43T, or 51T) for block positioning on the printed circuit board), and an ink sub-strategy (the selected ink color and ink application amount) are generated for the current processing sub-scenario. This eliminates the need for corresponding pre-processing strategies.

[0032] Specifically, the pre-processing strategy can be used to perform optimal and rapid preprocessing on the initial splicing board.

[0033] It is understandable that, in the above embodiments, by constructing a pre-processing model and an encoding production model, the manual debugging time during the PCB QR code production process is reduced. The system can quickly respond to PCB production tasks with different batches and requirements, while improving the versatility of production equipment (such as exposure machines and inkjet printing equipment), eliminating the need to rely on extremely expensive dedicated laser engraving equipment, and reducing the production and maintenance costs of enterprises.

[0034] In a preferred embodiment of this application, preprocessing parameters are set, including: Generate the initial feature package and the first-level similarity value between it and each processing sub-scene; Set the pre-processing sub-scenario corresponding to the maximum value among all first-level similarity values ​​as the pre-processing strategy to be executed; Set the first-level pickling instruction according to the pickling sub-strategy in the pre-execution strategy; Set the first-level screen printing instruction according to the screen printing sub-policy in the pre-execution policy; The ink sub-strategy in the pending pre-strategy is designated as the first-level ink strategy. Multiple positioning blocks are generated in the initial splicing board according to the first-level pickling instructions and the first-level screen printing instructions; Obtain feedback data from each positioning block to determine whether to correct the primary ink strategy.

[0035] Specifically, the initial feature package includes the real-time parameter values ​​of each basic feature indicator of the initial splicing board. By analyzing the difference between the parameter values ​​of each technical feature indicator in the initial feature package and the current processing sub-scene, a first-level similarity value is generated. The greater the difference, the smaller the corresponding first-level similarity value. The mapping relationship between the two can be set according to historical parameters.

[0036] Specifically, the larger the first-level similarity value, the better the preprocessing effect of the pre-processing strategy of the processing sub-scene on the current initial splicing board.

[0037] Specifically, by executing the first-level pickling instruction and the first-level screen printing instruction, positioning blocks (i.e., the areas for subsequent PCB QR code production) are created on each group of printed circuit boards of the initial splicing board.

[0038] Specifically, determining whether to modify the primary ink strategy includes: Multiple preset evaluation metrics for blocks; Select the target block sequentially from all the positioning blocks and obtain the feedback data from the target block; The processing deviation value f for generating the target block; f=[ (si-s'i)]; Where θ1 is the number of monitoring indicators; si is the expected reference value of the i-th monitoring indicator in the target block generated based on feedback data; and s'i is the expected reference value of the i-th monitoring indicator set according to the pre-execution strategy. Preset processing deviation threshold F1; If f > F1, generate the first-level correction instruction for the target block; Check each positioning block in turn to see if it has generated a first-level correction command.

[0039] Specifically, the monitoring indicators include, but are not limited to, structural parameters that affect the ink printing effect, such as surface roughness and surface height difference. By quantifying each monitoring indicator, the reference values ​​of each monitoring indicator are made to be within the same range.

[0040] Specifically, expected reference values ​​for each monitoring indicator are generated based on the pickling and screen printing sub-strategies set in the pre-execution strategy. These expected reference values ​​refer to the reference values ​​for each monitoring indicator when the positioning squares are in the optimal fit with the subsequent ink sub-strategies.

[0041] Specifically, the processing deviation threshold can be set based on historical parameters. If the processing deviation of the target block is greater than the preset processing deviation threshold, it means that the subsequent ink sub-strategy cannot make the target block achieve the best exposure state. The ink sub-strategy corresponding to the target block needs to be optimized and corrected in time (adjust the ink amount).

[0042] It is understood that in the above embodiments, by adding a pre-processing model, the automatic selection of pickling, screen printing and ink can be realized, and the ink strategy can be dynamically corrected by collecting real-time feedback data of positioning blocks, so as to achieve dynamic adaptation to different needs and improve the production efficiency of PCB QR codes.

[0043] In a preferred embodiment of this application, the preset coding production model includes: Multiple coding feature indicators are set based on historical production data; Multiple coding scenarios are constructed based on all coding feature indicators; Establish a sequence of encoded scenarios B, B=(b1,b2…bi…bn2), where bi is the i-th encoded scenario and n2 is the number of encoded scenarios; Based on the coding scenario sequence B, bi is sequentially set as the target coding scenario; Define the encoding sub-model for the target encoding scenario; The encoding sub-model includes: encoding rules and transformation sub-model; The coding sub-models for each coding scenario are set sequentially, and the coding production model is constructed based on all the coding sub-models.

[0044] Specifically, the encoding feature indicators include, but are not limited to, the number of data types contained in the QR code, the number and arrangement of printed circuit boards in the splicing board, and other parameters that affect the encoding difficulty. By quantifying each encoding feature indicator, the reference values ​​of each indicator are made to fall within the same range, and multiple value intervals for each indicator are generated. Multiple encoding scenarios are then set based on random combinations of all value intervals. Notably, the value intervals for each encoding feature indicator corresponding to any two encoding scenarios are not entirely identical.

[0045] Specifically, the encoding rules include the data meaning of each code segment, the incrementing code segment width, and the incrementing rule. By setting the encoding rules, codes corresponding to each printed circuit board within the splicing board can be continuously generated, and the codes can be converted into QR codes through a transformation sub-model. This achieves continuous incrementing and non-repeating QR codes for each printed circuit board within a single primary splicing board.

[0046] Specifically, the operation sub-strategies for each printed circuit board component are set, including: Establish a sequence W, W=(w1, w2…wi…wm) for the number of printing plate components, where wi is the i-th printing plate component and m is the number of printing plate components; Generate a coded feature package based on the number of printed plate components W, and set the anchor plate; Generate encoded feature packets and secondary similarity values ​​for each encoded scenario; The coding sub-strategy corresponding to the maximum value among all second-level similarity values ​​is set as the first-level coding strategy; QR code images for each printed circuit board component are generated based on the primary coding strategy; A positioning sub-strategy is generated based on the anchored printed plate; Based on the sequence of printed circuit board components W, wi is sequentially set as the target component; Set the operation sub-strategy for the target sub-component based on the positioning sub-strategy and the pre-execution strategy; Set the operation sub-strategies for each printing plate component in sequence.

[0047] Specifically, the anchoring printed circuit board is the first printed circuit board in the printed circuit board sub-component.

[0048] Specifically, the encoded feature package includes real-time reference values ​​for each encoded feature indicator of the current primary stitching board. A secondary similarity value is set by summing the differences between these values ​​and the corresponding reference values ​​for each encoded feature indicator in the current encoding scenario. The larger the sum of these differences, the smaller the corresponding secondary similarity value. The mapping relationship between the two can be set based on historical parameters.

[0049] Specifically, the higher the secondary similarity value, the higher the encoding efficiency of the encoding sub-strategy in the corresponding encoding scenario for each printed circuit board component in the current primary splicing board. Based on the selected primary encoding strategy, QR code images corresponding to each printed circuit board component are generated.

[0050] Specifically, the operation sub-strategy includes: using an LDI exposure machine for single-sided exposure, with energy depending on the ink color, typically between 500-1500 mj / cm2, followed by conventional development, with development conditions depending on the solder resist color, typically 0.8%-1.2% sodium carbonate development concentration, development temperature 30±2℃, and speed depending on the length of the development section, typically 2.5-4.5 m / min. After development, inspection is performed, and a scanning gun is used for sampling. If it passes inspection, final curing is performed at a temperature of 150±5 degrees Celsius for 30-40 minutes.

[0051] Specifically, before exposing the anchor printed circuit board (i.e., the first printed circuit board), optical positioning is required. Multiple optical positioning points are generated in the positioning blocks of the anchor printed circuit board to improve the exposure accuracy and the accuracy of PCB QR code production.

[0052] Specifically, subsequent printed circuit boards can directly adopt the positioning sub-strategy generated by anchoring the printed circuit board, achieving rapid positioning.

[0053] Specifically, the positioning sub-strategy is generated based on the anchored printed circuit board, including: Set the positioning block of the anchored printing plate as the block to be exposed; Multiple initial optical points are set based on the QR code image of the anchored printed plate; Obtain the exposure feedback data of the anchored printing plate, and determine whether to generate a positioning correction command based on the exposure feedback data; A positioning sub-strategy is generated based on all initial optical points and positioning correction instructions.

[0054] Specifically, the positioning sub-strategy includes the relative positions of each optical point in the positioning block and the relative positions in the corresponding QR code image.

[0055] Specifically, it determines whether there is a positional deviation in the PCB QR code generated after exposure on the anchored printed circuit board. If so, it optimizes the parameters of the initial optical point to improve the exposure accuracy of subsequent printed circuit board components.

[0056] It is understood that, in the above embodiments, by adding an encoding production model, encoding rules and conversion sub-models can be intelligently matched according to different board features (size, content), achieving continuous and non-duplicate QR codes for each printed circuit board within a single primary splicing board. Furthermore, by selecting an anchored printed board to generate a positioning sub-strategy, the positioning efficiency and accuracy of the QR code position for each printed board component are improved.

[0057] Another preferred embodiment of the method for creating high-speed, high-recognition-rate PCB QR codes based on any of the above preferred embodiments provides a method for creating high-speed, high-recognition-rate PCB QR codes, including: The central control unit is used to set the pre-processing parameters for the initial splicing board; The central control unit is also used to set the operation sub-strategies for each printed circuit board component; The pre-processing unit is used to execute pre-processing parameters and generate a first-level splicing board based on the execution results; The exposure unit is used to execute the operation sub-strategies of each printed circuit board component; The central control unit includes: The first control module is used to obtain the initial feature package of the initial splicing board; The first control module is also used to set preprocessing parameters according to the preset preprocessing model and the initial feature package; The second control module is used to set multiple printed circuit board components according to the first-level splicing board; The third control module is used to set the primary coding strategy based on the preset coding production model and all printed circuit board components; The third control module is also used to set the operation sub-strategies for each printed circuit board component.

[0058] Specifically, the front unit can perform acid washing, select QR code production positions on each printed circuit board using screen printing, and complete ink printing and pre-baking curing.

[0059] Specifically, the exposure unit is preferably an LDI exposure device, which can perform single-sided exposure and conventional development on the positioning blocks of each printed circuit board, thereby completing the production of PCB QR codes.

[0060] In a preferred embodiment of this application, the first control module is further configured to: Several basic characteristic indicators were set based on historical production data; Multiple processing sub-scenes are generated based on all basic feature indicators; Establish a sequence A of sub-scenarios to be processed, A=(a1, a2, ..., ai, ..., an1), where ai is the i-th sub-scenarios to be processed; n1 is the number of sub-scenarios to be processed; Based on the sequence of sub-scenes A, ai is sequentially set as the target processing scene; Pre-processing strategies for generating target scenarios; The pre-processing strategies include: acid washing sub-strategy, screen printing sub-strategy, and ink sub-strategy; Generate the pre-processing strategies for each sub-scenario in sequence; Establish a pre-processing model based on all pre-processing strategies.

[0061] In a preferred embodiment of this application, the second control module is further configured to: Multiple coding feature indicators are set based on historical production data; Multiple coding scenarios are constructed based on all coding feature indicators; Establish a sequence of encoded scenarios B, B=(b1,b2…bi…bn2), where bi is the i-th encoded scenario and n2 is the number of encoded scenarios; Based on the coding scenario sequence B, bi is sequentially set as the target coding scenario; Define the encoding sub-model for the target encoding scenario; The encoding sub-model includes: encoding rules and transformation sub-model; Sequentially set up the coding sub-models for each coding scenario, and construct the coding production model based on all the coding sub-models; Establish a sequence W, W=(w1, w2…wi…wm) for the number of printing plate components, where wi is the i-th printing plate component and m is the number of printing plate components; Generate a coded feature package based on the number of printed plate components W, and set the anchor plate; Generate encoded feature packets and secondary similarity values ​​for each encoded scenario; The coding sub-strategy corresponding to the maximum value among all second-level similarity values ​​is set as the first-level coding strategy; QR code images for each printed circuit board component are generated based on the primary coding strategy; A positioning sub-strategy is generated based on the anchored printed plate; Based on the sequence of printed circuit board components W, wi is sequentially set as the target component; Set the operation sub-strategy for the target sub-component based on the positioning sub-strategy and the pre-execution strategy; Set the operation sub-strategies for each printing plate component in sequence.

[0062] According to the first concept of this application, by adding a pre-processing model, the automatic selection of pickling, screen printing and ink can be realized. By collecting real-time feedback data from positioning blocks, the ink strategy can be dynamically corrected to achieve dynamic adaptation to different needs and improve the production efficiency of PCB QR codes.

[0063] According to the second concept of this application, by adding a coding production model, coding rules and conversion sub-models can be intelligently matched according to different board features (size, content), so as to achieve continuous and non-duplicate QR codes for each printed circuit board within a single primary splicing board. Furthermore, by selecting an anchored printed board to generate a positioning sub-strategy, the positioning efficiency and accuracy of the QR code position for each printed board component are improved.

[0064] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application.

Claims

1. A method for manufacturing high-speed, high-recognition-rate PCB QR codes, characterized in that, include: Obtain the initial feature package of the initial splicing board, and set the preprocessing parameters according to the preset preprocessing model and the initial feature package; The first-level splicing board is obtained based on the preprocessing parameters, and multiple printed circuit board components are set based on the first-level splicing board. Based on the preset coding production model and all printed circuit board components, a primary coding strategy is set, and an operation sub-strategy is set for each printed circuit board component.

2. The method for manufacturing high-speed, high-recognition-rate PCB QR codes as described in claim 1, characterized in that, The preset preprocessing model includes: Several basic characteristic indicators were set based on historical production data; Multiple processing sub-scenes are generated based on all basic feature indicators; Establish a sequence A of sub-scenarios to be processed, A=(a1, a2, ..., ai, ..., an1), where ai is the i-th sub-scenarios to be processed; n1 is the number of sub-scenarios to be processed; Based on the sequence of sub-scenes A, ai is sequentially set as the target processing scene; Pre-processing strategies for generating target scenarios; The pre-processing strategies include: acid washing sub-strategy, screen printing sub-strategy, and ink sub-strategy; Generate the pre-processing strategies for each sub-scenario in sequence; Establish a pre-processing model based on all pre-processing strategies.

3. The method for manufacturing high-speed, high-recognition-rate PCB QR codes as described in claim 2, characterized in that, The preprocessing parameters to be set include: Generate the initial feature package and the first-level similarity value between it and each processing sub-scene; Set the pre-processing sub-scenario corresponding to the maximum value among all first-level similarity values ​​as the pre-processing strategy to be executed; Set the first-level pickling instruction according to the pickling sub-strategy in the pre-execution strategy; Set the first-level screen printing instruction according to the screen printing sub-policy in the pre-execution policy; The ink sub-strategy in the pending pre-strategy is designated as the first-level ink strategy. Multiple positioning blocks are generated in the initial splicing board according to the first-level pickling instructions and the first-level screen printing instructions; Obtain feedback data from each positioning block to determine whether to correct the primary ink strategy.

4. The method for manufacturing high-speed, high-recognition-rate PCB QR codes as described in claim 3, characterized in that, The determination of whether to modify the primary ink strategy includes: Multiple preset evaluation metrics for blocks; Select the target block sequentially from all the positioning blocks and obtain the feedback data from the target block; The processing deviation value f for generating the target block; f=[ (si-s'i)]; Where θ1 is the number of monitoring indicators; si is the expected reference value of the i-th monitoring indicator in the target block generated based on feedback data; and s'i is the expected reference value of the i-th monitoring indicator set according to the pre-execution strategy. Preset processing deviation threshold F1; If f > F1, generate the first-level correction instruction for the target block; Check each positioning block in turn to see if it has generated a first-level correction command.

5. The method for manufacturing high-speed, high-recognition-rate PCB QR codes as described in claim 3, characterized in that, The preset coding production model includes: Multiple coding feature indicators are set based on historical production data; Multiple coding scenarios are constructed based on all coding feature indicators; Establish a sequence of encoded scenarios B, B=(b1,b2…bi…bn2), where bi is the i-th encoded scenario and n2 is the number of encoded scenarios; Based on the coding scenario sequence B, bi is sequentially set as the target coding scenario; Define the encoding sub-model for the target encoding scenario; The encoding sub-model includes: encoding rules and transformation sub-model; The coding sub-models for each coding scenario are set sequentially, and the coding production model is constructed based on all the coding sub-models.

6. The method for manufacturing high-speed, high-recognition-rate PCB QR codes as described in claim 5, characterized in that, The operation sub-strategies for setting each printed circuit board component include: Establish a sequence W, W=(w1, w2…wi…wm) for the number of printing plate components, where wi is the i-th printing plate component and m is the number of printing plate components; Generate a coded feature package based on the number of printed plate components W, and set the anchor plate; Generate encoded feature packets and secondary similarity values ​​for each encoded scenario; The coding sub-strategy corresponding to the maximum value among all second-level similarity values ​​is set as the first-level coding strategy; QR code images for each printed circuit board component are generated based on the primary coding strategy; A positioning sub-strategy is generated based on the anchored printed plate; Based on the sequence of printed circuit board components W, wi is sequentially set as the target component; Set the operation sub-strategy for the target sub-component based on the positioning sub-strategy and the pre-execution strategy; Set the operation sub-strategies for each printing plate component in sequence.

7. The method for manufacturing high-speed, high-recognition-rate PCB QR codes as described in claim 6, characterized in that, The method for generating a positioning sub-strategy based on the anchored printed board includes: Set the positioning block of the anchored printing plate as the block to be exposed; Multiple initial optical points are set based on the QR code image of the anchored printed plate; Obtain the exposure feedback data of the anchored printing plate, and determine whether to generate a positioning correction command based on the exposure feedback data; A positioning sub-strategy is generated based on all initial optical points and positioning correction instructions.

8. A high-speed motion, high-recognition-rate PCB QR code manufacturing system, employing the high-speed motion, high-recognition-rate PCB QR code manufacturing method according to any one of claims 1-7, characterized in that, include: The central control unit is used to set the pre-processing parameters for the initial splicing board; The central control unit is also used to set the operation sub-strategies for each printed circuit board component; The pre-processing unit is used to execute pre-processing parameters and generate a first-level splicing board based on the execution results; The exposure unit is used to execute the operation sub-strategies of each printed circuit board component; The central control unit includes: The first control module is used to obtain the initial feature package of the initial splicing board; The first control module is also used to set preprocessing parameters according to the preset preprocessing model and the initial feature package; The second control module is used to set multiple printed circuit board components according to the first-level splicing board; The third control module is used to set the primary coding strategy based on the preset coding production model and all printed circuit board components; The third control module is also used to set the operation sub-strategies for each printed circuit board component.

9. The high-speed motion, high-recognition-rate PCB QR code manufacturing system as described in claim 8, characterized in that, The first control module is also used for: Several basic characteristic indicators were set based on historical production data; Multiple processing sub-scenes are generated based on all basic feature indicators; Establish a sequence A of sub-scenarios to be processed, A=(a1, a2, ..., ai, ..., an1), where ai is the i-th sub-scenarios to be processed; n1 is the number of sub-scenarios to be processed; Based on the sequence of sub-scenes A, ai is sequentially set as the target processing scene; Pre-processing strategies for generating target scenarios; The pre-processing strategies include: acid washing sub-strategy, screen printing sub-strategy, and ink sub-strategy; Generate the pre-processing strategies for each sub-scenario in sequence; Establish a pre-processing model based on all pre-processing strategies.

10. The high-speed motion, high-recognition-rate PCB QR code manufacturing system as described in claim 9, characterized in that, The second control module is also used for: Multiple coding feature indicators are set based on historical production data; Multiple coding scenarios are constructed based on all coding feature indicators; Establish a sequence of encoded scenarios B, B=(b1,b2…bi…bn2), where bi is the i-th encoded scenario and n2 is the number of encoded scenarios; Based on the coding scenario sequence B, bi is sequentially set as the target coding scenario; Define the encoding sub-model for the target encoding scenario; The encoding sub-model includes: encoding rules and transformation sub-model; Sequentially set up the coding sub-models for each coding scenario, and construct the coding production model based on all the coding sub-models; Establish a sequence W, W=(w1, w2…wi…wm) for the number of printing plate components, where wi is the i-th printing plate component and m is the number of printing plate components; Generate a coded feature package based on the number of printed plate components W, and set the anchor plate; Generate encoded feature packets and secondary similarity values ​​for each encoded scenario; The coding sub-strategy corresponding to the maximum value among all second-level similarity values ​​is set as the first-level coding strategy; QR code images for each printed circuit board component are generated based on the primary coding strategy; A positioning sub-strategy is generated based on the anchored printed plate; Based on the sequence of printed circuit board components W, wi is sequentially set as the target component; Set the operation sub-strategy for the target sub-component based on the positioning sub-strategy and the pre-execution strategy; Set the operation sub-strategies for each printing plate component in sequence.

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