Final inspection appearance inspection system based on industrial camera shooting

Abstract

The present invention relates to the field of appearance inspection technology, specifically to a final inspection appearance inspection system based on industrial camera photography, the system comprising an image enhancement module, a three-dimensional modeling module, a feature mapping module, a parameter adjustment module, a quality trend analysis module, a structural integrity assessment module, an equipment maintenance strategy module, and a process optimization module. In the present invention, the image enhancement module removes noise by using Gaussian blur and sharpening algorithms, the histogram equalization enhances contrast, the three-dimensional modeling module optimizes texture by using stereo vision and surface reconstruction technology, the feature mapping module analyzes model features by using convolutional neural networks, maps product characteristics, the parameter adjustment module automatically optimizes inspection parameters by using an adaptive learning algorithm, the quality trend analysis and structural integrity assessment modules provide trend prediction and structural safety assessment, support the formulation of maintenance strategies, and the comprehensive application of the equipment maintenance strategy module and the process optimization module realizes predictive maintenance and process efficiency improvement.

Description

Technical Field

[0001] The present invention relates to the technical field of appearance inspection, and in particular to a final inspection appearance inspection system based on industrial camera photography. Background Art

[0002] Appearance inspection technology focuses on inspecting the appearance quality of products through various methods to ensure that the products meet specific standards. This field covers everything from simple visual inspection to complex automated image processing technology. With the increase in industrial automation and precision requirements, appearance inspection technology is becoming more and more important in quality control and production process optimization.

[0003] Among them, the final inspection appearance inspection system shot by industrial cameras is an advanced application in appearance inspection technology. Use industrial cameras to capture images of products, and then use software to analyze these images to detect surface defects, color inconsistencies, dimensional deviations and other problems. Its main purpose is to improve inspection efficiency and accuracy, reduce the need for manual inspection, and ensure product quality. This system usually includes high-resolution industrial cameras, appropriate lighting equipment, and software for image processing and analysis. The camera captures high-quality images of the product, while the software uses algorithms to detect and identify various possible defects. In this way, the system can quickly and accurately complete the quality inspection of a large number of products, thereby improving production efficiency and product quality.

[0004] Traditional systems have multiple shortcomings. Traditional image processing technology is often limited by low contrast and high noise, resulting in inaccurate information extraction, affecting the quality and efficiency of subsequent processing. Three-dimensional modeling usually lacks efficient algorithm support and has difficulty processing complex textures and details, resulting in inaccurate models. In terms of feature mapping, the lack of deep learning applications makes it difficult for traditional systems to fully utilize the complex patterns and features in the data, reducing the accuracy of product quality control. The parameter adjustment process in traditional systems is often manual, time-consuming and error-prone. Quality trend analysis and structural integrity assessment often rely on simplified models and limited data, fail to fully utilize existing information, and affect the effectiveness of maintenance strategies. Traditional process optimization methods are usually reactive rather than predictive, lacking systematic and comprehensive improvements, resulting in inefficiency and waste of resources. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a final inspection appearance inspection system based on industrial camera photography.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: the final inspection appearance detection system based on industrial camera shooting includes the system including an image enhancement module, a three-dimensional modeling module, a feature mapping module, a parameter adjustment module, a quality trend analysis module, a structural integrity assessment module, an equipment maintenance strategy module, and a process optimization module;

[0007] The image enhancement module uses a Gaussian blur algorithm to smooth the original image taken by the industrial camera, and uses a sharpening algorithm to improve the clarity of image details. It redistributes the image brightness and enhances the contrast through histogram equalization technology to generate an enhanced image.

[0008] The 3D modeling module reconstructs the object shape from multiple perspectives based on the enhanced image and the stereo vision algorithm, refines the model surface texture with surface reconstruction technology, and uses ray tracing to simulate the interaction between light and objects, optimizes the model texture and light and shadow effects, and generates a 3D visual model.

[0009] The feature mapping module is based on a three-dimensional visual model, uses a convolutional neural network to deeply analyze model features, extracts key structure and surface texture information, and maps model features through feature point detection technology to generate feature mapping analysis results;

[0010] The parameter adjustment module uses an adaptive learning algorithm to dynamically adjust model parameters based on feature feedback based on feature mapping analysis results, and uses neural networks and fuzzy logic to work together to optimize detection parameters and conditions and generate adaptive detection parameters;

[0011] The quality trend analysis module uses an autoregressive integrated moving average model to analyze the quality data time series based on adaptive detection parameters, identify trend changes and fluctuations, and predict the quality change trend in the future time period through a trend analysis algorithm to generate a trend forecast analysis;

[0012] The structural integrity assessment module uses finite element analysis to numerically simulate the product structure based on a three-dimensional visual model, and simulates the stress distribution under differentiated working conditions through a stress test algorithm to assess structural stability and potential risks, and generate a structural safety assessment;

[0013] The equipment maintenance strategy module uses data-driven machine learning methods to analyze maintenance needs based on structural safety assessment and equipment historical operation data, and identifies key maintenance points through support vector machines, formulates targeted maintenance strategies, and generates maintenance strategy plans;

[0014] The process optimization module integrates enhanced images, 3D visual models, feature mapping analysis, adaptive detection parameters, trend prediction analysis, structural safety assessment, maintenance strategy planning, practical process management and Six Sigma methods to generate an optimized detection process.

[0015] As a further solution of the present invention, the three-dimensional visual model includes product geometry, surface texture and lighting effects, the feature mapping analysis results include key structural features, surface texture features and abnormal feature points of the product, the adaptive detection parameters include adjusted sensitivity settings, recognition thresholds of target features and processing speeds, the trend prediction analysis includes changing trends and potential risk points of production quality in the future, the structural safety assessment includes potential structural weaknesses, stress distribution and durability predictions, the maintenance strategy plan includes maintenance priority sorting, maintenance activities and time scheduling, and the optimized detection process includes process simplification measures, efficiency improvement strategies and error prevention mechanisms.

[0016] As a further solution of the present invention, the image enhancement module includes a denoising submodule, a contrast enhancement submodule, an edge sharpening submodule, and a color adjustment submodule;

[0017] The denoising submodule uses a Gaussian blur algorithm to perform denoising based on the original image taken by the industrial camera, and reduces image noise by calculating the Gaussian weighted average of pixels in the image, keeping the main structure of the image unchanged, and generating a denoised image;

[0018] The contrast enhancement submodule improves the contrast of the image based on the denoising process image by applying the histogram equalization method, and by adjusting the brightness distribution of the image, enhancing the image brightness and distributing it evenly, strengthening the contrast of multiple brightness levels of the image, and generating a contrast enhanced image;

[0019] The edge sharpening submodule processes the image edge using the Laplace sharpening algorithm based on the contrast enhanced image, improves the image edge clarity by identifying the edge area of ​​the image and enhancing the brightness difference of the area, and generates an edge sharpened image;

[0020] The color adjustment submodule implements color adjustment technology to adjust the color attributes of the image based on the edge sharpening image, including adjusting the hue to match the target color temperature, increasing or decreasing the saturation, and adjusting the brightness and contrast to generate a color adjusted image.

[0021] As a further solution of the present invention, the three-dimensional modeling module includes a geometry reconstruction submodule, a texture mapping submodule, a depth estimation submodule, and a model correction submodule;

[0022] The geometric reconstruction submodule uses a stereoscopic vision algorithm to perform geometric analysis on the image based on the color adjustment image, calculates the disparity information by capturing image data from multiple perspectives, estimates the depth of each pixel, and generates a three-dimensional geometric structure model;

[0023] The texture mapping submodule uses surface reconstruction technology to perform texture mapping based on a three-dimensional geometric structure model, analyzes the surface of the three-dimensional model, extracts texture information through images taken from multiple perspectives, and uses a texture stitching algorithm to map the texture to the surface of the three-dimensional model to generate a texture mapping model;

[0024] The depth estimation submodule performs depth estimation based on the texture mapping model, creates a depth map, converts image data into depth information, and selects the position of each point in the three-dimensional space by analyzing the position relationship of the object in space to generate a depth estimation model;

[0025] The model correction submodule is based on the depth estimation model and combines the ray tracing method to perform model correction. By simulating the propagation path of light in three-dimensional space and referring to the reflection and absorption characteristics of multiple materials to light, it renders the lighting and shadow effects, optimizes the texture and details of the model, and generates the final three-dimensional visual model.

[0026] As a further solution of the present invention, the feature mapping module includes a feature extraction submodule, a pattern recognition submodule, a feature database submodule, and a mapping optimization submodule;

[0027] The feature extraction submodule is based on the final 3D visual model and uses a convolutional neural network analysis algorithm to perform deep feature analysis on the model. Based on the edge and texture of the model, it extracts features through multiple convolutional layers, reduces feature dimensions through pooling layers, locates and extracts key feature points in the model, and generates feature extraction analysis;

[0028] The pattern recognition submodule is based on feature extraction analysis, applies pattern recognition algorithms to classify and identify the extracted features, identifies known patterns through template matching, and uses cluster analysis to classify similar features, distinguish differentiated feature categories and patterns, and generate pattern recognition analysis;

[0029] The feature database submodule is based on pattern recognition analysis and uses machine learning algorithms to perform in-depth data analysis and processing. The decision tree algorithm makes classification decisions based on feature attributes, the support vector machine algorithm processes nonlinear feature relationships, and the neural network algorithm learns feature patterns. Through intelligent optimization, a feature database integration is generated;

[0030] The mapping optimization submodule is based on feature database integration, uses genetic algorithms to perform data association mapping, and performs efficiency optimization processing, including initializing the population to define the starting point of the search, evaluating the ability of individuals to adapt to the environment through the fitness function, evolving the population through selection operators, crossover operators and mutation operators, optimizing the data mapping process, and generating feature mapping optimization analysis.

[0031] As a further solution of the present invention, the parameter adjustment module includes a parameter learning submodule, an adaptive adjustment submodule, a process optimization submodule, and a real-time update submodule;

[0032] The parameter learning submodule uses an adaptive learning algorithm to learn and adjust the detection parameters based on feature mapping optimization analysis, and adopts feedback adjustment and parameter optimization mechanism to automatically adjust the parameters according to the differences in detection conditions, adapt to the differentiated detection environment, and generate parameter learning adjustments;

[0033] The adaptive adjustment submodule is based on parameter learning adjustment, and applies neural network and fuzzy logic to perform adaptive adjustment and optimize detection parameters and conditions. The neural network adjusts the learning mode information through weights, and the fuzzy logic processes uncertainty and fuzzy information according to fuzzy rules to generate adaptive parameter optimization;

[0034] The process optimization submodule uses a simulated annealing algorithm to optimize the structure of the detection process based on adaptive parameter optimization, including setting the initial solution, defining the neighborhood structure, implementing the temperature scheduling scheme, and automatically adjusting the parameters through the acceptance criterion to optimize the structure and efficiency of the entire detection process and generate a process optimization analysis;

[0035] The real-time update submodule is based on process optimization analysis and adopts the Bayesian optimization algorithm to update the detection parameters and conditions in real time, and perform adaptive adjustments, including selecting a priori models, a posteriori updates, defining acquisition functions, and selecting optimal points, to achieve real-time update and optimization of detection parameters and generate adaptive detection parameters.

[0036] As a further solution of the present invention, the quality trend analysis module includes a data acquisition submodule, a trend prediction submodule, an analysis and evaluation submodule, and a risk warning submodule;

[0037] The data acquisition submodule uses multi-source data integration technology to capture and pre-process data based on adaptive detection parameters, removes erroneous or inconsistent data through data cleaning, integrates multiple data through fusion strategies, improves data integration effects, and generates quality data sets;

[0038] The trend prediction submodule analyzes time series data based on the quality data set through the autoregressive integrated moving average model, detects and predicts trends, and uses machine learning time series analysis methods, including decision trees, support vector machines, and neural networks, to identify trends and anomalies, estimate trends in future time periods, and generate trend prediction reports;

[0039] The analysis and evaluation submodule is based on the trend forecast report, uses statistical hypothesis testing and variance analysis methods to perform in-depth analysis, analyzes differences between data groups through ANOVA, evaluates data distribution deviation through chi-square test, and compares differences between two groups of data through T test to generate an evaluation and analysis report;

[0040] The risk warning submodule is based on the assessment and analysis report, and uses risk matrix and sensitivity analysis to conduct risk assessment. The risk matrix is ​​used to conduct quantitative risk assessment, and sensitivity analysis is used to identify qualitative risk points. Risk levels are divided and a risk warning report is generated.

[0041] As a further solution of the present invention, the structural integrity assessment module includes a structural modeling submodule, a stress analysis submodule, a durability test submodule, and a safety assessment submodule;

[0042] The structural modeling submodule is based on a three-dimensional visual model, adopts computer-aided design and geometric modeling technology, uses computer-aided design to adjust and optimize the geometric parameters of the product, and generates a structural model through NURBS modeling and polygonal modeling;

[0043] The stress analysis submodule is based on the structural model, identifies and evaluates the stress distribution in the model through finite element analysis, and uses stress-strain simulation technology to perform static and dynamic load simulation, simulate the stress response of the material under differentiated conditions, and generate a stress analysis report;

[0044] The durability test submodule uses accelerated life testing to evaluate the strength of the structure based on the stress analysis report, and uses reliability analysis techniques, including stress testing and failure mode impact analysis, to detect and analyze potential failure modes, evaluate the durability of the product, and generate a durability evaluation report;

[0045] The safety assessment submodule evaluates the risk level of the product through safety indicators based on the durability assessment report, and adopts risk assessment standards, including safety factor calculation and fault tree analysis, to generate a structural safety assessment.

[0046] As a further solution of the present invention, the equipment maintenance strategy module includes a maintenance demand analysis submodule, a prediction modeling submodule, a strategy planning submodule, and a plan execution submodule;

[0047] The maintenance demand analysis submodule adopts fault tree analysis and failure mode impact analysis based on structural safety assessment and equipment historical operation data. Through fault tree analysis, it comprehensively refers to multiple potential failure causes and their interactions, and through failure mode impact analysis, it quantifies the reliability of multiple failure modes, locates key maintenance nodes, and generates a maintenance task list;

[0048] The predictive modeling submodule analyzes historical data based on the maintenance task list through support vector machines to identify the patterns and trends of maintenance requirements, and uses time series analysis of historical trends to predict the status in future time periods, optimize maintenance strategies, and generate maintenance prediction models;

[0049] The strategy planning submodule uses a genetic algorithm to generate an initial resource allocation plan based on a maintenance prediction model, mines the optimal solution by simulating natural selection and genetic variation processes, and uses linear programming to make adjustments to maintain the optimal configuration of resources and time, including timetables and resource allocation, and generates a maintenance action plan;

[0050] The plan execution submodule implements task scheduling and performance monitoring based on the maintenance action plan. Task scheduling ensures that multiple maintenance activities are executed in an orderly manner as planned. Performance monitoring tracks the progress and effects of maintenance operations in real time, evaluates and adjusts the effects of maintenance execution, and generates maintenance execution records.

[0051] As a further solution of the present invention, the process optimization module includes a process analysis submodule, an efficiency improvement submodule, a system integration submodule, and an improvement implementation submodule;

[0052] The process analysis submodule analyzes the operation log based on the enhanced image, 3D visual model and feature mapping analysis to identify the patterns and bottlenecks in the workflow, and uses data visualization technology to display the analysis results, assist in locating optimization goals and improvement points, and generate process optimization guidelines;

[0053] The efficiency improvement submodule is based on the process optimization guide, adopts the Six Sigma method and lean management technology, identifies defects in the process through the Six Sigma method, guides the reduction and elimination of defects, and analyzes the overall value stream through lean management technology, designs and implements work processes, and generates efficiency improvement plans;

[0054] The system integration submodule is based on the efficiency improvement plan, adopts big data analysis technology and service-oriented architecture, integrates and analyzes multi-source data through the big data platform, optimizes resource allocation and use, and uses modular services and system components to improve the overall coordination and efficiency of the system and generate a system coordination plan;

[0055] The improvement implementation submodule is based on a system coordination plan, adopts project management methods and continuous improvement techniques, ensures the setting of improvement goals and tracking of project progress through project management methods, and ensures the implementation of improvement measures through iterative evaluation and adjustment, and generates improvement implementation records.

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

[0057] In the present invention, the image enhancement module effectively removes noise and enhances image quality through Gaussian blur and sharpening algorithms, and histogram equalization greatly improves image contrast, making details clearer, and providing high-quality input for subsequent three-dimensional modeling and feature mapping. The three-dimensional modeling module uses stereo vision and surface reconstruction technology to generate an accurate three-dimensional visual model and optimize texture and detail performance. The feature mapping module uses convolutional neural networks to deeply analyze model features and accurately map product characteristics. The parameter adjustment module automatically optimizes detection parameters through an adaptive learning algorithm to improve detection efficiency and accuracy. The quality trend analysis and structural integrity assessment modules provide accurate assessments of trend prediction and structural safety, respectively, and provide data support for the formulation of maintenance strategies. The comprehensive application of the equipment maintenance strategy module and the process optimization module has achieved a significant improvement in predictive maintenance and process efficiency, and improved system performance and reliability as a whole. 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 It is a flow chart of the image enhancement module of the present invention;

[0061] Figure 4 It is a flow chart of the three-dimensional modeling module of the present invention;

[0062] Figure 5 It is a flow chart of the feature mapping module of the present invention;

[0063] Figure 6 It is a flow chart of the parameter adjustment module of the present invention;

[0064] Figure 7 It is a flow chart of the quality trend analysis module of the present invention;

[0065] Figure 8 It is a flow chart of the structural integrity assessment module of the present invention;

[0066] Fig. 9 It is a flow chart of the equipment maintenance strategy module of the present invention;

[0067] Fig.10 This is a flow chart of the process optimization 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 to Figure 2 The final inspection appearance inspection system based on industrial camera shooting includes image enhancement module, 3D modeling module, feature mapping module, parameter adjustment module, quality trend analysis module, structural integrity assessment module, equipment maintenance strategy module, and process optimization module;

[0072] The image enhancement module uses the Gaussian blur algorithm to smooth the original image taken by the industrial camera, and uses the sharpening algorithm to improve the clarity of image details. It also uses the histogram equalization technology to redistribute the image brightness, enhance the contrast, and generate an enhanced image.

[0073] The 3D modeling module uses the enhanced image and stereo vision algorithm to reconstruct the object shape from multiple perspectives, and the surface reconstruction technology to refine the surface texture of the model. It also uses the ray tracing method to simulate the interaction between light and objects, optimize the model texture and light and shadow effects, and generate a 3D visual model.

[0074] The feature mapping module is based on a 3D visual model and uses a convolutional neural network to deeply analyze model features, extract key structure and surface texture information, and map the model features through feature point detection technology to generate feature mapping analysis results;

[0075] The parameter adjustment module uses an adaptive learning algorithm to dynamically adjust model parameters based on feature feedback based on feature mapping analysis results, and uses neural networks and fuzzy logic to work together to optimize detection parameters and conditions and generate adaptive detection parameters.

[0076] The quality trend analysis module uses the autoregressive integral moving average model to analyze the quality data time series based on adaptive detection parameters, identify trend changes and fluctuations, and predict the quality change trend in the future time period through the trend analysis algorithm to generate trend forecast analysis;

[0077] The structural integrity assessment module uses finite element analysis to numerically simulate the product structure based on a three-dimensional visual model, and simulates the stress distribution under different working conditions through a stress test algorithm to evaluate the structural stability and potential risks and generate a structural safety assessment.

[0078] The equipment maintenance strategy module uses data-driven machine learning methods to analyze maintenance needs based on structural safety assessment and historical equipment operation data, and uses support vector machines to identify key maintenance points, develop targeted maintenance strategies, and generate maintenance strategy plans;

[0079] The process optimization module integrates enhanced images, 3D visual models, feature mapping analysis, adaptive detection parameters, trend prediction analysis, structural safety assessment, maintenance strategy planning, and combines practical process management and Six Sigma methods to generate optimized detection processes.

[0080] The 3D visual model includes the product's geometric structure, surface texture and lighting effects. The feature mapping analysis results include the product's key structural features, surface texture features and abnormal feature points. The adaptive detection parameters include adjusted sensitivity settings, target feature recognition thresholds and processing speeds. Trend forecasting analysis includes changing trends in production quality and potential risk points in the future. Structural safety assessment includes potential structural weaknesses, stress distribution and durability prediction. Maintenance strategy planning includes maintenance priority sorting, maintenance activities and scheduling. Optimization of the detection process includes process simplification measures, efficiency improvement strategies and error prevention mechanisms.

[0081] The image enhancement module improves image quality and lays the foundation for accurate detection. The 3D modeling and feature mapping module significantly improves the detection depth and accuracy by deeply analyzing the product structure and surface texture. The adaptive learning algorithm dynamically optimizes the detection parameters in the parameter adjustment module to enhance the system's ability to cope with complex situations. The quality trend analysis module uses time series analysis to predict quality changes and provide a basis for problem prevention. The structural integrity assessment module enhances product durability and safety assessment by simulating stress distribution under different working conditions. The equipment maintenance strategy module reduces costs and extends equipment life by optimizing maintenance plans. The process optimization module integrates the output of each link to improve the efficiency and reliability of the entire detection process.

[0082] See also Figure 3 ,The image enhancement module includes a denoising submodule, a contrast enhancement submodule, an edge sharpening submodule, and a color adjustment submodule;

[0083] The denoising submodule uses the Gaussian blur algorithm to perform denoising based on the original image taken by the industrial camera. It calculates the Gaussian weighted average of the pixels in the image to reduce image noise, keep the main structure of the image unchanged, and generate a denoised image.

[0084] The contrast enhancement submodule improves the contrast of the image based on the denoising process and applies the histogram equalization method. It adjusts the brightness distribution of the image, increases the image brightness and distributes it evenly, strengthens the contrast of the image at multiple brightness levels, and generates a contrast-enhanced image.

[0085] The edge sharpening submodule uses the Laplace sharpening algorithm to process the image edges based on the contrast enhancement image. It improves the image edge clarity by identifying the edge area of ​​the image and enhancing the brightness difference of the area, thus generating an edge sharpened image.

[0086] The color adjustment submodule implements color adjustment technology to adjust the color attributes of the image based on the edge sharpening image, including adjusting the hue to match the target color temperature, increasing or decreasing the saturation, and adjusting the brightness and contrast to generate a color adjusted image.

[0087] In the denoising submodule, the original image taken by the industrial camera is denoised by using the Gaussian blur algorithm. The algorithm calculates the Gaussian weighted average of each pixel in the image, effectively reducing image noise. In the specific implementation, the size and standard deviation of the Gaussian kernel are determined, and the Gaussian kernel is applied to each pixel in the image for convolution. In this process, the central pixel obtains a higher weight, and the surrounding pixels obtain decreasing weights according to the distance from the center, achieving denoising while keeping the main structure of the image unchanged. The resulting denoised image reduces noise while avoiding damage to the main content of the image.

[0088] The contrast enhancement submodule applies the histogram equalization method to improve the contrast of the denoised image. The core of this method is to redistribute the brightness values ​​of the image to make the image brightness distribution more uniform and wide. The operation includes calculating the grayscale histogram of the original image and adjusting the pixel values ​​according to the cumulative distribution function to achieve a uniform distribution effect. This processing improves the overall brightness of the image, strengthens the contrast between different brightness levels, makes the image details more prominent, and the generated contrast-enhanced image is visually clear and vivid.

[0089] The edge sharpening submodule uses the Laplace sharpening algorithm to process the edges of contrast-enhanced images. The Laplace algorithm achieves sharpening by identifying high-frequency areas in the image, namely edge areas, and enhancing the brightness differences in these areas. The implementation method is to apply the Laplace operator to the image, calculate the second-order derivative value of each pixel to detect the edge, and enhance the edge area. This processing method improves the clarity and contrast of the image edge, and the generated edge-sharpened image is more accurate in details, which is conducive to subsequent analysis and recognition.

[0090] The color adjustment submodule implements color adjustment techniques to adjust the color properties of edge-sharpened images. This process includes adjusting the hue to match the target color temperature, increasing or decreasing saturation, and adjusting brightness and contrast. Specific operations include independent adjustments to color channels through color space conversion (such as from RGB to HSV). In this way, the color balance of the image is finely controlled to make it closer to the natural state or specific visual requirements. The generated color-adjusted image is more visually in line with the detection requirements and provides a good foundation for high-quality visual detection.

[0091] See also Figure 4 ,The 3D modeling module includes a geometry reconstruction submodule, a texture mapping submodule, a depth estimation submodule, and a model correction submodule;

[0092] The geometric reconstruction submodule uses a stereoscopic vision algorithm to perform geometric analysis on the image based on the color adjustment image. By capturing image data from multiple perspectives, it calculates the disparity information, estimates the depth of each pixel, and generates a three-dimensional geometric structure model.

[0093] The texture mapping submodule uses surface reconstruction technology to perform texture mapping based on the 3D geometric structure model, analyzes the surface of the 3D model, extracts texture information through images taken from multiple perspectives, and uses a texture stitching algorithm to map the texture to the surface of the 3D model to generate a texture mapping model;

[0094] The depth estimation submodule performs depth estimation based on the texture mapping model, creates a depth map, converts image data into depth information, and selects the position of each point in three-dimensional space by analyzing the position relationship of objects in space to generate a depth estimation model;

[0095] The model correction submodule is based on the depth estimation model and combines the ray tracing method to perform model correction. It simulates the propagation path of light in three-dimensional space, refers to the reflection and absorption characteristics of multiple materials to light, renders lighting and shadow effects, optimizes the texture and details of the model, and generates the final three-dimensional visual model.

[0096] In the geometric reconstruction submodule of 3D modeling, the data format used is mainly color image data, such as RGB format. This module uses stereo vision algorithms, such as block matching-based stereo vision algorithms, to perform geometric analysis on images. This process involves matching the same object in the image from different perspectives, calculating the disparity between these matching points, and then estimating the depth information of each pixel. In the specific implementation process, the algorithm first adjusts the color of the input image to improve the consistency between images, then extracts features from the adjusted image, and searches for corresponding feature points in images from multiple perspectives. The disparity calculation of these feature points generates a 3D geometric structure model. This model can accurately represent the shape and size of objects in 3D space, providing a basis for subsequent texture mapping and depth estimation.

[0097] In the texture mapping submodule, surface reconstruction technology, such as multi-view stereo matching technology, is applied based on the three-dimensional geometric structure model. The data format of this module is still image data, but it focuses more on the texture information of the image. By analyzing the surface of the three-dimensional model, this module extracts texture information from images taken from multiple perspectives. In the texture extraction process, feature point matching and texture analysis algorithms, such as feature point-based texture matching algorithms, are used to determine the correspondence between images. Through texture stitching algorithms, such as texture stitching based on image fusion, these texture information are mapped to the surface of the three-dimensional model. The texture mapping model generated in this way has rich surface details, making the appearance of the three-dimensional model more realistic and helping to improve the final visual effect.

[0098] In the depth estimation submodule, the data format used is converted into depth information, such as a depth map, based on the texture mapping model. This module uses deep learning algorithms, such as a depth estimation algorithm based on a convolutional neural network, to perform depth analysis on the texture mapping model. Through this algorithm, depth information can be extracted from image data and converted into position information of each point in three-dimensional space. The specific process of depth estimation includes using a deep network to extract features from the input texture mapping model and generate a depth map based on these features. The depth estimation model generated in this way can accurately describe the positional relationship of objects in space and provide key data for further correction and optimization of the model.

[0099] In the model correction submodule, model correction is performed based on the depth estimation model combined with the ray tracing method. The data processing of this module includes not only geometry and texture information, but also lighting and material information. By simulating the propagation path of light in three-dimensional space, this module takes into account the reflection and absorption characteristics of light by various materials. The specific implementation of ray tracing includes building a light propagation model, calculating the interaction between light and the model surface, and simulating the influence of different materials on light. The generated three-dimensional visual model not only has accurate geometry and texture information, but also has realistic lighting and shadow effects, which greatly enhances the realism and visual effect of the model.

[0100] See also Figure 5 ,The feature mapping module includes a feature extraction submodule, a pattern recognition submodule, a feature database submodule, and a mapping optimization submodule;

[0101] The feature extraction submodule uses a convolutional neural network analysis algorithm to perform deep feature analysis on the final 3D visual model. Based on the edge and texture of the model, it extracts features through multiple convolutional layers, reduces feature dimensions through pooling layers, locates and extracts key feature points in the model, and generates feature extraction analysis.

[0102] The pattern recognition submodule is based on feature extraction analysis, applies pattern recognition algorithms to classify and identify the extracted features, identifies known patterns through template matching, and uses clustering analysis to classify similar features, distinguish differentiated feature categories and patterns, and generate pattern recognition analysis;

[0103] The feature database submodule is based on pattern recognition analysis and uses machine learning algorithms for in-depth data analysis and processing. The decision tree algorithm makes classification decisions based on feature attributes, the support vector machine algorithm handles nonlinear feature relationships, and the neural network algorithm learns feature patterns. Through intelligent optimization, feature database integration is generated.

[0104] The mapping optimization submodule is based on feature database integration, uses genetic algorithms for data association mapping, and performs efficiency optimization processing, including initializing the population to define the starting point of the search, evaluating the ability of individuals to adapt to the environment through the fitness function, evolving the population through selection operators, crossover operators, and mutation operators, optimizing the data mapping process, and generating feature mapping optimization analysis.

[0105] In the feature extraction submodule, the final 3D visual model is processed by a convolutional neural network (CNN) analysis algorithm. The data format is usually a numerical representation of the 3D model, including the edge and texture information of the model. The operation flow of this module includes the application of multiple convolutional layers to extract spatial features from the model; then, the dimensions of these features are reduced through pooling layers to retain important feature information. In this process, CNN can effectively locate and extract key feature points in the model, such as edges, corners, etc. The generated feature extraction analysis report describes the key features of the model and provides a basis for subsequent pattern recognition and data analysis.

[0106] In the pattern recognition submodule, the pattern recognition algorithm is applied to classify and identify the results of feature extraction analysis. In this process, the data format is converted into feature vectors to facilitate classification algorithm processing. The main methods used in the module include template matching and cluster analysis. Template matching is used to identify features that match known patterns, while cluster analysis classifies similar features and distinguishes different feature categories and patterns. Through these operations, the module generates a pattern recognition analysis report that shows the classification and recognition results between features, providing a clear direction for building a feature database and further mapping optimization.

[0107] In the feature database submodule, based on the results of pattern recognition analysis, machine learning algorithms are used for deep data processing. This module covers a variety of algorithms such as decision trees, support vector machines (SVMs), and neural networks. The decision tree algorithm focuses on making classification decisions through feature attributes; SVMs handle nonlinear feature relationships; and neural networks learn and simulate feature patterns. The output of this module is a feature database integration that not only stores a large amount of feature information, but also contains the relationships and patterns between features, providing rich data resources for the final mapping optimization.

[0108] In the mapping optimization submodule, based on the feature database integration, a genetic algorithm is used to optimize the data mapping. This module initializes the population to define the starting point of the search, and then evaluates the ability of each individual to adapt to the environment through the fitness function. The evolution of the population is achieved through the application of selection operators, crossover operators, and mutation operators, thereby optimizing the data mapping process. The generated feature mapping optimization analysis shows the efficiency and effect of data association mapping, which is crucial to improving the performance and accuracy of the entire system. Through these optimization measures, the efficiency and accuracy of the data mapping process are ensured, providing a guarantee for the stable operation and efficient performance of the system.

[0109] See also Figure 6 ,The parameter adjustment module includes a parameter learning submodule, an adaptive adjustment submodule, a process optimization submodule, and a real-time update submodule;

[0110] The parameter learning submodule uses an adaptive learning algorithm to learn and adjust the detection parameters based on feature mapping optimization analysis, and adopts feedback adjustment and parameter optimization mechanisms to automatically adjust parameters according to the differences in detection conditions, adapt to differentiated detection environments, and generate parameter learning adjustments;

[0111] The adaptive adjustment submodule is based on parameter learning and adjustment, and applies neural network and fuzzy logic to perform adaptive adjustment and optimize detection parameters and conditions. The neural network adjusts the learning pattern information through weights, and the fuzzy logic processes uncertainty and fuzzy information according to the fuzzy rules to generate adaptive parameter optimization.

[0112] The process optimization submodule is based on adaptive parameter optimization and uses simulated annealing algorithm to optimize the structure of the detection process, including setting the initial solution, defining the neighborhood structure, implementing the temperature scheduling plan, and automatically adjusting the parameters through the acceptance criterion to optimize the structure and efficiency of the entire detection process and generate process optimization analysis;

[0113] The real-time update submodule is based on process optimization analysis and adopts the Bayesian optimization algorithm to update the detection parameters and conditions in real time, and perform adaptive adjustments, including selecting a priori models, posterior updates, defining acquisition functions, and selecting optimal points, to achieve real-time update and optimization of detection parameters and generate adaptive detection parameters.

[0114] In the parameter learning submodule, an adaptive learning algorithm (such as gradient descent) is used.

[0115] Gradient Descent Optimization

[0116] Example code (Python):

[0117] def gradient_descent(learning_rate, n_iterations, params, data):

[0118] for _ in range(n_iterations):

[0119] gradients = compute_gradients(params, data)

[0120] params = params - learning_rate gradients

[0121] return params

[0122] # Example usage# params is the parameter set, data is the detection data

[0123] optimized_params = gradient_descent(0.01, 100, params, data)

[0124] In the adaptive adjustment submodule, neural network and fuzzy logic are used.

[0125] Neural Network Optimization

[0126] Example code (Python):

[0127] from keras.models import Sequentialfrom keras.layers import Dense

[0128] model = Sequential()

[0129] model.add(Dense(12, input_dim=len(params), activation='relu'))

[0130] model.add(Dense(len(params), activation='sigmoid'))

[0131] model.compile(loss='mean_squared_error', optimizer='adam')

[0132] model.fit(data, params, epochs=100, batch_size=10)

[0133] Fuzzy Logic Optimization

[0134] Example code (Python):

[0135] import numpy as npimport skfuzzy as fuzz

[0136] def fuzzy_logic_optimizer(data):

[0137] # Create a fuzzy logic system

[0138] # ...

[0139] return optimized_params

[0140] optimized_params = fuzzy_logic_optimizer(data)

[0141] In the process optimization submodule, the simulated annealing algorithm is used.

[0142] Simulated Annealing Algorithm

[0143] Example code (Python):

[0144] def simulated_annealing_optimization(params, data):

[0145] current_solution = params

[0146] current_cost = compute_cost(current_solution, data)

[0147] T = 1.0

[0148] T_min = 0.00001

[0149] alpha = 0.9

[0150] while T > T_min:

[0151] i = 1

[0152] while i <= 100:

[0153] new_solution = neighbor_solution(current_solution)

[0154] new_cost = compute_cost(new_solution, data)

[0155] cost_diff = new_cost - current_cost

[0156] if cost_diff < 0 or np.random.rand() < np.exp(-cost_diff / T):

[0157] current_solution = new_solution

[0158] current_cost = new_cost

[0159] i += 1

[0160] T = T alpha

[0161] return current_solution

[0162] optimized_params = simulated_annealing_optimization(params, data)

[0163] In the real-time update submodule, Bayesian optimization is adopted.

[0164] Bayesian Optimization

[0165] Example code (Python):

[0166] from bayes_opt import BayesianOptimization

[0167] def bayesian_optimization(data):

[0168] # Define the objective function for optimization

[0169] def target_function(params):

[0170] return compute_performance_metric(params, data)

[0171] optimizer = BayesianOptimization(

[0172] f=target_function,

[0173] pbounds={'param1': (0, 1), 'param2': (0, 1)},

[0174] random_state=1, )

[0176] optimizer.maximize(init_points=2, n_iter=3)

[0177] return optimizer.max['params']

[0178] optimized_params = bayesian_optimization(data)

[0179] See also Figure 7 ,The quality trend analysis module includes data acquisition submodule, trend prediction submodule, analysis and evaluation submodule, and risk early warning submodule;

[0180] The data acquisition submodule uses multi-source data integration technology to capture and pre-process data based on adaptive detection parameters, removes erroneous or inconsistent data through data cleaning, integrates multiple data through fusion strategies, improves data integration effects, and generates quality data sets;

[0181] The trend prediction submodule analyzes time series data based on quality data sets through the autoregressive integrated moving average model to detect and predict trends. It also uses machine learning time series analysis methods, including decision trees, support vector machines, and neural networks, to identify trends and anomalies, estimate trends in future time periods, and generate trend prediction reports.

[0182] The analysis and evaluation submodule uses statistical hypothesis testing and variance analysis methods to conduct in-depth analysis based on the trend forecast report. ANOVA is used to analyze the differences between data groups, the chi-square test is used to evaluate the data distribution deviation, and the T test is used to compare the differences between two groups of data to generate an evaluation and analysis report.

[0183] The risk warning submodule is based on the assessment and analysis report and uses risk matrix and sensitivity analysis to conduct risk assessment. The risk matrix is ​​used to conduct quantitative risk assessment, and sensitivity analysis is used to identify qualitative risk points. Risk levels are then divided to generate a risk warning report.

[0184] In the data acquisition submodule of the quality trend analysis module, the data formats mainly include structured data such as database tables, unstructured data such as text and images, and semi-structured data such as XML and JSON. This submodule uses multi-source data integration technology to capture and preprocess data based on adaptive detection parameters. This process involves data cleaning, eliminating erroneous or inconsistent data, and using algorithms such as data verification rules and anomaly detection methods. At the same time, the data fusion strategy integrates multiple data sources to improve the data integration effect, including data matching algorithms and data fusion models. The implementation process of this operation ensures the quality and consistency of the data, providing an accurate and comprehensive quality data set for subsequent analysis.

[0185] In the trend prediction submodule, the autoregressive integrated moving average (ARIMA) model and machine learning time series analysis methods are used based on the quality data set. The time series data is analyzed by the ARIMA model, which detects and predicts trends by identifying trends, seasonality, and noise components in the data. Machine learning time series analysis methods, including decision trees, support vector machines, and neural networks, are used to identify trends and anomalies. These methods learn the underlying patterns of the data through training and testing data sets and estimate trends in future time periods. The operation of this submodule generates a trend forecast report, which reveals the future development trend of the data and helps decision makers make more accurate predictions.

[0186] In the analysis and evaluation submodule, trend forecast reports are processed and in-depth analysis is performed using statistical hypothesis testing and variance analysis methods. ANOVA analysis is used to analyze differences between data groups, the chi-square test evaluates data distribution deviations, and the T test compares differences between two groups of data. These statistical methods provide an in-depth understanding of the data and reveal key trends and patterns in the data. Through the application of these methods, the generated evaluation and analysis reports provide an in-depth interpretation of the data and provide a scientific basis for further decision-making and analysis.

[0187] In the risk warning submodule, risk assessment is performed using risk matrix and sensitivity analysis based on the assessment analysis report. The risk matrix performs quantitative risk assessment, involving the calculation of risk probability and impact, as well as the classification of risk levels. Sensitivity analysis is used to identify qualitative risk points and analyze their impact on the results through changes in variables. The operation of this submodule generates a risk warning report, which not only provides a comprehensive assessment of potential risks, but also provides an important basis for the formulation of risk management and mitigation strategies.

[0188] See also Figure 8 ,The structural integrity assessment module includes the structural modeling submodule, the stress analysis submodule, the durability test submodule, and the safety assessment submodule;

[0189] The structural modeling submodule is based on a 3D visual model, using computer-aided design and geometric modeling technology. It uses computer-aided design to adjust and optimize the geometric parameters of the product, and generates a structural model through NURBS modeling and polygonal modeling.

[0190] The stress analysis submodule is based on the structural model. It uses finite element analysis to identify and evaluate the stress distribution in the model. It also uses stress-strain simulation technology to perform static and dynamic load simulations, simulate the stress response of materials under differentiated conditions, and generate a stress analysis report.

[0191] The durability test submodule uses accelerated life testing to evaluate the strength of the structure based on the stress analysis report, and uses reliability analysis techniques, including stress testing and failure mode effect analysis, to detect and analyze potential failure modes, evaluate the durability of the product, and generate a durability assessment report;

[0192] The safety assessment submodule is based on the durability assessment report, evaluates the risk level of the product through safety indicators, and adopts risk assessment standards, including safety factor calculation and fault tree analysis, to generate a structural safety assessment.

[0193] In the structural modeling submodule, the geometric parameters of the product are adjusted and optimized based on the 3D visual model through computer-aided design (CAD) and geometric modeling technology. In this process, NURBS (non-uniform rational B-spline) modeling and polygon modeling technology are used to accurately describe curves and surfaces, thereby generating high-quality structural models. During the operation, the precise construction of complex surfaces is achieved by adjusting the control points and weights. The completion of this step not only generates a structural model file, but also provides a solid foundation for subsequent stress analysis and durability testing.

[0194] In the stress analysis submodule, finite element analysis (FEA) and stress-strain simulation techniques are used based on the generated structural model. Through finite element analysis, the stress distribution in the model can be identified and evaluated, which involves detailed operations such as model meshing, applying loads and constraints. Stress-strain simulation technology is used to perform static and dynamic load simulations to simulate the stress response of materials under different conditions. After completing these operations, the generated stress analysis report reveals the performance of the structure under various load conditions, providing an important basis for product design optimization and structural improvement.

[0195] In the durability test submodule, based on the stress analysis report, accelerated life test and reliability analysis technology are used. During this process, the strength of the structure is comprehensively evaluated, and methods such as stress test and failure mode effect analysis (FMEA) are used to detect and analyze potential failure modes. Accelerated life test predicts the life of the product by simulating extreme working conditions, while reliability analysis focuses on the performance of the product under normal use conditions. Through these tests and analyses, the durability evaluation report generated not only reveals the durability of the product, but also provides key information for subsequent improvements and optimizations.

[0196] In the safety assessment submodule, safety indicators and risk assessment criteria are applied based on the durability assessment report. The risk level of the product is evaluated through safety indicators, and the safety of the product is comprehensively evaluated by combining methods such as safety factor calculation and fault tree analysis (FTA). The calculation of the safety factor takes into account the performance of the product under various possible working conditions, while the fault tree analysis identifies and evaluates the various factors that lead to potential failures. After completing these operations, the generated structural safety assessment report not only describes the safety performance of the product, but also provides strong support for ensuring user safety and compliance with relevant safety standards.

[0197] See also Fig. 9 ,The equipment maintenance strategy module includes the maintenance demand analysis submodule, the ,prediction modeling submodule, the strategy planning submodule, and the ,plan execution submodule;

[0198] The maintenance demand analysis submodule uses fault tree analysis and failure mode impact analysis based on structural safety assessment and equipment historical operation data. Through fault tree analysis, it comprehensively refers to multiple potential failure causes and their interactions, and through failure mode impact analysis, it quantifies the reliability of multiple failure modes, locates key maintenance nodes, and generates a maintenance task list;

[0199] The predictive modeling submodule is based on the maintenance task list. It uses support vector machines to analyze historical data, identify patterns and trends in maintenance needs, and uses time series analysis to predict the status in future time periods, optimize maintenance strategies, and generate maintenance prediction models.

[0200] The strategy planning submodule uses genetic algorithms to generate initial resource allocation plans based on the maintenance prediction model, and mines the optimal solution by simulating the natural selection and genetic variation process. It also uses linear programming to make adjustments to maintain the optimal configuration of resources and time, including schedules and resource allocation, and generates maintenance action plans.

[0201] The plan execution submodule implements task scheduling and performance monitoring based on the maintenance action plan. Task scheduling ensures that multiple maintenance activities are carried out in an orderly manner as planned. Performance monitoring tracks the progress and effects of maintenance operations in real time, evaluates and adjusts the effects of maintenance execution, and generates maintenance execution records.

[0202] In the maintenance demand analysis submodule, fault tree analysis (FTA) and failure mode effect analysis (FMEA) are used through structural safety assessment and historical equipment operation data. Fault tree analysis comprehensively considers multiple potential causes of failure and their interactions. In order to quantify the reliability of multiple failure modes, FMEA technology is used to locate key maintenance nodes. In this process, the data format usually includes equipment logs, maintenance records, and operating parameters. Through these analyses, the generated maintenance task list not only lists the maintenance areas that need attention, but also emphasizes the urgency and importance of each task, providing a solid foundation for the subsequent maintenance strategy formulation.

[0203] In the predictive modeling submodule, support vector machines (SVM) and time series analysis are used based on the maintenance task list. Support vector machines are used to analyze historical data and identify patterns and trends in maintenance needs, while time series analysis is used to predict the status of equipment in future time periods. In this step, the data format includes historical maintenance records and equipment operation data. After completing these operations, the generated maintenance prediction model not only optimizes the maintenance strategy, but also provides a scientific basis for reducing future maintenance costs and improving equipment operation efficiency.

[0204] In the strategy planning submodule, based on the maintenance prediction model, genetic algorithms and linear programming are used to generate an initial plan for resource allocation, simulate natural selection and genetic variation processes to mine the optimal solution, and use linear programming to make adjustments to maintain the optimal configuration of resources and time. This includes schedules and resource allocation plans. Through the application of these methods, the generated maintenance action plan not only ensures the effective use of resources, but also lays the foundation for the smooth completion of maintenance tasks.

[0205] In the plan execution submodule, task scheduling and performance monitoring are implemented based on the maintenance action plan. Task scheduling ensures that each maintenance activity is carried out in an orderly manner as planned, while performance monitoring tracks the progress and effect of maintenance operations in real time. Through these operations, the generated maintenance execution record not only provides a detailed record of maintenance activities, but also provides important information for the evaluation of maintenance effects and subsequent adjustments. The completion of this link ensures the effective implementation of equipment maintenance strategies and improves the transparency and traceability of the entire maintenance process.

[0206] See also Fig.10 ,The process optimization module includes the process analysis submodule, the efficiency improvement submodule, the system integration submodule, and the improvement implementation submodule;

[0207] The process analysis submodule analyzes the operation log based on enhanced images, 3D visual models and feature mapping, identifies patterns and bottlenecks in the workflow, and uses data visualization technology to display the analysis results, assist in locating optimization targets and improvement points, and generate process optimization guidelines;

[0208] The efficiency improvement submodule is based on the process optimization guide, adopts the Six Sigma method and lean management technology, identifies defects in the process through the Six Sigma method, guides the reduction and elimination of defects, and analyzes the overall value stream through lean management technology, designs and implements work processes, and generates efficiency improvement plans;

[0209] The system integration submodule is based on the efficiency improvement plan, adopts big data analysis technology and service-oriented architecture, integrates and analyzes multi-source data through the big data platform, optimizes resource allocation and use, and uses modular services and system components to improve the overall coordination and efficiency of the system and generate a system coordination plan;

[0210] The improvement implementation submodule is based on a system coordination plan and adopts project management methods and continuous improvement techniques. It ensures the setting of improvement goals and the tracking of project progress through project management methods, and ensures the implementation of improvement measures through iterative evaluation and adjustment, and generates improvement implementation records.

[0211] In the process analysis submodule, the data format is mainly enhanced images, 3D visual models and feature maps. This module uses process mining and data visualization technology. The specific operations include using image recognition algorithms to analyze enhanced images and using 3D reconstruction technology to process 3D visual models. Through these technologies, the module can identify workflow patterns and bottlenecks from operation logs. Data visualization technology plays an important role here. It presents complex data relationships through charts and graphs, making optimization goals and improvement points clear at a glance. The process optimization guide generated by this process contains improvement suggestions and steps, providing a solid foundation for subsequent efficiency improvement and system integration.

[0212] In the efficiency improvement submodule, the data format is mainly based on the indicators and suggestions in the process optimization guide. This module adopts the Six Sigma method and lean management technology, including the use of statistical analysis tools to identify defects in the process and systematically improve the process according to the Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) framework. Lean management technology is used to analyze the overall value stream and help design and implement more efficient workflows. The efficiency improvement plan generated by this module records the improvement measures and expected results of each step in detail. These results not only improve work efficiency, but also provide a clear blueprint for the optimization of the entire system.

[0213] In the system integration submodule, the data format is mainly the result of big data analysis and the components of the service-oriented architecture. This module uses big data technology to conduct comprehensive analysis of multi-source data, optimizes resource allocation and use through algorithms, and makes data-driven decisions more accurate and efficient. The application of service-oriented architecture makes the system more modular and flexible, improving overall coordination and efficiency. The generated system coordination plan describes in detail how each system component interacts with each other and how these interactions can improve overall performance.

[0214] In the improvement implementation submodule, the data format is transformed into a project management plan and continuous improvement record. This module ensures the setting of improvement goals and the tracking of project progress through project management methods, and uses iterative evaluation and adjustment techniques to ensure the implementation of improvement measures. The improvement implementation record records the execution process and results of each step in detail, ensuring that the improvement measures can be effectively tracked and evaluated. This process not only improves the efficiency of project management, but also ensures that the improvement measures can be continuously and effectively implemented.

[0215] 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. The final inspection appearance inspection system based on industrial camera shooting is characterized by: The system includes an image enhancement module, a three-dimensional modeling module, a feature mapping module, a parameter adjustment module, a quality trend analysis module, a structural integrity assessment module, an equipment maintenance strategy module, and a process optimization module; The image enhancement module uses a Gaussian blur algorithm to smooth the original image taken by the industrial camera, and uses a sharpening algorithm to improve the clarity of image details. It redistributes the image brightness and enhances the contrast through histogram equalization technology to generate an enhanced image. The 3D modeling module reconstructs the object shape from multiple perspectives based on the enhanced image and the stereo vision algorithm, refines the model surface texture with surface reconstruction technology, and uses ray tracing to simulate the interaction between light and objects, optimizes the model texture and light and shadow effects, and generates a 3D visual model. The feature mapping module is based on a three-dimensional visual model, uses a convolutional neural network to deeply analyze model features, extracts key structure and surface texture information, and maps model features through feature point detection technology to generate feature mapping analysis results; The parameter adjustment module uses an adaptive learning algorithm to dynamically adjust model parameters based on feature feedback based on feature mapping analysis results, and uses neural networks and fuzzy logic to work together to optimize detection parameters and conditions and generate adaptive detection parameters; The parameter adjustment module includes a parameter learning submodule, an adaptive adjustment submodule, a process optimization submodule, and a real-time update submodule; The parameter learning submodule uses an adaptive learning algorithm to learn and adjust the detection parameters based on feature mapping optimization analysis, and adopts feedback adjustment and parameter optimization mechanism to automatically adjust the parameters according to the differences in detection conditions, adapt to the differentiated detection environment, and generate parameter learning adjustments; The adaptive adjustment submodule is based on parameter learning adjustment, and applies neural network and fuzzy logic to perform adaptive adjustment and optimize detection parameters and conditions. The neural network adjusts the learning mode information through weights, and the fuzzy logic processes uncertainty and fuzzy information according to fuzzy rules to generate adaptive parameter optimization; The process optimization submodule uses a simulated annealing algorithm to optimize the structure of the detection process based on adaptive parameter optimization, including setting the initial solution, defining the neighborhood structure, implementing the temperature scheduling scheme, and automatically adjusting the parameters through the acceptance criterion to optimize the structure and efficiency of the entire detection process and generate a process optimization analysis; The real-time update submodule uses the Bayesian optimization algorithm to perform real-time update of detection parameters and conditions based on process optimization analysis, and performs adaptive adjustment, including selecting a priori models, a posteriori updates, defining acquisition functions, and selecting optimal points, to achieve real-time update and optimization of detection parameters and generate adaptive detection parameters; The quality trend analysis module uses an autoregressive integrated moving average model to analyze the quality data time series based on adaptive detection parameters, identify trend changes and fluctuations, and predict the quality change trend in the future time period through a trend analysis algorithm to generate a trend forecast analysis; The structural integrity assessment module uses finite element analysis to numerically simulate the product structure based on a three-dimensional visual model, and simulates the stress distribution under differentiated working conditions through a stress test algorithm to assess structural stability and potential risks, and generate a structural safety assessment; The equipment maintenance strategy module uses data-driven machine learning methods to analyze maintenance needs based on structural safety assessment and equipment historical operation data, and identifies key maintenance points through support vector machines, formulates targeted maintenance strategies, and generates maintenance strategy plans; The process optimization module integrates enhanced images, 3D visual models, feature mapping analysis, adaptive detection parameters, trend prediction analysis, structural safety assessment, maintenance strategy planning, and practical process management and Six Sigma methods to generate an optimized detection process; The process optimization module includes a process analysis submodule, an efficiency improvement submodule, a system integration submodule, and an improvement implementation submodule; The process analysis submodule analyzes the operation log based on the enhanced image, 3D visual model and feature mapping analysis to identify the patterns and bottlenecks in the workflow, and uses data visualization technology to display the analysis results, assist in locating optimization goals and improvement points, and generate process optimization guidelines; The efficiency improvement submodule is based on the process optimization guide, adopts the Six Sigma method and lean management technology, identifies defects in the process through the Six Sigma method, guides the reduction and elimination of defects, and analyzes the overall value stream through lean management technology, designs and implements work processes, and generates efficiency improvement plans; The system integration submodule is based on the efficiency improvement plan, adopts big data analysis technology and service-oriented architecture, integrates and analyzes multi-source data through the big data platform, optimizes resource allocation and use, and uses modular services and system components to improve the overall coordination and efficiency of the system and generate a system coordination plan; The improvement implementation submodule is based on a system coordination plan, adopts project management methods and continuous improvement techniques, ensures the setting of improvement goals and tracking of project progress through project management methods, and ensures the implementation of improvement measures through iterative evaluation and adjustment, and generates improvement implementation records.

2. The final inspection appearance detection system based on industrial camera shooting according to claim 1 is characterized in that: The three-dimensional visual model includes the product's geometric structure, surface texture and lighting effects. The feature mapping analysis results include the product's key structural features, surface texture features and abnormal feature points. The adaptive detection parameters include adjusted sensitivity settings, target feature recognition thresholds and processing speeds. The trend prediction analysis includes changing trends and potential risk points in production quality over a period of time in the future. The structural safety assessment includes potential structural weaknesses, stress distribution and durability predictions. The maintenance strategy plan includes maintenance priority sorting, maintenance activities and time scheduling. The optimized detection process includes process simplification measures, efficiency improvement strategies and error prevention mechanisms.

3. The final inspection appearance detection system based on industrial camera shooting according to claim 1 is characterized in that: The image enhancement module includes a denoising submodule, a contrast enhancement submodule, an edge sharpening submodule, and a color adjustment submodule; The denoising submodule uses a Gaussian blur algorithm to perform denoising based on the original image taken by the industrial camera, and reduces image noise by calculating the Gaussian weighted average of pixels in the image, keeping the main structure of the image unchanged, and generating a denoised image; The contrast enhancement submodule improves the contrast of the image based on the denoising process image by applying the histogram equalization method, and by adjusting the brightness distribution of the image, enhancing the image brightness and distributing it evenly, strengthening the contrast of multiple brightness levels of the image, and generating a contrast enhanced image; The edge sharpening submodule processes the image edge using the Laplace sharpening algorithm based on the contrast enhanced image, improves the image edge clarity by identifying the edge area of ​​the image and enhancing the brightness difference of the area, and generates an edge sharpened image; The color adjustment submodule implements color adjustment technology to adjust the color attributes of the image based on the edge sharpening image, including adjusting the hue to match the target color temperature, increasing or decreasing the saturation, and adjusting the brightness and contrast to generate a color adjusted image.

4. The final inspection appearance inspection system based on industrial camera shooting according to claim 1 is characterized in that: The three-dimensional modeling module includes a geometry reconstruction submodule, a texture mapping submodule, a depth estimation submodule, and a model correction submodule; The geometric reconstruction submodule uses a stereoscopic vision algorithm to perform geometric analysis on the image based on the color adjustment image, calculates the disparity information by capturing image data from multiple perspectives, estimates the depth of each pixel, and generates a three-dimensional geometric structure model; The texture mapping submodule uses surface reconstruction technology to perform texture mapping based on a three-dimensional geometric structure model, analyzes the surface of the three-dimensional model, extracts texture information through images taken from multiple perspectives, and uses a texture stitching algorithm to map the texture to the surface of the three-dimensional model to generate a texture mapping model; The depth estimation submodule performs depth estimation based on the texture mapping model, creates a depth map, converts image data into depth information, and selects the position of each point in the three-dimensional space by analyzing the position relationship of the object in space to generate a depth estimation model; The model correction submodule is based on the depth estimation model and combines the ray tracing method to perform model correction. By simulating the propagation path of light in three-dimensional space and referring to the reflection and absorption characteristics of multiple materials to light, it renders the lighting and shadow effects, optimizes the texture and details of the model, and generates the final three-dimensional visual model.

5. The final inspection appearance detection system based on industrial camera shooting according to claim 1 is characterized in that: The feature mapping module includes a feature extraction submodule, a pattern recognition submodule, a feature database submodule, and a mapping optimization submodule; The feature extraction submodule is based on the final 3D visual model and uses a convolutional neural network analysis algorithm to perform deep feature analysis on the model. Based on the edge and texture of the model, it extracts features through multiple convolutional layers, reduces feature dimensions through pooling layers, locates and extracts key feature points in the model, and generates feature extraction analysis; The pattern recognition submodule is based on feature extraction analysis, applies pattern recognition algorithms to classify and identify the extracted features, identifies known patterns through template matching, and uses cluster analysis to classify similar features, distinguish differentiated feature categories and patterns, and generate pattern recognition analysis; The feature database submodule is based on pattern recognition analysis and uses machine learning algorithms to perform in-depth data analysis and processing. The decision tree algorithm makes classification decisions based on feature attributes, the support vector machine algorithm processes nonlinear feature relationships, and the neural network algorithm learns feature patterns. Through intelligent optimization, a feature database integration is generated; The mapping optimization submodule is based on feature database integration, uses genetic algorithms to perform data association mapping, and performs efficiency optimization processing, including initializing the population to define the starting point of the search, evaluating the ability of individuals to adapt to the environment through the fitness function, evolving the population through selection operators, crossover operators and mutation operators, optimizing the data mapping process, and generating feature mapping optimization analysis.

6. The final inspection appearance detection system based on industrial camera shooting according to claim 1 is characterized in that: The quality trend analysis module includes a data acquisition submodule, a trend prediction submodule, an analysis and evaluation submodule, and a risk warning submodule; The data acquisition submodule uses multi-source data integration technology to capture and pre-process data based on adaptive detection parameters, removes erroneous or inconsistent data through data cleaning, integrates multiple data through fusion strategies, improves data integration effects, and generates quality data sets; The trend prediction submodule analyzes time series data based on the quality data set through the autoregressive integrated moving average model, detects and predicts trends, and uses machine learning time series analysis methods, including decision trees, support vector machines, and neural networks, to identify trends and anomalies, estimate trends in future time periods, and generate trend prediction reports; The analysis and evaluation submodule is based on the trend forecast report, uses statistical hypothesis testing and variance analysis methods to perform in-depth analysis, analyzes differences between data groups through ANOVA, evaluates data distribution deviation through chi-square test, and compares differences between two groups of data through T test to generate an evaluation and analysis report; The risk warning submodule is based on the assessment and analysis report, and uses risk matrix and sensitivity analysis to conduct risk assessment. The risk matrix is ​​used to conduct quantitative risk assessment, and sensitivity analysis is used to identify qualitative risk points. Risk levels are divided and a risk warning report is generated.

7. The final inspection appearance detection system based on industrial camera shooting according to claim 1 is characterized in that: The structural integrity assessment module includes a structural modeling submodule, a stress analysis submodule, a durability test submodule, and a safety assessment submodule; The structural modeling submodule is based on a three-dimensional visual model, adopts computer-aided design and geometric modeling technology, uses computer-aided design to adjust and optimize the geometric parameters of the product, and generates a structural model through NURBS modeling and polygonal modeling; The stress analysis submodule is based on the structural model, identifies and evaluates the stress distribution in the model through finite element analysis, and uses stress-strain simulation technology to perform static and dynamic load simulation, simulate the stress response of the material under differentiated conditions, and generate a stress analysis report; The durability test submodule uses accelerated life testing to evaluate the strength of the structure based on the stress analysis report, and uses reliability analysis techniques, including stress testing and failure mode impact analysis, to detect and analyze potential failure modes, evaluate the durability of the product, and generate a durability evaluation report; The safety assessment submodule evaluates the risk level of the product through safety indicators based on the durability assessment report, and adopts risk assessment standards, including safety factor calculation and fault tree analysis, to generate a structural safety assessment.

8. The final inspection appearance detection system based on industrial camera shooting according to claim 1 is characterized in that: The equipment maintenance strategy module includes a maintenance demand analysis submodule, a prediction modeling submodule, a strategy planning submodule, and a plan execution submodule; The maintenance demand analysis submodule adopts fault tree analysis and failure mode impact analysis based on structural safety assessment and equipment historical operation data. Through fault tree analysis, it comprehensively refers to multiple potential failure causes and their interactions, and through failure mode impact analysis, it quantifies the reliability of multiple failure modes, locates key maintenance nodes, and generates a maintenance task list; The predictive modeling submodule analyzes historical data based on the maintenance task list through support vector machines to identify the patterns and trends of maintenance requirements, and uses time series analysis of historical trends to predict the status in future time periods, optimize maintenance strategies, and generate maintenance prediction models; The strategy planning submodule uses a genetic algorithm to generate an initial resource allocation plan based on a maintenance prediction model, mines the optimal solution by simulating natural selection and genetic variation processes, and uses linear programming to make adjustments to maintain the optimal configuration of resources and time, including timetables and resource allocation, and generates a maintenance action plan; The plan execution submodule implements task scheduling and performance monitoring based on the maintenance action plan. Task scheduling ensures that multiple maintenance activities are executed in an orderly manner as planned. Performance monitoring tracks the progress and effects of maintenance operations in real time, evaluates and adjusts the effects of maintenance execution, and generates maintenance execution records.

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