Jewelry jade color intelligent grading system and method based on hyperspectrum

Abstract

The invention relates to the technical field of jewelry and jade grading, in particular to a jewelry and jade color intelligent grading system and method based on hyperspectrum, and the system comprises a uniform illumination module which is used for uniformly illuminating a jewelry and jade sample placed on an illumination station; the spectrum acquisition module is used for acquiring a visible spectrum of the jewelry jade sample; the spectral analysis module is used for carrying out spectral analysis on the visible light spectrum of the jewelry jade sample to obtain spectral reflectivity; the information acquisition module is used for acquiring basic sample information of a jewelry jade sample; the model matching module is used for matching the color model of the jewelry jade sample according to the basic information of the sample; the spectrum conversion module is used for converting the spectrum reflectivity into corresponding color parameters according to the color model matched with the jewelry jade sample; and the grading module is used for outputting a color grade corresponding to the jewelry jade sample based on a preset color grading model according to the color parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of jewelry and jade grading, and in particular to a jewelry and jade color intelligent grading system and method based on a hyperspectral spectrum. Background Art

[0002] The color grade of jewelry and jade is one of the important factors that determine its value. Especially for precious gems such as ruby ​​and sapphire, a specific color grade can make its carat price reach nearly RMB 100,000, which is much higher than similar gems of other color grades. Therefore, accurate color grading is crucial to assessing the value of jewelry and jade.

[0003] At present, the color grading of gemstones mainly relies on visual colorimetry. This method requires experienced gemstone color graders to operate in a stable and uniform environment, using standard colorimetric cards and standard samples to measure the color parameters of gemstones such as hue, lightness and chroma to ultimately determine the color grade of the sample. Although visual colorimetry is relatively simple and intuitive, it still has some problems:

[0004] Different graders may give different color grades to the same gemstone due to individual visual differences or experience levels, resulting in inconsistent and subjective results;

[0005] Visual colorimetry needs to be performed in a stable, uniform environment without interfering light. Any changes in light conditions may affect the accuracy of grading. It is highly dependent on the environment and has poor grading reliability.

[0006] The manual grading process is slow, especially when dealing with a large number of samples, which is inefficient, increases costs, and is time-consuming and labor-intensive.

[0007] Based on this, there is an urgent need for a jewelry and jade color intelligent grading system and method based on hyperspectral, which can realize the intelligent and quantified grading of jewelry and jade, and greatly improve the grading efficiency of jewelry and jade. Summary of the invention

[0008] One of the purposes of the present invention is to provide a hyperspectral-based intelligent grading system and method for jewelry and jadeite colors, which can realize the intelligent and quantitative grading of jewelry and jadeite and greatly improve the grading efficiency of jewelry and jadeite.

[0009] In order to achieve the above purpose, a hyperspectral-based intelligent grading system for gemstone color is provided, comprising:

[0010] Uniform lighting module, used to evenly illuminate the jewelry and jade samples placed on the lighting station;

[0011] Spectrum acquisition module, used to collect visible light spectra of jewelry and jade samples on the lighting station;

[0012] The spectrum analysis module is used to perform spectrum analysis on the visible light spectrum corresponding to the collected jewelry and jade samples and obtain the corresponding spectrum reflectance;

[0013] An information acquisition module is used to acquire basic information of jewelry and jade samples placed on the lighting station;

[0014] The model matching module is used to match the color model corresponding to the jewelry and jade samples according to the obtained basic information of the samples and based on the preset model matching strategy;

[0015] A spectrum conversion module, for converting the spectrum reflectance corresponding to the jewelry and jade samples into color parameters corresponding to the color model matched by the jewelry and jade samples according to the color model matched by the jewelry and jade samples;

[0016] The grading module is used to output the color grade corresponding to the jewelry and jade sample based on the color parameters corresponding to the color model matched by the jewelry and jade and based on the preset color grading model.

[0017] Technical principle and effect of this solution: In this solution, firstly, the jewelry and jade samples are placed on the lighting station for uniform lighting, which eliminates the error caused by uneven light source and improves the accuracy of spectrum acquisition. Then, the spectrum acquisition module is used to collect the corresponding visible light spectrum of the jewelry and jade samples on the lighting station, and the spectrum image of the jewelry and jade samples is collected. After that, the collected visible light spectrum is analyzed to obtain the spectrum reflectance of the jewelry and jade samples.

[0018] Then the next step is to match the corresponding color model. Specifically, it is to obtain the basic information of the jewelry and jade samples, and match the color model corresponding to the jewelry and jade samples through the basic information of the samples and the preset model matching strategy.

[0019] After matching the corresponding color model, the corresponding spectral reflectance will be used as input, and the spectral reflectance will be converted into the color parameters corresponding to the matching model. Then, the color parameters are combined with the preset color grading model to determine the color grade corresponding to the jewelry and jade sample.

[0020] Compared with the traditional visual colorimetry method, this solution does not require human intervention in the entire process from sample placement, information acquisition, spectral collection and analysis, to color model matching, parameter conversion and final grading, which greatly improves work efficiency and realizes the intelligent and quantitative identification of jewelry and jade. It can accurately and intelligently judge the color of jewelry and jade samples without destroying the samples.

[0021] Furthermore, the preset model matching strategy is:

[0022] According to the basic information of the jewellery and jade samples, the sample types corresponding to the jewellery and jade samples are identified;

[0023] The corresponding jewelry model matching table is retrieved from the database, and the color model corresponding to the sample type corresponding to the jewelry and jade sample is identified based on the sample type corresponding to the jewelry and jade sample and the retrieved jewelry model matching table; the jewelry model matching table records the matching suitability of each sample type and each of the three color models.

[0024] Beneficial effects: In this solution, by identifying the specific type of the sample and combining it with the jewelry model matching table in the database, the most suitable color model can be selected for each type of jewelry and jade. This targeted selection ensures the accuracy of color parameter conversion. At the same time, the jewelry model matching table records the matching suitability of each sample type with each of the three color models, which makes the model selection process more scientific and quantitative, avoiding errors caused by subjective judgment.

[0025] The entire model matching process can be highly automated, reducing the need for manual intervention and thus improving evaluation efficiency. Technicians only need to input the basic information of the jewellery and jade, and the system can automatically complete type identification and selection of the best colour model.

[0026] Furthermore, the color model includes a CIE color model, an HSB color model, and a Munsell color model.

[0027] Furthermore, the uniform lighting module is a dome lighting LED light source, which provides a uniform diffuse reflection light source, and the illumination angle corresponding to the dome lighting LED light source is between 0 degrees and 90 degrees.

[0028] Furthermore, it also includes a light source calibration module, which is used to place a standard whiteboard on the lighting station before placing the jewelry and jade samples on the lighting station, and calibrate and calculate the environment and light source corresponding to the lighting station based on a preset calibration formula until the calibrated spectral image meets the preset requirements;

[0029] The preset calibration formula is:

[0030]

[0031] In the formula, R c is the spectral image after calibration, I c is the spectral image before calibration, A c is the full brightness spectrum image, B c It is a full dark spectrum image.

[0032] Beneficial effects: The light source calibration module uses a standard whiteboard to calculate the calibration formula before each measurement, ensuring that the environment and light source at the lighting station meet the preset requirements. This eliminates errors caused by changes in light sources or environmental factors and ensures consistency in each measurement condition.

[0033] The calibration formula ensures that the calibrated spectral image can truly reflect the color characteristics of jewelry and jade, and reduces measurement deviations caused by uneven light source intensity or ambient light interference.

[0034] Furthermore, it also includes a model training construction module, which is used to construct and train the color grading model according to the model parameters corresponding to the color model matched by each historical jewelry and jade sample in the historical database.

[0035] Furthermore, the model training construction module includes:

[0036] A historical data retrieval module is used to retrieve the model parameters corresponding to each historical jewelry and jade sample output by the same color model in the historical database;

[0037] A labeling module, used to label the model parameters corresponding to each historical jewelry sample output by the same color model, wherein the labeling content is the color grade corresponding to the corresponding historical jewelry jade sample;

[0038] A division module, used to divide the model parameters corresponding to each historical jewelry sample output by the same color model with annotated content into a training set and a validation set according to a preset division ratio;

[0039] A construction module, used to construct a color grading model corresponding to each color model;

[0040] A random generation module, used for randomly generating a number of parameter groups corresponding to the color grading model corresponding to a certain color model when training the color grading model corresponding to the color model;

[0041] The training module is used to input the model parameters corresponding to each historical jewelry sample output by the same color model corresponding to the training set as input data into the color grading model corresponding to the corresponding color model, and output the training prediction grade results corresponding to each parameter group;

[0042] An evaluation module is used to evaluate the prediction reliability corresponding to each parameter group based on the training prediction level results and the corresponding annotation content, and sort the parameter groups in descending order of prediction reliability to form a reliability ranking table;

[0043] An updating module is used to remove some parameter groups in the reliability ranking list based on a preset ratio, pair the remaining parameter groups in the reliability ranking list, and randomly select a number of parameters to exchange them in pairs to form a new parameter group, and re-execute the training module until a preset number of iterations is reached, and the color grading model corresponding to the parameter group ranked at a preset ranking in the reliability ranking list corresponding to the evaluation module at this time is taken as the model excellent set;

[0044] A selection module is used to input the model parameters corresponding to each historical jewelry sample corresponding to the corresponding color model in the verification set as input data into the color grading model corresponding to the corresponding color model in the excellent model set, input the verification prediction grade results corresponding to each color grading model, and based on the verification prediction grade results corresponding to each color grading model and the annotation content corresponding to each model parameter in the corresponding verification set, select the color grading model with the highest prediction reliability as the final color grading model corresponding to the corresponding color model.

[0045] Beneficial effects: In this solution, the existing data resources are fully utilized through the historical data retrieval module, providing rich basic materials for model training. This not only saves data collection costs, but also improves the starting point of model training. The labeling module ensures that each sample has an accurate color grade label, and the partitioning module divides the data into a training set and a validation set according to a preset ratio, ensuring the independence and fairness of the training and validation processes. The construction module can process multiple color models (such as CIE, HSB, Munsell) at the same time, and build corresponding color grading models respectively, realizing multi-model parallel training and improving overall efficiency. The random generation module generates multiple parameter combinations at the beginning of training, which increases the possibility of model exploration and helps to find a better solution. The evaluation module quantitatively evaluates the prediction reliability of each parameter group based on the training prediction grade results and annotation content, and forms a reliability ranking table. This evaluation mechanism ensures the scientificity and objectivity of model selection. The update module eliminates inefficient parameter groups according to the reliability ranking table, and continuously optimizes the parameter combination by pairing and random exchange. This process helps to gradually improve model performance.

[0046] The selection module further tests the color grading models in the excellent model set using the validation set to ensure that they still perform well on unseen data. Based on the prediction results of the validation set, the color grading model with the highest prediction reliability is selected as the final model. This process ensures that the model not only performs well on the training data, but also has good generalization ability.

[0047] The present invention also provides a hyperspectral-based intelligent grading method for jewellery and jade colours, using the hyperspectral-based intelligent grading system for jewellery and jade colours. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a logic block diagram of the hyperspectral-based intelligent grading system for jewelry and jade colors in Example 1 of the present invention. DETAILED DESCRIPTION

[0049] The following is further described in detail through specific implementation methods:

[0050] Embodiment 1

[0051] The intelligent grading system for gemstone color based on hyperspectral is basically Figure 1 As shown, including:

[0052] The uniform lighting module is used to uniformly illuminate the jewelry and jade samples placed on the lighting station; in this embodiment, the uniform lighting module is a dome lighting LED light source, which provides a uniform diffuse reflection light source, and the illumination angle corresponding to the dome lighting LED light source is between 0 degrees and 90 degrees.

[0053] It also includes a light source calibration module, which is used to place a standard white board on the lighting station before placing the jewelry and jade samples on the lighting station, and calibrate and calculate the environment and light source corresponding to the lighting station based on a preset calibration formula until the calibrated spectral image meets the preset requirements;

[0054] The preset calibration formula is:

[0055]

[0056] In the formula, R c is the spectral image after calibration, I c is the spectral image before calibration, A c is the full brightness spectrum image, B c It is a full dark spectrum image.

[0057] Spectrum acquisition module, used to collect visible light spectra of jewelry and jade samples at the lighting station;

[0058] The spectrum analysis module is used to perform spectrum analysis on the visible light spectrum corresponding to the collected jewelry and jade samples and obtain the corresponding spectrum reflectance;

[0059] An information acquisition module is used to acquire basic information of jewelry and jade samples placed on the lighting station;

[0060] The model matching module is used to match the color model corresponding to the jewelry and jade samples according to the obtained basic information of the samples and based on the preset model matching strategy;

[0061] The preset model matching strategy is:

[0062] According to the basic information of the jewellery and jade samples, the sample types corresponding to the jewellery and jade samples are identified;

[0063] The corresponding jewelry model matching table is retrieved from the database, and the color model corresponding to the sample type corresponding to the jewelry and jade sample is identified based on the sample type corresponding to the jewelry and jade sample and the retrieved jewelry model matching table; the jewelry model matching table records the matching suitability of each sample type and each of the three color models.

[0064] The color models include CIE color model, HSB color model and Munsell color model.

[0065] In this embodiment, the CIE color model converts the spectral reflectance into a three-dimensional uniform color space CIE1976L * a * b * , the calculation formula is:

[0066] L * =116f(Y / Y n )-16

[0067] a * =500[f(X / X n )-f(Y / Y n )]

[0068] b * =500[f(Y / Y n )-f(Z / Z n )]

[0069]

[0070] In the formula, X, Y, and Z are the three stimulus values ​​of the color stimulus of the object to be tested, X n , Y n , Z n is the tristimulus value of the light reflected by the same light source as the object to be measured, which is reflected by the completely diffuse reflector. n , Y n , Z n That is, the three stimulus values ​​of the light source and Y n =100. In this color space, L * Represents brightness, +a * The axis approximately represents the red stimulus direction, -a * The axis approximately represents the direction of green stimulus, +b * The axis approximately represents the direction of the yellow stimulus, -b * The axis approximately represents the blue stimulus direction.

[0071] In the HSB color model, the spectral reflectance is converted into the color parameters corresponding to the HSB color model, and the RGB color parameters are first normalized;

[0072]

[0073] Then the normalized RGB color parameters are converted to HSB according to the following formula:

[0074]

[0075] V(B)=MAX×100%

[0076] Where MAX is the maximum value of the RGB color parameters, and MIN is the minimum value of the RGB color parameters.

[0077] A spectrum conversion module, for converting the spectrum reflectance corresponding to the jewelry and jade samples into color parameters corresponding to the color model matched by the jewelry and jade samples according to the color model matched by the jewelry and jade samples;

[0078] The grading module is used to output the color grade corresponding to the jewelry and jade sample based on the color parameters corresponding to the color model matched by the jewelry and jade and based on the preset color grading model.

[0079] It also includes a model training construction module, which is used to construct and train a color grading model based on the model parameters corresponding to the color model matched by each historical jewelry and jade sample in the historical database.

[0080] The model training building block includes:

[0081] A historical data retrieval module is used to retrieve the model parameters corresponding to each historical jewelry and jade sample output by the same color model in the historical database;

[0082] A labeling module, used to label the model parameters corresponding to each historical jewelry sample output by the same color model, wherein the labeling content is the color grade corresponding to the corresponding historical jewelry jade sample;

[0083] A division module, used to divide the model parameters corresponding to each historical jewelry sample output by the same color model with annotated content into a training set and a validation set according to a preset division ratio;

[0084] A construction module is used to construct a color grading model corresponding to each color model; the color grading model can be constructed using BP neural network, KNN, Bayesian and other methods. In this embodiment, a BP neural network model is used to construct the corresponding model. Specifically, a three-layer BP neural network model is first constructed, including an output layer, a hidden layer and an output layer. In this embodiment, the model parameters are input as input layer inputs, and the corresponding output is the color grade. BP neural networks usually use Sigmoid differentiable functions and linear functions as the excitation functions of the network. This paper selects the S-type tangent function tansig as the excitation function of the hidden layer neurons. The prediction model selects the S-type logarithmic function tansig as the excitation function of the output layer neurons.

[0085] A random generation module, used for randomly generating a number of parameter groups corresponding to the color grading model corresponding to a certain color model when training the color grading model corresponding to the color model;

[0086] The training module is used to input the model parameters corresponding to each historical jewelry sample output by the same color model in the training set as input data into the color grading model corresponding to the corresponding color model, and output the training prediction grade results corresponding to each parameter group;

[0087] The evaluation module is used to evaluate the prediction reliability corresponding to each parameter group based on the training prediction level results and the corresponding annotation content, and sort the parameter groups in order of prediction reliability from large to small to form a reliability arrangement table; in this embodiment, the calculation logic corresponding to the prediction reliability evaluation is: through all the training prediction level results of the same parameter group and the corresponding annotation content, the corresponding accuracy is statistically calculated as the corresponding prediction reliability.

[0088] An updating module is used to remove some parameter groups in the reliability ranking list based on a preset ratio, pair the remaining parameter groups in the reliability ranking list, and randomly select a number of parameters to exchange them in pairs to form a new parameter group, and re-execute the training module until a preset number of iterations is reached, and the color grading model corresponding to the parameter group ranked at a preset ranking in the reliability ranking list corresponding to the evaluation module at this time is taken as the model excellent set;

[0089] A selection module is used to input the model parameters corresponding to each historical jewelry sample corresponding to the corresponding color model in the verification set as input data into the color grading model corresponding to the corresponding color model in the excellent model set, input the verification prediction grade results corresponding to each color grading model, and based on the verification prediction grade results corresponding to each color grading model and the annotation content corresponding to each model parameter in the corresponding verification set, select the color grading model with the highest prediction reliability as the final color grading model corresponding to the corresponding color model. For example, a turquoise sample is placed in a uniform illumination module, the sample is focused using a microscope, and then the visible light spectrum of the sample is collected using a spectrum acquisition module, the collected light signal is converted into an electrical signal and finally into a digital signal, the spectrum information is converted into reflectance information, the spectrum reflectance is converted into a tristimulus value XYZ, and the color parameters of the turquoise are digitized using the color model HSB. The hue H of the sample is 180.2°, the saturation S is 25.2%, and the lightness B is 62.3%. The color grade is intelligently graded using the corresponding color grading model. The hue of the turquoise color grade is blue B, the saturation is medium S3, and the lightness is bright B2. The turquoise color grade is comprehensively determined to be the second grade. In this embodiment, the color grade is divided into 4 grades. Of course, the color grade in other embodiments can also be changed, such as three grades, five grades, etc.

[0090] This embodiment also discloses a method for intelligent grading of jewelry and jade colors based on hyperspectrum, using the above-mentioned intelligent grading system for jewelry and jade colors based on hyperspectrum.

[0091] The above is only an embodiment of the present invention. The common sense such as the known specific structure and characteristics in the scheme is described too much here. The ordinary technicians in the relevant field know all the common technical knowledge in the technical field of the invention before the application date or priority date, can know all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the scope of protection of the present invention, which will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. Intelligent grading system for gemstone color based on hyperspectral, characterized by: include: Uniform lighting module, used to evenly illuminate the jewelry and jade samples placed on the lighting station; Spectrum acquisition module, used to collect visible light spectra of jewelry and jade samples on the lighting station; The spectrum analysis module is used to perform spectrum analysis on the visible light spectrum corresponding to the collected jewelry and jade samples and obtain the corresponding spectrum reflectance; An information acquisition module is used to acquire basic information of jewelry and jade samples placed on the lighting station; The model matching module is used to match the color model corresponding to the jewelry and jade samples according to the obtained basic information of the samples and based on the preset model matching strategy; A spectrum conversion module, for converting the spectrum reflectance corresponding to the jewelry and jade samples into color parameters corresponding to the color model matched by the jewelry and jade samples according to the color model matched by the jewelry and jade samples; The grading module is used to output the color grade corresponding to the jewelry and jade sample based on the color parameters corresponding to the color model matched by the jewelry and jade and based on the preset color grading model.

2. The hyperspectral-based intelligent grading system for gemstone colors according to claim 1, characterized in that: The preset model matching strategy is: According to the basic information of the jewellery and jade samples, the sample types corresponding to the jewellery and jade samples are identified; The corresponding jewelry model matching table is retrieved from the database, and the color model corresponding to the sample type corresponding to the jewelry and jade sample is identified based on the sample type corresponding to the jewelry and jade sample and the retrieved jewelry model matching table; the jewelry model matching table records the matching suitability of each sample type and each of the three color models.

3. The hyperspectral-based intelligent grading system for gemstone colors according to claim 2, characterized in that: The color models include CIE color model, HSB color model and Munsell color model.

4. The hyperspectral-based intelligent grading system for gemstone colors according to claim 3 is characterized in that: The uniform lighting module is a dome lighting LED light source, which provides a uniform diffuse reflection light source. The illumination angle corresponding to the dome lighting LED light source is between 0 degrees and 90 degrees.

5. The hyperspectral-based intelligent grading system for gemstone colors according to claim 4 is characterized in that: It also includes a light source calibration module, which is used to place a standard white board on the lighting station before placing the jewelry and jade samples on the lighting station, and calibrate and calculate the environment and light source corresponding to the lighting station based on a preset calibration formula until the calibrated spectral image meets the preset requirements; The preset calibration formula is: In the formula, R c is the spectral image after calibration, I c is the spectral image before calibration, A c is the full brightness spectrum image, B c It is a full dark spectrum image.

6. The hyperspectral-based intelligent grading system for gemstone colors according to claim 5 is characterized in that: It also includes a model training construction module, which is used to construct and train a color grading model based on the model parameters corresponding to the color model matched by each historical jewelry and jade sample in the historical database.

7. The hyperspectral-based intelligent grading system for gemstone colors according to claim 6, characterized in that: The model training building block includes: A historical data retrieval module is used to retrieve the model parameters corresponding to each historical jewelry and jade sample output by the same color model in the historical database; A labeling module, used to label the model parameters corresponding to each historical jewelry sample output by the same color model, wherein the labeling content is the color grade corresponding to the corresponding historical jewelry jade sample; A division module, used to divide the model parameters corresponding to each historical jewelry sample output by the same color model with annotated content into a training set and a validation set according to a preset division ratio; A construction module, used to construct a color grading model corresponding to each color model; A random generation module, used for randomly generating a number of parameter groups corresponding to the color grading model corresponding to a certain color model when training the color grading model corresponding to the color model; The training module is used to input the model parameters corresponding to each historical jewelry sample output by the same color model in the training set as input data into the color grading model corresponding to the corresponding color model, and output the training prediction grade results corresponding to each parameter group; An evaluation module is used to evaluate the prediction reliability corresponding to each parameter group based on the training prediction level results and the corresponding annotation content, and sort the parameter groups in descending order of prediction reliability to form a reliability ranking table; An updating module is used to remove some parameter groups in the reliability ranking list based on a preset ratio, pair the remaining parameter groups in the reliability ranking list, and randomly select a number of parameters to exchange them in pairs to form a new parameter group, and re-execute the training module until a preset number of iterations is reached, and the color grading model corresponding to the parameter group ranked at a preset ranking in the reliability ranking list corresponding to the evaluation module at this time is taken as the model excellent set; A selection module is used to input the model parameters corresponding to each historical jewelry sample corresponding to the corresponding color model in the verification set as input data into the color grading model corresponding to the corresponding color model in the excellent model set, input the verification prediction grade results corresponding to each color grading model, and based on the verification prediction grade results corresponding to each color grading model and the annotation content corresponding to each model parameter in the corresponding verification set, select the color grading model with the highest prediction reliability as the final color grading model corresponding to the corresponding color model.

8. The intelligent grading method of jewelry and jade color based on hyperspectral is characterized by: A hyperspectral-based intelligent grading system for gemstone color using any one of claims 1 to 7 above.

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