A data analysis system for ICT testing tooling of automotive parts

Abstract

The present invention relates to the field of circuit board assembly technology, specifically a data analysis system for an automotive parts ICT test tooling, the system includes a thermal imaging monitoring module, a machine learning analysis module, a component positioning calibration module, a test strategy generation module, a production process optimization module, an electromagnetic test adjustment module, a fault diagnosis prediction module, and a system performance feedback module. In the present invention, thermal imaging monitoring and machine learning identify welding defects to improve product quality, component positioning calibration uses machine vision and deep learning to greatly improve assembly accuracy and reduce manual calibration, the test strategy generation module uses genetic algorithms to optimize test paths and improve test efficiency, the production process optimization and electromagnetic test adjustment modules optimize resources and test processes through advanced algorithms, significantly improving production efficiency and test accuracy, and the fault diagnosis prediction module and system performance feedback ensure continuous system improvement and performance optimization, providing strong support for long-term operation.

Description

Technical Field

[0001] The invention relates to the technical field of circuit board assembly, and in particular to a data analysis system for an automotive parts ICT testing tool. Background Art

[0002] The field of circuit board assembly technology focuses on the assembly process of printed circuit boards (PCBs), including component placement, soldering and connection, as well as subsequent testing and inspection. This field covers everything from basic manual assembly to highly automated production lines. The core purpose is to ensure that the components on the circuit board are installed correctly and efficiently, and that the performance of the circuit board meets the design specifications. Key components of this technology field include precise component placement, high-quality soldering technology, effective connection methods and comprehensive quality control processes. With the development of technology, circuit board assembly is also constantly introducing more advanced automation technology and testing equipment, such as automated placement machines, wave soldering machines and various circuit testing equipment to improve production efficiency and product quality.

[0003] The data analysis system for the ICT test fixture of automotive parts is a system specially designed to analyze and optimize the test data of automotive parts during the printed circuit board (PCB) assembly process. The main purpose of this system is to improve the manufacturing quality and reliability of automotive parts through in-depth analysis of test data. This usually includes the analysis of data collected during circuit board functional testing (such as ICT-integrated circuit testing) to identify potential quality problems, performance deviations or failure modes. The introduction of such a system aims to improve the defect detection rate in the production process, reduce rework and scrap in production, and thus improve overall production efficiency and cost-effectiveness. The means to achieve this goal usually include advanced data analysis technologies, such as machine learning algorithms and statistical analysis tools, which can quickly and accurately analyze large amounts of test data to provide support for manufacturing decisions.

[0004] The main drawback of traditional systems is that they lack sufficient flexibility and accuracy for complex production and testing processes. Traditional methods usually rely on more manual operations and lower levels of automation technology in welding quality control, component positioning and testing strategies, which limits production efficiency and product quality. In addition, traditional systems are often not intelligent enough in fault diagnosis and performance feedback, and it is difficult to respond to problems in the production process in a timely manner, resulting in inefficient resource utilization, and unsatisfactory optimization of production rhythm and production line operation speed. These shortcomings have led to increased production costs, reduced production efficiency and unstable product quality. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a data analysis system for an automotive parts ICT testing tool.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solutions: a data analysis system for an automotive parts ICT test fixture includes a thermal imaging monitoring module, a machine learning analysis module, a component positioning calibration module, a test strategy generation module, a production process optimization module, an electromagnetic test adjustment module, a fault diagnosis prediction module, and a system performance feedback module;

[0007] The thermal imaging monitoring module uses thermal imaging technology to capture temperature distribution based on the welding process, extracts temperature features through image processing algorithms, and generates welding thermal image data;

[0008] The machine learning analysis module uses a convolutional neural network to analyze welding quality, identify defects, and generate a welding quality analysis report based on welding thermal image data;

[0009] The component positioning and calibration module uses machine vision technology and deep learning algorithms to locate components based on circuit board design, automatically calibrate, and generate component placement calibration data;

[0010] The test strategy generation module optimizes the test path and generates an optimized test strategy based on component placement calibration data, applying a genetic algorithm and historical test data;

[0011] The production process optimization module adjusts production resources and generates a production process optimization plan based on an optimization test strategy using a simulated annealing algorithm and reinforcement learning;

[0012] The electromagnetic test adjustment module uses adaptive multi-band electromagnetic test technology based on the production process optimization solution to adjust the test signal and generate electromagnetic test results;

[0013] The fault diagnosis prediction module applies a deep learning algorithm to perform fault diagnosis, predict performance, and generate a fault prediction report based on the electromagnetic test results;

[0014] The system performance feedback module uses a simulated annealing algorithm to optimize the system based on the fault prediction report and combines real-time data to generate system performance optimization feedback;

[0015] The welding thermal image data includes temperature distribution diagram, hot spot identification, and temperature anomaly index of welding area; the welding quality analysis report includes welding defect type, defect location, and welding quality evaluation; the component placement calibration data includes component position accuracy, placement angle, and calibration deviation value; the optimization test strategy specifically includes test point priority, test sequence, and test parameter setting; the production process optimization plan includes resource utilization, production rhythm adjustment, and production line operation speed optimization; the electromagnetic test results include test signal frequency band selection, circuit board fault detection results, and signal strength adjustment; the fault prediction report specifically includes potential fault type, fault probability, and performance prediction index; the system performance optimization feedback includes production process adjustment plan, performance improvement measures, and long-term operation efficiency evaluation.

[0016] As a further solution of the present invention, the thermal imaging monitoring module includes a temperature capturing submodule, a feature extraction submodule, and a first data analysis submodule;

[0017] The machine learning analysis module includes a model training submodule, a defect identification submodule, and a result output submodule;

[0018] The component positioning and calibration module includes an image capture submodule, a positioning analysis submodule, and a calibration feedback submodule;

[0019] The test strategy generation module includes a second data analysis submodule, a strategy optimization submodule, and a strategy application submodule;

[0020] The production process optimization module includes a resource allocation submodule, a scheduling algorithm submodule, and a performance monitoring submodule;

[0021] The electromagnetic test adjustment module includes a frequency band adjustment submodule, a signal analysis submodule, and a fault detection submodule;

[0022] The fault diagnosis prediction module includes a data collection submodule, a prediction modeling submodule, and a result analysis submodule;

[0023] The system performance feedback module includes a performance evaluation submodule, an optimization strategy submodule, and a feedback implementation submodule.

[0024] As a further solution of the present invention, the temperature capture submodule uses thermal imaging technology based on the welding process to capture the temperature distribution of the soldering point, and performs real-time analysis to generate soldering point temperature data;

[0025] The feature extraction submodule extracts thermal features based on the solder point temperature data and applies edge detection and pattern recognition algorithms to generate soldering thermal feature data;

[0026] The first data analysis submodule uses statistical analysis technology based on welding thermal feature data to identify temperature anomalies and generate welding thermal image data;

[0027] The thermal imaging technology specifically uses an infrared thermal imager to capture the temperature changes of solder joints in real time. The edge detection and pattern recognition algorithm specifically includes a Sobel operator for edge detection. The template matching technology is used to identify preset thermal image patterns. The statistical analysis technology specifically uses standard deviation and mean analysis to identify temperature anomalies.

[0028] As a further solution of the present invention, the model training submodule uses a convolutional neural network based on the welding thermal image data to train the model to identify welding defects and generate a welding defect recognition model;

[0029] The defect recognition submodule analyzes welding quality, identifies defect types and locations, and generates welding defect recognition results based on the welding defect recognition model;

[0030] The result output submodule uses data visualization technology based on the welding defect identification results to present defect details and generate a welding quality analysis report;

[0031] The convolutional neural network is specifically a multi-layer perceptron structure, which is used to extract and learn the characteristics of welding defects. The defect type and location analysis is specifically to determine the defect type using the probability distribution of the model output. The spatial positioning technology determines the defect location. The data visualization technology is specifically interactive charts and heat maps, which are used to display the location and severity of the defects.

[0032] As a further solution of the present invention, the image capture submodule uses machine vision technology to capture images based on circuit board design, performs visual analysis of components and circuit boards, and generates circuit board and component image data;

[0033] The positioning analysis submodule uses a deep learning positioning algorithm based on the circuit board and component image data, and uses an improved YOLO model to perform positioning and generate component positioning analysis data;

[0034] The calibration feedback submodule applies automatic calibration technology based on component positioning analysis data to adjust the component to a predetermined position and generate component placement calibration data;

[0035] The machine vision technology specifically uses advanced CMOS sensors and image processing algorithms, the deep learning positioning algorithm specifically includes feature extraction and space recognition technology, and the automatic calibration technology specifically is based on the automatic adjustment mechanism and closed-loop feedback control of the robot arm.

[0036] As a further solution of the present invention, the second data analysis submodule uses data mining technology based on component placement calibration data to analyze component test requirements and characteristics through association rule analysis to generate test point analysis data;

[0037] The strategy optimization submodule optimizes the test path and method based on the test point analysis data, using a genetic algorithm combined with a simulated annealing algorithm, and generates test strategy optimization data;

[0038] The strategy application submodule formulates a test strategy based on the test strategy optimization data, including test point priority, test sequence and parameter setting, applies it to circuit board testing, and generates an optimized test strategy;

[0039] The data mining technology specifically includes data preprocessing and pattern recognition, and the genetic algorithm and simulated annealing algorithm include population initialization, crossover, mutation and selection operations.

[0040] As a further solution of the present invention, the resource allocation submodule performs resource allocation based on the optimization test strategy, adopts linear programming and optimization algorithms, and generates resource optimization allocation data, specifically including the optimal solution of resource allocation and cost-benefit analysis;

[0041] The scheduling algorithm submodule performs scheduling optimization of the production process based on the resource optimization allocation data, and uses the simulated annealing algorithm and reinforcement learning to generate production scheduling optimization data, specifically the global optimal solution and real-time adjustment strategy for scheduling;

[0042] The performance monitoring submodule monitors and evaluates the production process based on the production scheduling optimization data, and generates a production process optimization plan using a real-time monitoring system and performance analysis tools, specifically including real-time monitoring of production status and performance trend prediction;

[0043] The linear programming model is used to optimize resource allocation, the optimization algorithm is used for cost-benefit analysis, the simulated annealing algorithm is used to find the global optimal solution, the reinforcement learning is used to adjust the scheduling strategy, the industrial Internet of Things technology monitors the production status in real time, and the performance analysis tool performs data analysis and trend prediction.

[0044] As a further solution of the present invention, the frequency band adjustment submodule performs frequency band adjustment of the test signal based on the production process optimization solution, and adopts adaptive signal processing technology to generate test signal frequency band adjustment data;

[0045] The signal analysis submodule performs in-depth signal analysis based on the test signal frequency band adjustment data, applies the signal analysis algorithm to perform waveform analysis and spectrum analysis, and generates signal analysis results, specifically the spectrum characteristics and characteristic frequencies of the signal;

[0046] The fault detection submodule identifies faults and performance issues based on the signal analysis results, and uses machine learning and pattern recognition techniques to generate electromagnetic test results, specifically fault pattern recognition on the circuit board;

[0047] The adaptive signal processing technology specifically includes automatically selecting a test frequency band based on circuit board characteristics; the signal analysis algorithm includes Fourier transform and spectrum analysis methods to analyze test signals; the machine learning and pattern recognition technology specifically includes using a trained machine learning model to classify signal features and then identify fault modes.

[0048] As a further solution of the present invention, the data collection submodule performs data collection based on the electromagnetic test results and uses data integration technology to generate a fault data set;

[0049] The prediction modeling submodule applies a deep learning algorithm based on the fault data set, establishes a fault prediction model through a convolutional neural network and a recursive neural network, performs model training, and generates a fault prediction model;

[0050] The result analysis submodule performs fault diagnosis and performance prediction based on the fault prediction model and generates a fault prediction report using data visualization technology;

[0051] The data integration technology specifically extracts key information from electromagnetic test results, including signal characteristics and test parameters. The deep learning algorithm specifically uses neural networks to perform feature extraction and pattern recognition. The data visualization technology specifically presents the prediction results in the form of charts and graphs.

[0052] As a further solution of the present invention, the performance evaluation submodule performs performance evaluation based on the fault prediction report and generates a preliminary performance evaluation report using statistical analysis techniques;

[0053] The optimization strategy submodule formulates the optimization strategy of the production process and generates the production process optimization plan based on the preliminary performance evaluation report by applying the simulated annealing algorithm and data analysis technology;

[0054] The feedback implementation submodule executes optimization measures based on the production process optimization plan in combination with real-time production data, adopts a feedback mechanism, and generates system performance optimization feedback;

[0055] The statistical analysis technology is specifically used to analyze production efficiency and product quality data, the simulated annealing algorithm and data analysis technology are specifically used to combine algorithms to find the optimal solution and analyze production data, and the feedback mechanism is specifically used to monitor and adjust the effects of optimization measures.

[0056] Compared with the prior art, the advantages and positive effects of the present invention are:

[0057] In the present invention, through thermal imaging monitoring and machine learning analysis, the system can more accurately identify welding defects and improve product quality. The introduction of the component positioning and calibration module uses machine vision and deep learning to greatly improve assembly accuracy and reduce the need for manual calibration. The test strategy generation module uses genetic algorithms to optimize test paths and improve test efficiency. The use of the production process optimization module and the electromagnetic test adjustment module optimizes production resources and test processes through advanced algorithms, significantly improving production efficiency and test accuracy. The fault diagnosis prediction module and the system performance feedback module ensure continuous improvement and performance optimization of the system, providing strong support for long-term operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a system flow chart of the present invention;

[0059] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0060] Figure 3 This is a flow chart of the thermal imaging monitoring module of the present invention;

[0061] Figure 4 This is a flow chart of the machine learning analysis module of the present invention;

[0062] Figure 5 It is a flow chart of the component positioning and calibration module of the present invention;

[0063] Figure 6 Generate a module flow chart for the test strategy of the present invention;

[0064] Figure 7 It is a flow chart of the production process optimization module of the present invention;

[0065] Figure 8 It is a flow chart of the electromagnetic test adjustment module of the present invention;

[0066] Fig. 9 This is a flow chart of the fault diagnosis prediction module of the present invention;

[0067] Fig.10 It is a flow chart of the system performance feedback module of the present invention. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0069] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0070] Embodiment 1

[0071] See also Figure 1 ,A data analysis system for ICT test fixtures for automotive parts includes a thermal imaging monitoring module, a machine learning analysis module, a component positioning calibration module, a test strategy generation module, a production process optimization module, an electromagnetic test adjustment module, a fault diagnosis prediction module, and a system performance feedback module;

[0072] The thermal imaging monitoring module uses thermal imaging technology to capture temperature distribution based on the welding process, extracts temperature features through image processing algorithms, and generates welding thermal image data;

[0073] The machine learning analysis module uses convolutional neural networks to analyze welding quality, identify defects, and generate welding quality analysis reports based on welding thermal image data;

[0074] The component positioning and calibration module uses machine vision technology and deep learning algorithms to locate components based on circuit board design, automatically calibrate, and generate component placement calibration data;

[0075] The test strategy generation module optimizes the test path and generates an optimized test strategy based on component placement calibration data, applying genetic algorithms and historical test data;

[0076] The production process optimization module uses simulated annealing algorithm and reinforcement learning based on the optimization test strategy to adjust production resources and generate production process optimization solutions;

[0077] The electromagnetic test adjustment module is based on the production process optimization solution and adopts adaptive multi-band electromagnetic test technology to adjust the test signal and generate electromagnetic test results;

[0078] The fault diagnosis and prediction module uses deep learning algorithms to perform fault diagnosis, predict performance, and generate fault prediction reports based on electromagnetic test results;

[0079] The system performance feedback module uses simulated annealing algorithm to optimize the system based on fault prediction reports and combines real-time data to generate system performance optimization feedback;

[0080] The welding thermal image data includes temperature distribution map, hot spot identification, and temperature anomaly index of the welding area. The welding quality analysis report includes welding defect type, defect location, and welding quality evaluation. The component placement calibration data includes component position accuracy, placement angle, and calibration deviation value. The optimized test strategy specifically includes test point priority, test sequence, and test parameter setting. The production process optimization plan includes resource utilization, production rhythm adjustment, and production line operation speed optimization. The electromagnetic test results include test signal frequency band selection, circuit board fault detection results, and signal strength adjustment. The fault prediction report specifically includes potential fault type, fault probability, and performance prediction indicators. The system performance optimization feedback includes production process adjustment plan, performance improvement measures, and long-term operation efficiency evaluation.

[0081] Through the thermal imaging monitoring module, the system captures the temperature distribution of the welding process in real time, thereby improving welding quality and reducing defects. The machine learning analysis module uses convolutional neural network technology to automatically identify welding defects, accelerate detection speed and improve accuracy to ensure the quality of the final product. The component positioning and calibration module uses machine vision and deep learning technology to ensure the precise placement of components and improve product consistency and reliability.

[0082] The synergy between the test strategy generation module and the production process optimization module optimizes the test process and production line efficiency, reduces production costs and increases production speed through genetic algorithms and simulated annealing algorithms. The electromagnetic test adjustment module further ensures the quality and functional integrity of electronic components through adaptive multi-band electromagnetic testing technology. The fault diagnosis prediction module identifies potential faults in advance, reducing downtime and repair costs. The system performance feedback module continuously optimizes the entire system to ensure long-term efficiency and stability and reduce operating costs.

[0083] See also Figure 2 ,The thermal imaging monitoring module includes a temperature capture submodule, a feature extraction submodule, and a first data analysis submodule;

[0084] The machine learning analysis module includes a model training submodule, a defect identification submodule, and a result output submodule;

[0085] The component positioning and calibration module includes an image capture submodule, a positioning analysis submodule, and a calibration feedback submodule;

[0086] The test strategy generation module includes a second data analysis submodule, a strategy optimization submodule, and a strategy application submodule;

[0087] The production process optimization module includes resource allocation submodule, scheduling algorithm submodule, and performance monitoring submodule;

[0088] The electromagnetic test adjustment module includes a frequency band adjustment submodule, a signal analysis submodule, and a fault detection submodule;

[0089] The fault diagnosis and prediction module includes a data collection submodule, a prediction modeling submodule, and a result analysis submodule;

[0090] The system performance feedback module includes a performance evaluation submodule, an optimization strategy submodule, and a feedback implementation submodule.

[0091] In the thermal imaging monitoring module, the temperature capture submodule monitors the temperature distribution during the welding process in real time, the feature extraction submodule extracts key temperature features from these data, and finally the first data analysis submodule integrates these features to generate detailed welding thermal image data.

[0092] In the machine learning analysis module, the model training submodule uses a convolutional neural network to analyze welding thermal image data, the defect recognition submodule uses these training models to automatically identify welding defects, and the result output submodule generates a welding quality analysis report containing defect types and locations.

[0093] In the component positioning and calibration module, the image capture submodule uses machine vision technology to capture component images on the circuit board, the positioning analysis submodule analyzes these images to determine the exact position of the components, and the calibration feedback submodule automatically calibrates the component placement based on the analysis results to generate accurate calibration data.

[0094] In the test strategy generation module, the second data analysis submodule analyzes the component placement calibration data, the strategy optimization submodule uses a genetic algorithm to optimize the test path, and the strategy application submodule generates and applies efficient test strategies based on these optimization results.

[0095] In the production process optimization module, the resource allocation submodule is responsible for adjusting and allocating production resources, the scheduling algorithm submodule uses simulated annealing algorithm and reinforcement learning method to optimize the production process, and the performance monitoring submodule ensures the efficiency and quality of the optimized process.

[0096] In the electromagnetic test adjustment module, the frequency band adjustment submodule adjusts the frequency band of the electromagnetic test to adapt to different test requirements, the signal analysis submodule analyzes the test signal to ensure accuracy, and the fault detection submodule is responsible for detecting potential faults of the circuit board.

[0097] In the fault diagnosis and prediction module, the data collection submodule collects electromagnetic test results and other relevant data, the predictive modeling submodule uses a deep learning algorithm to establish a fault prediction model, and the result analysis submodule analyzes the prediction results to generate a fault prediction report.

[0098] In the system performance feedback module, the performance evaluation submodule evaluates the performance of the entire system, the optimization strategy submodule proposes improvement strategies, and the feedback implementation submodule implements these strategies to continuously optimize system performance.

[0099] See also Figure 3 ,The temperature capture submodule uses thermal imaging technology based on the welding process, ,captures the temperature distribution of the solder point, and performs real-time analysis to generate the solder point temperature ,data;

[0100] The feature extraction submodule applies edge detection and pattern recognition algorithms based on solder point temperature data to extract thermal features and generate welding thermal feature data;

[0101] The first data analysis submodule uses statistical analysis technology based on welding thermal characteristic data to identify temperature anomalies and generate welding thermal image data;

[0102] Thermal imaging technology specifically uses an infrared thermal imager to capture the temperature changes of solder joints in real time. Edge detection and pattern recognition algorithms specifically include the Sobel operator for edge detection, template matching technology for identifying preset thermal image patterns, and statistical analysis technology specifically uses standard deviation and mean analysis to identify temperature anomalies.

[0103] The temperature capture submodule captures the temperature distribution of the solder joint during the welding process. This device generates detailed temperature data. These data are transmitted to the analysis software in real time for further processing and analysis. This process ensures that the temperature changes during the welding process are accurately recorded and monitored, providing basic data for subsequent feature extraction and data analysis.

[0104] The feature extraction submodule uses edge detection techniques such as the Sobel operator to identify the edges of solder joints, and then uses pattern recognition techniques such as template matching to identify specific thermal features. This not only extracts useful information from temperature data, but also identifies key thermal features in the soldering process, providing important input for the final data analysis.

[0105] The first data analysis submodule uses statistical analysis techniques to perform in-depth analysis on the data extracted and generated by the first two submodules. By calculating the standard deviation and mean of the solder joint temperature data, this module identifies any temperature anomalies. This not only helps to promptly detect and correct problems in the welding process, but is also used to generate detailed welding thermal image data to further evaluate welding quality and monitor the entire welding process.

[0106] See also Figure 4 ,The model training submodule uses a convolutional neural network to train the model to identify welding defects and generate a welding defect recognition model based on the welding thermal image data;

[0107] The defect recognition submodule analyzes welding quality, identifies defect types and locations, and generates welding defect recognition results based on the welding defect recognition model;

[0108] The result output submodule uses data visualization technology to present defect details and generate a welding quality analysis report based on the welding defect identification results;

[0109] The convolutional neural network is specifically a multi-layer perceptron structure, which is used to extract and learn the characteristics of welding defects. The defect type and location analysis is specifically to determine the defect type using the probability distribution of the model output, and the spatial positioning technology is to determine the defect location. The data visualization technology is specifically interactive charts and heat maps, which are used to display the location and severity of the defects.

[0110] The model training submodule uses a convolutional neural network (CNN) to analyze welding thermal image data. This module uses a multi-layer perceptron structure to effectively extract and learn the various defect features that occur during the welding process. During the training phase, through the input of a large amount of welding image data, the model learns to identify different types of welding defects, such as holes and cracks, and gradually optimizes its recognition ability, ultimately generating an efficient welding defect recognition model.

[0111] The defect recognition submodule uses the trained welding defect recognition model to perform real-time analysis of welding quality. This submodule determines the defect type through the probability distribution of the model output and uses spatial positioning technology to accurately identify the specific location of the defect.

[0112] The result output submodule uses data visualization technology to display defect details based on the defect identification results. This module uses visualization tools such as interactive charts and heat maps to intuitively display the location and severity of defects. This not only enables operators to quickly identify and understand welding quality issues, but also generates detailed welding quality analysis reports to provide a basis for quality control and subsequent improvements.

[0113] See also Figure 5 ,The image capture submodule is based on the circuit board design, adopts machine vision technology to capture images, conducts visual analysis of components and circuit boards, and generates circuit board and component image data;

[0114] The positioning analysis submodule uses deep learning positioning algorithm based on circuit board and component image data and an improved YOLO model to perform positioning and generate component positioning analysis data;

[0115] The calibration feedback submodule applies automatic calibration technology based on component positioning analysis data, adjusts components to predetermined positions, and generates component placement calibration data;

[0116] Machine vision technology specifically uses advanced CMOS sensors and image processing algorithms, deep learning positioning algorithms specifically include feature extraction and spatial recognition technology, and automatic calibration technology specifically refers to the automatic adjustment mechanism and closed-loop feedback control based on the robot arm.

[0117] In the image capture submodule,

[0118] Task: Capture images of circuit boards and components using machine vision technology.

[0119] Operation: Use CMOS sensor to acquire circuit board images and apply image processing algorithms for pre-processing, such as noise reduction, contrast enhancement, etc.

[0120] Code example (assuming Python and OpenCV):

[0121] import cv2

[0122] #Assume there is already a camera interface

[0123] cap = cv2.VideoCapture(0)

[0124] ret,image=cap.read()

[0125] if ret:

[0126] # Apply image preprocessing

[0127] processed_image=cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)

[0128] processed_image=cv2.GaussianBlur(processed_image,(5,5),0)

[0129] cap.release()

[0130] In the positioning analysis submodule,

[0131] Task: Use deep learning positioning algorithm based on improved YOLO model to locate components on the circuit board.

[0132] Operation: Train a modified YOLO model to identify different components on the circuit board and locate their locations.

[0133] Code examples (using Python and PyTorch):

[0134] import torch

[0135] from models import YOLOv3#Assuming there is an improved YOLO model implementation

[0136] # Load the model

[0137] model = YOLOv3()

[0138] model.load_state_dict(torch.load('yolov3.pth'))

[0139] #Predict the circuit board image

[0140] predictions=model(processed_image)

[0141] In the calibration feedback submodule,

[0142] Task: Based on component positioning analysis data, use automatic calibration technology to adjust components to the predetermined position.

[0143] Operation: Use a robotic arm and closed-loop feedback control system to move components to a predetermined location based on component position data.

[0144] Program instructions (pseudo code):

[0145] #Assume there is a robot arm interface and control function

[0146] from robot_arm_control import RobotArm

[0147] robot_arm=RobotArm()

[0148] for component in predictions:

[0149] desired_position=get_desired_position(component)

[0150] current_position=get_current_position(component)

[0151] adjustment=calculate_adjustment(current_position,desired_position)

[0152] robot_arm.move(adjustment)

[0153] See also Figure 6 ,The second data analysis submodule uses data mining technology based on ,component placement calibration data and analyzes the test requirements and ,characteristics of the components through association rule analysis to generate ,test point analysis data;

[0154] The strategy optimization submodule analyzes the data based on the test points, uses genetic algorithms combined with simulated annealing algorithms to optimize the test paths and methods, and generates test strategy optimization data;

[0155] The strategy application submodule formulates the test strategy based on the test strategy optimization data, including test point priority, test sequence and parameter setting, applies it to the circuit board test, and generates the optimized test strategy;

[0156] Data mining technology specifically includes data preprocessing and pattern recognition, and genetic algorithms and simulated annealing algorithms include population initialization, crossover, mutation, and selection operations.

[0157] The second data analysis submodule uses data mining technology to process and analyze component placement calibration data. In this stage, data preprocessing is performed, including data cleaning, sorting and standardization, to ensure data quality and consistency. Through association rule analysis, this module deeply mines the test requirements and characteristics of components, uses pattern recognition technology to extract valuable information from large amounts of data, and finally generates test point analysis data.

[0158] The strategy optimization submodule takes over and, based on these analysis data, uses a composite optimization strategy that combines genetic algorithms and simulated annealing algorithms to fine-tune the test paths and methods. This process covers population initialization, crossover, mutation, and selection operations, and by imitating the principles of natural selection and thermodynamics, it searches for the optimal test strategy and generates optimized test strategy data.

[0159] The strategy application submodule formulates specific test strategies based on these optimization data, determines the priority, test sequence and parameter settings of the test points, and applies these strategies to circuit board testing to improve the efficiency and accuracy of the test.

[0160] See also Figure 7 ,The resource allocation submodule performs resource allocation based on the ,optimization test strategy, and uses linear programming and ,optimization algorithms to generate resource optimization allocation data, ,including the optimal solution of resource allocation and cost-benefit ,analysis;

[0161] The scheduling algorithm submodule optimizes the scheduling of the production process based on the resource optimization allocation data, and uses the simulated annealing algorithm and reinforcement learning to generate production scheduling optimization data, specifically the global optimal solution and real-time adjustment strategy for scheduling;

[0162] The performance monitoring submodule monitors and evaluates the production process based on the production scheduling optimization data, and generates a production process optimization plan using a real-time monitoring system and performance analysis tools, including real-time monitoring of production status and performance trend prediction;

[0163] Linear programming models are used to optimize resource allocation, optimization algorithms are used for cost-benefit analysis, simulated annealing algorithms are used to find the global optimal solution, reinforcement learning is used to adjust scheduling strategies, industrial Internet of Things technology monitors production status in real time, and performance analysis tools perform data analysis and trend prediction.

[0164] The resource allocation submodule uses a linear programming model to determine the optimal solution for resource allocation, and applies an optimization algorithm for cost-benefit analysis. The linear programming model calculates the best solution for resource utilization based on production demand and resource constraints, while the optimization algorithm evaluates the cost-benefit ratio of different resource configuration solutions to ensure that resource allocation is both efficient and economical.

[0165] The scheduling algorithm submodule uses simulated annealing algorithm and reinforcement learning algorithm. The simulated annealing algorithm searches for the optimal production scheduling solution in the global scope to avoid falling into the local optimum, while the reinforcement learning algorithm is used to dynamically adjust the scheduling strategy to cope with real-time changes and uncertainties in the production process.

[0166] The performance monitoring submodule uses industrial Internet of Things technology to achieve real-time monitoring of production status, and uses performance analysis tools to conduct in-depth analysis of production data to predict performance trends.

[0167] See also Figure 8 ,The frequency band adjustment submodule adjusts the frequency band of the test signal based on the ,production process optimization scheme, and adopts adaptive signal processing ,technology to generate the frequency band adjustment data of the test signal;

[0168] The signal analysis submodule performs in-depth signal analysis based on the test signal frequency band adjustment data, applies the signal analysis algorithm to perform waveform analysis and spectrum analysis, and generates signal analysis results, specifically the signal's spectrum characteristics and characteristic frequencies;

[0169] The fault detection submodule identifies faults and performance issues based on the signal analysis results, using machine learning and pattern recognition techniques to generate electromagnetic test results, specifically fault pattern recognition on the circuit board;

[0170] Adaptive signal processing technology specifically involves automatically selecting the test frequency band based on the characteristics of the circuit board. The signal analysis algorithm includes Fourier transform and spectrum analysis methods to analyze the test signal. Machine learning and pattern recognition technology specifically involves using the trained machine learning model to classify signal features and then identify fault modes.

[0171] The frequency band adjustment submodule adjusts the frequency band of the test signal according to the production process optimization plan. At this stage, the system uses adaptive signal processing technology to automatically select the most appropriate test frequency band based on the characteristics of the circuit board. This adaptive technology ensures that the test signal can effectively cover the fault frequency range of the circuit board, thereby improving the accuracy and efficiency of the test.

[0172] The signal analysis submodule uses signal analysis algorithms, including Fourier transform and spectrum analysis methods, to analyze the test signal. Through these analyses, the system can derive the spectral characteristics and characteristic frequencies of the signal. These data are essential for understanding the electromagnetic characteristics of the circuit board and provide the necessary basic information for fault detection.

[0173] The fault detection submodule uses machine learning and pattern recognition techniques to classify signal features using trained models to identify fault modes on the circuit board. This approach effectively identifies a variety of fault modes, including subtle faults that are difficult to detect with traditional detection methods.

[0174] See also Fig. 9 ,The data collection submodule performs data collection based on the ,electromagnetic test results and uses data integration ,techniques to generate fault data sets;

[0175] The prediction modeling submodule applies deep learning algorithms based on the fault data set, establishes a fault prediction model through convolutional neural networks and recurrent neural networks, performs model training, and generates a fault prediction model;

[0176] The result analysis submodule performs fault diagnosis and performance prediction based on the fault prediction model and generates a fault prediction report using data visualization technology;

[0177] The data integration technology specifically involves extracting key information from electromagnetic test results, including signal characteristics and test parameters. The deep learning algorithm specifically involves using neural networks for feature extraction and pattern recognition. The data visualization technology specifically involves presenting the prediction results in the form of charts and graphs.

[0178] The data collection submodule collects data based on the electromagnetic test results. At this stage, the submodule uses data integration technology to extract key information from the electromagnetic test results, such as signal characteristics and test parameters, and generates a fault data set. This step is the basis for the subsequent prediction model to ensure the integrity and relevance of the data.

[0179] The predictive modeling submodule applies deep learning algorithms to build and train models based on the collected fault data sets. This step mainly uses convolutional neural networks (CNN) and recurrent neural networks (RNN), which are combined to effectively extract features from data and identify fault modes. Through training, this submodule generates a fault prediction model, providing a powerful tool for subsequent fault diagnosis and performance prediction.

[0180] The result analysis submodule performs fault diagnosis and performance prediction based on the fault prediction model. At this stage, data visualization technology is used to present the prediction results in the form of charts and graphs, so that users can intuitively understand and analyze the results of fault prediction. This visualization method helps users quickly identify the problem and take appropriate measures.

[0181] See also Fig.10 ,The performance evaluation submodule performs performance evaluation based on the ,fault prediction report and uses statistical analysis techniques to ,generate a preliminary performance evaluation report;

[0182] The optimization strategy submodule applies simulated annealing algorithm and data analysis technology based on the preliminary performance evaluation report to formulate the optimization strategy of the production process and generate the production process optimization plan;

[0183] The feedback implementation submodule is based on the production process optimization plan, combined with real-time production data, to execute optimization measures and adopt feedback mechanisms to generate system performance optimization feedback;

[0184] The statistical analysis technology is specifically used to analyze production efficiency and product quality data. The simulated annealing algorithm and data analysis technology are specifically used to combine the algorithm to find the optimal solution and analyze production data. The feedback mechanism is specifically used to monitor and adjust the effects of optimization measures.

[0185] The performance evaluation submodule uses statistical analysis techniques to focus on analyzing production efficiency and product quality data and generate a preliminary performance evaluation report. This report will provide the necessary data support for the formulation of optimization strategies.

[0186] The optimization strategy submodule applies simulated annealing algorithm and data analysis technology to formulate optimization strategies for production processes. The simulated annealing algorithm is used here to find the optimal solution in the production process, while the data analysis technology is used to deeply understand the production data to guide the formulation of strategies.

[0187] The feedback implementation submodule uses a feedback mechanism to monitor and adjust the effects of optimization measures in real time. This feedback loop ensures that the optimization strategy can effectively respond to changes in the production environment, thereby achieving continuous optimization of system performance.

[0188] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A data analysis system for ICT testing tooling of automotive parts, characterized by: The system includes a thermal imaging monitoring module, a machine learning analysis module, a component positioning and calibration module, a test strategy generation module, a production process optimization module, an electromagnetic test adjustment module, a fault diagnosis and prediction module, and a system performance feedback module; The thermal imaging monitoring module uses thermal imaging technology to capture temperature distribution based on the welding process, extracts temperature features through image processing algorithms, and generates welding thermal image data; The machine learning analysis module uses a convolutional neural network to analyze welding quality, identify defects, and generate a welding quality analysis report based on welding thermal image data; The component positioning and calibration module uses machine vision technology and deep learning algorithms to locate components based on circuit board design, automatically calibrate, and generate component placement calibration data; The test strategy generation module optimizes the test path and generates an optimized test strategy based on component placement calibration data, applying a genetic algorithm and historical test data; The production process optimization module adjusts production resources and generates a production process optimization plan based on an optimization test strategy using a simulated annealing algorithm and reinforcement learning; The production process optimization module includes a resource allocation submodule, a scheduling algorithm submodule, and a performance monitoring submodule; The resource allocation submodule performs resource allocation based on the optimization test strategy, adopts linear programming and optimization algorithms, and generates resource optimization allocation data, including the optimal solution of resource allocation and cost-benefit analysis; The scheduling algorithm submodule performs scheduling optimization of the production process based on the resource optimization allocation data, and uses the simulated annealing algorithm and reinforcement learning to generate production scheduling optimization data, specifically the global optimal solution and real-time adjustment strategy for scheduling; The performance monitoring submodule monitors and evaluates the production process based on the production scheduling optimization data, and generates a production process optimization plan using a real-time monitoring system and performance analysis tools, specifically including real-time monitoring of production status and performance trend prediction; The linear programming model is used to optimize resource allocation, the optimization algorithm is used for cost-benefit analysis, the simulated annealing algorithm is used to find the global optimal solution, the reinforcement learning is used to adjust the scheduling strategy, and the performance analysis tool is used for data analysis and trend prediction; The electromagnetic test adjustment module uses adaptive multi-band electromagnetic test technology based on the production process optimization solution to adjust the test signal and generate electromagnetic test results; The electromagnetic test adjustment module includes a frequency band adjustment submodule, a signal analysis submodule, and a fault detection submodule; The frequency band adjustment submodule adjusts the frequency band of the test signal based on the production process optimization solution, and uses adaptive signal processing technology to generate test signal frequency band adjustment data; The signal analysis submodule performs in-depth signal analysis based on the test signal frequency band adjustment data, applies the signal analysis algorithm to perform waveform analysis and spectrum analysis, and generates signal analysis results, specifically the spectrum characteristics and characteristic frequencies of the signal; The fault detection submodule identifies faults and performance issues based on the signal analysis results, and uses machine learning and pattern recognition techniques to generate electromagnetic test results, specifically fault pattern recognition on the circuit board; The adaptive signal processing technology specifically includes automatically selecting a test frequency band based on circuit board characteristics, the signal analysis algorithm includes Fourier transform and spectrum analysis methods to analyze test signals, and the machine learning and pattern recognition technology specifically includes using a trained machine learning model to classify signal features and then identify fault modes; The fault diagnosis prediction module applies a deep learning algorithm to perform fault diagnosis, predict performance, and generate a fault prediction report based on the electromagnetic test results; The system performance feedback module uses a simulated annealing algorithm to optimize the system based on the fault prediction report and combines real-time data to generate system performance optimization feedback; The system performance feedback module includes a performance evaluation submodule, an optimization strategy submodule, and a feedback implementation submodule; The performance evaluation submodule performs performance evaluation based on the fault prediction report and generates a preliminary performance evaluation report using statistical analysis techniques; The optimization strategy submodule formulates the optimization strategy of the production process and generates the production process optimization plan based on the preliminary performance evaluation report by applying the simulated annealing algorithm and data analysis technology; The feedback implementation submodule executes optimization measures based on the production process optimization plan in combination with real-time production data, adopts a feedback mechanism, and generates system performance optimization feedback; The statistical analysis technology is specifically used to analyze production efficiency and product quality data, the simulated annealing algorithm and data analysis technology are specifically used to combine algorithms to find the optimal solution and analyze production data, and the feedback mechanism is specifically used to monitor and adjust the effects of optimization measures.

2. The data analysis system for the automotive parts ICT testing tooling according to claim 1 is characterized by: The thermal imaging monitoring module includes a temperature capture submodule, a feature extraction submodule, and a first data analysis submodule; The temperature capture submodule uses thermal imaging technology to capture the temperature distribution of solder joints based on the soldering process, and performs real-time analysis to generate solder joint temperature data; The feature extraction submodule extracts thermal features based on the solder point temperature data and applies edge detection and pattern recognition algorithms to generate soldering thermal feature data; The first data analysis submodule uses statistical analysis technology based on welding thermal feature data to identify temperature anomalies and generate welding thermal image data; The thermal imaging technology specifically uses an infrared thermal imager to capture the temperature changes of solder joints in real time. The edge detection and pattern recognition algorithm specifically includes a Sobel operator for edge detection. The statistical analysis technology specifically uses standard deviation and mean analysis to identify temperature anomalies.

3. The data analysis system for the automotive parts ICT testing tooling according to claim 1 is characterized in that: The machine learning analysis module includes a model training submodule, a defect identification submodule, and a result output submodule; The model training submodule uses a convolutional neural network based on the welding thermal image data to train a model to identify welding defects and generate a welding defect recognition model; The defect recognition submodule analyzes welding quality, identifies defect types and locations, and generates welding defect recognition results based on the welding defect recognition model; The result output submodule uses data visualization technology based on the welding defect identification results to present defect details and generate a welding quality analysis report; The convolutional neural network is specifically a multi-layer perceptron structure, which is used to extract and learn the characteristics of welding defects. The defect type and location analysis is specifically to determine the defect type using the probability distribution of the model output. The data visualization technology is specifically interactive charts and heat maps, which are used to display the location and severity of defects.

4. The data analysis system for the automotive parts ICT testing tooling according to claim 1 is characterized in that: The component positioning and calibration module includes an image capture submodule, a positioning analysis submodule, and a calibration feedback submodule; The image capture submodule uses machine vision technology to capture images based on circuit board design, conducts visual analysis of components and circuit boards, and generates circuit board and component image data; The positioning analysis submodule uses a deep learning positioning algorithm based on the circuit board and component image data, and uses an improved YOLO model to perform positioning and generate component positioning analysis data; The calibration feedback submodule applies automatic calibration technology based on component positioning analysis data to adjust the component to a predetermined position and generate component placement calibration data; The machine vision technology specifically uses advanced CMOS sensors and image processing algorithms, the deep learning positioning algorithm specifically includes feature extraction and space recognition technology, and the automatic calibration technology specifically is based on the automatic adjustment mechanism and closed-loop feedback control of the robot arm.

5. The data analysis system for the automotive parts ICT testing tooling according to claim 1 is characterized by: The test strategy generation module includes a second data analysis submodule, a strategy optimization submodule, and a strategy application submodule; The second data analysis submodule uses data mining technology based on component placement calibration data to analyze component test requirements and characteristics through association rule analysis to generate test point analysis data; The strategy optimization submodule optimizes the test path and method based on the test point analysis data, using a genetic algorithm combined with a simulated annealing algorithm, and generates test strategy optimization data; The strategy application submodule formulates a test strategy based on the test strategy optimization data, including test point priority, test sequence and parameter setting, applies it to circuit board testing, and generates an optimized test strategy; The data mining technology specifically includes data preprocessing and pattern recognition, and the genetic algorithm and simulated annealing algorithm include population initialization, crossover, mutation and selection operations.

6. The data analysis system for the automotive parts ICT testing tooling according to claim 1 is characterized by: The fault diagnosis prediction module includes a data collection submodule, a prediction modeling submodule, and a result analysis submodule; The data collection submodule collects data based on the electromagnetic test results and generates a fault data set using data integration technology; The prediction modeling submodule applies a deep learning algorithm based on the fault data set, establishes a fault prediction model through a convolutional neural network and a recursive neural network, performs model training, and generates a fault prediction model; The result analysis submodule performs fault diagnosis and performance prediction based on the fault prediction model and generates a fault prediction report using data visualization technology; The data integration technology specifically extracts key information from electromagnetic test results, including signal characteristics and test parameters. The deep learning algorithm specifically uses neural networks to perform feature extraction and pattern recognition. The data visualization technology specifically presents the prediction results in the form of charts and graphs.

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