Diamond painting color durability intelligent evaluation system based on AI

Abstract

An AI-based diamond picture color durability intelligent evaluation system of the invention belongs to the technical performance evaluation field, and comprises the following steps: constructing multi-field coupling stress equivalent simulation of a diamond picture composite interface, and simulating a real service environment; establishing a visual sampling rule engine, collecting a multispectral image, extracting CIELAB parameters by using the rule engine, and generating a dynamic natural color difference matrix; an interface failure chain reaction knowledge graph is established, and a dynamic correction formula correction knowledge graph is established; dynamic color attenuation time sequence prediction analysis is designed, a color difference value time change curve is generated, curve characteristics are identified through reinforcement learning, attenuation stages are divided, and a four-dimensional dynamic correlation model is established; stratified sampling with enhanced sample representativeness is established, feature areas are selected according to two-dimensional stratification, and a standardized sampling evaluation result is formed; and constructing a full-link intelligent conversion link according to a standardized sampling evaluation result. According to the method, durability is accurately analyzed through AI full-link evaluation, and conversion from the technology to the industrial value is achieved.

Description

Technical Field

[0001] This invention belongs to the field of performance evaluation and analysis technology, specifically an AI-based intelligent evaluation system for the color durability of diamond paintings. Background Technology

[0002] The diamond painting industry has developed rapidly with the rise of home decoration demand, but its diamond-glue-canvas composite structure is easily affected by environmental factors such as light, temperature and humidity, resulting in problems such as color fading and yellowing of the glue layer.

[0003] The core drawback of traditional intelligent assessment methods for the color durability of diamond paintings lies in their environmental simulation. These methods often rely on single factors like light and temperature / humidity, failing to replicate the simultaneous effects of four factors in real-world service environments: temperature and humidity cycles, physical friction, dust adhesion, and oxygen concentration gradients. Furthermore, they neglect the stress transmission path at the diamond-adhesive-canvas composite interface, a crucial target point, leading to significant discrepancies between assessment results and actual usage. Sampling and data processing depend on manual location of typical areas, making micron-level precision marking difficult. Additionally, the use of a single color difference threshold criterion fails to consider the coupled effects of interface failure characteristics such as adhesive yellowing, diamond coating damage, and canvas ink penetration. The lack of a scientific calibration mechanism limits assessment accuracy and efficiency. Traditional methods only output basic color decay data, failing to establish a link between data and process optimization or scenario adaptation. This prevents guidance for adhesive formulation upgrades and coating modifications in production, and also fails to provide targeted post-production protection recommendations for end-users, resulting in a significant disconnect between assessment results and industrial applications. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention proposes an AI-based intelligent evaluation system for the color durability of diamond paintings. This invention primarily addresses the problem of significant discrepancies between the color durability evaluation results and actual usage conditions of diamond paintings.

[0005] The AI-based intelligent evaluation system for the color durability of diamond paintings provided by this invention includes: a coupling simulation module, used to construct a multi-field coupling stress equivalent simulation of the diamond-adhesive-canvas composite interface of the diamond painting, simulating the real service environment of dynamic indoor natural light irradiation and seasonal temperature and humidity changes, and outputting basic simulation samples.

[0006] The visual sampling module is used to build a visual sampling rule engine based on basic simulated samples. It identifies typical regions and marks micron-level reference points through deep learning, acquires multispectral images, and uses the rule engine to extract CIELAB parameters from the multispectral images to generate a dynamic chromaticity difference matrix.

[0007] The interface criterion module is used to construct a knowledge graph of interface failure chain reactions based on the dynamic intrinsic difference matrix, establish a dynamic correction formula to correct the knowledge graph of interface failure chain reactions using three-dimensional correlation criteria, and output coupled evaluation data.

[0008] The time-series prediction module is used to design dynamic color decay time-series prediction analysis based on coupled evaluation data. It adopts an adaptive acquisition strategy to generate color difference value time change curves, identifies curve features through reinforcement learning to divide decay stages, and establishes a four-dimensional dynamic correlation model.

[0009] The stratified sampling module is used to build stratified sampling and transfer learning rules to enhance sample representativeness based on the four-dimensional dynamic association model. It selects feature regions in a two-dimensional stratification and forms standardized sampling evaluation results after the weights are corrected by the algorithm.

[0010] The closed-loop conversion module is used to construct a complete closed-loop conversion process based on standardized sampling evaluation results, including evaluation, diagnosis, optimization, and verification. It matches the causes of attenuation, outputs customized process solutions, and generates integrated reports by linking with the scenario database, thus constructing a full-link intelligent conversion link.

[0011] The AI-based intelligent evaluation system for the color durability of diamond paintings provided by this invention includes a coupled simulation module comprising:

[0012] The parameter framework construction unit is used to build a multi-field coupled stress simulation framework adapted to the composite interface of diamond painting, determine the parameter range of indoor natural light dynamic spectrum and seasonal temperature and humidity fluctuation, and output parameterized simulation scheme.

[0013] The device debugging and adaptation unit is used to build a low-cost biomimetic simulation device based on the parameterized simulation scheme. It integrates light adjustment and temperature and humidity control modules, calibrates the matching degree between device parameters and simulation scheme, and outputs the debugging simulation device.

[0014] The coupled simulation running unit is used to place diamond painting samples using a debugging simulation device, start a multi-field coupled stress application program, replicate the real service environment, and run continuously for a set period to output basic simulation samples.

[0015] According to the AI-based intelligent evaluation system for the color durability of diamond paintings provided by the present invention, the visual sampling module includes:

[0016] The engine rule preset unit is used to build a visual sampling rule engine for eliminating basal interference based on basic simulation samples, and presets typical region recognition standards and micron-level reference point marking rules.

[0017] Typical region labeling units are used to perform a full-domain scan of the basic simulation samples based on the rule engine and deep learning algorithms, automatically identify three typical regions: solid color, gradient, and AB diamond, and label the micron-level reference points of each region.

[0018] The image preprocessing optimization unit is used to acquire multispectral images of samples using calibrated equipment based on the marked reference points. The images are then input into the rule engine, which automatically crops and removes background noise pixels, simultaneously corrects errors caused by uneven illumination, and outputs a preprocessed image.

[0019] The difference matrix generation unit is used to extract CIELAB color parameters of typical regions in the preprocessed image, calculate the original color difference by comparing with the initial parameters, integrate the difference data by region and time dimension, and generate a dynamic original color difference matrix.

[0020] According to the AI-based intelligent evaluation system for the color durability of diamond paintings provided by this invention, the specific steps for generating a dynamic natural color difference matrix in the difference matrix generation unit are as follows:

[0021] CIELAB color parameters corresponding to reference points in typical regions of the preprocessed image are extracted to form a multidimensional dataset.

[0022] The initial CIELAB color parameters of the sample are retrieved and compared one by one with the corresponding parameters of the preprocessed image. The color difference of each reference point is calculated to generate the difference base dataset.

[0023] The difference dataset is classified and integrated according to the region type and sampling time dimension, and a two-dimensional array with rows and columns corresponding to the region and time is constructed to generate a dynamic intrinsic difference matrix.

[0024] The AI-based intelligent evaluation system for the color durability of diamond paintings provided by this invention includes an interface criterion module comprising:

[0025] The graph construction unit is used to construct a knowledge graph of interface failure chain reaction based on the dynamic intrinsic difference matrix and output the knowledge graph.

[0026] The formula modeling unit is used to build a three-dimensional association criterion based on the knowledge graph, extract the color deviation correction coefficients corresponding to the failure features, establish a dynamic correction formula, and output a parameterized correction model.

[0027] The data calibration unit is used to run the data calibration unit according to the parametric correction model, substitute coefficients to correct the deviation of the color measurement data of the diamond's true color, and output coupled evaluation data.

[0028] According to the AI-based intelligent evaluation system for the color durability of diamond paintings provided by this invention, the specific steps for constructing a knowledge graph of interface failure chain reactions in the graph construction unit are as follows:

[0029] Based on the dynamic color difference matrix, the color deviation data of the diamond painting samples are decomposed, and the results of coating integrity, adhesive crosslinking degree, and substrate permeability are correlated to output a correlation dataset of color deviation and interface failure characteristics.

[0030] Based on the classification and statistical analysis of color deviation patterns under different combinations of failure features in the associated dataset, the quantitative mapping relationship between three-dimensional failure features and color deviation is extracted, and a knowledge graph of interface failure chain reaction is output.

[0031] The time-series prediction module of the AI-based intelligent evaluation system for the color durability of diamond paintings provided by the present invention includes:

[0032] The framework design unit is used to design dynamic color decay time-series predictive analysis based on coupled evaluation data, determine the prediction dimensions and accuracy requirements, and output the time-series analysis framework.

[0033] The curve generation unit is used to implement an adaptive acquisition strategy based on the time series analysis framework, dynamically adjust the data acquisition frequency according to the attenuation process, and generate a color difference value time change curve.

[0034] The model building unit is used to identify trend inflection points and slope changes based on the color difference value over time curve through reinforcement learning, and to divide the decay stages. Based on the stage characteristics and time and environment, a four-dimensional dynamic correlation model is established.

[0035] According to the AI-based intelligent evaluation system for the color durability of diamond paintings provided by this invention, the specific steps for establishing a four-dimensional dynamic correlation model in the model building unit are as follows:

[0036] By using reinforcement learning algorithms to capture the trend inflection points and slope abrupt change points of the color difference value change curve over time, the color decay is divided into three stages: initial stabilization, rapid decay, and slow stabilization, and the stage division results are output.

[0037] Based on the stage division results, the decay rate and feature threshold of each stage are extracted, and a three-dimensional feature set is constructed by associating time and environmental variables.

[0038] A four-dimensional dynamic correlation model is established by using the composite structure durability index as the fourth dimension variable based on the three-dimensional feature set.

[0039] The AI-based intelligent evaluation system for the color durability of diamond paintings provided by this invention includes a stratified sampling module comprising:

[0040] The index grading unit is used to extract quantitative grading indicators of pattern complexity and interface stress concentration based on a four-dimensional dynamic correlation model, determine the two-dimensional layer boundary and level division standard, and output a basic grading scheme.

[0041] The sample selection unit is used to select three types of typical samples—edge stress concentration area, central uniform stress area, and high contrast boundary penetration area—within each level according to the basic grading scheme, and to construct a hierarchical sample set.

[0042] The weight correction unit is used to correct the weight coefficients of each layer based on the stratified sample set and the transfer learning algorithm to transfer similar structural durability data, thereby forming a standardized sampling evaluation result.

[0043] The AI-based intelligent evaluation system for the color durability of diamond paintings provided by this invention extracts feature parameters of each layer of samples from a hierarchical sample set, matches the feature dimensions of historical durability data of similar structures, establishes feature mapping relationships through transfer learning algorithms, and outputs sample association data.

[0044] Calculate the initial weight coefficients for each layer based on the sample association data, substitute the actual evaluation data of the stratified sample set to verify the weights, iteratively adjust the coefficient values, and output the weight coefficient table for each layer.

[0045] The weighted calculation of the sample evaluation data of each layer is performed according to the weight coefficient table of each layer to eliminate the bias caused by the difference between layers and form a standardized sampling evaluation result.

[0046] The AI-based intelligent evaluation system for the color durability of diamond paintings provided by this invention replicates the real service environment of diamond paintings through a full-link technical architecture of multi-field coupling simulation, AI visual sampling, multi-dimensional criterion analysis, time series prediction and hierarchical sampling. It accurately evaluates the color durability, reverse-matches the causes of color decay, and outputs process optimization and scene protection solutions, thus realizing the value transformation from technical evaluation to industrial application.

[0047] The beneficial effects of this invention are as follows:

[0048] 1. This invention constructs a full-link technical architecture encompassing coupled simulation, visual sampling, interface criterion, temporal prediction, and stratified sampling. It replicates the real-world service environment through multi-field coupled stress equivalent simulation, focusing on the core target of stress transmission at the diamond-adhesive-canvas interface. This addresses the shortcomings of traditional evaluation environments, which often rely on singular simulations and are detached from real-world application scenarios. The visual sampling stage utilizes deep learning to accurately identify typical regions and eliminate substrate interference. The interface criterion module overcomes the limitations of a single color difference threshold, establishing quantitative correlation rules between three-dimensional failure characteristics and color deviation. Combined with dynamic correction formulas, it calibrates data deviations, resulting in coupled evaluation data that is both accurate and correlated. Subsequent temporal prediction and stratified sampling modules further extend the evaluation dimensions, achieving a leap from static detection to dynamic prediction, providing scientific and systematic technical support for the color durability analysis of diamond paintings.

[0049] 2. This invention deeply integrates AI technologies such as deep learning, reinforcement learning, and transfer learning into each core unit, significantly breaking through the efficiency bottlenecks and accuracy limitations of traditional manual evaluation. In the visual sampling stage, deep learning algorithms automatically identify three typical regions and mark micron-level reference points, replacing manual point-by-point positioning and greatly improving sampling efficiency and accuracy. In the temporal prediction stage, reinforcement learning algorithms autonomously capture the inflection points and slope changes of the color difference curve, accurately dividing the color decay stage and avoiding subjective errors from human experience. The stratified sampling module introduces transfer learning algorithms, transferring durability data of similar structures to correct weight coefficients, solving the problem of insufficient small-sample evaluation data. The full-process application of AI algorithms improves system evaluation efficiency several times over, while controlling color deviation calibration accuracy within a precise matching range of micron-level and dimensionless precision, meeting the high-precision detection requirements of industrial production.

[0050] 3. This invention achieves deep integration from technology assessment to industrial application by constructing a closed-loop practical transformation unit encompassing assessment, diagnosis, optimization, and verification. Based on standardized sampling assessment results, the rule engine reverse-matches the core causes of color decay, outputting customized process optimization solutions such as adhesive formulation upgrades, diamond coating modification, and canvas waterproofing pretreatment. Simultaneously, it connects with a usage scenario database to generate an integrated report covering process parameters, scenario adaptation, and post-production protection, providing direction for process improvement for production and maintenance guidance for end-users. This transformation link breaks down the industry barrier of disconnect between assessment and application, transforming technology assessment results into tangible industrial value and helping the diamond painting industry achieve the dual goals of product quality upgrading and production process optimization. Attached Figure Description

[0051] The invention will now be further described with reference to the accompanying drawings.

[0052] Figure 1 This is a block diagram of the AI-based intelligent evaluation system for the color durability of diamond paintings provided in an embodiment of the present invention;

[0053] Figure 2 This is a flowchart of the AI-based intelligent evaluation system for the color durability of diamond paintings provided in an embodiment of the present invention;

[0054] Figure 3 This is a flowchart illustrating the steps for establishing a four-dimensional dynamic association model provided in an embodiment of the present invention. Detailed Implementation

[0055] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below according to specific embodiments.

[0056] like Figures 1 to 3As shown in the embodiment of the present invention, the AI-based intelligent evaluation system for the color durability of diamond paintings includes:

[0057] The coupling simulation module constructs a multi-field coupled stress equivalent simulation of the diamond-adhesive-canvas composite interface. It adopts a low-cost biomimetic device that simultaneously applies four factors: temperature and humidity cycle, physical friction, dust adhesion, and oxygen concentration gradient. It simulates the real service environment of dynamic indoor natural light irradiation and seasonal temperature and humidity changes. It focuses on the interface stress transmission path as the core evaluation target and outputs basic simulation samples.

[0058] The parameter framework construction unit is used to build a multi-field coupled stress simulation framework for the diamond painting composite interface, determine the parameter range of indoor natural light dynamic spectrum and seasonal temperature and humidity fluctuation, and output a parameterized simulation scheme.

[0059] Data from the actual service environment of the diamond painting was collected, including the spectral distribution of indoor natural light, duration of illumination, and temperature and humidity fluctuations in different seasons. This data determined the boundaries of the core simulation parameters, forming the original environmental parameter dataset. Combining the stress transfer characteristics of the diamond-adhesive-canvas composite interface, the original dataset was filtered and optimized to define the key parameter ranges for multi-field coupled simulations, determining the timing and intensity thresholds of factors such as illumination, temperature, humidity, friction, and dust. By integrating the parameter ranges and action rules, a multi-field coupled stress simulation framework was constructed, forming a complete parameterized simulation scheme including parameter settings, operational procedures, and evaluation criteria.

[0060] The device debugging and adaptation unit is used to build a low-cost biomimetic simulation device based on the parameterized simulation scheme. It integrates light adjustment and temperature and humidity control modules, calibrates the matching degree between device parameters and simulation scheme, and outputs the debugging simulation device.

[0061] Based on the parametric simulation scheme, low-cost materials such as household transparent containers, small temperature and humidity regulators, and natural light filter components were selected to build the main structure of the biomimetic simulation device, integrating modules for light regulation, temperature and humidity control, and friction dust application.

[0062] According to the parameter range in the plan, each module of the device was calibrated, the matching degree of the illumination spectrum with natural light and the accuracy of temperature and humidity fluctuations were adjusted, and the uniformity of friction and dust application was tested to ensure that the device operating parameters meet the simulation requirements.

[0063] Conduct no-load test runs of the device, monitor the collaborative working status of each module, record parameter deviations during operation, adjust module parameters accordingly, complete device debugging, and output a debugging simulation device that can be directly used for experiments.

[0064] The coupled simulation running unit is used to place diamond painting samples using a debugging simulation device, start a multi-field coupled stress application program, replicate the real service environment, and run continuously for a set period to output basic simulation samples.

[0065] Prepare unit samples of the diamond-adhesive-canvas composite interface, ensuring that the samples contain a complete three-layer structure and typical color areas. Fix the samples in the designated position of the debugging simulation device to ensure that each coupling factor can act uniformly on the interface.

[0066] The multi-field coupling stress application program is initiated, and factors such as light, temperature and humidity cycle, physical friction, and dust adhesion are applied synchronously according to the period and sequence set by the parameterization scheme to replicate the multi-field coupling process of the real service environment.

[0067] After running for a set period, stop the program and remove the sample. Observe whether there are any micro-damages or color changes on the interface, record the state data of the sample, and finally output a basic simulation sample carrying information about the interface environment.

[0068] The visual sampling module is used to establish a visual sampling rule engine for eliminating basis interference based on basic simulated samples. It automatically identifies three typical regions—solid color, gradient, and AB diamond—using deep learning algorithms and marks micron-level reference points. Multispectral images are acquired using a calibrated smartphone, and the engine performs real-time cropping to remove background noise pixels and correct for uneven lighting errors. It accurately extracts CIELAB parameters (Lab color space) and calculates the dynamic color difference matrix. The deep learning-based identification of typical regions and marking of micron-level reference points essentially defines a precise sampling range for AI-acquired multispectral images, avoiding the acquisition of invalid pixels in canvas gaps or non-target diamond areas.

[0069] The engine rule preset unit is used to build a visual sampling rule engine for eliminating basal interference based on basic simulation samples, and presets typical region recognition standards and micron-level reference point marking rules.

[0070] Based on the structural feature data analysis of the basic simulation samples, the color distribution pattern of the drill-glue-canvas composite interface was analyzed, and the pixel feature thresholds of three typical areas—solid color, gradient, and AB drill—were determined as the core basis for the recognition standard.

[0071] The marking spacing and positioning rules of micron-level reference points are set in combination with the visual sampling accuracy requirements. At the same time, the logic for eliminating substrate interference is embedded, and the algorithm parameters for removing background noise pixels and correcting illumination errors are defined.

[0072] By integrating the above-mentioned identification standards, marking rules and interference elimination logic, a visual sampling rule engine for basal interference elimination is built, forming a set of engine operation parameters that can be directly called.

[0073] Typical region labeling units are used to perform a full-domain scan of the basic simulation samples based on the rule engine and deep learning algorithms, automatically identify three typical regions: solid color, gradient, and AB diamond, and label the micron-level reference points of each region.

[0074] Input the basic simulated samples into the established rule engine, start the deep learning algorithm to perform a full-domain pixel scan of the samples, match the preset typical region feature thresholds, and initially locate the boundary range of the three types of regions.

[0075] Based on the micron-level reference point marking rules within the engine, reference points are evenly distributed within the already located typical area to generate a marked dataset containing the area location and reference point coordinates.

[0076] The labeling results are verified, misidentified areas and reference points with positioning deviations are removed, the accuracy of the labeled dataset is optimized, and the final core sampling range is locked.

[0077] The image preprocessing optimization unit is used to acquire multispectral images of samples using calibrated equipment based on the marked reference points. The images are then input into the rule engine, which automatically crops and removes background noise pixels, simultaneously corrects errors caused by uneven illumination, and outputs a preprocessed image.

[0078] Using the coordinates of the reference points in the labeled dataset as the basis for positioning, the calibrated image acquisition device is controlled to acquire multispectral images of typical regions of the sample, obtaining original images containing complete color information.

[0079] The original image is input into the rule engine, which calls the background noise pixel removal module to crop pixels in atypical areas according to preset conditions, while retaining image data in the core sampling area.

[0080] The lighting error correction module within the engine is activated, adjusting the brightness and color uniformity of the image according to preset algorithm parameters to eliminate interference caused by ambient lighting fluctuations and output a standardized pre-processed image.

[0081] The difference matrix generation unit is used to extract CIELAB color parameters of typical regions in the preprocessed image, calculate the original color difference by comparing with the initial parameters, integrate the difference data by region and time dimension, and generate a dynamic original color difference matrix.

[0082] Read the pixel data of the preprocessed image, extract the CIELAB color parameters corresponding to the reference points in each typical region, and form a multidimensional dataset containing region, coordinates, and color parameters.

[0083] The initial CIELAB color parameters of the sample are retrieved and compared one by one with the corresponding parameters of the preprocessed image. The color difference of each reference point is calculated to generate the difference base dataset.

[0084] The difference dataset is classified and integrated according to the region type and sampling time dimension, and a two-dimensional array with rows and columns corresponding to the region and time is constructed to generate a dynamic intrinsic difference matrix.

[0085] The interface criterion module is used to construct a knowledge graph and fuzzy reasoning criterion system for interface failure chain reactions based on the dynamic color difference matrix. It introduces a three-dimensional correlation criterion of diamond coating integrity, adhesive layer crosslinking degree, and canvas substrate permeability, breaking through the limitations of a single ΔEab threshold (CIELab color difference threshold). It establishes a dynamic correction formula for adhesive layer yellowing level, color difference, and interface peeling force under multi-factor coupling. The measured values ​​are corrected through formula iteration, and the overall durability coupling assessment data of the composite structure with interface failure risk warning is output.

[0086] The knowledge graph construction unit is used to construct a knowledge graph of interface failure chain reaction based on the dynamic color difference matrix, sort out the quantitative correlation rules between the three-dimensional failure characteristics of coating integrity, adhesive crosslinking degree, and substrate penetration and color deviation, and output the knowledge graph.

[0087] Based on the dynamic color difference matrix, the ΔEab color deviation data of each region of the sample is split, and the coating integrity, adhesive crosslinking degree and substrate permeability detection values ​​of the corresponding regions are collected simultaneously to establish a one-to-one correspondence dataset between color deviation and three-dimensional failure characteristics.

[0088] Statistical analysis was performed on the dataset to extract the color deviation variation patterns under different combinations of failure characteristics, construct quantitative association rules between failure characteristics and deviation degree, and integrate them to form a knowledge graph of interface failure chain reaction.

[0089] The specific steps for constructing a knowledge graph of interface failure chain reactions are as follows:

[0090] Based on the dynamic color difference matrix, the color deviation data of diamond painting samples are decomposed, and the detection results of coating integrity, adhesive crosslinking degree, and substrate permeability in the corresponding areas are correlated to output a correlation dataset of color deviation and interface failure characteristics. Based on this correlation dataset, the color deviation patterns under different combinations of failure characteristics are classified and statistically analyzed. The quantitative mapping relationship between three-dimensional failure characteristics and color deviation is extracted, and a knowledge graph of interface failure chain reaction containing correlation rules is output.

[0091] The formula modeling unit is used to build a three-dimensional association criterion based on the knowledge graph, extract the color deviation correction coefficients corresponding to the failure features, establish a dynamic correction formula to correct the three-dimensional failure features in the knowledge graph, and output a parameterized correction model.

[0092] The classification criteria for three-dimensional failure features and the corresponding color deviation impact weights are extracted from the knowledge graph, and a three-dimensional association criterion system is built based on this. The specific steps for building the three-dimensional association criterion system are as follows: extract the classification indicators of three-dimensional failure features of coating integrity, adhesive crosslinking degree, and substrate permeability from the knowledge graph; match the color deviation ranges corresponding to each level; and statistically analyze the deviation impact weights under different failure combinations to form a quantifiable three-dimensional association criterion system.

[0093] The influence weights of each failure feature are converted into mathematical correction coefficients, and the dynamic correlation between the coefficients and the degree of failure is determined. A preliminary dynamic correction formula with three-dimensional failure parameters as input is constructed. The specific steps for establishing the dynamic correction formula are as follows: Based on the three-dimensional correlation criteria, the influence weights of each failure feature are converted into mathematical coefficients; a functional relationship is constructed with three-dimensional failure parameters as independent variables and color deviation as dependent variable; the accuracy of the formula is verified by substituting the association rule data in the knowledge graph; the coefficient values ​​are iteratively adjusted to form a precise dynamic correction formula, which is then encapsulated as a parameterized correction model.

[0094] The three-dimensional failure features in a knowledge graph include:

[0095] Yellowing of the adhesive layer: Oxidation and yellowing of the adhesive layer will cover the surface of the diamond, causing the collected color data to appear yellowish and deviating from the true color of the diamond.

[0096] Ink penetration into the canvas: The ink on the canvas substrate migrates to the adhesive layer, contaminating the area around the diamond and causing the background color to interfere with the extraction of the diamond's true color.

[0097] Damage to the diamond coating: Wear and cracking of the diamond surface coating will change its reflectivity, causing optical distortion and directly affecting the accuracy of CIELAB parameters.

[0098] The data calibration unit is used to run the data calibration unit according to the parametric correction model, substitute coefficients to correct the deviation of the color measurement data of the diamond's true color, and output coupled evaluation data.

[0099] Import the original color measurement data of the diamond and the corresponding three-dimensional failure characteristic parameters, and call the parameterized correction model to load the dynamic correction formula.

[0100] The core correction formula is expressed as:

[0101]

[0102] In the formula, This is the true color deviation value after calibration. These are the original, uncalibrated color deviation values. This is the amount of color deviation correction caused by 3D failure features.

[0103] The color deviation correction value is calculated by substituting the failure characteristic parameters into the formula. The original measurement data is then calibrated using the correction value to eliminate the interference caused by interface failure and output the overall durability coupling evaluation data of the composite structure.

[0104] The time-series prediction module is used to design dynamic color decay time-series prediction analysis based on coupled evaluation data. It adopts an adaptive acquisition strategy to generate the ΔEab value time change curve, and uses a reinforcement learning rule engine to identify the curve inflection point and slope change characteristics. It accurately divides the three stages of yellowing incubation period, rapid fading period and failure critical period, and establishes a four-dimensional dynamic correlation model of environmental factors-time-color decay-interface failure.

[0105] The framework design unit is used to design dynamic color decay time-series predictive analysis based on coupled evaluation data, determine the prediction dimensions and accuracy requirements, and output the time-series analysis framework.

[0106] Based on the coupled assessment data, key durability indicators of composite structures are extracted, core influencing factors of color decay are correlated, and the accuracy requirements such as the time span and data resolution for time series prediction are clarified, resulting in a preliminary prediction scheme. Based on this preliminary prediction scheme, the correlation dimensions between color decay and environment and time are integrated to build a hierarchical analysis logic, ultimately outputting a standardized time series analysis framework.

[0107] The curve generation unit is used to implement an adaptive acquisition strategy based on the time series analysis framework, dynamically adjust the data acquisition frequency according to the attenuation process, and generate a color difference value time change curve.

[0108] Based on the preset decay process threshold of the time-series analysis framework, color difference data is collected at low frequency intervals during the initial stable period of color decay, outputting a sparsely collected dataset for the stable period. The slope of the collected data is monitored in real time. When the rate of change of color difference values ​​exceeds the threshold and enters the rapid decay period, the system automatically switches to a high-frequency collection mode, outputting a densely collected dataset for the rapid decay period. The sparsely collected dataset for the stable period and the densely collected dataset for the rapid decay period are integrated, and time axis alignment and data smoothing and denoising are performed to generate a continuous and complete time-varying curve of color difference values.

[0109] The model building unit is used to identify trend inflection points and slope changes based on the color difference value over time curve through reinforcement learning, and to divide the decay stages. Based on the stage characteristics and time and environment, a four-dimensional dynamic correlation model is established.

[0110] By capturing the trend inflection points and slope abrupt changes in the color difference value's time-varying curve using reinforcement learning algorithms, the color decay process is divided into three stages: initial stabilization, rapid decay, and slow stabilization. The stage division results are then output. Based on these results, key parameters such as decay rate and feature thresholds for each stage are extracted, and a three-dimensional feature set is constructed by associating these parameters with time and environmental variables.

[0111] The decay rate threshold, time interval boundary, and environmental variable influence coefficients for each decay stage are extracted from the 3D feature set to form a standardized 3D feature parameter matrix. The composite structure durability index is simultaneously imported, and the 3D feature parameter matrix is ​​aligned according to the decay stage to generate a 4D feature fusion dataset. Based on the 4D feature fusion dataset, a nonlinear mapping relationship is constructed with time, environmental variables, decay rate, and composite structure durability index as independent variables, and color decay degree as the dependent variable. Gradient boosting tree or neural network algorithms are used to fit the dynamic coupling law between variables, outputting a preliminary 4D association model. Reinforcement learning algorithms are used to iteratively optimize the preliminary 4D association model, using the matching degree of the stage division results as the optimization objective, adjusting the weight coefficients of each dimension variable to eliminate the deviation between the model's predicted values ​​and the actual color difference values. The model's generalization ability is cross-validated to finally form a stable 4D dynamic association model.

[0112] The stratified sampling module is used to build stratified sampling and transfer learning rules to enhance sample representativeness based on the four-dimensional dynamic association model. It is divided into layers according to two dimensions: pattern complexity and interface stress concentration. Each layer specifically selects edge stress concentration areas, central uniform stress areas, and high-contrast boundary penetration areas. Combined with the transfer learning algorithm, the durability weights of each layer are corrected to form standardized sampling evaluation results.

[0113] The index grading unit is used to extract quantitative grading indicators of pattern complexity and interface stress concentration based on a four-dimensional dynamic correlation model, determine the two-dimensional layer boundary and level division standard, and output a basic grading scheme.

[0114] Based on a four-dimensional dynamic correlation model, the pattern texture parameters and interface stress distribution data of the composite structure are decomposed. Texture density and morphological entropy indices of pattern complexity, as well as stress peak value and distribution gradient indices of interface stress concentration, are extracted, outputting a two-dimensional quantitative index set. Based on this two-dimensional quantitative index set, the level intervals of complexity and stress concentration are divided, the two-dimensional cross-layer boundaries and level naming rules are determined, and a standardized basic hierarchical scheme is output.

[0115] The sample selection unit is used to select three types of typical samples—edge stress concentration area, central uniform stress area, and high contrast boundary penetration area—within each level according to the basic grading scheme, and to construct a hierarchical sample set.

[0116] Based on the basic grading scheme, the patterns and stress characteristics corresponding to each level are matched one by one. The selection range and judgment criteria of edge stress concentration areas, central uniform stress areas, and high-contrast boundary penetration areas within each level are clarified, and stratified sampling guidelines are output. Following these stratified sampling guidelines, a sufficient number of typical samples are randomly selected within each level. The regional attributes and characteristic parameters of the samples are recorded, and integrated to form a stratified sample set covering all levels and all regional types.

[0117] The weight correction unit is used to correct the weight coefficients of each layer based on the stratified sample set and the transfer learning algorithm to transfer similar structural durability data, thereby forming a standardized sampling evaluation result.

[0118] Based on the stratified sample set, historical durability data of similar structures are imported using a transfer learning algorithm to establish a correlation mapping between sample features and durability decay rates, outputting a preliminary weight allocation matrix. Using this matrix, the durability contribution deviation of samples at each stratum is calculated, and the weight coefficients are iteratively optimized through backpropagation to eliminate the adaptation error between historical data and target samples, outputting a corrected stratified weight coefficient table. Based on this corrected table, the evaluation data of the stratified sample set is weighted to generate directly applicable standardized sampling evaluation results.

[0119] Based on standardized sampling evaluation results, a practical conversion unit with a closed loop of evaluation, diagnosis, optimization, and verification is constructed. Through a rule engine, the core causes of color decay are matched in reverse, and customized process optimization solutions such as adhesive formula upgrades, diamond coating modification, and canvas waterproof pretreatment are intelligently output. At the same time, it links with a database of usage scenarios such as indoor light intensity and humidity distribution to generate an integrated report of process optimization parameters, scenario adaptation suggestions, and post-protection solutions, thus constructing a full-link intelligent conversion chain from evaluation data to production practice and end-user maintenance.

[0120] In summary, this embodiment provides an AI-based intelligent evaluation system for the color durability of diamond paintings. Through a full-link technical architecture of multi-field coupling simulation, AI visual sampling, multi-dimensional criterion analysis, time-series prediction, and hierarchical sampling, it replicates the real service environment of diamond paintings, accurately evaluates color durability, reverse-matches the causes of attenuation, and outputs process optimization and scene protection solutions, realizing the value transformation from technical evaluation to industrial application.

[0121] This invention constructs a full-link technical architecture encompassing coupled simulation, visual sampling, interface criterion, temporal prediction, and stratified sampling. It replicates the real-world service environment through multi-field coupled stress equivalent simulation, focusing on the core target of stress transmission at the diamond-adhesive-canvas interface. This addresses the shortcomings of traditional assessment environments, which often rely on singular simulations and are detached from real-world application scenarios. The visual sampling stage utilizes deep learning to accurately identify typical regions and eliminate substrate interference. The interface criterion module overcomes the limitations of a single color difference threshold, establishing quantitative correlation rules between three-dimensional failure characteristics and color deviation. Combined with dynamic correction formulas, it calibrates data deviations, resulting in coupled assessment data that is both accurate and correlated. Subsequent temporal prediction and stratified sampling modules further extend the assessment dimensions, achieving a leap from static detection to dynamic prediction, providing scientific and systematic technical support for the color durability analysis of diamond paintings.

[0122] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An AI-based intelligent evaluation system for the color durability of diamond paintings, characterized in that, include: The coupling simulation module is used to construct a multi-field coupled stress equivalent simulation of the diamond-adhesive-canvas composite interface of the diamond painting, simulate the real service environment of dynamic indoor natural light illumination and seasonal temperature and humidity changes, and output basic simulation samples. The visual sampling module is used to build a visual sampling rule engine based on the basic simulated samples, identify typical regions and mark micron-level reference points through deep learning, acquire multispectral images and use the rule engine to extract CIELAB parameters in the multispectral images, and generate a dynamic color difference matrix. The interface criterion module is used to construct an interface failure chain reaction knowledge graph based on the dynamic intrinsic difference matrix, establish a dynamic correction formula to correct the interface failure chain reaction knowledge graph using three-dimensional correlation criteria, and output coupled evaluation data. The timing prediction module is used to design dynamic color decay timing prediction analysis based on the coupled evaluation data. It adopts an adaptive acquisition strategy to generate a color difference value time change curve, identifies curve features through reinforcement learning to divide decay stages, and establishes a four-dimensional dynamic correlation model. The stratified sampling module is used to build stratified sampling and transfer learning rules to enhance sample representativeness based on the four-dimensional dynamic association model, select feature regions in a two-dimensional stratified manner, and form standardized sampling evaluation results after the weights are corrected by the algorithm. The closed-loop conversion module is used to construct a complete closed-loop conversion process based on standardized sampling evaluation results, including evaluation, diagnosis, optimization, and verification. It matches the causes of attenuation, outputs customized process solutions, and generates integrated reports by linking with the scenario database, thus constructing a full-link intelligent conversion link.

2. The AI-based intelligent evaluation system for the color durability of diamond paintings according to claim 1, characterized in that: The coupled simulation module includes: The parameter framework construction unit is used to build a multi-field coupled stress simulation framework adapted to the diamond painting composite interface, determine the parameter range of indoor natural light dynamic spectrum and seasonal temperature and humidity fluctuation, and output parameterized simulation scheme. The device debugging and adaptation unit is used to build a low-cost biomimetic simulation device according to the parameterized simulation scheme, integrates light adjustment and temperature and humidity control modules, calibrates the matching degree between device parameters and simulation scheme, and outputs the debugging simulation device. The coupled simulation running unit is used to place diamond painting samples using a debugging simulation device, start a multi-field coupled stress application program, replicate the real service environment, and run continuously for a set period to output basic simulation samples.

3. The AI-based intelligent evaluation system for the color durability of diamond paintings according to claim 1, characterized in that: The visual sampling module includes: The engine rule preset unit is used to build a visual sampling rule engine for substrate interference elimination based on the basic simulation samples, and preset typical region recognition standards and micron-level reference point marking rules. The typical region marking unit is used to perform a full-domain scan of the basic simulation sample based on the rule engine using a deep learning algorithm, automatically identify three types of typical regions: solid color, gradient, and AB diamond, and mark the micron-level reference points of each region. The image preprocessing optimization unit is used to acquire multispectral images of samples with calibrated equipment based on the marked reference points, input the images into the rule engine, automatically crop and remove background noise pixels, simultaneously correct errors caused by uneven illumination, and output preprocessed images. The difference matrix generation unit is used to extract CIELAB color parameters of typical regions of the preprocessed image, calculate the original color difference by comparing with the initial parameters, integrate the difference data according to the region and time dimensions, and generate a dynamic original color difference matrix.

4. The AI-based intelligent evaluation system for the color durability of diamond paintings according to claim 3, characterized in that: In the difference matrix generation unit, the specific steps for generating the dynamic intrinsic difference matrix are as follows: Extract CIELAB color parameters corresponding to reference points in typical regions of the preprocessed image to form a multidimensional dataset; The initial CIELAB color parameters of the sample are retrieved and compared one by one with the corresponding parameters of the preprocessed image. The color difference of each reference point is calculated to generate the difference base dataset. The difference dataset is classified and integrated according to the region type and sampling time dimension, and a two-dimensional array with rows and columns corresponding to the region and time is constructed to generate a dynamic intrinsic difference matrix.

5. The AI-based intelligent evaluation system for the color durability of diamond paintings according to claim 1, characterized in that: The interface criteria module includes: The graph construction unit is used to construct a knowledge graph of interface failure chain reaction based on the dynamic color difference matrix and output the knowledge graph. The formula modeling unit is used to build a three-dimensional association criterion based on the knowledge graph, extract the color deviation correction coefficient corresponding to the failure feature, establish a dynamic correction formula, and output a parameterized correction model. The data calibration unit is used to run the data calibration unit according to the parameterized correction model, substitute coefficients to correct the color measurement data deviation of the diamond's true color, and output coupled evaluation data.

6. The AI-based intelligent evaluation system for the color durability of diamond paintings according to claim 5, characterized in that: In the knowledge graph construction unit, the specific steps for constructing the interface failure chain reaction knowledge graph are as follows: Based on the dynamic color difference matrix, the color deviation data of the diamond painting sample is decomposed, and the detection results of coating integrity, adhesive crosslinking degree, and substrate permeability are correlated to output a correlation dataset of color deviation and interface failure characteristics. Based on the classification and statistical analysis of color deviation patterns under different combinations of failure features in the associated dataset, the quantitative mapping relationship between three-dimensional failure features and color deviation is extracted, and a knowledge graph of interface failure chain reaction is output.

7. The AI-based intelligent evaluation system for the color durability of diamond paintings according to claim 1, characterized in that: The time series prediction module includes: The framework design unit is used to design dynamic color decay time-series prediction analysis based on the coupled evaluation data, determine the prediction dimension and accuracy requirements, and output the time-series analysis framework. The curve generation unit is used to perform an adaptive acquisition strategy based on the time series analysis framework, dynamically adjust the data acquisition frequency according to the attenuation process, and generate a color difference value time change curve. The model building unit is used to identify trend inflection points and slope changes based on the color difference value over time curve through reinforcement learning, and to divide the decay stages according to the stage characteristics, time, and environment to establish a four-dimensional dynamic correlation model.

8. The AI-based intelligent evaluation system for the color durability of diamond paintings according to claim 7, characterized in that: The specific steps for establishing a four-dimensional dynamic association model in the model building unit are as follows: By using reinforcement learning algorithms to capture the trend inflection points and slope change points of the color difference value change curve over time, the color decay is divided into three stages: initial stabilization, rapid decay, and slow stabilization, and the stage division results are output. Based on the stage division results, the decay rate and feature threshold of each stage are extracted, and a three-dimensional feature set is constructed by associating time and environmental variables. Based on the three-dimensional feature set, a four-dimensional dynamic correlation model is established using the composite structure durability index as the fourth dimension variable.

9. The AI-based intelligent evaluation system for the color durability of diamond paintings according to claim 1, characterized in that: The stratified sampling module includes: The index grading unit is used to extract quantitative grading indicators of pattern complexity and interface stress concentration based on the four-dimensional dynamic correlation model, determine the two-dimensional layer boundary and level division standard, and output the basic grading scheme. The sample selection unit is used to select three types of typical samples—edge stress concentration area, central uniform stress area, and high contrast boundary penetration area—within each level according to the basic grading scheme, and to construct a hierarchical sample set. The weight correction unit is used to correct the weight coefficients of each layer based on the hierarchical sample set by using a transfer learning algorithm to transfer similar structural durability data, thereby forming a standardized sampling evaluation result.

10. The AI-based intelligent evaluation system for the color durability of diamond paintings according to claim 9, characterized in that: Based on the hierarchical sample set, feature parameters of each layer of samples are extracted, and feature dimensions of historical durability data of similar structures are matched. Feature mapping relationship is established through transfer learning algorithm, and sample association data is output. Calculate the initial weight coefficients of each layer based on the sample association data, verify the weights by substituting the actual evaluation data of the stratified sample set, iteratively adjust the coefficient values, and output the weight coefficient table of each layer. The weighted calculation of the sample evaluation data of each layer is performed according to the weight coefficient table of each layer to eliminate the bias caused by the difference between layers and form a standardized sampling evaluation result.

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