An intelligent method and system for implementing PCBA board testing

Abstract

The present invention relates to the field of intelligent testing technology, and in particular to an intelligent system and method for implementing PCBA board testing. The method comprises the following steps: obtaining a scanned image of a PCBA board; extracting key structural feature points of the scanned image of the PCBA board to obtain surface feature data of the PCBA board and internal structure data of the PCBA board; performing three-dimensional reconstruction based on the surface feature data of the PCBA board and the internal structure data of the PCBA board to generate a three-dimensional structural model of the PCBA board; performing initial surface structural defect detection on the three-dimensional structural model of the PCBA board to generate an initial defect area of ​​the PCBA board and a normal area of ​​the PCBA board. The present invention improves the comprehensiveness, accuracy and intelligence of PCBA board testing by performing implicit circuit association on defective parts of the PCBA board and by using three-dimensional reconstruction and multi-dimensional evaluation technology.

Description

Technical Field

[0001] The present invention relates to the field of intelligent testing technology, and in particular to an intelligent system and method for implementing PCBA board testing. Background Art

[0002] At the beginning of the 21st century, with the rapid development of computer technology and data processing capabilities, PCBA testing began to introduce more intelligent solutions. The intelligence level of automatic test equipment (ATE) has been continuously improved, using more advanced sensor technology, automatic control systems and data analysis software. The combination of these technologies makes the test process more accurate and efficient, and can achieve coverage of a wider range of functions, including electrical performance, communication interfaces, power consumption and environmental adaptability. With the rise of artificial intelligence (AI) and machine learning (ML) technology, PCBA testing methods have reached a new milestone. The application of AI and ML enables the test system to automatically optimize test strategies and parameter settings through big data analysis and pattern recognition, and improve test coverage and accuracy. For example, by identifying patterns in large amounts of data, AI can detect potential product defects or failure modes in advance, thereby reducing the failure rate on the production line and improving the overall quality level. However, the current traditional method for PCBA board defect testing still tests according to the defect location, and cannot well associate the circuit where the defect is located for invisible performance evaluation, resulting in an inaccurate assessment of the impact on the overall quality of the PCBA board. Summary of the invention

[0003] Based on this, it is necessary to provide an intelligent system and method for implementing PCBA board testing to solve at least one of the above technical problems.

[0004] To achieve the above object, an intelligent method for implementing PCBA board testing is provided, the method comprising the following steps:

[0005] Step S1: Acquire a scanned image of a PCBA board; extract key structural feature points of the scanned image of the PCBA board to obtain surface feature data of the PCBA board and internal structure data of the PCBA board; perform three-dimensional reconstruction based on the surface feature data of the PCBA board and the internal structure data of the PCBA board to generate a three-dimensional structural model of the PCBA board;

[0006] Step S2: Performing initial surface structural defect detection on the three-dimensional structural model of the PCBA board to generate an initial defect area and a normal area of ​​the PCBA board; screening the actual associated circuit of the PCBA board according to the initial defect area of ​​the PCBA board to obtain a defect associated circuit of the PCBA board; performing electrical testing of the defect area of ​​the defect associated circuit of the PCBA board to generate defect electrical test data; performing defect impact performance evaluation on the defect electrical test data to generate defect area performance impact data;

[0007] Step S3: Perform multi-dimensional functional testing on the PCBA board through the normal area of ​​the PCBA board to generate functional test data of the normal area of ​​the PCBA board; perform defect area repair performance comparative analysis based on the functional test data of the normal area of ​​the PCBA board and the defect performance impact data to generate defect area repair data of the PCBA board;

[0008] Step S4: Perform a repair quality assessment on the three-dimensional structure model of the PCBA board according to the repair data of the defective area of ​​the PCBA board, thereby generating a PCBA board defect repair test report.

[0009] The present invention can provide surface feature data and internal structure data of the PCBA board by acquiring a scanned image of the PCBA board and extracting key structural feature points. These data can be used for subsequent three-dimensional reconstruction to generate a three-dimensional structural model of the PCBA board. This helps to understand the overall shape and layout of the PCBA board. Performing initial surface structural defect detection on the three-dimensional structural model of the PCBA board can detect potential defect areas and normal areas. The circuit part related to the defect area can be determined by screening the actual associated circuit. Electrical testing of the defect-associated circuit can generate defect electrical test data, thereby evaluating the impact of the defect on the performance. These data help to locate and analyze the defects of the PCBA board. Functional test data of the normal area can be generated by performing multi-dimensional functional testing on the normal area of ​​the PCBA board. Comparing and analyzing the normal area functional test data with the defect performance impact data can evaluate the repair performance of the defect area. This can help determine whether the repaired PCBA board is similar to the normal board in function. The three-dimensional structural model is repaired according to the repair data of the defect area of ​​the PCBA board, and a defect repair test report is generated, which can provide an overall quality evaluation of the repaired PCBA board, including the performance and structure of the repair area. Therefore, the present invention improves the comprehensiveness, accuracy and intelligence of PCBA board testing by performing implicit circuit correlation on defective parts of PCBA board and using three-dimensional reconstruction and multi-dimensional evaluation technology.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: using a multispectral camera to capture a multispectral image of the PCBA board to obtain a scanned image of the PCBA board;

[0012] Step S12: performing image preprocessing on the scanned image of the PCBA board to generate a standard PCBA board multispectral image, wherein the image preprocessing includes image denoising, image enhancement and color correction;

[0013] Step S13: Identify the structure center point of the standard PCBA board multispectral image to obtain the structure center point data of the PCBA board; extract key feature points of the standard PCBA board multispectral image according to the structure center point data of the PCBA board to obtain the surface feature data of the PCBA board and the internal structure data of the PCBA board;

[0014] Step S14: Perform three-dimensional reconstruction based on the surface feature data of the PCBA board and the internal structure data of the PCBA board to generate a three-dimensional structure model of the PCBA board.

[0015] The present invention can provide rich spectral information by using multispectral images captured by a multispectral camera, which is helpful to more accurately detect subtle defects and imperceptible problems of PCBA boards. Through image preprocessing steps such as image denoising, image enhancement and color correction, the image quality can be improved, noise interference can be reduced, image details can be enhanced, and subsequent processing can be more accurate and reliable. By identifying the structural center point of the PCBA board, the accuracy and consistency in the subsequent key feature point extraction and three-dimensional reconstruction process can be ensured, thereby improving the accuracy of detection and analysis. The surface feature data and internal structure data of the PCBA board are extracted, so that not only surface defects can be detected, but also the internal structure can be analyzed to provide a comprehensive quality assessment. The three-dimensional structural model of the PCBA board is generated based on the multispectral image, which can more intuitively and comprehensively display the structure and characteristics of the PCBA board, which is helpful for in-depth analysis and optimization design. The automated image capture, preprocessing, feature extraction and three-dimensional reconstruction process can greatly improve the detection efficiency, reduce manual intervention and subjective errors, and improve production efficiency and product quality. The generated three-dimensional structural model and feature data can be saved for subsequent analysis and quality tracking, which is helpful for product improvement and quality management.

[0016] Preferably, step S2 comprises the following steps:

[0017] Step S21: performing initial surface structural defect detection on the three-dimensional structural model of the PCBA board to generate an initial defect area and a normal area of ​​the PCBA board; performing defect morphology analysis on the initial defect area of ​​the PCBA board to generate initial defect morphology data of the PCBA board;

[0018] Step S22: confirming the defect boundary coordinates of the initial defect area of ​​the PCBA board according to the initial defect morphology data of the PCBA board, and obtaining the defect area boundary coordinate data of the PCBA board;

[0019] Step S23: based on the PCBA board defect area boundary coordinate data, the PCBA board is realistically mapped to generate the PCBA board realistic area mapping coordinates; the PCBA board is associated with the circuits screened by the PCBA board realistic area mapping coordinates to obtain the PCBA board defect associated circuits;

[0020] Step S24: performing an electrical test on the defect area of ​​the defect-associated circuit of the PCBA board to generate defect electrical test data; performing a defect impact performance evaluation on the defect electrical test data to generate defect area performance impact data.

[0021] The present invention quickly identifies and classifies the initial defect area and normal area of ​​the PCBA board, thereby laying the foundation for subsequent defect analysis. By generating initial defect morphological data through morphological analysis, the characteristics of the defect can be described in more detail, providing detailed information support for subsequent processing. Based on the initial defect morphological data, the boundary coordinates of the defect area are accurately confirmed to ensure the accuracy and reliability of subsequent processing. The defect area is mapped to the actual PCBA board to generate the real area mapping coordinates to ensure the accuracy of defect positioning. The circuit associated with the defect is screened out through the real area mapping coordinates, which helps to carry out electrical testing and performance evaluation in a targeted manner. Electrical testing is carried out on the associated circuit to obtain the electrical test data of the defect area, and the impact of the defect on the circuit performance can be accurately evaluated. By analyzing the electrical test data, the impact of the defect area on the overall performance of the PCBA board is evaluated, and performance impact data is generated, which provides an important reference for product quality evaluation and improvement. From initial defect detection to detailed morphological analysis, and then to electrical testing and performance evaluation, a multi-level detection and analysis method is provided to ensure a comprehensive understanding and control of the PCBA board.

[0022] Preferably, step S21 includes the following steps:

[0023] Step S211: performing surface meshing on the three-dimensional structure model of the PCBA board to generate three-dimensional mesh data of the PCBA board;

[0024] Step S212: Calculate the mesh surface curvature of the three-dimensional mesh data of the PCBA board to obtain the three-dimensional mesh curvature data of the PCBA board;

[0025] Step S213: Compare the three-dimensional grid curvature data of the PCBA board with a preset standard grid curvature threshold. When the three-dimensional grid curvature data of the PCBA board is greater than or equal to the preset standard grid curvature threshold, mark the grid area corresponding to the three-dimensional grid curvature data of the PCBA board as the initial defect area of ​​the PCBA board; when the three-dimensional grid curvature data of the PCBA board is less than the preset standard grid curvature threshold, mark the grid area corresponding to the three-dimensional grid curvature data of the PCBA board as the normal area of ​​the PCBA board;

[0026] Step S214: using the three-dimensional shape descriptor to identify the defect type of the initial defect area of ​​the PCBA board, and generating defect type identification data; performing defect morphology analysis on the initial defect area of ​​the PCBA board through the defect type identification data, and generating initial defect morphology data of the PCBA board.

[0027] The present invention generates detailed three-dimensional grid data by surface meshing the three-dimensional structure model of the PCBA board, provides a more accurate surface description, and helps to more accurately identify and locate the defect area. The surface curvature of the three-dimensional grid data of the PCBA board is calculated to obtain the three-dimensional grid curvature data, which accurately reflects the slight shape changes and defect characteristics of the surface. The three-dimensional grid curvature data of the PCBA board is compared with the preset standard grid curvature threshold, and the initial defect area and the normal area are automatically marked, which reduces human intervention and improves the automation and consistency of detection. When the curvature data is greater than or equal to the standard threshold, the corresponding grid area is marked as the initial defect area; when the curvature data is less than the standard threshold, it is marked as the normal area, so as to achieve efficient separation of defect areas. The defect type of the initial defect area is identified by using the three-dimensional shape descriptor, and detailed defect type identification data is generated to provide a comprehensive understanding of the defect. Based on the defect type identification data, the initial defect area is subjected to a detailed defect morphology analysis, and the initial defect morphology data is generated, which provides detailed data support for subsequent processing and evaluation. Surface meshing and curvature calculation improve the accuracy of defect detection, can identify subtle defects, and improve the overall detection quality. From curvature calculation to morphology analysis, multi-level detection and analysis methods ensure a comprehensive understanding and accurate assessment of PCBA board defects.

[0028] Preferably, step S24 includes the following steps:

[0029] Step S241: performing a digital Domitor test on the PCBA board defect-related circuit, and recording the test results in real time to obtain PCBA board defect circuit test data, wherein the PCBA board defect circuit test data includes defect area circuit resistance data, defect area circuit capacitance data, and defect area circuit inductance data;

[0030] Step S242: using the defective area circuit resistance data to perform circuit signal transmission acquisition to generate a defective area circuit signal transmission waveform; performing signal waveform distortion analysis on the defective area circuit signal transmission waveform to generate defective area circuit resistance performance impact data;

[0031] Step S243: using the defective area circuit capacitance data to collect signal frequency changes, generating defective area circuit signal frequency data; performing frequency change response analysis on the defective area circuit signal frequency data according to the defective area circuit capacitance data, generating defective area circuit capacitance performance impact data;

[0032] Step S244: collecting circuit power noise using the defective area circuit inductance data to obtain circuit power noise data; performing noise suppression analysis on the circuit power noise data using the defective area circuit inductance data to generate defective area circuit inductance performance impact data;

[0033] Step S245: performing a comprehensive defect impact performance evaluation on the defect area circuit resistance performance impact data, the defect area circuit capacitance performance impact data and the defect area circuit inductance performance impact data to generate defect area performance impact data.

[0034] The present invention performs comprehensive tests on the resistance, capacitance and inductance of the defect-related circuit of the PCBA board, records the test results in real time, generates detailed defect circuit test data, and provides reliable basic data for subsequent analysis. The signal transmission waveform is collected through the resistance data of the defective area circuit, and the signal waveform distortion analysis is performed to generate resistance performance impact data to help evaluate the impact of resistance on signal transmission quality. The signal frequency change is collected using the capacitance data of the defective area circuit, and the frequency change response analysis is performed to generate capacitance performance impact data to evaluate the impact of capacitance on circuit frequency response. The power supply noise is collected through the inductance data of the defective area circuit, and the noise suppression analysis is performed to generate inductance performance impact data to evaluate the suppression effect of inductance on circuit noise. The resistance, capacitance and inductance performance impact data are comprehensively analyzed to generate comprehensive defect area performance impact data, providing detailed basis for the overall performance evaluation of the PCBA board. Through detailed testing and analysis of the three key electrical parameters of resistance, capacitance and inductance, a multi-dimensional performance evaluation of the defective area circuit is achieved, and the accuracy and reliability of detection are improved. Digital multimeter testing and real-time recording of test results ensure the real-time and accuracy of the data, providing reliable data support for subsequent analysis.

[0035] Preferably, step S3 comprises the following steps:

[0036] Step S31: Perform multi-dimensional functional testing on the PCBA board through the normal area of ​​the PCBA board to generate functional test data of the normal area of ​​the PCBA board;

[0037] Step S32: constructing a full-area performance model of the PCBA board according to the normal-area function test data of the PCBA board and the defect performance impact data, and obtaining a full-area performance detection model of the PCBA board; using the full-area performance detection model of the PCBA board to perform a first full-area performance evaluation of the PCBA board on the normal-area function test data of the PCBA board and the defect performance impact data, and generating first full-area performance evaluation data of the PCBA board;

[0038] Step S33: repairing the defective area of ​​the PCBA board based on the defect performance impact data to obtain PCBA board defective area optimization data;

[0039] Step S34: using the PCBA board full-area performance detection model to perform a second PCBA board full-area performance evaluation on the PCBA board normal area function test data and the PCBA board defect area optimization data, and generate the PCBA board second full-area performance evaluation data; by comparing and analyzing the PCBA board first full-area performance evaluation data and the PCBA board second full-area performance evaluation data, generate the PCBA board defect area repair data.

[0040] The present invention performs multi-dimensional functional testing on the normal area of ​​the PCBA board, generates detailed normal area functional test data, and provides comprehensive baseline performance data. According to the normal area functional test data and the defect performance impact data, a full-area performance detection model of the PCBA board is constructed. The model can comprehensively consider the performance characteristics of the normal area and the defect area, and realize accurate modeling of the full board performance. Using the full-area performance detection model, the normal area functional test data and the defect performance impact data are subjected to the first full-area performance evaluation of the PCBA board, and the first full-area performance evaluation data is generated to provide baseline data for subsequent analysis and repair. Based on the defect performance impact data, targeted defect area repair is performed, the defect area performance is optimized, and defect area optimization data is obtained. The repaired defect area data helps to improve the overall performance of the PCBA board and ensure the effectiveness and pertinence of the repair measures. Using the full-area performance detection model, the normal area functional test data and the defect area optimization data are subjected to the second full-area performance evaluation of the PCBA board, and the second full-area performance evaluation data is generated to evaluate the repair effect. By comparing the first and second full-area performance evaluation data, PCBA board defect area repair data is generated, and the performance changes before and after the repair are evaluated in detail to verify the effect of the repair measures. Through comparative analysis, the performance improvement before and after the repair can be clearly identified, providing an important reference for subsequent optimization and quality improvement. By building a full-area performance detection model, the performance evaluation is automated and systematized, improving the efficiency and accuracy of detection and evaluation. Repairs are performed based on detailed defect performance impact data and evaluation models, which improves the accuracy and effectiveness of repairs and reduces rework and resource waste.

[0041] Preferably, performing multi-dimensional functional testing on the PCBA board through the normal area of ​​the PCBA board includes:

[0042] Conduct voltage stability test on PCBA board through normal area of ​​PCBA board, and generate voltage stability test data of normal area of ​​PCBA board;

[0043] Perform a current response time test on the PCBA board through the normal area of ​​the PCBA board to generate current response time test data for the normal area of ​​the PCBA board;

[0044] Perform signal transmission rate test on the PCBA board through the normal area of ​​the PCBA board to generate signal transmission rate test data of the normal area of ​​the PCBA board;

[0045] Conduct temperature effect test on PCBA board through normal area of ​​PCBA board, and generate temperature effect test data of normal area of ​​PCBA board;

[0046] Perform fault injection test on the PCBA board through the normal area of ​​the PCBA board to generate fault injection test data for the normal area of ​​the PCBA board;

[0047] A comprehensive performance evaluation is performed based on the voltage stability test data of the PCBA board normal area, the current response time test data of the PCBA board normal area, the signal transmission rate test data of the PCBA board normal area, the temperature effect test data of the PCBA board normal area and the fault injection test data of the PCBA board normal area to generate the functional test data of the PCBA board normal area.

[0048] The present invention performs multi-dimensional functional testing on the normal area of ​​the PCBA board, generates detailed normal area functional test data, and provides comprehensive baseline performance data. According to the normal area functional test data and the defect performance impact data, a full-area performance detection model of the PCBA board is constructed. The model can comprehensively consider the performance characteristics of the normal area and the defect area, and realize accurate modeling of the full board performance. Using the full-area performance detection model, the normal area functional test data and the defect performance impact data are subjected to the first full-area performance evaluation of the PCBA board, and the first full-area performance evaluation data is generated to provide baseline data for subsequent analysis and repair. Based on the defect performance impact data, targeted defect area repair is performed, the defect area performance is optimized, and defect area optimization data is obtained. The repaired defect area data helps to improve the overall performance of the PCBA board and ensure the effectiveness and pertinence of the repair measures. Using the full-area performance detection model, the normal area functional test data and the defect area optimization data are subjected to the second full-area performance evaluation of the PCBA board, and the second full-area performance evaluation data is generated to evaluate the repair effect. By comparing the first and second full-area performance evaluation data, PCBA board defect area repair data is generated, and the performance changes before and after the repair are evaluated in detail to verify the effect of the repair measures. Through comparative analysis, the performance improvement before and after the repair can be clearly identified, providing an important reference for subsequent optimization and quality improvement. By building a full-area performance detection model, the performance evaluation is automated and systematized, improving the efficiency and accuracy of detection and evaluation. Repairs are performed based on detailed defect performance impact data and evaluation models, which improves the accuracy and effectiveness of repairs and reduces rework and resource waste.

[0049] Preferably, constructing a full-area performance model of a PCBA board based on normal-area function test data and defect performance impact data of the PCBA board includes:

[0050] Merge the PCBA board normal area function test data and defect performance impact data to generate a PCBA board integrated data set;

[0051] The integrated data set of the PCBA board is divided into data sets to generate a model training set and a model test set; the model training set is trained using the support vector machine algorithm to generate a full-area performance training model for the PCBA board; the full-area performance training model of the PCBA board is verified and evaluated using the model test set to generate a full-area performance model for the PCBA board.

[0052] The present invention generates a comprehensive PCBA board integrated data set by merging the normal area function test data of the PCBA board and the defect performance impact data. This data set contains all relevant performance data of the normal and defective areas of the PCBA board, providing a comprehensive perspective. The PCBA board integrated data set is reasonably divided to generate a model training set and a model test set. Ensuring that the division of the training set and the test set is scientific and reasonable can improve the training effect and evaluation accuracy of the model. The model training set is trained using a support vector machine (SVM) algorithm to generate a PCBA board full-area performance training model. SVM is an effective classification and regression algorithm suitable for high-dimensional data, can handle complex nonlinear relationships, and ensure the efficiency and accuracy of the training model. The training model is verified and evaluated by the model test set to ensure the reliability and accuracy of the model. The generated PCBA board full-area performance model can accurately reflect the overall performance of the PCBA board. Based on the generated full-area performance model, the overall performance of the PCBA board can be comprehensively analyzed and evaluated. The model combines the performance data of the normal area and the defective area, providing a comprehensive performance evaluation tool. The performance model can be used to predict the performance of PCBA boards under different conditions, help identify potential problems, propose improvement measures, and improve product quality and reliability. By analyzing and integrating data sets and performance models, weak links in the design and manufacturing process can be discovered, guiding the optimization of design and improving production processes.

[0053] Preferably, step S4 comprises the following steps:

[0054] Step S41: performing a repair quality assessment on the three-dimensional structure model of the PCBA board according to the repair data of the defective area of ​​the PCBA board, and generating repair quality detection data of the PCBA board;

[0055] Step S42: Visualize the PCBA board repair quality inspection data to generate a PCBA board defect repair test report.

[0056] The present invention performs a detailed repair quality assessment on the three-dimensional structural model of the PCBA board according to the repair data of the defective area of ​​the PCBA board. The generated PCBA board repair quality detection data can fully reflect the repair effect and ensure the accuracy and reliability of the repair. Through the evaluation of the repair quality, detailed detection data are generated, including data on the structural integrity, material consistency and functional recovery after the repair, so as to provide a quantitative basis for the repair effect. The PCBA board repair quality detection data is visualized to generate intuitive charts and reports for easy understanding and analysis. The visual display can clearly show the comparison and repair effect before and after the repair, and help to quickly judge the repair quality. Through data visualization, a PCBA board defect repair test report is generated. The report records the repair process, detection data and repair effect in detail, providing a basis for subsequent quality control and improvement. Through detailed repair quality assessment and data visualization, problems in the repair process can be found, improvement measures can be proposed, and the repair quality and efficiency can be further improved. The generated repair test report can be used for quality tracking and management to ensure the traceability and consistency of the repair process.

[0057] In this specification, an intelligent system for implementing PCBA board testing is provided, which is used to execute the above-mentioned intelligent method for implementing PCBA board testing. The intelligent system for implementing PCBA board testing includes:

[0058] The 3D reconstruction module is used to obtain the scanned image of the PCBA board; extract the key structural feature points of the scanned image of the PCBA board to obtain the surface feature data of the PCBA board and the internal structure data of the PCBA board; perform 3D reconstruction based on the surface feature data of the PCBA board and the internal structure data of the PCBA board to generate a 3D structural model of the PCBA board;

[0059] The defect circuit association module is used to perform initial surface structural defect detection on the three-dimensional structural model of the PCBA board, generate the initial defect area and normal area of ​​the PCBA board; screen the actual associated circuit of the PCBA board according to the initial defect area of ​​the PCBA board to obtain the defect associated circuit of the PCBA board; perform electrical test on the defect area of ​​the PCBA board defect associated circuit to generate defect electrical test data; perform defect impact performance evaluation on the defect electrical test data to generate defect area performance impact data;

[0060] The repair performance analysis module is used to perform multi-dimensional functional tests on the PCBA board through the normal area of ​​the PCBA board to generate the functional test data of the normal area of ​​the PCBA board; perform a comparative analysis of the repair performance of the defective area based on the functional test data of the normal area of ​​the PCBA board and the defect performance impact data to generate the repair data of the defective area of ​​the PCBA board;

[0061] The repair quality assessment module is used to perform repair quality assessment on the three-dimensional structure model of the PCBA board according to the repair data of the defective area of ​​the PCBA board, so as to generate a PCBA board defect repair test report.

[0062] The beneficial effect of the present invention is that by using the scanned image of the PCBA board to extract the key structural features, the three-dimensional reconstruction of the surface and internal structure of the PCBA board is realized, and fine three-dimensional structural information is provided for subsequent defect detection and positioning. The defect detection results are classified into the initial defect area and the normal area, and the associated circuits of the defect area are further screened and locked to the specific defective circuit, providing an accurate detection object for subsequent electrical testing. The defective circuit is tested for electrical properties to evaluate the impact of the defect on the performance, providing an electrical basis for confirming the type and degree of the defect. The normal area is tested for multi-dimensional functions, and compared with the impact of the defect, the impact range of the defect on the overall performance can be comprehensively evaluated. The three-dimensional model is evaluated according to the defect repair effect, and a specific repair report is given, which can feedback the repair quality in real time and provide a basis for subsequent decision-making. The overall process combines three-dimensional physical information with electrical functional information, evaluates defects from different angles, and has a very high guarantee for the reliability and accuracy of defect detection and repair. Therefore, the present invention improves the comprehensiveness, accuracy and intelligence of PCBA board testing by performing implicit circuit association on the defective part of the PCBA board and using three-dimensional reconstruction and multi-dimensional evaluation technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A schematic diagram of a process flow of an intelligent method for implementing PCBA board testing;

[0064] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0065] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0066] Figure 4 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0067] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0068] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0069] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0070] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0071] To achieve this, please refer to Figures 1 to 4 , an intelligent method for implementing PCBA board testing, the method comprising the following steps:

[0072] Step S1: Acquire a scanned image of a PCBA board; extract key structural feature points of the scanned image of the PCBA board to obtain surface feature data of the PCBA board and internal structure data of the PCBA board; perform three-dimensional reconstruction based on the surface feature data of the PCBA board and the internal structure data of the PCBA board to generate a three-dimensional structural model of the PCBA board;

[0073] Step S2: Performing initial surface structural defect detection on the three-dimensional structural model of the PCBA board to generate an initial defect area and a normal area of ​​the PCBA board; screening the actual associated circuit of the PCBA board according to the initial defect area of ​​the PCBA board to obtain a defect associated circuit of the PCBA board; performing electrical testing of the defect area of ​​the defect associated circuit of the PCBA board to generate defect electrical test data; performing defect impact performance evaluation on the defect electrical test data to generate defect area performance impact data;

[0074] Step S3: Perform multi-dimensional functional testing on the PCBA board through the normal area of ​​the PCBA board to generate functional test data of the normal area of ​​the PCBA board; perform defect area repair performance comparative analysis based on the functional test data of the normal area of ​​the PCBA board and the defect performance impact data to generate defect area repair data of the PCBA board;

[0075] Step S4: Perform a repair quality assessment on the three-dimensional structure model of the PCBA board according to the repair data of the defective area of ​​the PCBA board, thereby generating a PCBA board defect repair test report.

[0076] The present invention can provide surface feature data and internal structure data of the PCBA board by acquiring a scanned image of the PCBA board and extracting key structural feature points. These data can be used for subsequent three-dimensional reconstruction to generate a three-dimensional structural model of the PCBA board. This helps to understand the overall shape and layout of the PCBA board. Performing initial surface structural defect detection on the three-dimensional structural model of the PCBA board can detect potential defect areas and normal areas. The circuit part related to the defect area can be determined by screening the actual associated circuit. Electrical testing of the defect-associated circuit can generate defect electrical test data, thereby evaluating the impact of the defect on the performance. These data help to locate and analyze the defects of the PCBA board. Functional test data of the normal area can be generated by performing multi-dimensional functional testing on the normal area of ​​the PCBA board. Comparing and analyzing the normal area functional test data with the defect performance impact data can evaluate the repair performance of the defect area. This can help determine whether the repaired PCBA board is similar to the normal board in function. The three-dimensional structural model is repaired according to the repair data of the defect area of ​​the PCBA board, and a defect repair test report is generated, which can provide an overall quality evaluation of the repaired PCBA board, including the performance and structure of the repair area. Therefore, the present invention improves the comprehensiveness, accuracy and intelligence of PCBA board testing by performing implicit circuit correlation on defective parts of PCBA board and using three-dimensional reconstruction and multi-dimensional evaluation technology.

[0077] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the steps of an intelligent method for implementing PCBA board testing of the present invention. In this example, the intelligent method for implementing PCBA board testing includes the following steps:

[0078] Step S1: Acquire a scanned image of a PCBA board; extract key structural feature points of the scanned image of the PCBA board to obtain surface feature data of the PCBA board and internal structure data of the PCBA board; perform three-dimensional reconstruction based on the surface feature data of the PCBA board and the internal structure data of the PCBA board to generate a three-dimensional structural model of the PCBA board;

[0079] In an embodiment of the present invention, the PCBA board is scanned by using a suitable scanning device (such as an optical scanner or a CT scanner) to obtain high-quality image data. Computer vision technology is used to process the scanned image to extract key feature points on the surface of the PCBA board and structural data inside the PCBA board. Specific feature points include surface features such as solder joints, components, and wires, as well as the internal connections and structures of the PCB hierarchy. Using the feature points and data extracted from step S2, a three-dimensional reconstruction algorithm (such as a reconstruction method based on image processing or a structured light scanning technique) is used to generate a three-dimensional structural model of the PCBA board. This step can cover a complete three-dimensional model from the surface features of the PCBA board to the overall internal structure.

[0080] Step S2: Performing initial surface structural defect detection on the three-dimensional structural model of the PCBA board to generate an initial defect area and a normal area of ​​the PCBA board; screening the actual associated circuit of the PCBA board according to the initial defect area of ​​the PCBA board to obtain a defect associated circuit of the PCBA board; performing electrical testing of the defect area of ​​the defect associated circuit of the PCBA board to generate defect electrical test data; performing defect impact performance evaluation on the defect electrical test data to generate defect area performance impact data;

[0081] In an embodiment of the present invention, the three-dimensional structural model of the PCBA board is analyzed by using image processing and computer vision algorithms to identify the existing surface structural defects. Defects specifically include welding problems (such as contact problems or defects at welding points), damage or misalignment of components, etc. The location and nature of the defects are determined, and the surface of the PCBA board is divided into defective areas and normal areas. Based on the defective area information obtained in step 1, the circuits associated with these areas are identified, involving circuit design files or circuit connection diagrams of the PCBA board to determine the circuit elements and paths directly related to the detected defective areas. Based on the screened circuit elements and path information, a list of PCBA board defect-related circuits is generated. The screened defect-related circuits are subjected to electrical performance tests. The test includes the measurement of parameters such as resistance, current, and voltage to determine the actual electrical performance state of the defective area. The results of the electrical performance test are recorded as defect electrical test data. The data should include specific electrical performance parameters of each defective area, such as whether the resistance value meets the specification, the normal range of current and voltage, etc. Based on the defect electrical test data, the impact of each defective area on the overall performance of the PCBA board is evaluated, and a comparative analysis of the electrical performance data of the defective area and the design specifications or standard requirements is required. Combined with the assessment results, a detailed performance impact data report of the defective area is generated. The report should include information such as the impact of each defective area, the recommended repair or treatment measures, etc.

[0082] Step S3: Perform multi-dimensional functional testing on the PCBA board through the normal area of ​​the PCBA board to generate functional test data of the normal area of ​​the PCBA board; perform defect area repair performance comparative analysis based on the functional test data of the normal area of ​​the PCBA board and the defect performance impact data to generate defect area repair data of the PCBA board;

[0083] In an embodiment of the present invention, a multi-dimensional functional test is performed on the normal area of ​​the PCBA board by using appropriate testing equipment and tools. The functional test specifically covers multiple aspects such as electronic performance (such as resistance, current, voltage), communication performance (such as signal transmission quality), and power consumption characteristics. During the functional test process, various test data are recorded and collected. The data should include the test results of each normal area, such as the electrical performance parameters of each component, the stability and rate of the communication interface, etc. All functional test data are sorted and analyzed to generate a functional test data report for the normal area of ​​the PCBA board. The report should include detailed test results, performance evaluation of each normal area, and comparative analysis with design requirements. The defect area performance impact data generated in step S2 and the normal area functional test data in step S3 are used for comparative analysis. Compare the functional test data of the defect area with the normal area to evaluate the actual impact of the defect on the functional performance of the PCBA board. According to the results of the comparative analysis, evaluate the expected performance of each defect area after repair. Consider the improvement effect of the repair scheme, such as correcting welding contact, replacing damaged components, etc. The results of the comparative analysis of the repair performance are sorted into a defect area repair data report. The report should include a comparison of functional performance before and after repair of each defect area, a prediction of the repair effect, and recommended repair measures.

[0084] Step S4: Perform a repair quality assessment on the three-dimensional structure model of the PCBA board according to the repair data of the defective area of ​​the PCBA board, thereby generating a PCBA board defect repair test report.

[0085] In an embodiment of the present invention, the repair quality of the three-dimensional structural model of the PCBA board is evaluated by using the defective area repair data generated in step S3. The performance data of the repaired defective area is compared with the conformity of the design requirements or standards to evaluate the effectiveness and quality of the repair. Determine indicators for evaluating the quality of the repair, such as the degree of improvement of electrical performance parameters, the improvement of communication stability, the optimization of power consumption efficiency, etc. According to the specific repair content and the performance data affected by the defect, formulate corresponding evaluation standards and quantitative indicators. Use appropriate three-dimensional modeling and analysis tools, such as CAD software or professional PCBA analysis software, to import the repaired PCBA board structure model. Analyze the geometry, connectivity and physical properties of the repair area to verify the accuracy and completeness of the repair. Based on the evaluation results, write a PCBA board defect repair test report. The report content should include: performance comparison analysis before and after repair, including specific data on electrical performance, communication performance, etc. Qualitative and quantitative evaluation of the repair quality, indicating the feasibility and actual effect of the repair effect. If necessary, provide recommended further improvement measures or future maintenance suggestions. Submit the generated test report to the relevant quality control department or engineer team for review and approval. Ensure accuracy and completeness of reports to support subsequent production processes or customer delivery.

[0086] Preferably, step S1 comprises the following steps:

[0087] Step S11: using a multispectral camera to capture a multispectral image of the PCBA board to obtain a scanned image of the PCBA board;

[0088] Step S12: performing image preprocessing on the scanned image of the PCBA board to generate a standard PCBA board multispectral image, wherein the image preprocessing includes image denoising, image enhancement and color correction;

[0089] Step S13: Identify the structure center point of the standard PCBA board multispectral image to obtain the structure center point data of the PCBA board; extract key feature points of the standard PCBA board multispectral image according to the structure center point data of the PCBA board to obtain the surface feature data of the PCBA board and the internal structure data of the PCBA board;

[0090] Step S14: Perform three-dimensional reconstruction based on the surface feature data of the PCBA board and the internal structure data of the PCBA board to generate a three-dimensional structure model of the PCBA board.

[0091] In an embodiment of the present invention, a multispectral camera is prepared to ensure that it has sufficient band coverage to capture the multispectral image of the PCBA board. The PCBA board is fixed on the scanning platform to ensure that it remains stationary during the scanning process. The parameters of the multispectral camera, including exposure time, spectral band range, resolution, etc., are set to obtain the best image quality. The camera focal length and shooting angle are adjusted to ensure that the surface of the PCBA board can be fully covered. The multispectral camera is started to scan the PCBA board line by line to capture multispectral images of different bands. It is ensured that the captured image covers all areas of the PCBA board and there is no overlap and gap between the images. The multispectral image is denoised using image processing software to remove image noise caused by device noise and ambient light. Specifically, denoising algorithms such as median filtering and Gaussian filtering are used to improve the clarity of the image. Image enhancement techniques such as contrast adjustment and brightness balance are used to improve the details and recognizability of the image. It is ensured that the important features in the image are fully highlighted and displayed. Color correction is performed on the multispectral image to eliminate color distortion caused by lighting conditions and camera characteristics. Use standard color plates for calibration to ensure the accuracy and consistency of image colors. Use image processing algorithms to identify the structural center points on the PCBA board, such as the center position of components, connection points, etc. Specifically, edge detection, corner point detection and other technologies are used for center point identification. According to the identified structural center point data, key feature points are extracted from the multispectral image. Extract the surface feature data of the PCBA board, including the outline of components, the location of solder joints, etc. Extract the internal structure data of the PCBA board, such as wiring paths, internal layers, etc. Combine the extracted surface feature data and internal structure data to generate three-dimensional point cloud data. Ensure that the point cloud data covers all important structures and details of the PCBA board. Use three-dimensional reconstruction algorithms to process the point cloud data and generate a three-dimensional structural model of the PCBA board. Specifically, surface reconstruction algorithms such as Delaunay triangulation and Marching Cubes are used to realize the construction of three-dimensional models. Optimize the generated three-dimensional structural model, including removing redundant points and smoothing the surface. Ensure the accuracy and integrity of the three-dimensional model, which can accurately reflect the structural characteristics of the PCBA board.

[0092] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0093] Step S21: performing initial surface structural defect detection on the three-dimensional structural model of the PCBA board to generate an initial defect area and a normal area of ​​the PCBA board; performing defect morphology analysis on the initial defect area of ​​the PCBA board to generate initial defect morphology data of the PCBA board;

[0094] Step S22: confirming the defect boundary coordinates of the initial defect area of ​​the PCBA board according to the initial defect morphology data of the PCBA board, and obtaining the defect area boundary coordinate data of the PCBA board;

[0095] Step S23: based on the PCBA board defect area boundary coordinate data, the PCBA board is realistically mapped to generate the PCBA board realistic area mapping coordinates; the PCBA board is associated with the circuits screened by the PCBA board realistic area mapping coordinates to obtain the PCBA board defect associated circuits;

[0096] Step S24: performing an electrical test on the defect area of ​​the defect-associated circuit of the PCBA board to generate defect electrical test data; performing a defect impact performance evaluation on the defect electrical test data to generate defect area performance impact data.

[0097] In an embodiment of the present invention, an initial surface structure defect detection is performed on a three-dimensional structure model of a PCBA board by using an image processing algorithm or a machine learning technique. According to a preset defect detection algorithm, defect areas and normal areas on the PCBA board are identified. Determine the threshold and standard for defect detection, such as surface flatness, hole detection, foreign body detection, etc. Perform morphological analysis on the detected initial defect area. Extract the shape, size, position and other features of the defect to generate initial defect morphological data of the PCBA board. Analyze the type of defect, such as cracks, unevenness of the defect area. According to the initial defect morphological data, use edge detection or contour analysis and other techniques to determine the boundary of the defect area. Confirm the boundary coordinates of each defect area for subsequent reality mapping and circuit screening process. Map the boundary coordinate data of the defect area of ​​the PCBA board to the coordinate system on the actual PCBA board. Determine the actual position and range of each defect area on the PCBA board, and generate the mapping coordinates of the actual area of ​​the PCBA board. Based on the mapping coordinates of the actual area of ​​the PCBA board, screen out the circuit elements and connections associated with the defect area from the design file of the PCBA board or a related database. Determine which circuit components are affected by the defect or are located inside the defect area. Conduct electrical tests on the identified defect-related circuits, such as resistance, capacitance, inductance and other parameter tests. Use digital Domitor and other testing equipment to record circuit parameters in real time and generate defect electrical test data. Evaluate the impact of defects on circuit performance based on defect electrical test data. Analyze the changes in resistance, capacitance, inductance and other parameters, and evaluate the impact of defects on circuit transmission, frequency response, power supply noise and other performance. Generate defect area performance impact data to provide a basis for subsequent repairs and improvements.

[0098] Preferably, step S21 includes the following steps:

[0099] Step S211: performing surface meshing on the three-dimensional structure model of the PCBA board to generate three-dimensional mesh data of the PCBA board;

[0100] Step S212: Calculate the mesh surface curvature of the three-dimensional mesh data of the PCBA board to obtain the three-dimensional mesh curvature data of the PCBA board;

[0101] Step S213: Compare the three-dimensional grid curvature data of the PCBA board with a preset standard grid curvature threshold. When the three-dimensional grid curvature data of the PCBA board is greater than or equal to the preset standard grid curvature threshold, mark the grid area corresponding to the three-dimensional grid curvature data of the PCBA board as the initial defect area of ​​the PCBA board; when the three-dimensional grid curvature data of the PCBA board is less than the preset standard grid curvature threshold, mark the grid area corresponding to the three-dimensional grid curvature data of the PCBA board as the normal area of ​​the PCBA board;

[0102] Step S214: using the three-dimensional shape descriptor to identify the defect type of the initial defect area of ​​the PCBA board, and generating defect type identification data; performing defect morphology analysis on the initial defect area of ​​the PCBA board through the defect type identification data, and generating initial defect morphology data of the PCBA board.

[0103] In an embodiment of the present invention, a three-dimensional structural model of a PCBA board is obtained by using a three-dimensional scanner or a method based on multispectral image reconstruction. Ensure that the three-dimensional model covers all surface features and structural details of the PCBA board. The three-dimensional structural model of the PCBA board is subjected to surface meshing processing, and the surface is divided into small meshes. The meshing process specifically uses professional three-dimensional modeling software (such as MeshLab, Blender, etc.) to generate three-dimensional mesh data of the PCBA board. The curvature of the generated three-dimensional mesh data of the PCBA board is calculated. Specifically, mathematical methods such as finite difference method or normal-based method are used to calculate the curvature of each grid point. Curvature calculation can help identify the flatness and concave-convexity of the surface, which is helpful for subsequent defect detection and morphological analysis. Set a preset standard grid curvature threshold, and determine the threshold according to the design requirements and standards of the PCBA board. A typical approach is to set the threshold according to the average curvature and standard deviation of a normal PCBA board, and the area exceeding the threshold is marked as a potential defect area. Compare the calculated grid curvature data with the preset threshold. When the grid curvature data is greater than or equal to the preset standard grid curvature threshold, the corresponding grid area is marked as the initial defect area of ​​the PCBA board. Conversely, the area where the mesh curvature data is less than the preset threshold is marked as the normal area of ​​the PCBA board. For the mesh area marked as the initial defect area, the defect type is identified using a three-dimensional shape descriptor. The three-dimensional shape descriptor specifically includes features such as area, perimeter, and concavity, which are used to distinguish different types of surface defects, such as cracks, holes, and depressions. Based on the identified defect type, a morphological analysis is performed. The shape, size, and location of each defect area and its impact on the function and performance of the PCBA board are analyzed. The initial defect morphological data of the PCBA board is generated, including a detailed description of the defect, the location coordinates, and its distribution on the PCBA board.

[0104] Preferably, step S24 includes the following steps:

[0105] Step S241: performing a digital Domitor test on the PCBA board defect-related circuit, and recording the test results in real time to obtain PCBA board defect circuit test data, wherein the PCBA board defect circuit test data includes defect area circuit resistance data, defect area circuit capacitance data, and defect area circuit inductance data;

[0106] Step S242: using the defective area circuit resistance data to perform circuit signal transmission acquisition to generate a defective area circuit signal transmission waveform; performing signal waveform distortion analysis on the defective area circuit signal transmission waveform to generate defective area circuit resistance performance impact data;

[0107] Step S243: using the defective area circuit capacitance data to collect signal frequency changes, generating defective area circuit signal frequency data; performing frequency change response analysis on the defective area circuit signal frequency data according to the defective area circuit capacitance data, generating defective area circuit capacitance performance impact data;

[0108] Step S244: collecting circuit power noise using the defective area circuit inductance data to obtain circuit power noise data; performing noise suppression analysis on the circuit power noise data using the defective area circuit inductance data to generate defective area circuit inductance performance impact data;

[0109] Step S245: performing a comprehensive defect impact performance evaluation on the defect area circuit resistance performance impact data, the defect area circuit capacitance performance impact data and the defect area circuit inductance performance impact data to generate defect area performance impact data.

[0110] In an embodiment of the present invention, a detailed test is performed on the circuit marked as a defective area on the PCBA board by ensuring that a digital Domitor or other suitable test equipment is used. The test equipment is set to measure the resistance, capacitance and inductance data of the circuit. The digital Domitor test is performed on the circuit of each defective area respectively. The test results of each defective area circuit are recorded in real time, including the resistance value, capacitance value and inductance value. Ensure that the test process is accurate and the recorded data should accurately reflect the actual performance state of the defective circuit. The resistance data of the defective area circuit is used to perform a circuit signal transmission experiment. The transmission waveform of the defective area circuit signal is collected through a suitable signal generator and oscilloscope. A detailed distortion analysis is performed on the collected signal transmission waveform. The waveform distortion, signal attenuation and other problems generated by the signal during the transmission process of the defective area are analyzed to evaluate the impact on the circuit performance. The capacitance data of the defective area circuit is used to perform a circuit signal frequency change test. The signal response of the defective area circuit at different frequencies is measured. According to the collected frequency response data, the performance of the defective area circuit at different frequencies is analyzed. The impact of capacitance performance is evaluated, including frequency distortion, signal attenuation or other frequency-related problems. Conduct circuit power supply noise testing using the inductance data of the defective area circuit. Measure the noise level of the circuit power supply in the defective area. Analyze the noise suppression effect of the defective area circuit based on the inductance data and noise acquisition results. Evaluate the stability of the circuit and the potential impact of noise on performance. Integrate the resistance, capacitance, and inductance performance impact data obtained in steps S242 to S244. Compare and analyze the data to determine the comprehensive impact of the defective area on the overall circuit performance. Generate a defective area performance impact data report that describes in detail the impact of the circuit performance problem in each defective area. Provide targeted suggestions or measures to optimize or repair the circuit performance in the defective area.

[0111] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0112] Step S31: Perform multi-dimensional functional testing on the PCBA board through the normal area of ​​the PCBA board to generate functional test data of the normal area of ​​the PCBA board;

[0113] Step S32: constructing a full-area performance model of the PCBA board according to the normal-area function test data of the PCBA board and the defect performance impact data, and obtaining a full-area performance detection model of the PCBA board; using the full-area performance detection model of the PCBA board to perform a first full-area performance evaluation of the PCBA board on the normal-area function test data of the PCBA board and the defect performance impact data, and generating first full-area performance evaluation data of the PCBA board;

[0114] Step S33: repairing the defective area of ​​the PCBA board based on the defect performance impact data to obtain PCBA board defective area optimization data;

[0115] Step S34: using the PCBA board full-area performance detection model to perform a second PCBA board full-area performance evaluation on the PCBA board normal area function test data and the PCBA board defect area optimization data, and generate the PCBA board second full-area performance evaluation data; by comparing and analyzing the PCBA board first full-area performance evaluation data and the PCBA board second full-area performance evaluation data, generate the PCBA board defect area repair data.

[0116] In an embodiment of the present invention, by ensuring that all test equipment and tools can accurately and completely test each functional module of the PCBA board. Perform multi-dimensional functional tests on the normal area of ​​the PCBA board, including but not limited to tests on electrical characteristics, communication performance, stability, etc. Use appropriate test instruments and equipment to perform functional tests to ensure that the test results are accurate and reliable. Record and organize the data of each functional test in real time, including test conditions, test results, and any abnormal conditions. Integrate and process all functional test results to generate a functional test data set for the normal area of ​​the PCBA board. Integrate the functional test data of the normal area of ​​the PCBA board obtained in step S31 with the defect performance impact data in step S23. Divide the integrated data set into a model training set and a model test set. Use appropriate machine learning or statistical modeling methods (such as support vector machines, neural networks, etc.) to train the model training set and establish a full-area performance detection model for the PCBA board. During the training process, ensure that the model can accurately capture the relationship between the functional test data of the PCBA board and the defect impact data. Use the model test set to verify and evaluate the trained full-area performance model of the PCBA board to ensure that the model has good generalization ability and prediction accuracy. According to the defect analysis results in steps S21 to S23, the defective area of ​​the PCBA board that needs to be repaired is determined. Specific repair plans and strategies are formulated, such as replacing components, adjusting circuit design or other technical means. Repair work in the defective area is performed to ensure that the repair operation is carried out in accordance with the design plan and meets the quality control standards. After the repair of the defective area is completed, the PCBA board defective area optimization data is recorded and sorted, including detailed records of the repair operation and effect evaluation. Using the full-area performance model of the PCBA board established in step S32, the PCBA board normal area function test data obtained in step S31 and the defective area optimization data in step S33 are evaluated for the first full-area performance. Generate the first full-area performance evaluation data of the PCBA board to evaluate the performance changes and impacts before and after the repair. Compare and analyze the results of the first evaluation with the initial evaluation data (if any) to evaluate the effectiveness and improvement space of the repair operation. Based on the results of the comparative analysis, the same full-area performance model of the PCBA board is used to perform a second full-area performance evaluation on the PCBA board. Generate the second full-area performance evaluation data of the PCBA board to further confirm the repair effect and performance improvement.

[0117] Preferably, performing multi-dimensional functional testing on the PCBA board through the normal area of ​​the PCBA board includes:

[0118] Conduct voltage stability test on PCBA board through normal area of ​​PCBA board, and generate voltage stability test data of normal area of ​​PCBA board;

[0119] Perform a current response time test on the PCBA board through the normal area of ​​the PCBA board to generate current response time test data for the normal area of ​​the PCBA board;

[0120] Perform signal transmission rate test on the PCBA board through the normal area of ​​the PCBA board to generate signal transmission rate test data of the normal area of ​​the PCBA board;

[0121] Conduct temperature effect test on PCBA board through normal area of ​​PCBA board, and generate temperature effect test data of normal area of ​​PCBA board;

[0122] Perform fault injection test on the PCBA board through the normal area of ​​the PCBA board to generate fault injection test data for the normal area of ​​the PCBA board;

[0123] A comprehensive performance evaluation is performed based on the voltage stability test data of the PCBA board normal area, the current response time test data of the PCBA board normal area, the signal transmission rate test data of the PCBA board normal area, the temperature effect test data of the PCBA board normal area and the fault injection test data of the PCBA board normal area to generate the functional test data of the PCBA board normal area.

[0124] In the embodiment of the present invention, by using a suitable voltage source and measuring equipment, different voltage values ​​are applied, the voltage output of the normal area of ​​the PCBA board is recorded, and the stability is detected, and the stable time and fluctuation range under each voltage value are recorded. Using a current source and a fast measuring device, a pulse current or a rapidly changing current is applied, and the response time and waveform characteristics of the normal area of ​​the PCBA board are recorded, and the changes in the response time and current waveform are recorded. Using a signal generator and a receiver, signals of different frequencies and amplitudes are applied, and the signal transmission rate and transmission quality of the normal area of ​​the PCBA board are recorded, and indicators such as the signal transmission rate and bit error rate are recorded. Using a temperature control device and a temperature sensor, the ambient temperature is adjusted, and the performance changes of the normal area of ​​the PCBA board at different temperatures, such as resistance, capacitance, frequency, etc., are recorded, and the impact of temperature changes on the performance of the PCBA board are recorded. Using a fault injection device and a monitoring tool, a known fault signal or noise is injected, and the response and recovery capabilities of the normal area of ​​the PCBA board to the fault are detected, and indicators such as the response time and error detection rate after the fault injection are recorded. The data of the above tests are integrated into a functional test data set for the normal area of ​​the PCBA board. Use statistical methods or professional tools to analyze voltage stability, current response time, signal transmission rate, temperature effect and fault injection test data to evaluate the comprehensive performance of the normal area of ​​the PCBA board. Generate a PCBA board normal area functional test data report based on the evaluation results, including specific data, analysis results and conclusions for each test.

[0125] Preferably, constructing a full-area performance model of a PCBA board based on normal-area function test data and defect performance impact data of the PCBA board includes:

[0126] Merge the PCBA board normal area function test data and defect performance impact data to generate a PCBA board integrated data set;

[0127] The integrated data set of the PCBA board is divided into data sets to generate a model training set and a model test set; the model training set is trained using the support vector machine algorithm to generate a full-area performance training model for the PCBA board; the full-area performance training model of the PCBA board is verified and evaluated using the model test set to generate a full-area performance model for the PCBA board.

[0128] In an embodiment of the present invention, the normal area functional test data of the PCBA board and the defect performance impact data are integrated into a data set to ensure data format and label consistency, and a training set and a test set are divided from the integrated data set. The training set is a data set used to train the model, which usually accounts for the majority of the total data. The test set is an independent data set used to evaluate the generalization ability of the trained model. SVM is a powerful supervised learning algorithm suitable for processing problems with a small amount of data but high feature dimensions, such as performance model construction. Feature selection or conversion is performed according to data characteristics to ensure the effectiveness of training data and the efficiency of the model. The SVM model is trained using the training set, with the goal of finding an optimal decision boundary so that the normal area functional test data and the defect performance impact data can be effectively classified or regressed. The trained SVM model is evaluated and verified using the model test set, and appropriate evaluation indicators such as classification accuracy, regression error, F1-score, etc. are selected according to specific problems to analyze the performance of the model on the test set and evaluate the generalization ability and prediction accuracy of the model. After verification, the final PCBA board full-area performance model is generated, which can predict the performance status of other areas of the PCBA board based on the input normal area functional test data and defect performance impact data.

[0129] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes:

[0130] Step S4 includes the following steps:

[0131] Step S41: performing a repair quality assessment on the three-dimensional structure model of the PCBA board according to the repair data of the defective area of ​​the PCBA board, and generating repair quality detection data of the PCBA board;

[0132] Step S42: Visualize the PCBA board repair quality inspection data to generate a PCBA board defect repair test report.

[0133] In an embodiment of the present invention, the three-dimensional structural model of the PCBA board generated by step S14 is used as a basis. According to the PCBA board defect area optimization data obtained in step S33, the three-dimensional structural model of the PCBA board is partially repaired, and the quality of the repair is evaluated using quantitative and qualitative methods. For example, the deviation of the size, shape and position of the repair area from the standard is measured, and the appearance and structural integrity of the repaired area are evaluated by visual inspection or other detection means. The quantitative and qualitative data of the evaluation are organized into the format and structure required for the PCBA board repair quality inspection report, including the description of the repair area, size data, shape evaluation, structural integrity analysis, etc. Suitable data visualization tools are selected, such as chart software, CAD software or professional three-dimensional modeling tools. Based on the repair quality evaluation data, a visual chart, image or three-dimensional model is generated to clearly show the difference and repair effect before and after the repair. According to the evaluation results of the implementation step S41, a PCBA board defect repair test report is written, including a comparison chart before and after the repair, a summary of quantitative evaluation data, a description of the effect of the repair area, and further operations (if necessary) recommended, etc., and the visualization results and evaluation data are integrated into a complete PCBA board defect repair test report.

[0134] In this specification, an intelligent system for implementing PCBA board testing is provided, which is used to execute the above-mentioned intelligent method for implementing PCBA board testing. The intelligent system for implementing PCBA board testing includes:

[0135] The 3D reconstruction module is used to obtain the scanned image of the PCBA board; extract the key structural feature points of the scanned image of the PCBA board to obtain the surface feature data of the PCBA board and the internal structure data of the PCBA board; perform 3D reconstruction based on the surface feature data of the PCBA board and the internal structure data of the PCBA board to generate a 3D structural model of the PCBA board;

[0136] The defect circuit association module is used to perform initial surface structural defect detection on the three-dimensional structural model of the PCBA board, generate the initial defect area and normal area of ​​the PCBA board; screen the actual associated circuit of the PCBA board according to the initial defect area of ​​the PCBA board to obtain the defect associated circuit of the PCBA board; perform electrical test on the defect area of ​​the PCBA board defect associated circuit to generate defect electrical test data; perform defect impact performance evaluation on the defect electrical test data to generate defect area performance impact data;

[0137] The repair performance analysis module is used to perform multi-dimensional functional tests on the PCBA board through the normal area of ​​the PCBA board to generate the functional test data of the normal area of ​​the PCBA board; perform a comparative analysis of the repair performance of the defective area based on the functional test data of the normal area of ​​the PCBA board and the defect performance impact data to generate the repair data of the defective area of ​​the PCBA board;

[0138] The repair quality assessment module is used to perform repair quality assessment on the three-dimensional structure model of the PCBA board according to the repair data of the defective area of ​​the PCBA board, so as to generate a PCBA board defect repair test report.

[0139] The beneficial effect of the present invention is that by using the scanned image of the PCBA board to extract the key structural features, the three-dimensional reconstruction of the surface and internal structure of the PCBA board is realized, and fine three-dimensional structural information is provided for subsequent defect detection and positioning. The defect detection results are classified into the initial defect area and the normal area, and the associated circuits of the defect area are further screened and locked to the specific defective circuit, providing an accurate detection object for subsequent electrical testing. The defective circuit is tested for electrical properties to evaluate the impact of the defect on the performance, providing an electrical basis for confirming the type and degree of the defect. The normal area is tested for multi-dimensional functions, and compared with the impact of the defect, the impact range of the defect on the overall performance can be comprehensively evaluated. The three-dimensional model is evaluated according to the defect repair effect, and a specific repair report is given, which can feedback the repair quality in real time and provide a basis for subsequent decision-making. The overall process combines three-dimensional physical information with electrical functional information, evaluates defects from different angles, and has a very high guarantee for the reliability and accuracy of defect detection and repair. Therefore, the present invention improves the comprehensiveness, accuracy and intelligence of PCBA board testing by performing implicit circuit association on the defective part of the PCBA board and using three-dimensional reconstruction and multi-dimensional evaluation technology.

[0140] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0141] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An intelligent method for implementing PCBA board testing, characterized in that: The following steps are involved: Step S1: Acquire a scanned image of a PCBA board; extract key structural feature points of the scanned image of the PCBA board to obtain surface feature data of the PCBA board and internal structure data of the PCBA board; perform three-dimensional reconstruction based on the surface feature data of the PCBA board and the internal structure data of the PCBA board to generate a three-dimensional structural model of the PCBA board; Step S2: Performing initial surface structural defect detection on the three-dimensional structural model of the PCBA board to generate an initial defect area of ​​the PCBA board and a normal area of ​​the PCBA board; Screen the actual associated circuits of the PCBA board according to the initial defect area of ​​the PCBA board to obtain the defect associated circuit of the PCBA board; perform electrical tests on the defect area of ​​the defect associated circuit of the PCBA board to generate defect electrical test data; Conduct defect impact performance evaluation on defect electrical test data and generate defect area performance impact data; Step S3: Perform multi-dimensional functional testing on the PCBA board through the normal area of ​​the PCBA board to generate functional test data of the normal area of ​​the PCBA board; Compare and analyze the defect area repair performance based on the PCBA board normal area function test data and defect performance impact data, and generate PCBA board defect area repair data; Step S3 includes the following steps: Step S31: Perform multi-dimensional functional testing on the PCBA board through the normal area of ​​the PCBA board to generate functional test data of the normal area of ​​the PCBA board; Step S32: constructing a full-area performance model of the PCBA board according to the normal-area function test data of the PCBA board and the defect performance impact data, and obtaining a full-area performance detection model of the PCBA board; using the full-area performance detection model of the PCBA board to perform a first full-area performance evaluation of the PCBA board on the normal-area function test data of the PCBA board and the defect performance impact data, and generating first full-area performance evaluation data of the PCBA board; Step S33: repairing the defective area of ​​the PCBA board based on the defect performance impact data to obtain PCBA board defective area optimization data; Step S34: using the PCBA board full-area performance detection model to perform a second PCBA board full-area performance evaluation on the PCBA board normal area function test data and the PCBA board defect area optimization data, and generate the PCBA board second full-area performance evaluation data; by comparing and analyzing the PCBA board first full-area performance evaluation data and the PCBA board second full-area performance evaluation data, generate the PCBA board defect area repair data; Step S4: Perform a repair quality assessment on the three-dimensional structure model of the PCBA board according to the repair data of the defective area of ​​the PCBA board, thereby generating a PCBA board defect repair test report.

2. The intelligent method for implementing PCBA board testing according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: using a multispectral camera to capture a multispectral image of the PCBA board to obtain a scanned image of the PCBA board; Step S12: performing image preprocessing on the scanned image of the PCBA board to generate a standard PCBA board multispectral image, wherein the image preprocessing includes image denoising, image enhancement and color correction; Step S13: Identify the structure center point of the standard PCBA board multispectral image to obtain the structure center point data of the PCBA board; extract key feature points of the standard PCBA board multispectral image according to the structure center point data of the PCBA board to obtain the surface feature data of the PCBA board and the internal structure data of the PCBA board; Step S14: Perform three-dimensional reconstruction based on the surface feature data of the PCBA board and the internal structure data of the PCBA board to generate a three-dimensional structure model of the PCBA board.

3. The intelligent method for implementing PCBA board testing according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing initial surface structural defect detection on the three-dimensional structural model of the PCBA board to generate an initial defect area and a normal area of ​​the PCBA board; performing defect morphology analysis on the initial defect area of ​​the PCBA board to generate initial defect morphology data of the PCBA board; Step S22: confirming the defect boundary coordinates of the initial defect area of ​​the PCBA board according to the initial defect morphology data of the PCBA board, and obtaining the defect area boundary coordinate data of the PCBA board; Step S23: based on the PCBA board defect area boundary coordinate data, the PCBA board is realistically mapped to generate the PCBA board realistic area mapping coordinates; the PCBA board is associated with the circuits screened by the PCBA board realistic area mapping coordinates to obtain the PCBA board defect associated circuits; Step S24: performing an electrical test on the defect area of ​​the defect-associated circuit of the PCBA board to generate defect electrical test data; performing a defect impact performance evaluation on the defect electrical test data to generate defect area performance impact data.

4. The intelligent method for implementing PCBA board testing according to claim 3, characterized in that: Step S21 includes the following steps: Step S211: performing surface meshing on the three-dimensional structure model of the PCBA board to generate three-dimensional mesh data of the PCBA board; Step S212: Calculate the mesh surface curvature of the three-dimensional mesh data of the PCBA board to obtain the three-dimensional mesh curvature data of the PCBA board; Step S213: Compare the three-dimensional grid curvature data of the PCBA board with a preset standard grid curvature threshold. When the three-dimensional grid curvature data of the PCBA board is greater than or equal to the preset standard grid curvature threshold, mark the grid area corresponding to the three-dimensional grid curvature data of the PCBA board as the initial defect area of ​​the PCBA board; when the three-dimensional grid curvature data of the PCBA board is less than the preset standard grid curvature threshold, mark the grid area corresponding to the three-dimensional grid curvature data of the PCBA board as the normal area of ​​the PCBA board; Step S214: using the three-dimensional shape descriptor to identify the defect type of the initial defect area of ​​the PCBA board, and generating defect type identification data; performing defect morphology analysis on the initial defect area of ​​the PCBA board through the defect type identification data, and generating initial defect morphology data of the PCBA board.

5. The intelligent method for implementing PCBA board testing according to claim 3, characterized in that: Step S24 includes the following steps: Step S241: performing a digital Domitor test on the PCBA board defect-related circuit, and recording the test results in real time to obtain PCBA board defect circuit test data, wherein the PCBA board defect circuit test data includes defect area circuit resistance data, defect area circuit capacitance data, and defect area circuit inductance data; Step S242: using the defective area circuit resistance data to perform circuit signal transmission acquisition to generate a defective area circuit signal transmission waveform; performing signal waveform distortion analysis on the defective area circuit signal transmission waveform to generate defective area circuit resistance performance impact data; Step S243: using the defective area circuit capacitance data to collect signal frequency changes, generating defective area circuit signal frequency data; performing frequency change response analysis on the defective area circuit signal frequency data according to the defective area circuit capacitance data, generating defective area circuit capacitance performance impact data; Step S244: collecting circuit power noise using the defective area circuit inductance data to obtain circuit power noise data; performing noise suppression analysis on the circuit power noise data using the defective area circuit inductance data to generate defective area circuit inductance performance impact data; Step S245: performing a comprehensive defect impact performance evaluation on the defect area circuit resistance performance impact data, the defect area circuit capacitance performance impact data and the defect area circuit inductance performance impact data to generate defect area performance impact data.

6. The intelligent method for implementing PCBA board testing according to claim 1, characterized in that: Multi-dimensional functional testing of PCBA boards through normal areas of PCBA boards includes: Conduct voltage stability test on PCBA board through normal area of ​​PCBA board, and generate voltage stability test data of normal area of ​​PCBA board; Perform a current response time test on the PCBA board through the normal area of ​​the PCBA board to generate current response time test data for the normal area of ​​the PCBA board; Perform signal transmission rate test on the PCBA board through the normal area of ​​the PCBA board to generate signal transmission rate test data of the normal area of ​​the PCBA board; Conduct temperature effect test on PCBA board through normal area of ​​PCBA board, and generate temperature effect test data of normal area of ​​PCBA board; Perform fault injection test on the PCBA board through the normal area of ​​the PCBA board to generate fault injection test data for the normal area of ​​the PCBA board; A comprehensive performance evaluation is performed based on the voltage stability test data of the PCBA board normal area, the current response time test data of the PCBA board normal area, the signal transmission rate test data of the PCBA board normal area, the temperature effect test data of the PCBA board normal area and the fault injection test data of the PCBA board normal area to generate the functional test data of the PCBA board normal area.

7. The intelligent method for implementing PCBA board testing according to claim 1, characterized in that: Based on the normal area function test data of PCBA board and the defect performance impact data, the full area performance model of PCBA board is constructed, including: Merge the PCBA board normal area function test data and defect performance impact data to generate a PCBA board integrated data set; The integrated data set of the PCBA board is divided into data sets to generate a model training set and a model test set; the model training set is trained using the support vector machine algorithm to generate a full-area performance training model for the PCBA board; the full-area performance training model of the PCBA board is verified and evaluated using the model test set to generate a full-area performance model for the PCBA board.

8. The intelligent method for implementing PCBA board testing according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing a repair quality assessment on the three-dimensional structure model of the PCBA board according to the repair data of the defective area of ​​the PCBA board, and generating repair quality detection data of the PCBA board; Step S42: Visualize the PCBA board repair quality inspection data to generate a PCBA board defect repair test report.

9. An intelligent system for implementing PCBA board testing, characterized in that: The intelligent method for implementing PCBA board testing as claimed in claim 1, wherein the intelligent system for implementing PCBA board testing comprises: The 3D reconstruction module is used to obtain the scanned image of the PCBA board; extract the key structural feature points of the scanned image of the PCBA board to obtain the surface feature data of the PCBA board and the internal structure data of the PCBA board; perform 3D reconstruction based on the surface feature data of the PCBA board and the internal structure data of the PCBA board to generate a 3D structural model of the PCBA board; The defect circuit association module is used to perform initial surface structural defect detection on the three-dimensional structural model of the PCBA board, generate the initial defect area and normal area of ​​the PCBA board; screen the actual associated circuit of the PCBA board according to the initial defect area of ​​the PCBA board to obtain the defect associated circuit of the PCBA board; perform electrical test on the defect area of ​​the PCBA board defect associated circuit to generate defect electrical test data; perform defect impact performance evaluation on the defect electrical test data to generate defect area performance impact data; The repair performance analysis module is used to perform multi-dimensional functional tests on the PCBA board through the normal area of ​​the PCBA board to generate the functional test data of the normal area of ​​the PCBA board; perform a comparative analysis of the repair performance of the defective area based on the functional test data of the normal area of ​​the PCBA board and the defect performance impact data to generate the repair data of the defective area of ​​the PCBA board; The repair quality assessment module is used to perform repair quality assessment on the three-dimensional structure model of the PCBA board according to the repair data of the defective area of ​​the PCBA board, thereby generating a PCBA board defect repair test report.

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