Visual characteristic correction and self-adaptive detection method for color development of noble metal solution

Abstract

The invention provides a precious metal solution color development visual feature correction and self-adaptive detection method, which is used for solving the problems in the aspects of manual identification precision, process control stability and digital traceability. The method comprises the following steps: constructing a multi-stage sampling point precious metal solution color development detection test tube array, and obtaining a color development visual image; using the improved pix2pix network model to carry out color correction on the color development visual image; establishing a standard database containing sample images, corrected images and component parameters; extracting RGB color space features of the color development visual image of the precious metal solution by utilizing a histogram, and performing data mapping on color data and parameter data in combination with trilinear interpolation so as to perfect a database; and finally obtaining the category and concentration information of the noble metal through the color information, thereby deducing the purification reaction state. Through the non-linear mapping model of the color features, the solution category, the concentration and the reaction process, self-adaptive analysis of the color development visual image information of the precious metal ion solution is realized.

Description

Technical Field

[0001] The invention belongs to the technical field of precious metal detection, and in particular relates to a precious metal solution color development visual feature correction and adaptive detection method. Background Art

[0002] Precious metal purification is the final treatment link in precious metal smelting, which requires the complete replacement of precious metals in the refined mother liquor. At present, the replacement state of precious metal ions is determined by the color information presented after the color development reaction of precious metal ions in the solution to determine whether the precious metal ions are completely replaced. This on-site manual detection operation method has the problems of low operation efficiency and relatively lagging feedback of ion components in the solution, which affects the control of key process parameters of precious metals and is difficult to meet the needs of digital development of manufacturing workshops; and there are metal dust, acid mist environment and heavy metal ion emission pollution in the process of precious metal purification operation, which affects the safety, health and environment of operators. Therefore, in order to carry out technical research on the above-mentioned bottleneck links, it is necessary to use modern machine vision technology to promote the digital transformation and upgrading of key purification processes, continuously optimize the precious metal purification process, improve the efficiency of precious metal purification replacement and energy saving and consumption reduction, improve the intelligence of production lines, and release the operation capacity of existing precious metal purification enterprises, so that they can better serve the smelting state detection and production of precious metals. Summary of the invention

[0003] In view of the problems of strong subjectivity and low detection accuracy in the color information of the reaction solution after color development judged by artificial vision in the above-mentioned precious metal purification process, the present invention aims to provide a method for color development visual feature correction and adaptive detection of precious metal solutions. The chromaticity feature information of the precious metal solution in the sample liquid test tube is automatically extracted by machine vision, and a quantitative correlation model between the chromaticity parameters of the solution and the type of precious metal, ion concentration and reaction state is established in combination with the improved pix2pix color correction algorithm and the R, G, B three-channel color space modeling technology, so as to realize the color development visual feature correction and adaptive component detection of the solution in the precious metal purification process.

[0004] In order to achieve the above object, the present invention proposes a method for calibrating and adaptively detecting color visual characteristics of a precious metal solution, comprising the following steps:

[0005] Step 1) Construct a multi-level sampling point inspection test tube array, implement dynamic solution collection in the continuous reaction process of the precious metal reactor, and accurately capture the color change characteristics of the solution under different reaction states. By clarifying the detection targets and required data of each key reaction stage in the complex purification process, the system collects and analyzes the precious metal solution sample sequences under different precious metal reactors and different reaction time nodes, providing data support for the subsequent detection model establishment and process optimization.

[0006] Step 2): Sequentially place the multi-level sampling point detection test tubes obtained in Step 1) into the dark box device, and collect images of the precious metal solutions one by one to avoid interference of ambient light on the image color. Then, carry out automated image analysis based on the constructed vision detection system. This automated image analysis covers four core links: image acquisition, feature analysis, data association, and result feedback, and specifically includes the following steps:

[0007] Step 21): For the color changes caused by the turbidity gradient change and the tube reflection, as well as the color deviation in the collected image caused by the distribution gradient of the solution suspension and the tube wall refraction effect, propose an improved pix2pix method to complete the color deviation correction of the precious metal solution image;

[0008] Step 22): Construct a sample image database, a standard image color database, a corrected image database, and a corrected image color information database, and store the captured sample images, standard images, corrected images, and the color information extracted from the corrected images into the corresponding databases respectively;

[0009] Step 23): Establish a precious metal component feature database, which mainly includes the types of precious metals, ion concentrations, and reaction processes. Then, realize data coupling by establishing the mapping relationship between the solution chromaticity coordinates and the types of precious metals, ion concentrations, and reaction process parameters;

[0010] Step 24): Based on the data mapping relationship, map the color information of the corrected image to the component feature library, and thereby judge the reaction process of the precious metal purification process, generate a visual component analysis result, and provide support for process monitoring and process optimization.

[0011] Preferably, the improved pix2pix network in Step 21) consists of a generator, a discriminator, and a loss function. Among them, the generator adopts a lightweight network structure with a three-layer U-Net architecture, and the discriminator adopts a Markov discriminator integrating a spatial attention mechanism; the loss function integrates the Lab three-channel color loss, including the L-channel loss, the losses of the a-channel and the b-channel, where: the L-channel loss is:

[0012]

[0013] In the formula: n is the total number of image pixels, is the L-channel information of pixel i in the predicted image, is the L-channel information of pixel i in the real image; the a-channel loss and the b-channel loss use the Huber loss:

[0014]

[0015] where: n is the total number of image pixels, and is the color information of pixel i in the real image, and is the color information of pixel i in the generated image; since the weighted absolute error has better robustness, the losses of the three channels are weighted and averaged, and the total color loss is:

[0016]

[0017] where: is a hyperparameter used to optimize the influence of the brightness loss and the chroma loss on the model training.

[0018] Furthermore, the improved pix2pix network in step 21) is characterized in that its data set is composed of a sample image set and a standard image set, wherein the sample image set is obtained by standardizing and photographing the reaction solution test tube through a dark box device, and the standard image set is generated by using the method of image region segmentation and filling. First, the noble metal solution test tube region in the sample image is segmented by the K-Means algorithm, and the Euclidean distance is used when the K-Means algorithm assigns pixel points for assignment, and the distance formula is:

[0019]

[0020] where: m is the total number of pixels in the sample image, is the i-th pixel value of the image, is the i-th clustering center; secondly, "PANTONE METALLIC standard metallic color" is filled into the segmented solution test tube image region to generate a standard image.

[0021] Preferably, the color deviation correction of the sample image by the improved pix2pix network in step 21) includes the following steps:

[0022] Step 211): Construct a sample image data set and a standard image data set from the collected sample images and the generated standard images.

[0023] Step 212): Use the improved pix2pix network to train the input data set and save the training model.

[0024] Step 213): Predict the sample image through the training model to obtain a corrected image.

[0025] Further, the corrected image is preprocessed for noise suppression using the median filtering algorithm. Then, based on the color consistency feature of the test tube area, the chromaticity values corresponding to the peaks in the histogram distributions of the R, G, and B channels are extracted respectively, and the chromaticity values are used as the r, g, and b feature data of the standard color scale in this area, which are saved into the corrected image color information database to realize the quantitative characterization of the color information of the precious metal solution.

[0026] Further, a mapping relationship between the solution chromaticity coordinates, the precious metal type, the ion concentration, and the reaction process parameters is established to realize data coupling. In order to improve the content of the chromaticity coordinate database and the composition database, first, the composition data corresponding to the color data of 20 groups of solution sample images at the key nodes of the purification process are obtained from the NIST national standard database, and 20 groups of basic data nodes are established based on this; secondly, taking two adjacent groups of color data among them as the starting chromaticity coordinates and the ending chromaticity coordinates, a coupling relationship between the three-dimensional chromaticity coordinates and the composition data is constructed using the trilinear interpolation algorithm to supplement the unsampled data samples. The specific interpolation process is as follows:

[0027]

[0028] In the formula: and are the proportionality coefficients, and are the chromaticity features of the adjacent sampling points collected manually, is the interpolated color information; first, interpolation is performed in the r-axis direction:

[0029]

[0030] In the formula: and are the intermediate coefficients, represents the value of this function at and then interpolation is performed along the g-axis:

[0031]

[0032] and are the interpolation coefficients, and then interpolation is performed along the b-axis:

[0033]

[0034] In the formula: represents the ion concentration value of the precious metal solution; through the trilinear interpolation method, each item in the chromaticity-ion concentration correlation database is interpolated and completed to realize the construction of the continuous mapping relationship between the chromaticity features of the database and the ion concentration parameters. Finally, the reaction process of the current precious metal purification can be judged by the precious metal ion type and the ion concentration of the solution.

[0035] Further, obtain the component data corresponding to the color data of the solution samples at the key nodes of the 20 purification process in the NIST national standard database, and thereby establish 20 basic data nodes. Store the chromaticity information and component parameters in the corresponding databases respectively. Then, manually calibrate the mapping relationship between the chromaticity characteristics of adjacent sampling points and the precious metal component parameters, perform different calibrations and trilinear interpolations for different types of precious metal solutions, and obtain the chromaticity values at the ion concentration boundary thresholds of each type of precious metal solution as boundary data. Moreover, the independent color regions formed by the chromaticity parameters of different types of precious metal solutions in the RGB color space are all non-overlapping, and thereby construct the mapping relationship between the color and component characteristic data of the precious metal purification liquid.

[0036] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0037] Aiming at the problems of low efficiency, high cost, and detection lag existing in traditional manual detection, a color vision feature correction and adaptive detection method for precious metal solutions is proposed. By constructing a multi-level sampling point test tube array, dynamically collect solution samples during the continuous reaction process of the precious metal reaction kettle, and accurately capture the color changes under different reaction states; place the samples in a dark box to collect images, and combine the improved pix2pix color correction algorithm and the R, G, B three-channel color space modeling technology to construct a standardized color feature library. Subsequently, establish the mapping relationship between the chromaticity information of the corrected images and the precious metal ion concentration, type, purity, and reaction state, realize the visual analysis of component characteristics, and provide data support for the automatic monitoring and process optimization of the purification process. The results show that: compared with the traditional manual detection method, the present invention shows better performance in terms of detection accuracy and detection efficiency.

[0038] Further, the color vision feature correction and adaptive detection method for precious metal solutions proposed by the present invention can accurately judge the purification state based on color information, and has stronger robustness and adaptability. In the case of a large amount of collected data, the system can still maintain stable and reliable detection performance, showing good practical effects and application prospects.

[0039] Further, the present invention can bring certain guiding significance to the detection of the purification state of precious metal solutions under actual complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flowchart of the color vision feature correction and adaptive detection method for precious metal solutions of the present invention;

[0041] Figure 2 is an overall structure diagram of the color vision feature correction and adaptive detection method for precious metal solutions of the present invention;

[0042] Figure 3 is a schematic diagram of the improved pix2pix network structure of the present invention;

[0043] Figure 4 is a schematic diagram of the structure of the generator of the improved pix2pix network of the present invention;

[0044] Figure 5 is a schematic diagram of the discriminator structure of the improved pix2pix network of the present invention;

[0045] Figure 6 is a color loss result diagram of color deviation correction of the present invention;

[0046] Figure 7 is a color loss result diagram of each channel of Lab of color deviation correction of the present invention;

[0047] Figure 8 is a schematic diagram of the trilinear interpolation method of the present invention;

[0048] Figure 9 is a result diagram of color-ion concentration data mapping of the present invention; Detailed implementation manners

[0049] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention.

[0050] The present invention proposes a method for correcting the visual characteristics of the color development of a precious metal solution and adaptive detection. In the present invention, the solutions corresponding to different precious metals and their component information have different colors and do not overlap with each other;

[0051] The following further describes the present invention in detail with reference to the accompanying drawings:

[0052] In view of the problems of low operation efficiency and lag in feedback of solution components existing in the existing precious metal solution detection methods, the present invention proposes a method for correcting the visual characteristics of the color development of a precious metal solution and adaptive detection. The working flow chart of this method is as Figure 1As shown in the figure, first, a multi-level sampling point detection test tube array is constructed, and dynamic solution sampling is implemented in the continuous reaction process of the precious metal reactor to determine the detection targets and detection data of the complex purification process to be analyzed, and obtain the precious metal solution detection sample sequences at different time nodes in the continuous reaction processes of different precious metal purification reactors, and add them to the test tubes; secondly, the test tubes are placed into the dark box device one by one to obtain images of the precious metal solutions, avoiding the influence of light on the image color. Then, for the problems of turbidity gradient changes caused by the distribution of solution components and color deviation of the images caused by the reflection of the test tubes, an improved pix2pix algorithm is used to correct the color deviation of the images; then, by constructing the mapping relationship between color and precious metal components, the mapping between chromaticity coordinates and components is realized. Manually calibrate 20 groups of key node data (such as the minimum and maximum ion concentrations of different types of precious metals and their corresponding colors), and use trilinear interpolation to complete other color-component information, and establish a complete database. This method has been verified in the detection system for the precious metal purification reaction process, and the results show that it has significant advantages in terms of detection accuracy and efficiency compared with traditional human eye recognition.

[0053] Specifically, with reference to Figure 2 , a method for correcting the color vision characteristics and adaptive detection of precious metal solutions of the present invention will be described in detail. This method includes the following steps:

[0054] Step 1): Construct a multi-level sampling point detection test tube array, implement dynamic solution sampling in the continuous reaction process of the precious metal reactor, determine the detection targets and detection data of the complex purification process to be analyzed, obtain the precious metal solution detection sample sequences at different time nodes in the continuous reaction processes of different precious metal purification reactors, and add them to the test tubes as the basis for subsequent image detection.

[0055] Step 2): Sequentially place the multi-level sampling point detection test tubes obtained in Step 1) into the dark box device, and collect images of the precious metal solutions one by one to avoid the interference of ambient light on the image color; then, carry out automated image analysis based on the constructed visual detection system; this automated image analysis covers four core links: image acquisition, feature analysis, data association, and result feedback, and specifically includes the following steps:

[0056] Step 21): For the color changes caused by turbidity gradient changes and test tube reflection, as well as the color deviation in the collected images caused by the distribution gradient of solution suspended matter and the tube wall refraction effect, an improved pix2pix method is proposed to complete the color deviation correction of the precious metal solution images;

[0057] Step 22): Construct a sample image database, a standard image color database, a database of calibrated images, and a database of calibrated image color information, and store the captured sample images, standard images, calibrated images, and color information extracted from the calibrated images into the corresponding databases respectively;

[0058] Step 23): Establish a precious metal component feature database, which mainly includes the types of precious metals, ion concentrations, and reaction processes. Then, realize data coupling by establishing the mapping relationship between the solution chromaticity coordinates and the types of precious metals, ion concentrations, and reaction process parameters;

[0059] Step 24): Based on the data mapping relationship, map the calibrated image color information to the component feature library, and thereby judge the reaction process of the precious metal purification process to generate a visual component analysis result.

[0060] The following will describe the present invention in detail in combination with the embodiment of the gold purification solution.

[0061] In this embodiment, the precious metal components of the solution image in the gold purification process are detected, including the following steps:

[0062] (1) Construct a multi-level sampling point detection test tube array, perform dynamic solution sampling in the continuous reaction process of the gold purification liquid reactor, and add the solution into the test tubes;

[0063] (2) Place the test tubes in a dark box environment equipped with a standardized light source system, perform image acquisition through an industrial camera, and store the obtained original image data into a time-sequential sample database;

[0064] (3) Apply an improved pix2pix network model to adaptively adjust the color deviation of the sample image data to generate calibrated image data with very small color errors;

[0065] (4) Based on the calibrated image data in step (3), combined with the color consistency feature of the test tube area, use the histogram statistical method to extract the chromaticity values corresponding to the peaks in the histogram distributions of the R, G, and B channels respectively, and use this chromaticity value as the r, g, b feature data of the standard color scale in this area, save it into the calibrated image color information database, realize the quantitative characterization of the gold purification solution color information, and map the r, g, b feature data to a preset precious metal component feature database to realize the data coupling between the image color and the component data;

[0066] (5) Output the corresponding type, ion concentration, and reaction process attribute information according to the mapping result to complete the automatic recognition and analysis of the gold purification solution sample.

[0067] In this embodiment, as Figure 3As shown in the figure, an improved pix2pix method is used to perform color correction on the gold purification solution. The improved pix2pix network consists of a generator, a discriminator, and a loss function. Among them, the generator adopts a lightweight network structure with a three-layer U-Net architecture, as Figure 4 shown, the discriminator adopts a Markov discriminator that integrates a spatial attention mechanism, as Figure 5 shown, and the loss function integrates the color loss of the Lab three channels; during the training process, the changes in color error are respectively as Figure 6 and Figure 7 shown. When training reaches the 100th round, the color error basically tends to be stable, and the loss values in both the Lab color space and the RGB color space gradually tend to 0, and the loss values of each channel in the Lab color space also tend to 0.

[0068] In this embodiment, after obtaining the corrected image, a histogram color statistics method is used to extract the color distribution information in the image, locate and extract the color values of the gold purification solution test tube area, and obtain the r, g, b values of the area as r = 229, g = 188, b = 82.

[0069] Correspondingly, according to the color information of the 20 groups of sample solutions extracted, the color information r = 229, g = 188, b = 82 of the purification solution is between the color information r0 = 204, g0 = 153, b0 = 0 and the color information r1 = 255, g1 = 223, b1 = 128. According to the precious metal types corresponding to the color information between the color information r0 = 204, g0 = 153, b0 = 0 and the color information r1 = 255, g1 = 223, b1 = 128 is gold, and the color information r = 229, g = 188, b = 82 of the purification solution is within this range. Therefore, the color of the precious metal solution is within the color change range of the gold purification solution, and then it is determined that the precious metal type is gold.

[0070] In this embodiment, there is no solution ion concentration value corresponding to r = 229, g = 188, b = 82 in the gold database. Therefore, it is necessary to use the trilinear interpolation method to interpolate this chromaticity information. After completion, the ion concentration value can be mapped through the color value, and then the current purification reaction process can be judged.

[0071] In this embodiment, as Figure 8 shown, for the calculation of the trilinear interpolation method, first, the color information of the gold purification solution at r0 = 204, g0 = 153, b0 = 0 and r1 = 255, g1 = 223, b1 = 128 is used as the interpolation starting point and the interpolation ending point. Therefore, the starting point value is r0 = 204, g0 = 153, b0 = 0, and the ending point value is r1 = 255, g1 = 223, b1 = 128. Query the vertex ion concentration values in the NIST national standard database for value taking:

[0072]

[0073] Secondly, calculate the proportionality coefficient:

[0074]

[0075] Then interpolate the r-axis to obtain:

[0076]

[0077] Interpolate the g-axis to obtain:

[0078]

[0079] Interpolate the b-axis to obtain:

[0080]

[0081] That is, the ion concentration value c = 0.5111 mol / L of the gold purification solution is obtained; for other un-sampled color information, the trilinear interpolation method is used to calculate the ion concentration value to supplement the data information in the database. The interpolation results are shown in Table 4, and the detailed visualization results are as Figure 9 shown.

[0082] Table 4 Trilinear Interpolation Results of Gold Purification Solution Serial number Attribute r, g, b Ion concentration (mol / L) 1 Gold purification solution （204，153，0） 0.761 2 Gold purification solution （210，170，10） 0.686 ┇ ┇ ┇ ┇ 3 Gold purification solution （245，210，110） 0.406 4 Gold purification solution （255，223，128） 0.342

[0083] In this embodiment, the image colors of different types of precious metal solutions are obtained according to the above steps, a relevant database is established, and the trilinear interpolation method is used to improve the database. Different colors are mapped to different ion concentration results of the gold purification solution.

[0084] Furthermore, after the database is supplemented, the rgb values r = 229, g = 188, and b = 82 of the obtained image area can be mapped to its category as gold, the ion concentration is c = 0.5111, and the reaction process is judged based on this ion concentration.

[0085] In this embodiment, the method for correcting the color visual characteristics and adaptively detecting the gold purification solution includes: using a machine vision system to collect images of the gold purification solution to obtain image data of the gold purification solution, and using an improved pix2pix method for color deviation correction; then, based on the corrected image data, interpolation calculations are performed by extracting the r, g, and b values of its test tube area, and the trilinear interpolation method is used to process the color information in the image to deduce the ion concentration information of the corresponding gold purification solution. All of the gold ion types, gold purification solution image data, corrected image data, extracted r, g, and b values of the test tube area, and the mapped ion concentration information are saved in the database and corresponding to each other to improve the database. Then, the type and ion concentration of the gold purification solution can be directly retrieved through the r, g, and b values, and the purification reaction process can be inferred. This method can achieve an accurate mapping from the image color characteristics to the gold ion types and ion concentration ranges, improving the accuracy and automation of detection.

[0086] In summary, in view of the problems of strong subjectivity and poor accuracy caused by the fact that the color of the reaction solution in the precious metal purification process is usually judged by the human eye, that is, the reaction situation is judged by the on-site technicians, starting from the perspective of color detection in machine vision, combined with the color correction of the improved pix2pix method and the data filling of trilinear interpolation, the present invention proposes a method for correcting the color visual characteristics and adaptively detecting the precious metal solution. First, collect the reaction solution during the purification process and collect sample images with the solution placed in a test tube; secondly, use the improved pix2pix method to correct the color of the collected sample images, correct the color of the test tube area to the standard color, and then use histogram equalization to obtain the r, g, and b values of the test tube area in the image; finally, obtain the types and ion concentrations of precious metal ions through the color-component mapping relationship, and judge the current reaction process based on the types and ion concentrations of precious metal ions in the solution. This method is verified by being applied to the detection of the gold purification solution. The results show that: the method of the present invention shows good performance in both color correction and the detection of precious metal purification reaction solutions, and the proposed method has established a relatively complete database and can be used for the detection of different precious metals.

[0087] That is to say, the present invention proposes a method for quickly detecting the ion concentration state during the precious metal purification process, promoting the intelligent upgrade of the purification process, improving the replacement efficiency, energy conservation and consumption reduction, and the intelligent level of the production line, releasing the operation potential of precious metal purification enterprises, and further improving the detection and production capabilities of precious metal purification.

[0088] Those of ordinary skill in the art can understand that the above-described embodiments are specific examples for implementing the present application. In actual applications, various changes can be made in form and details without departing from the spirit and scope of the present application. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application. Therefore, the protection scope of the present application should be subject to the scope defined by the claims.

Claims

1. A method for correcting the visual characteristics and adaptively detecting the color development of a precious metal solution, characterized in that It includes the following steps: Step 1): Construct a multi-level sampling point detection test tube array, implement dynamic solution sampling in the precious metal purification process, clarify the color changes corresponding to different reaction state points, determine the detection targets and detection data of the complex purification process to be analyzed, and obtain the precious metal solution detection sample sequences at different time nodes during the continuous reaction process of different precious metal purification reactors; Step 2): Put the multi-level sampling point detection test tubes obtained in Step 1) into a dark box device one by one to obtain images of precious metal solutions, avoiding the influence of light on the image color, and then perform automated analysis based on the constructed visual detection system; The automated analysis in Step 2) includes image acquisition, feature analysis, data association, and result feedback, mainly including the following steps: Step 21): For the color changes caused by the turbidity gradient change and the tube reflection, as well as the color deviation in the acquired image caused by the distribution gradient of solution suspended matter and the tube wall refraction effect, propose an improved pix2pix method to complete the color deviation correction of the precious metal solution image; Step 22): Construct a sample image database, a standard image color database, a database of corrected images, and a database of corrected image color information, and store the captured sample images, standard images, corrected images, and color information extracted from the corrected images into the corresponding databases respectively; Step 23): Establish a precious metal component feature database, which mainly includes the types and ion concentrations of precious metal purification solutions, and then realize data coupling by establishing the mapping relationship between the solution chromaticity coordinates and the parameters of precious metal types and ion concentrations; Step 24): Based on the data mapping relationship, map the corrected image color information to the component feature library, and thereby judge the reaction process of the precious metal purification process to generate a component analysis result.

2. The method for correcting visual characteristics of noble metal solution color development and adaptive detection according to claim 1, characterized in that, The improved pix2pix network in Step 21) consists of a generator, a discriminator, and a loss function. Among them, the generator adopts a lightweight network structure with a three-layer U-Net architecture, and the discriminator adopts a Markov discriminator integrating a spatial attention mechanism; the loss function integrates the Lab three-channel color loss, including the L-channel loss, the losses of the a-channel and the b-channel, where: L-channel loss is as follows: Where: n is the total number of image pixels, is the L-channel information of pixel i in the predicted image, is the L-channel information of pixel i in the real image; a-channel loss and b-channel loss Use Huber loss: Where: n is the total number of image pixels, and are the color information of pixel i in the real image, and are the color information of pixel i in the generated image; Since the weighted absolute error has better robustness, the losses of the three channels are weighted and averaged, and the total color loss is: In the formula: is a hyperparameter used to optimize the influence of luminance loss and chrominance loss on model training.

3. A method for correcting the colorimetric visual characteristics and adaptively detecting a precious metal solution according to claim 2, characterized in that The dataset of the improved pix2pix method consists of a sample image set and a standard image set. The sample image set is obtained by standardizing the shooting of the reaction solution test tube through a dark box device. The standard image set is generated by using the method of image region segmentation and filling. First, the noble metal solution test tube region in the sample image is segmented by the K-Means algorithm. When the K-Means algorithm assigns pixel points, the Euclidean distance is used for assignment. The distance formula is: Where: m is the total number of pixels of the sample image, is the i-th pixel value of the image, is the i-th clustering center; secondly, fill the "PANTONE METALLIC standard metallic color" into the segmented solution test tube image area to generate a standard image.

4. A method for correcting the colorimetric visual characteristics and adaptively detecting a precious metal solution according to claim 1, characterized in that The improved pix2pix network in Step 21) performs color deviation correction on the sample image, including the following steps: Step 211): Construct a sample image data set and a standard image data set from the collected sample images and the generated standard images. Step 212): Use the improved pix2pix network to train the input data set and save the training model. Step 213): Predict the sample image through the training model to obtain a corrected image.

5. A method for correcting visual characteristics and adaptively detecting color development of a precious metal solution according to claim 1, characterized in that, Perform noise suppression preprocessing on the corrected image in Step 22) using the median filtering algorithm, and then based on the color consistency feature of the test tube area, extract the chromaticity values corresponding to the peaks in the R, G, and B three-channel histogram distributions respectively. Use this chromaticity value as the r, g, b feature data of the standard color scale in this area and save it to the color information database extracted from the corrected image to realize the quantitative characterization of the precious metal solution color information.

6. A method for correcting the visual characteristics of a precious metal solution color display and adaptive detection according to claim 1, characterized in that, In step 23), a mapping relationship between the chromaticity coordinates of the solution and the types and ion concentration parameters of precious metals is established to achieve data coupling. To improve the content of the chromaticity coordinate database and the composition database, first, the composition data corresponding to the color data of 20 solution sample images at the key nodes of the purification process are obtained from the NIST national standard database, and 20 groups of basic data nodes are established. Secondly, taking two adjacent groups of color data as the starting chromaticity coordinates and the ending chromaticity coordinates, a coupling relationship between the three-dimensional chromaticity coordinates and the composition data is constructed using the trilinear interpolation algorithm to supplement the unsampled data samples. The specific interpolation process is as follows: Wherein: and are proportionality coefficients, and are the chromaticity characteristics of adjacent sampling points collected manually, is the interpolated color information; interpolation is first performed in the r-axis direction: In the formula: and are intermediate coefficients, represents the value of the function at , and then interpolates along the g-axis: In the formula: and are interpolation coefficients, and then interpolate along the b-axis: Where: represents the ionic concentration value of the precious metal solution; by means of this trilinear interpolation method, item-by-item interpolation and completion are performed on the chromaticity-ion concentration correlation database to establish a continuous mapping relationship between the chromaticity characteristics of the database and the ionic concentration parameters. Finally, the reaction process of the current precious metal purification can be judged by the type and ionic concentration of the precious metal ions in the solution.

7. A method for correcting the colorimetric visual characteristics and adaptively detecting a precious metal solution according to claim 6, characterized in that The color deviation of the image of the precious metal solution is corrected by the improved pix2pix method to obtain chromaticity information, the composition of the precious metal solution is analyzed by chromatographic analysis, the chromaticity information and the composition parameters are stored in the corresponding databases respectively, and then the mapping relationship between the chromaticity characteristics of adjacent sampling points and the precious metal composition parameters is manually calibrated. Different calibrations and trilinear interpolations are performed for different types of precious metal solutions to obtain the chromaticity values at the ion concentration boundary thresholds of each precious metal solution as boundary data, and the independent color regions formed by the chromaticity parameters corresponding to different types of precious metal solutions in the RGB color space are all non-overlapping distributions, so as to construct the mapping relationship between the color and the composition characteristic data of the precious metal purification solution.

Images