Detection Method for 3D Hard Gold Processing Based on Weighted Calculation

Abstract

The present invention belongs to the technical field of hard gold processing, and specifically relates to a detection method for 3D hard gold processing based on weighted calculation. It not only covers the complete process from sample pretreatment to final report compilation, but more importantly, introduces a weight factor to adjust the density distribution of each point in the virtual mapping, forming an optimized internal structure diagram. Through high-resolution scanning and local enhanced scanning, it focuses on suspected defects to refine the image quality, and combines quantitative evaluation to ensure that all internal micro-defects can be accurately detected, and comprehensively analyzes their size, morphology and distribution characteristics. Finally, a detailed detection report is generated, providing a reliable basis for product quality control. This method based on weighted calculation effectively solves the problem of insufficient resolution in the prior art, realizes more refined and reliable internal defect detection, and helps to improve the overall quality level of 3D hard gold processing products.

Description

Technical Field

[0001] The present invention belongs to the technical field of hard gold processing, and particularly relates to a detection method for 3D hard gold processing based on weighted calculation. Background Art

[0002] In the field of 3D hard gold processing, ensuring the quality and performance of the finished product is of utmost importance. Traditional detection methods mainly rely on surface visual inspection or destructive testing, such as slice analysis. Although these methods can ensure product quality to a certain extent, they have obvious limitations. For example, surface visual inspection is difficult to detect hidden internal defects, while destructive testing will damage the sample itself and is not suitable for widespread use in mass production.

[0003] In recent years, with the development of non-destructive testing technologies, especially those based on optical imaging and multi-angle light source illumination, they have gradually been applied to the detection of 3D hard gold processed products. By applying different lighting conditions and recording the reflection spectral characteristics, such methods can construct a virtual mapping of the internal structure of the sample, thereby achieving a preliminary identification of internal defects. However, when dealing with complex structures, the existing technologies often face the problem of insufficient resolution. Especially when it is necessary to accurately reflect tiny defects, traditional methods are difficult to provide sufficient detailed information, resulting in inaccurate detection results. Summary of the Invention

[0004] The purpose of the present invention is to provide a detection method for 3D hard gold processing based on weighted calculation, which effectively solves the problem of insufficient resolution in the existing technology, realizes more refined and reliable internal defect detection, and helps to improve the overall quality level of 3D hard gold processed products.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions: A detection method for 3D hard gold processing based on weighted calculation, including the following steps:

[0006] Prepare the sample to be inspected, preprocess the sample, apply multi-angle light source illumination to the sample, and record the reflection spectral characteristics under different lighting conditions;

[0007] Based on the obtained reflection spectral characteristics, construct an initial virtual mapping of the internal structure of the sample, and the initial virtual mapping covers all known physical properties;

[0008] On the basis of the formed virtual mapping, adjust the density distribution of each point by introducing a weight factor to form an optimized internal structure diagram. According to the generated structure diagram, select several representative regions and perform high-resolution scanning to obtain detailed data;

[0009] Compare the obtained detailed data with the original virtual mapping, identify the differences and mark the defect locations. Using the marked defect locations, perform local enhanced scanning, focus on the suspected defect areas, and refine the image quality.

[0010] Quantitatively evaluate each defect area to determine its size, shape, and distribution characteristics. Integrate various indicators to compile a complete inspection report, indicating all detected internal micro-defects and their degrees of influence.

[0011] Preferably, the pretreatment of the sample includes:

[0012] Clean the surface of the sample with a cleaner to remove all non-metallic residues, and purge it with a dry gas to eliminate the moisture left after cleaning.

[0013] Measure and record the reflectance DR of the sample surface using an optical measuring instrument for the processed sample, where the reflectance DR is calculated by the formula DR = Ir / Ii, Ir represents the reflected light intensity, and Ii represents the incident light intensity.

[0014] Based on the obtained reflectance DR value, adjust the output intensity F of the light source E to the preset threshold G, and the adjustment process follows the formula F = G * DR.

[0015] Preferably, irradiate the sample with multi-angle light sources, and record the reflection spectral characteristics under different lighting conditions, including:

[0016] Select a set of light sources H, each with a different wavelength , ,..., , and irradiate the pretreated sample in sequence;

[0017] For each wavelength under the lighting condition, record the data of the corresponding reflected light intensity , forming a series of reflected light intensity data sets { , ,..., };

[0018] Based on the obtained reflected light intensity data sets, calculate the reflection spectral characteristic values for each wavelength , using the formula = / ;

[0019] According to all the obtained values, construct the reflection spectral curve C, and the reflection spectral curve C is obtained by connecting each point ( , ), ( , ),...,( , ) is represented by

[0020] Preferably, based on the obtained reflection spectral characteristics, an initial virtual mapping of the internal structure of the sample is constructed. The initial virtual mapping covers all known physical properties, including:

[0021] According to the recorded reflection spectral characteristic values , combined with the geometric shape parameters GP of the sample surface, an initial mapping relationship M1 is established. The relationship is expressed by the formula M1 = f( , GP), where f is the process of constructing the mapping;

[0022] Using the mapping relationship M1, the interior of the sample is preliminarily divided to determine multiple discrete regions , ,..., ;

[0023] For each divided region , analyze the corresponding reflection spectral characteristic values , calculate the estimated value of the physical property QA in this region, and use the formula = g( ), where g is the process of converting to the estimated value of the physical property;

[0024] Combining all the obtained values, and the spatial distribution information of each region , construct a three-dimensional virtual mapping M2 that includes all known physical properties. The three-dimensional virtual mapping M2 consists of a series of point sets {( , )}.

[0025] Preferably, based on the formed virtual mapping, by introducing a weight factor to adjust the density distribution of each point, an optimized internal structure diagram is formed, including:

[0026] For each point in the formed three-dimensional virtual mapping M2 , evaluate the reliability of its corresponding physical property , and assign an initial weight WF0. The weight is calculated by the formula WF0 = h( ), where h is the process of determining the weight;

[0027] Based on the obtained initial weight WF0, calculate the relative distance between each point ; The representative point and The distance between them is calculated using the formula:

[0028] =sqrt((xri - xrj)^2+(yri - yrj)^2+(zri - zrj)^2), where (xri,yri,zri) are the coordinates of point and (xrj,yrj,zrj) are the coordinates of point ;

[0029] Combined with the calculated distance ; Adjust the new weight and connected between each pair of points , and the new weight value is determined by the formula =WF0*exp(-α* ), where α is a predefined proportionality coefficient;

[0030] Using the updated weight , recalculate the density distribution DDn of all points to form an optimized internal structure diagram M3. The new density distribution DDn is calculated by the formula DDn( )=Σ( ) / N, where N is the number of all points connected to point , and Σ represents the summation operation.

[0031] Preferably, according to the generated structure diagram, several representative regions are selected and high-resolution scanning is performed to obtain detailed data, including:

[0032] Based on the optimized internal structure diagram M3, identify and mark multiple candidate regions with significant physical property differences , ,..., , by comparing the property change rate ΔQA between adjacent points , and the formula is ΔQA = | - | / ;

[0033] Based on the determined candidate regions , select those standard regions that meet the preset property change threshold TL , ,..., , and the selection of the standard regions follows the condition TL ≤ ΔQA;

[0034] For each selected standard region , plan a high-resolution scanning path PLi; the path design ensures that the entire area is covered while minimizing the scanning time, and the path length PLi is calculated by the formula PLi = Σ( )

[0035] According to the set path PLi, perform a detailed high-resolution scan on each standard area Execute a detailed high-resolution scan, record the finer structural information FIi within the area, and use it to construct a higher-precision local map MIi.

[0036] Preferably, comparing the obtained detailed data with the original virtual map for analysis, identifying differences and marking the defect positions, including:[[]]

[0037] Using the obtained detailed data set DF5 and combining it with the original virtual map VMAP, generate a comparative analysis matrix BM; the comparative analysis matrix BM quantifies the data differences at the corresponding positions of the two into a value ; The formula is expressed as = | (DF5) - (VMAP)|;

[0038] Based on the formed comparative analysis matrix BM, identify all coordinate points that exceed the preset difference threshold TL to form a set of suspected abnormal points PS;

[0039] For all marked suspected abnormal points PSi, apply a local refinement algorithm to calculate the neighborhood structure feature vector FVi of each point; the feature vector FVi is determined by the local pattern composed of the current point and its n closest surrounding points;

[0040] Based on the obtained feature vector FVi, perform a depth evaluation on each suspected abnormal point PSi to determine whether it is finally marked as a defect position. The evaluation process introduces a confidence score CScore, and the calculation method is CScore(PSi) = h(FVi).

[0041] Preferably, using the marked defect positions, perform a local enhancement scan, focus on the suspected defects, and refine the image quality, including:[[]]

[0042] Based on the marked set of suspected defect positions DS, determine the coordinate range RSi of each defect position DSi. The coordinate range RSi is defined by the expansion distance δ of the defect point, and the formula is RSi = {P|d(P, DSi) ≤ δ};

[0043] For each determined coordinate range RSi, plan a high-precision local scanning path PLi; the path design ensures that the entire area is covered while optimizing the scanning path length PLi;

[0044] According to the set path PLi, perform enhanced resolution scanning at each suspected defect, and record the detailed structural information FIi; the structural information FIi is used to construct the local refinement map MIi;

[0045] Using the obtained local refinement map MIi, perform image quality assessment on each suspected defect position DSi; the clarity score IScore is introduced in the assessment process, and the calculation method is IScore(MIi)=h(Isharp,Inoise); Isharp is the sharpness index, and Inoise is the noise level.

[0046] Preferably, the quantitative evaluation of each defect area to determine its size, morphology and distribution characteristics includes:

[0047] Based on the local refinement map MIi, calculate the area AS of each suspected defect position DSi; the area is calculated by the formula AS(DSi)=Σ(Pij), where Pij represents the set of all pixel points belonging to the defect area DSi;

[0048] According to the obtained area AS(DSi), analyze the morphological characteristics FShape of the suspected defect area; the morphological characteristics FShape are determined by the defect boundary shape descriptor SD, and the expression is FShape(DSi)=g(SD);

[0049] Combined with the morphological characteristics FShape, evaluate the spatial distribution characteristics DDist of each suspected defect area; the spatial distribution characteristics are analyzed by the distance between adjacent defects and their relative angles to measure;

[0050] Using the obtained spatial distribution characteristics DDist, together with the area AS and morphological characteristics FShape, comprehensively and quantitatively evaluate the overall attribute QTotal of each suspected defect area; the overall attribute is calculated by the formula QTotal(DSi)=w1*AS(DSi)+w2*FShape(DSi)+w3*DDist(DSi); w1, w2, w3 are weight factors.

[0051] Preferably, the comprehensive compilation of various indicators to prepare a complete inspection report includes:

[0052] According to the overall attribute QTotal(DSi) obtained from the quantitative evaluation, determine the impact level GL for each suspected defect position DSi; the impact level GL is calculated by the formula GL(DSi)=f(QTotal(DSi));

[0053] Based on the determined impact level GL, all suspected defects are classified and summarized to form a defect list LS; each entry in the list includes the defect location DSi, the area AS(DSi), the morphological feature FShape(DSi), the spatial distribution characteristic DDist(DSi), and the corresponding impact level GL(DSi).

[0054] Using the formed defect list LS, a preliminary inspection report RDraft is compiled; the report structure follows the template TM, and the template TM defines how to organize and present defect information.

[0055] Review the compiled preliminary report RDraft and add a comprehensive conclusion CC; the conclusion summarizes the quantity, location, and impact degree of all internal micro-defects, and proposes recommended measures SM; the final inspection report RFinal is composed of the formula RFinal = RDraft + CC + SM.

[0056] Technical effects and advantages of the present invention: The detection method for 3D hard gold processing based on weighted calculation proposed by the present invention has the following advantages compared with the prior art:

[0057] The present invention not only covers the complete process from sample pretreatment to final report compilation, but more importantly, introduces a weight factor to adjust the density distribution of each point in the virtual mapping, forming an optimized internal structure diagram. This method can significantly improve the detection accuracy, especially the ability to identify micro-defects has a qualitative leap. Through high-resolution scanning and local enhanced scanning, focus on the suspected defects to refine the image quality, combined with quantitative evaluation, ensure that all internal micro-defects can be accurately detected, and comprehensively analyze their size, morphology, and distribution characteristics. Finally, a detailed inspection report is generated, providing a reliable basis for product quality control. This method based on weighted calculation effectively solves the problem of insufficient resolution in the prior art, realizes more refined and reliable internal defect detection, and helps to improve the overall quality level of 3D hard gold processing products. Description of the Drawings

[0058] Figure 1 It is a flowchart of the detection method for 3D hard gold processing based on weighted calculation of the present invention. Detailed Embodiments

[0059] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0060] The present invention provides a detection method for 3D hard gold processing based on weighted calculation as shown in Figure 1 the following, including the steps below:

[0061] Step 1: Prepare the sample to be detected and preprocess the sample; further including:

[0062] Use a cleaner to clean the surface of the sample to remove all non-metallic residues, and use a drying gas to purge to eliminate the moisture left after cleaning; by removing the non-metallic residues and moisture on the surface of the sample, it is ensured that there is no external substance interference during the subsequent detection process, so that the reflection spectrum characteristics can truly reflect the internal structure of the sample. The cleaning and drying treatments can ensure the consistency of the sample surface and reduce the measurement errors caused by surface state differences.

[0063] For the processed sample, use an optical measuring instrument to measure and record the reflectance DR of the sample surface, where the reflectance DR is calculated by the formula DR = Ir / Ii, Ir represents the intensity of the reflected light, and Ii represents the intensity of the incident light; the reflectance refers to the ratio of the intensity of the light reflected from the sample surface to the intensity of the light incident on the sample. It reflects the reflection characteristics of the sample surface for light of a specific wavelength and is one of the important parameters for evaluating the surface properties of the sample. This formula is used to calculate the reflectance, where Ir is the intensity of the reflected light, that is, the intensity of the light reflected from the sample surface; Ii is the intensity of the incident light, that is, the intensity of the light irradiated on the sample surface. The value range of the reflectance DR is usually between 0 and 1, and the higher the value, the stronger the reflection.

[0064] Based on the obtained reflectance DR value, adjust the output intensity F of the light source E to the preset threshold G, and the adjustment process follows the formula F = G*DR. This formula is used to determine the final output intensity of the light source. By multiplying the preset threshold G by the reflectance DR, the light source output can be dynamically adjusted to keep the light intensity received by the sample surface within an ideal range, neither overexposed nor underexposed.

[0065] Light source output intensity F: The light source output intensity refers to the ability or brightness of the light source to emit light, and the unit is usually watt (W) or candela (cd), depending on the application scenario.

[0066] Preset threshold G: This is a pre-set target value, aiming to ensure that different samples receive consistent light intensity under the same lighting conditions, thus ensuring the consistency and comparability of the detection results.

[0067] By adjusting the light source output intensity, it is ensured that each sample can be under the same lighting conditions when being detected, improving the reliability and repeatability of the detection results.

[0068] Example 1

[0069] Suppose there is a 3D hard gold processing sample. After its surface is cleaned and dried, the incident light intensity Ii measured by an optical measuring instrument is 100 units, and the reflected light intensity Ir is 85 units. According to the formula:

[0070] DR = Ir / Ii, DR = 85 / 100;

[0071] DR = 0.85;

[0072] Next, the preset threshold G is known to be 120 units. To adjust the output intensity F of the light source E, the formula is applied:

[0073] F = G * DR, F = 120 * 0.85;

[0074] F = 102;

[0075] Therefore, the output intensity of the light source E should be adjusted to 102 units to ensure that the light intensity received by the sample surface is moderate, neither too bright nor too dark, thus providing ideal conditions for subsequent multi-angle light source irradiation and recording of reflection spectral characteristics.

[0076] Through the above embodiments, it can be seen how to achieve precise control of the light source output intensity through specific measurements and calculations, thereby improving the accuracy and reliability of the entire detection process.

[0077] Step 2: Apply multi-angle light source irradiation to the sample and record the reflection spectral characteristics under different lighting conditions; further including:

[0078] Select a set of light sources H, each light source having a different wavelength , ,..., , and irradiate the pre-treated sample in sequence; by selecting light sources with multiple different wavelengths, a wide spectral range from ultraviolet to infrared can be covered, ensuring that the characteristics of the sample under various spectral conditions can be detected. Lights with different wavelengths have different penetration depths and reflection characteristics for materials. Using multi-wavelength light sources can improve the detection sensitivity and help discover subtle internal structure changes or defects.

[0079] For each wavelength under the lighting condition, record the corresponding data of the reflected light intensity , forming a series of reflected light intensity data sets { , ,..., }; the data of the reflected light intensity : at a specific wavelength Under this condition, the light intensity reflected from the sample surface. It reflects the reflection ability of the sample to the light of this wavelength and is the basic data for constructing the reflection spectrum characteristics. By recording the reflected light intensity at different wavelengths, detailed information about the optical properties of the sample can be obtained, providing rich data support for subsequent analysis. Multiple measurements and forming a data set help reduce the random error that may be brought by a single measurement and improve the accuracy and reliability of the data.

[0080] Based on the obtained data set of reflected light intensity, calculate the reflection spectrum characteristic value for each wavelength under , using the formula = / ; The reflection spectrum characteristic value : This refers to the reflectivity of the sample at a specific wavelength . It is calculated by dividing the data of the reflected light intensity by the incident light intensity Ii, reflecting the reflection characteristics of the sample at this wavelength. By calculating the reflection spectrum characteristic value, the reflected light intensity at different wavelengths can be standardized, facilitating comparison and analysis. The reflection spectrum characteristic value directly reflects the optical properties of the sample at different wavelengths and is very important for understanding the internal structure and composition of the material.

[0081] According to all the obtained values, construct the reflection spectrum curve C. The reflection spectrum curve C is represented by connecting each point ([[]] , ), ([[]] , ),..., ([[]] , ). The reflection spectrum curve intuitively shows the reflection characteristics of the sample at different wavelengths, enabling researchers to quickly understand the optical behavior of the sample. The reflection spectrum curve provides rich information and can be used to assist in diagnosing whether there are defects, impurities or other abnormal conditions inside the sample.

[0082] Example 2

[0083] Suppose there is a pre-treated 3D hard gold processing sample. Now select five light sources H with different wavelengths, which are respectively = 450nm, = 550nm, = 650nm, = 750nm and = 850nm. For each wavelength, the measured incident light intensity Ii is 100 units (for simplicity of calculation), and the data of the reflected light intensity are respectively:

[0084] : = 70 units;

[0085] : = 85 units;

[0086] : = 90 units;

[0087] : = 80 units;

[0088] : = 75 units;

[0089] According to the formula = / , the reflection spectral characteristic values at each wavelength can be calculated as follows:

[0090] = / = 70 / 100 = 0.70;

[0091] = / = 85 / 100 = 0.85;

[0092] = / = 90 / 100 = 0.90;

[0093] = / = 80 / 100 = 0.80;

[0094] = / = 75 / 100 = 0.75;

[0095] Next, based on these values, construct the reflection spectral curve C, which is represented by connecting the points ( , ), ( , ),..., ( , ). The specific points are as follows:

[0096] (0.70, 450 nm);

[0097] (0.85, 550 nm);

[0098] (0.90, 650 nm);

[0099] (0.80, 750 nm);

[0100] (0.75, 850 nm);

[0101] This reflection spectrum curve shows the reflection characteristics of the sample at different wavelengths, which can help further analyze the internal structure of the sample and possible existing defects. For example, if the characteristic value of the reflection spectrum at a certain wavelength is significantly lower than that at other wavelengths, this may be caused by the existence of some absorptive defects or compositional changes inside the sample.

[0102] Step 3: Based on the obtained reflection spectrum characteristics, construct an initial virtual mapping of the internal structure of the sample. The initial virtual mapping covers all known physical properties; further including:

[0103] According to the recorded reflection spectrum characteristic values , combined with the geometric shape parameters GP of the sample surface, establish an initial mapping relationship M1. The relationship is expressed by the formula M1 = f( , GP), where f is the process of constructing the mapping; combining the reflection spectrum characteristic values with the geometric shape parameters GP of the sample surface can more accurately reflect the actual physical state of the sample. Establishing the initial mapping relationship M1 provides a basic framework for subsequent detailed analysis and regional division, ensuring that the optical characteristics of each point correspond to its spatial position.

[0104] Using the mapping relationship M1, conduct a preliminary division of the sample interior to determine multiple discrete regions , ,..., ; Through the preliminary division, the sample interior can be divided into multiple discrete regions, facilitating the detailed physical property evaluation for different regions. The regional division simplifies the subsequent analysis process, enabling the computational resources to be concentrated on important or suspicious regions and improving the overall analysis efficiency.

[0105] For each divided region , analyze the corresponding reflection spectrum characteristic values , calculate the estimated value of the physical property QA within this region, using the formula = g( ), where g is the process of converting to the estimated value of the physical property; through the formula = g( ), the optical measurement results can be converted into specific physical properties, providing a quantitative basis for subsequent quality assessment. The estimated value Help to identify abnormal regions, such as uneven hardness or compositional changes, which may be signs of potential defects.

[0106] All the combined values, as well as the spatial distribution information of each region , construct a three-dimensional virtual map M2 that includes all known physical properties. The three-dimensional virtual map M2 consists of a series of point sets {( , )}. The three-dimensional virtual map M2 visually shows the physical properties and their spatial distribution of each region inside the sample, providing a visualization tool for a comprehensive understanding of the sample. Through the three-dimensional mapping, researchers can quickly locate problem areas, supporting further defect analysis and quality control decisions.

[0107] Example Three

[0108] Suppose there is a 3D hard gold processing sample that has undergone the first two steps of processing and obtained the reflection spectral characteristic values . Now the following specific operations will be performed:

[0109] Establish the initial mapping relationship M1: According to the reflection spectral characteristic values and the geometric shape parameters GP of the sample surface, use the formula M1 = f( , GP) to construct the initial mapping relationship. Assume that the mapping relationship M1 is established through a certain mapping method (such as interpolation or fitting), and this step provides the basis for subsequent analysis.

[0110] Preliminarily divide the inside of the sample: Based on the mapping relationship M1, divide the inside of the sample into 5 discrete regions to . Each region covers a different part, and each region contains a set of reflection spectral characteristic values .

[0111] Calculate the estimated physical property values : For each region , use the formula = g( ) to calculate the estimated physical property value within that region. Assume that hardness is selected as the physical property QA, and through a certain conversion method (such as looking up a table or model prediction), the hardness estimated values for each region are obtained as follows:

[0112] : = 80HV;

[0113] : = 75HV;

[0114] : = 82 HV;

[0115] : = 78 HV;

[0116] : = 81 HV, where HV represents the Vickers hardness unit.

[0117] Construct the three - dimensional virtual mapping M2: Finally, integrating all the values and the spatial distribution information of each region to construct the three - dimensional virtual mapping M2. The mapping M2 consists of a series of point sets {( , )}, and the specific points are as follows:

[0118] (80 HV, );

[0119] (75 HV, );

[0120] (82 HV, );

[0121] (78 HV, );

[0122] (81 HV, );

[0123] Through the above - mentioned embodiments, it can be seen how to gradually construct a three - dimensional virtual mapping covering all known physical properties starting from the reflection spectral characteristic values. This method not only improves the accuracy of detection but also provides strong support for subsequent quality assessment and defect analysis.

[0124] Step Four: On the basis of the formed virtual mapping, adjust the density distribution of each point by introducing a weight factor to form an optimized internal structure diagram; further including:

[0125] For each point in the formed three - dimensional virtual mapping M2, evaluate the reliability of its corresponding physical property and assign an initial weight WF0. The weight is calculated by the formula WF0 = h( The reliability is ensured and the initial weight WF0 is assigned, which can ensure that those more reliable data points account for a larger proportion in subsequent analyses, thereby improving the accuracy of the overall detection results. Points with low reliability may indicate potential measurement errors or sample defects, and assigning lower weights can help filter out these outliers and reduce their impact on the final results.

[0126] Based on the obtained initial weight WF0, calculate the relative distance between each point representing points and using the formula:

[0127] =sqrt((xri - xrj)^2+(yri - yrj)^2+(zri - zrj)^2), where (xri,yri,zri) are the coordinates of point and (xrj,yrj,zrj) are the coordinates of point ; By calculating the relative distance between each point , their spatial relationship can be quantified, which is crucial for understanding the internal structure of the sample. The distance information helps identify local features, such as similarities and differences between adjacent regions.

[0128] Combined with the calculated distance , adjust the new weight and connected between each pair of points , and the new weight value is determined by the formula =WF0*exp(-α* ), where α is a predefined proportionality coefficient; By introducing the distance factor, the weights can be dynamically adjusted according to the relative positions of the points, making the connection between neighboring points closer and weakening the influence of distant points.

[0129] Using the updated weight , recalculate the density distribution DDn of all points to form the optimized internal structure diagram M3. The new density distribution DDn is calculated by the formula DDn( )=Σ( ) / N, where N is the number of all points connected to point , and Σ represents the summation operation. By recalculating the density distribution, the actual structure inside the sample can be more accurately reflected, improving the detection resolution and accuracy. The optimized internal structure diagram M3 visually shows the density distribution inside the sample, providing strong support for quality assessment.

[0130] Example Four

[0131] Assume there is a 3D hard gold processing sample, and a three-dimensional virtual mapping M2 has been formed, and the physical properties of each point have been obtained. Now the following specific operations will be performed:

[0132] Evaluate the reliability and assign an initial weight WF0: According to the reliability of the physical properties , use the formula WF0 = h( ) to assign an initial weight to each point. Assume a linear mapping method is adopted (for example, the reliability score is directly used as the weight), and the initial weights WF0 of each point are obtained as follows:

[0133] : = 80HV -> WF0_1 = 0.9;

[0134] : = 75HV -> WF0_2 = 0.8;

[0135] : = 82HV -> WF0_3 = 0.95;

[0136] : = 78HV -> WF0_4 = 0.85;

[0137] : = 81HV -> WF0_5 = 0.92;

[0138] Calculate the relative distance between each point : For each pair of points and , use the formula sqrt((xri - xrj)^2+(yri - yrj)^2+(zri - zrj)^2) to calculate their relative distance. Assume the following coordinates:

[0139] : (1, 2, 3);

[0140] : (4, 5, 6);

[0141] : (7, 8, 9);

[0142] : (10, 11, 12);

[0143] : (13, 14, 15);

[0144] Calculate the following relative distances:

[0145] = 5.196;

[0146] = 10.392;

[0147] = 15.588;

[0148] = 20.785;

[0149] ... (and so on);

[0150] Adjust the new weights of the connections between each pair of points : Based on the calculated distances , use the formula = WF0 * exp(-α * ) to update the weights. Assume the proportionality coefficient α = 0.01, and calculate the new weights as follows:

[0151] = WF0_1 * exp(-0.01 * DL1,2^2) = 0.9 * exp(-0.01 * 5.196^2) ≈ 0.82;

[0152] = WF0_1 * exp(-0.01 * DL1,3^2) = 0.9 * exp(-0.01 * 10.392^2) ≈ 0.67;

[0153] = WF0_1 * exp(-0.01 * DL1,4^2) = 0.9 * exp(-0.01 * 15.588^2) ≈ 0.48;

[0154] = WF0_1 * exp(-0.01 * DL1,5^2) = 0.9 * exp(-0.01 * 20.785^2) ≈ 0.33;

[0155] ... (and so on);

[0156] Recalculate the density distribution DDn and form the optimized internal structure diagram M3: Finally, using the updated weights , through the formula DDn( ) = Σ( ) / N to recalculate the density distribution of all points. Assume that each point is connected to its four nearest neighbors, and calculate the following density distribution values:

[0157] DDn( )=( + + + ) / 4;

[0158] ... (and so on);

[0159] Through the above embodiments, it can be seen how to start from the initial virtual mapping, gradually introduce the weight factor to adjust the density distribution of each point, and finally form the optimized internal structure diagram M3.

[0160] Step Five: According to the generated structure diagram, select several representative regions and perform high-resolution scanning to obtain detailed data; further including:

[0161] Based on the optimized internal structure diagram M3, identify and mark multiple candidate regions with significant physical property differences , ,..., , by comparing the property change rate ΔQA between adjacent points The formula is ΔQA = | - | / ; By calculating the physical property change rate between adjacent points, the regions with obvious physical property differences can be accurately located, and these regions may be defects, boundaries or other important structures.

[0162] Based on the determined candidate regions , select the standard regions that meet the preset property change threshold TL , ,..., , The selection of standard regions follows the condition TL ≤ ΔQA; By setting a property change threshold TL, the truly worthy standard regions can be selected from many candidate regions to ensure that only those regions of great significance are further analyzed.

[0163] For each selected standard region , plan the high-resolution scanning path PLi; The path design ensures covering the entire region while minimizing the scanning time, and the path length PLi is calculated by the formula PLi = Σ( ); Reasonable path planning can not only ensure that all regions of interest are fully covered, but also minimize the scanning time and improve work efficiency.

[0164] According to the set path PLi, for each standard region Perform a detailed high-resolution scan to record more refined structural information FIi within the region for constructing a more accurate local map MIi. The high-resolution scan provides more detailed data than the conventional scan, helping to detect subtle structural features or defects. The refined local map provides a solid foundation for subsequent quality assessment, fault diagnosis, etc., facilitating more accurate and reliable judgments.

[0165] Example Five

[0166] Assume that an optimized internal structure diagram M3 of a hard gold sample has been obtained and it is desired to select some regions with significant differences in physical properties for high-resolution scanning. The following steps will be carried out:

[0167] Identify candidate regions CR: First, use the formula ΔQA = | - | / to calculate the property change rate between adjacent points. For example, in a specific direction, the following results may be obtained:

[0168] Δ = |80HV - 75HV| / 5.196 ≈ 0.96;

[0169] Δ = |75HV - 82HV| / 10.392 ≈ 0.67;

[0170] Δ = |82HV - 78HV| / 15.588 ≈ 0.26;

[0171] Δ = |78HV - 81HV| / 20.785 ≈ 0.14;

[0172] Assume that several positions with high ΔQA are found and they are marked as candidate regions , ,...

[0173] Select standard region Sr: Next, set a property change threshold TL, such as 0.5. Then, check which candidate regions have ΔQA greater than or equal to this threshold. If in the above example, only Δ exceeds the threshold, then is selected as the standard region .

[0174] Plan the scanning path PL: For the selected standard region , a path that covers the entire region and can minimize the scanning time needs to be planned. Assume consists of five key points, and the path length can be calculated based on the distance between points:

[0175] PL1 = + + + ;

[0176] If these distances are 5.196, 10.392, 15.588, 20.785 respectively, then PL1 = 5.196 + 10.392 + 15.588 + 20.785 = 51.961.

[0177] Perform high - resolution scanning: Finally, perform high - resolution scanning on the standard area along the planned path PL1 and record more detailed structural information FI1. This may involve collecting additional images, spectra, or other forms of data, which are ultimately used to construct a more accurate local map MI1.

[0178] From the above embodiments, it can be seen how to start from the optimized internal structure diagram, gradually identify, select, and scan the areas with significant physical property differences, so as to obtain more detailed data and provide strong support for subsequent analysis.

[0179] Step Six: Compare and analyze the obtained detailed data with the original virtual map, identify the differences and mark the defect positions; further including:

[0180] Using the obtained detailed data set DF5, combined with the original virtual map VMAP, generate a comparative analysis matrix BM; the comparative analysis matrix BM quantifies the data differences at the corresponding positions of the two into values The formula is expressed as = | (DF5) - (VMAP)|; Through the comparative analysis matrix BM, the differences between the detailed data after high - resolution scanning and the original virtual map can be accurately quantified, making any potential changes or anomalies obvious at a glance.

[0181] Based on the formed comparative analysis matrix BM, identify all coordinate points that exceed the preset difference threshold TL, and form a set of suspected anomaly points PS; by setting the difference threshold TL, those coordinate points with significant differences can be screened out from the comparative analysis matrix, and these points may be defects or other anomalies inside the sample.

[0182] For all the marked suspected anomaly points PSi, apply a local refinement algorithm to calculate the neighborhood structure feature vector FVi of each point; the feature vector FVi is determined by the local pattern composed of the current point and its n closest surrounding points; by calculating the feature vector FVi, more detailed structural information about the suspected anomaly points PSi and their adjacent regions can be obtained, which helps to further evaluate the properties of these points.

[0183] Based on the obtained feature vector FVi, each suspected abnormal point PSi is deeply evaluated to determine whether it is finally marked as a defect location. A confidence score CScore is introduced in the evaluation process, and the calculation method is CScore(PSi)=h(FVi). By introducing the confidence score CScore, it is possible to more accurately determine whether a suspected abnormal point is indeed a defect, reducing the possibility of mislabeling.

[0184] Example Six

[0185] Assume that a detailed data set DF5 of a certain standard area has been obtained and the original virtual map VMAP is available. The following steps will be taken:

[0186] Generate a comparative analysis matrix BM: First, use the formula =| (DF5)- (VMAP)| to calculate the data difference at each corresponding position between the detailed data set DF5 and the original virtual map VMAP. For example, in a specific area, the following results may be obtained:

[0187] Δ =|80HV - 79HV| = 1;

[0188] Δ =|75HV - 76HV| = 1;

[0189] Δ =|82HV - 83HV| = 1;

[0190] Δ =|78HV - 77HV| = 1;

[0191] These difference values form part of the comparative analysis matrix BM.

[0192] Identify the set of suspected abnormal points PS: Next, set a difference threshold TL, such as 2. Then, check which points in the comparative analysis matrix BM have difference values exceeding this threshold. If in the above example, all difference values are less than 2, then no points are selected into the set of suspected abnormal points PS. But assume that at another location, Δ = 3, then this point will be marked as a suspected abnormal point PS1.

[0193] Calculate the feature vector FVi: For each suspected abnormal point PSi (such as PS1), apply a local refinement algorithm to calculate its neighborhood structure feature vector FVi. Assume that the three nearest points around PS1 are P1, P2, and P3 respectively, and the feature vector FV1 can be constructed based on their physical properties and spatial relationships.

[0194] Deeply evaluate and mark the defect locations: Finally, use the formula CScore(PSi)=h(FVi) to deeply evaluate each suspected abnormal point PSi. Assume that a machine learning-based classification method is used to determine the confidence score, and the following results are obtained:

[0195] PS1: CScore(PS1)=h(FV1)=0.85 (high confidence);

[0196] If the confidence score is higher than a preset threshold (e.g., 0.8), then the current point is officially marked as the defect location.

[0197] From the above embodiments, it can be seen how to start from the comparative analysis matrix, gradually identify, evaluate, and finally mark the defect locations, thus providing a scientific basis and technical support for quality control and defect management. This method not only improves the accuracy of detection but also provides guidance for subsequent repair and improvement measures.

[0198] Step 7: Utilize the marked defect locations to perform local enhanced scanning, focus on the suspected defect areas, and refine the image quality; further including:

[0199] Based on the set of marked suspected defect locations DS, determine the coordinate range RSi of each defect location DSi. The coordinate range RSi is defined by the expansion distance δ of the defect point, and the formula is RSi={P|d(P,DSi)≤δ}; expanding a certain distance δ from the defect point can ensure that the scan covers the defect and the surrounding areas that may be affected, avoiding missing any potential problems.

[0200] For each determined coordinate range RSi, plan a high-precision local scanning path PLi; the path design ensures covering the entire area while optimizing the scanning path length PLi; reasonable path planning can ensure that the scanning device effectively covers the entire specified area while minimizing unnecessary movement, saving time and resources.

[0201] According to the set path PLi, perform enhanced resolution scanning at each suspected defect, and record the detailed structure information FIi; the structure information FIi is used to construct the local refinement mapping MIi; high-resolution scanning provides more detailed data than conventional scanning, which helps to discover subtle structural features or defects. The detailed structure information FIi provides a solid foundation for subsequent in-depth analysis and the construction of the local refinement mapping MIi.

[0202] Using the obtained local refinement mapping MIi, image quality assessment is performed on each suspected defect location DSi; the clarity score IScore is introduced in the assessment process, and the calculation method is IScore(MIi)=h(Isharp,Inoise); Isharp is the sharpness index, and Inoise is the noise level. By introducing the clarity score IScore, the quality of the local refinement mapping can be objectively evaluated to ensure that the final result meets the expected standard.

[0203] Example Seven

[0204] Suppose several suspected defect locations have been marked and local enhanced scanning is desired. The following steps will be taken:

[0205] Determine the coordinate range RSi: First, use the formula RSi={P|d(P,DSi)≤δ} to define the coordinate range of each defect location DSi. For example, if a defect location D1 is at the coordinates (5,6,7) and the expansion distance δ = 2 is set, the coordinate range RS1 will include all points P that are no more than 2 units away from D1.

[0206] RS1={P|d(P,(5,6,7))≤2};

[0207] Plan the high-precision local scanning path PLi: Next, for each coordinate range RSi, plan a high-precision local scanning path PLi that covers the entire area and minimizes the path length as much as possible. Assume that RS1 consists of several key points, and an optimal path can be designed based on the positions of these points. For example, by connecting adjacent key points, ensure that the path is as short as possible and comprehensively covers the area.

[0208] Perform enhanced resolution scanning and record the structural information FIi: According to the set path PLi, perform high-resolution scanning at each suspected defect and record the detailed structural information FIi. This may involve collecting additional images, spectra, or other forms of data, which are ultimately used to construct the local refinement mapping MIi.

[0209] For RS1, detailed structural information FI1 is obtained and the local refinement mapping MI1 is constructed based on this.

[0210] Image quality assessment and calculate the clarity score IScore: Finally, use the formula IScore(MIi)=h(Isharp,Inoise) to perform image quality assessment on each suspected defect location DSi. Assume that the sharpness index Isharp = 0.9 (high sharpness) and the noise level Inoise = 0.1 (low noise) of MI1, then the clarity score is as follows:

[0211] IScore(MI1)=h(0.9,0.1);

[0212] If a comprehensive evaluation method, such as weighted average or machine learning model, is adopted to calculate the clarity score, the following results may be obtained:

[0213] IScore(MI1) = 0.85 (high clarity);

[0214] From the above embodiments, it can be seen how to start from the marked defect positions, gradually perform local enhanced scanning, path planning, high-resolution data acquisition, and image quality evaluation, so as to obtain a high-quality local refined mapping, providing a scientific basis and technical support for subsequent quality evaluation and defect management.

[0215] Step eight: Quantitatively evaluate each defect area to determine its size, morphology, and distribution characteristics; further including:

[0216] Based on the local refined mapping MIi, calculate the area AS for each suspected defect position DSi; the area is calculated by the formula AS(DSi) = Σ(Pij), where Pij represents the set of all pixel points belonging to the defect area DSi; by calculating the area AS, the actual size of each defect area can be intuitively quantified, providing basic data for subsequent analysis.

[0217] According to the obtained area AS(DSi), analyze the morphological characteristics FShape of the suspected defect area; the morphological characteristics FShape are determined by the defect boundary shape descriptor SD, and the expression is FShape(DSi) = g(SD); the morphological characteristics FShape provide information about the geometric shape of the defect area, such as circular, elliptical, or irregular shape, which helps to understand the formation reason of the defect.

[0218] Combined with the morphological characteristics FShape, evaluate the spatial distribution characteristics DDist of each suspected defect area; the spatial distribution characteristics are evaluated by analyzing the distance between adjacent defects and their relative angles; by analyzing the spatial distribution characteristics DDist, the mutual relationship between defect areas and their distribution pattern in the whole sample can be revealed, so as to better understand the generation mechanism of defects.

[0219] Using the obtained spatial distribution characteristic DDist, together with the area AS and the morphological feature FShape, comprehensively quantify and evaluate the overall property QTotal of each suspected defect area; the overall property is calculated by the formula QTotal(DSi)=w1*AS(DSi)+w2*FShape(DSi)+w3*DDist(DSi); w1, w2, and w3 are weighting factors. By comprehensively considering the area, morphology, and distribution characteristics, a comprehensive quantitative evaluation can be performed on each suspected defect area to ensure the integrity and accuracy of the evaluation results.

[0220] Example Eight

[0221] Suppose a local refined map MI1 of a suspected defect position DS1 has been obtained and it is desired to perform a quantitative evaluation on it. The following steps will be taken:

[0222] Calculate the defect area AS: First, use the formula AS(DSi)=Σ(Pij) to calculate the area of the defect area. Suppose DS1 contains 50 pixel points, then:

[0223] AS(DS1)=50;

[0224] Analyze the morphological feature FShape: Next, according to the area AS(DS1), analyze the morphological feature FShape of the suspected defect area. Suppose the morphological feature of DS1 is determined through the defect boundary shape descriptor SD (such as perimeter, convex hull, major axis direction, etc.) as follows:

[0225] SD(DS1)={Perimeter: 25, Convex hull area: 60, Major axis direction: 45 degrees};

[0226] Calculate the morphological feature value using a certain method (such as a machine learning model or an empirical formula):

[0227] FShape(DS1)=g(SD(DS1))=0.7 (high morphological complexity);

[0228] Evaluate the spatial distribution characteristic DDist: Combining the morphological feature FShape, evaluate the spatial distribution characteristic DDist of each suspected defect area. Suppose there are two other defects DS2 and DS3 near DS1, calculate the distance and the relative angle as follows:

[0229] =10, θ1,2 = 30 degrees;

[0230] =15, θ1,3 = 60 degrees;

[0231] Calculate the spatial distribution characteristic value using a certain method (such as statistical analysis or clustering algorithm):

[0232] DDist(DS1)=h( , ) = 0.6 (medium aggregation degree);

[0233] Comprehensively and quantitatively evaluate the overall property QTotal: Finally, use the formula QTotal(DSi)=w1*AS(DSi)+w2*FShape(DSi)+w3*DDist(DSi) to comprehensively and quantitatively evaluate the suspected defect areas. Assume that the weight factors are set as w1 = 0.5, w2 = 0.3, and w3 = 0.2, then:

[0234] QTotal(DS1)=0.5*50 + 0.3*0.7 + 0.2*0.6 = 25 + 0.21 + 0.12 = 25.33;

[0235] From the above embodiments, it can be seen how to start from local refinement mapping, gradually calculate the area, analyze the morphological characteristics, evaluate the spatial distribution characteristics, and finally comprehensively and quantitatively evaluate the overall property of each suspected defect area.

[0236] Step Nine: Synthesize various indicators, compile a complete inspection report, and point out all detected internal micro-defects and their influence degrees; further include:

[0237] According to the overall property QTotal(DSi) obtained from the quantitative evaluation, determine the influence level GL for each suspected defect position DSi; the influence level GL is calculated by the formula GL(DSi)=f(QTotal(DSi)); by calculating the influence level GL, the influence degree of each defect on the overall performance of the sample can be intuitively evaluated, which helps to prioritize the treatment of high-risk defects.

[0238] Based on the determined influence level GL, classify and summarize all suspected defects to form a defect list LS; each entry in the list includes the defect position DSi, area AS(DSi), morphological characteristic FShape(DSi), and spatial distribution characteristic DDist(DSi), as well as the corresponding influence level GL(DSi); through classification and summarization, all suspected defects can be organized into an ordered list, which is convenient for subsequent analysis and report compilation.

[0239] Use the formed defect list LS to compile a preliminary inspection report RDraft; the report structure follows the template TM, and the template TM defines how to organize and present defect information; by following the template TM, it is ensured that all inspection reports have a consistent structure and content, improving the professionalism and readability of the reports.

[0240] Review and prepare the preliminary report RDraft, adding the comprehensive conclusion CC; the conclusion summarizes the quantity, location, and degree of influence of all internal minor defects, and proposes suggested measures SM; the final inspection report RFinal is composed of the formula RFinal = RDraft + CC + SM. By adding the comprehensive conclusion CC, the inspection results can be more comprehensively summarized, providing profound insights into the overall condition of the sample. The suggested measures SM provide specific guidance for subsequent work, helping to take appropriate repair or improvement measures to reduce potential risks.

[0241] Example Nine

[0242] Assume that the quantitative evaluation of several suspected defect locations has been completed, and a complete inspection report is desired. The following steps will be followed:

[0243] Determine the influence level GL: First, use the formula GL(DSi) = f(QTotal(DSi)) to calculate the influence level GL for each suspected defect location DSi. Assume the following overall property QTotal values:

[0244] QTotal(DS1) = 25.33;

[0245] QTotal(DS2) = 18.75;

[0246] QTotal(DS3) = 30.20;

[0247] Using a certain method (such as threshold classification or machine learning model), the influence level GL can be determined as follows:

[0248] GL(DS1) = f(25.33) = High;

[0249] GL(DS2) = f(18.75) = Medium;

[0250] GL(DS3) = f(30.20) = High;

[0251] Form the defect list LS: Next, based on the determined influence level GL, all suspected defects are classified and summarized to form the defect list LS. Each entry in the list contains the defect location DSi, area AS(DSi), morphological feature FShape(DSi), spatial distribution characteristic DDist(DSi), and the corresponding influence level GL(DSi). For example:

[0252] DS1: Area = 50, Morphological Feature = 0.7, Spatial Distribution Characteristic = 0.6, Influence Level = High;

[0253] DS2: Area = 40, Morphological Feature = 0.5, Spatial Distribution Characteristic = 0.4, Influence Level = Medium;

[0254] DS3: Area = 60, Morphological characteristics = 0.8, Spatial distribution characteristics = 0.7, Impact level = high;

[0255] Prepare the preliminary inspection report RDraft: Using the formed defect list LS, prepare the preliminary inspection report RDraft according to the requirements of the template TM. Assume that the template TM requires the report to include the following parts:

[0256] Overview: Summarize the inspection process and the main problems found.

[0257] Detailed description: List the information of each defect item by item.

[0258] Chart display: Use charts to assist in explaining the defect situation.

[0259] Review and add the comprehensive conclusion CC: Finally, review the prepared preliminary report RDraft and add the comprehensive conclusion CC. The conclusion summarizes the quantity, location, and impact degree of all internal minor defects, and proposes recommended measures SM. For example:

[0260] Comprehensive conclusion CC:

[0261] A total of 3 internal minor defects were detected, 2 of which were located in the middle of the sample and 1 was located at the edge.

[0262] The defects are mainly manifested as high-complexity morphological characteristics and a relatively high degree of spatial aggregation.

[0263] All defects were rated as medium to high impact level and need to be dealt with in a timely manner.

[0264] Recommended measures SM:

[0265] For the high-impact level defects DS1 and DS3, immediately initiate the repair procedure to prevent further deterioration.

[0266] For the medium-impact level defect DS2, regularly monitor its development and take remedial measures if necessary.

[0267] Strengthen the quality control process to reduce the probability of similar defects occurring.

[0268] Form the final inspection report RFinal: The final inspection report RFinal consists of the preliminary report RDraft, the comprehensive conclusion CC, and the recommended measures SM, ensuring that the report content is complete and has practical operation value.

[0269] From the above embodiments, it can be seen how to start from quantitative evaluation, gradually determine the impact level, compile a defect list, write a preliminary report, and finally add comprehensive conclusions and recommended measures to form a detailed inspection report. This method not only improves the professionalism and accuracy of the report, but also provides valuable guidance and support for subsequent quality control and defect management.

[0270] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A 3D hard gold processing detection method based on weighted calculation, characterized in that: The following steps are involved: Prepare the samples to be tested, pre-treat them, apply multi-angle light sources to the samples, and record the reflection spectrum characteristics under different lighting conditions; Based on the obtained reflection spectrum characteristics, an initial virtual map of the internal structure of the sample is constructed, and the initial virtual map covers all known physical properties; On the basis of the virtual mapping, the density distribution of each point is adjusted by introducing a weight factor to form an optimized internal structure map. According to the generated structure map, several representative areas are selected and high-resolution scanning is performed to obtain detailed data. The obtained detailed data is compared and analyzed with the original virtual mapping, the differences are identified and the defect locations are marked, and the marked defect locations are used to perform local enhanced scanning, focus on the suspected defects, and refine the image quality; Conduct quantitative assessment of each defect area to determine its size, shape and distribution characteristics, and comprehensively analyze various indicators to compile a complete test report, pointing out all detected internal micro defects and their impact.

2. The 3D hard gold processing detection method based on weighted calculation according to claim 1, characterized in that: The pretreatment of the sample comprises: Clean the sample surface with a detergent to remove all non-metallic residues, and purge with dry gas to eliminate moisture left after cleaning; The treated samples are measured and recorded with an optical measuring instrument for the reflectivity DR of the sample surface, where the reflectivity DR is calculated using the formula DR=Ir / Ii, where Ir represents the intensity of reflected light and Ii represents the intensity of incident light; Based on the obtained reflectivity DR value, the output intensity F of the light source E is adjusted to a preset threshold G, and the adjustment process follows the formula F=G*DR.

3. The 3D hard gold processing detection method based on weighted calculation according to claim 2, characterized in that: The step of applying multi-angle light source illumination to the sample and recording the reflection spectrum characteristics under different illumination conditions includes: Select a set of light sources H, each with a different wavelength , ,..., , and irradiate the pretreated samples in sequence; For each wavelength Under the lighting conditions, record the corresponding reflected light intensity data , forming a series of reflected light intensity data sets { , ,..., }; Based on the obtained reflected light intensity data set, calculate the Lower reflectance spectrum characteristic value , using the formula = / ; According to all the obtained value, construct the reflection spectrum curve C, which is constructed by connecting each point ( , ),( , ),...,( , )express.

4. The 3D hard gold processing detection method based on weighted calculation according to claim 3 is characterized in that: Based on the obtained reflection spectrum characteristics, an initial virtual map of the internal structure of the sample is constructed, and the initial virtual map covers all known physical properties, including: According to the recorded reflectance spectrum characteristic value , combined with the sample surface geometric shape parameter GP, establish the initial mapping relationship M1, the relationship is expressed by the formula M1=f( ,GP) expression, f is the process of building the mapping; Using the mapping relationship M1, the sample is initially divided to determine multiple discrete areas. , ,..., ; For each divided area , analyze the corresponding reflectance spectrum characteristic value , calculate the estimated value of the physical property QA in the area , using the formula =g( ), g is the process of converting to physical property estimates; All the comprehensive Values, and regions The spatial distribution information of , constructs a three-dimensional virtual map M2 containing all known physical properties. The three-dimensional virtual map M2 consists of a series of point sets {( , )}composition.

5. The 3D hard gold processing detection method based on weighted calculation according to claim 4, characterized in that: The method of adjusting the density distribution of each point by introducing a weight factor based on the formed virtual mapping to form an optimized internal structure diagram includes: For each point in the formed three-dimensional virtual map M2 , evaluate its corresponding physical properties The reliability of the system is calculated and given an initial weight WF0, which is calculated by the formula WF0=h( ) is calculated, h is the process of determining the weight; Based on the initial weight WF0, calculate each point The relative distance between ; Representative Points and The distance between them is calculated using the formula: =sqrt((xri-xrj)^2+(yri-yrj)^2+(zri-zrj)^2), where (xri,yri,zri) is the point The coordinates of the point (xrj, yrj, zrj) The coordinates of Combined with the calculated distance ; Adjust each pair of points and The new weights of the connections between ; The new weight value is given by the formula =WF0*exp(-α* ) is determined, α is a predefined proportional coefficient; Using the updated weights ; Recalculate all points The density distribution DDn is formed to form the optimized internal structure diagram M3. The new density distribution DDn is calculated by the formula DDn( )=Σ( ) / N, where N is the number of points The number of all connected points, Σ represents the summation operation.

6. The 3D hard gold processing detection method based on weighted calculation according to claim 5, characterized in that: According to the generated structural map, several representative areas are selected and high-resolution scanning is performed to obtain detailed data, including: Based on the optimized internal structure map M3, multiple candidate regions with significant differences in physical properties are identified and marked. , ,..., , by comparing adjacent points The attribute change rate ΔQA is realized by the formula ΔQA=| - | / ; Based on the determined candidate regions , select those standard areas that meet the preset attribute change threshold TL , ,..., , the selection of the standard area follows the condition TL≤ΔQA; For each standard area selected , planning a high-resolution scanning path PLi; the path design ensures that the entire area is covered while minimizing the scanning time, and the path length PLi is given by the formula PLi=Σ( ) is calculated; According to the set path PLi, for each standard area A detailed high-resolution scan is performed to record finer structural information FIi within the region, which is used to construct a higher-precision local map MIi.

7. The 3D hard gold processing detection method based on weighted calculation according to claim 6, characterized in that: The detailed data obtained is compared and analyzed with the original virtual map to identify differences and mark defect locations, including: The obtained detailed data set DF5 is combined with the original virtual map VMAP to generate a comparative analysis matrix BM. The comparative analysis matrix BM quantifies the data differences between the corresponding positions of the two into values ; The formula is expressed as =| (DF5)- (VMAP)|; Based on the formed comparative analysis matrix BM, all coordinate points exceeding the preset difference threshold TL are identified to form a set of suspected outlier points PS; For all the marked suspected outlier points PSi, a local refinement algorithm is applied to calculate the neighborhood structure feature vector FVi of each point; the feature vector FVi is determined by the local pattern formed by the current point and its surrounding n closest points; Based on the obtained feature vector FVi, each suspected abnormal point PSi is deeply evaluated to decide whether it is finally marked as a defect location. The confidence score CScore is introduced into the evaluation process, and the calculation method is CScore(PSi)=h(FVi).

8. The 3D hard gold processing detection method based on weighted calculation according to claim 7, characterized in that: The method uses the marked defect position to perform local enhanced scanning, focuses on the suspected defect, and refines the image quality, including: Based on the marked suspected defect position set DS, determine the coordinate range RSi of each defect position DSi. The coordinate range RSi is defined by the extended distance δ of the defect point, and the formula is RSi={P|d(P,DSi)≤δ}; For each determined coordinate range RSi, a high-precision local scanning path PLi is planned; the path design ensures that the entire area is covered while optimizing the scanning path length PLi; According to the set path PLi, an enhanced resolution scan is performed at each suspected defect to record detailed structural information FIi; the structural information FIi is used to construct a local refinement map MIi; The obtained local refinement map MIi is used to evaluate the image quality of each suspected defect position DSi. The evaluation process introduces the clarity score IScore, which is calculated as IScore(MIi)=h(Isharp,Inoise); Isharp is the sharpness index and Inoise is the noise level.

9. The 3D hard gold processing detection method based on weighted calculation according to claim 8, characterized in that: The quantitative evaluation of each defect area is performed to determine its size, shape and distribution characteristics, including: Based on the local refinement map MIi, the area AS of each suspected defect location DSi is calculated; the area is calculated by the formula AS(DSi)=Σ(Pij), where Pij represents the set of all pixels belonging to the defect area DSi; According to the obtained area AS(DSi), the morphological feature FShape of the suspected defect area is analyzed; the morphological feature FShape is determined by the defect boundary shape descriptor SD, and the expression is FShape(DSi)=g(SD); Combined with the morphological feature FShape, the spatial distribution characteristics DDist of each suspected defect area are evaluated; the spatial distribution characteristics are analyzed by analyzing the distance between adjacent defects and the relative angles between them measure; The obtained spatial distribution characteristics DDist, together with the area AS and the morphological characteristics FShape, are used to comprehensively and quantitatively evaluate the overall attributes QTotal of each suspected defect area; the overall attributes are calculated by the formula QTotal(DSi)=w1*AS(DSi)+w2*FShape(DSi)+w3*DDist(DSi); w1, w2, w3 are weight factors.

10. The 3D hard gold processing detection method based on weighted calculation according to claim 9, characterized in that: The above comprehensive indicators are used to prepare a complete test report, including: According to the overall property QTotal(DSi) obtained by quantitative evaluation, the impact level GL is determined for each suspected defect location DSi; the impact level GL is calculated by the formula GL(DSi)=f(QTotal(DSi)); Based on the determined impact level GL, all suspected defects are classified and summarized to form a defect list LS; each entry in the list contains the defect location DSi, area AS(DSi), shape feature FShape(DSi) and spatial distribution characteristic DDist(DSi), as well as the corresponding impact level GL(DSi); Using the defect list LS, prepare a preliminary inspection report RDraft; the report structure follows the template TM, which defines how to organize and present defect information; Review the prepared preliminary report RDraft and add the comprehensive conclusion CC; the conclusion summarizes the number, location and impact of all internal minor defects, and proposes recommended measures SM; the final inspection report RFinal is composed of the formula RFinal=RDraft+CC+SM.

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