A method and system for measuring a sample of a wire insulation layer

Abstract

The present invention relates to the technical field of measurement and evaluation of wire insulation layers, and particularly to a method and system for measuring wire insulation layer samples. The method includes the following steps: using a laser thickness gauge to scan and measure the insulation layer thickness of numbered samples to generate two-dimensional thickness distribution map data; performing thickness uniformity analysis based on the two-dimensional thickness distribution map data to obtain thickness uniformity data; selecting tensile strength test samples according to the thickness uniformity data to obtain tensile strength test sample selection data; performing a tensile strength test according to the tensile strength test sample selection data to obtain tensile strength test data; evaluating the performance of the wire insulation layer based on the two-dimensional thickness distribution map data, the thickness uniformity data, and the tensile strength test data to obtain wire insulation layer performance evaluation data for assisting the operation of wire insulation layer sample measurement. The present invention can effectively reduce material waste in the production process through precise thickness measurement and uniformity analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of measurement and evaluation of wire insulation layers, and particularly to a method and system for measuring samples of wire insulation layers. Background Art

[0002] The quality of the wire insulation layer is crucial for the safety, stability, and reliability of the cable. With the continuous development of power systems and electronic devices, the performance requirements for wire insulation layers are also increasing day by day. Especially in the fields of high-voltage power transmission, electronic device connection, and communication lines, the thickness uniformity and tensile strength of the wire insulation layer directly affect its durability and safety. Therefore, how to efficiently and accurately detect and evaluate the wire insulation layer has become an important research topic in materials science, engineering technology, and quality control.

[0003] Traditional methods for measuring wire insulation layers rely on manual inspection or contact measurement devices. These methods have problems such as low accuracy, low efficiency, and cumbersome operation. For example, when using tools such as vernier calipers and micrometers for measurement, only limited measurement point data can be obtained, and the uniformity analysis requirements of large-scale samples cannot be met. In addition, these traditional methods are mostly manually operated, with high human errors and are easily affected by environmental factors during the detection process, making it impossible to ensure the accuracy and reliability of the data. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention proposes a method and system for measuring samples of wire insulation layers to solve at least one of the above technical problems.

[0005] The present application provides a method for measuring samples of wire insulation layers, including the following steps:

[0006] Step S1: Use a laser thickness gauge to scan and measure the thickness of the insulation layer of the numbered samples to generate two-dimensional thickness distribution map data;

[0007] Step S2: Perform thickness uniformity analysis based on the two-dimensional thickness distribution map data to obtain thickness uniformity data;

[0008] Step S3: Select samples for tensile strength testing based on the thickness uniformity data to obtain data for sample selection for tensile strength testing;

[0009] Step S4: Perform tensile strength testing based on the data for sample selection for tensile strength testing to obtain tensile strength test data;

[0010] Step S5: Evaluate the performance of the wire insulation layer based on the two-dimensional thickness distribution map data, the thickness uniformity data, and the tensile strength test data to obtain wire insulation layer performance evaluation data for assisting in the operation of measuring wire insulation layer samples.

[0011] In the present invention, the sample is scanned by a laser thickness gauge, enabling high-precision and non-contact measurement of the insulation layer thickness. Compared with traditional physical contact measurement methods, it has higher precision and efficiency, and will not damage the surface of the sample. By generating a thickness distribution map, the thickness variation of the insulation layer at different positions can be presented more intuitively. Thickness uniformity is a key factor affecting the performance of the wire insulation layer. By analyzing the uniformity of the two-dimensional thickness distribution map data, non-uniform regions can be identified, which lead to unstable performance of the insulation layer and thus affect the electrical performance and safety of the wire. Selecting the tensile strength test samples based on the thickness uniformity data can effectively reduce unnecessary sample tests, while ensuring that representative samples with potential problems are selected for in-depth testing. This not only improves the test efficiency, but also saves time and costs. Combining the two-dimensional thickness distribution map data, thickness uniformity data, and tensile strength test data for performance evaluation of the wire insulation layer. The multi-dimensional data fusion provides a more comprehensive and accurate performance evaluation, enabling not only understanding of the thickness distribution of the insulation layer, but also grasping of its mechanical properties, thus comprehensively evaluating its quality.

[0012] Preferably, step S1 is specifically as follows:

[0013] Perform spatial positioning and optical axis calibration on the laser thickness gauge to generate laser positioning data;

[0014] Reconstruct the sample reference plane by the laser thickness gauge according to the laser positioning data to obtain reference plane data;

[0015] Perform multi-point thickness sampling by the laser thickness gauge according to the reference plane data to obtain preliminary thickness sampling matrix data;

[0016] Perform thickness distribution interpolation calculation according to the preliminary thickness sampling matrix data to obtain thickness distribution interpolation data;

[0017] Generate a dynamic distribution map according to the thickness distribution interpolation data to obtain two-dimensional thickness distribution map data.

[0018] In the present invention, the sample is scanned and data is collected by a laser thickness gauge, avoiding the contact error generated in the traditional measurement method. By using the laser thickness gauge to reconstruct the sample reference plane, a suitable reference plane can be selected on the surface of the irregular sample, and the generated reference plane data provides a reliable reference framework for thickness measurement, avoiding the error caused by the irregular shape of the sample surface. Through interpolation calculation, the multi-point sampling data is converted into continuous thickness distribution data. This interpolation method can help fill the spatial data gaps existing in the sampling process, generate a smoother and more accurate thickness distribution data, and avoid the influence of rough discrete data on subsequent analysis. From laser positioning to sample reference plane reconstruction, then to thickness data sampling, interpolation calculation and dynamic distribution map generation, the whole process realizes hierarchical measurement and analysis from coarse to fine.

[0019] Preferably, the spatial positioning and optical axis calibration are specifically as follows:

[0020] By using the inertial sensor and external vision sensor integrated in the laser thickness gauge, the spatial position data of the device is captured in real time;

[0021] According to the spatial position data, attitude calculation is performed to obtain the device attitude data;

[0022] According to the device attitude data, attitude error analysis is performed to obtain the attitude error data;

[0023] According to the attitude error data, attitude calibration is performed to obtain the attitude calibration data;

[0024] The laser optical axis is aligned with the attitude calibration data to obtain the optical axis alignment data;

[0025] According to the optical axis alignment data, error correction is performed to obtain the error correction data;

[0026] According to the spatial position data, attitude calibration data, optical axis alignment data and error correction data, laser positioning data is generated to obtain the laser positioning data.

[0027] In the present invention, real-time spatial position data is captured by using an integrated inertial sensor and an external vision sensor, which can effectively track the spatial position and attitude changes of the device. Through attitude error analysis and attitude calibration, the attitude deviation of the device can be accurately corrected, and the errors caused by instability or external interference during the scanning process of the device can be eliminated. The device after attitude calibration can work stably in a more complex or dynamic environment, thereby improving the accuracy of measurement data. The optical axis calibration step eliminates the errors caused by misalignment of the laser beam during the laser measurement process by precisely aligning the laser optical axis. Through fine optical axis alignment, the perpendicular relationship between the laser beam and the sample surface can be ensured, thereby minimizing the laser measurement error and ensuring the accuracy and consistency of the scanned data. By correcting the optical axis error, the measurement accuracy can be further improved, especially in a complex environment (such as under the influence of factors such as device vibration and thermal expansion), and the error correction can dynamically adjust the error of the device to ensure the reliability of each measurement result.

[0028] Preferably, the reconstruction of the sample reference plane is specifically as follows:

[0029] Perform a preliminary scan of the sample by a laser thickness gauge according to the laser positioning data to obtain sample scan data;

[0030] Perform local reference plane reconstruction based on the sample scan data to obtain first reference plane data;

[0031] Perform minimum convex hull reconstruction based on the sample scan data to obtain second reference plane data;

[0032] Perform shape feature fusion on the first reference plane data and the second reference plane data to obtain reference plane data;

[0033] The preliminary scan of the sample is specifically as follows:

[0034] Scan the surface of the sample by a laser thickness gauge to obtain sample surface point cloud data;

[0035] Perform particle iterative filtering on the sample surface point cloud data to obtain scanned noise reduction data;

[0036] Perform KD tree region division according to the scanned noise reduction data to obtain scanned division data;

[0037] Perform voxelization and resampling on the scanned division data to obtain sample resampled data;

[0038] Perform spatial coordinate alignment according to the sample scan data and the sample resampled data to obtain sample scan data.

[0039] In the present invention, the surface of the sample is scanned by a laser thickness gauge, and particle iterative filtering is used for denoising, which can effectively remove the noise in the scanned data and ensure the high quality of the data. According to the complexity of the sample surface, the accuracy of regional division is flexibly adjusted, so as to adapt to samples of different shapes, sizes and complexities. It can effectively process samples with different surface features and avoid the influence of overly simple or overly complex division methods on the final data quality. Through local reference plane reconstruction, more refined processing of the sample surface can be carried out. When aligning the reference planes, the local changes of the sample surface are taken into account. The minimum convex hull algorithm can effectively find the most compact outer enclosing area of the sample surface and use it as the reference plane. This method can provide a stable and relatively robust reference plane solution when the sample surface has an irregular shape or protrusions. Through minimum convex hull reconstruction, the influence of surface noise on the results can be avoided, and the stability and reliability of the reference plane data can be improved. By fusing the shape features of the first reference plane data and the second reference plane data, the advantages of local reference plane reconstruction and minimum convex hull reconstruction can be comprehensively considered to obtain more accurate reference plane data. The fusion can reduce the deviation caused by a single method and improve the accuracy and applicability of the entire reference plane reconstruction at the same time.

[0040] Preferably, the local reference plane reconstruction is specifically as follows:

[0041] Extract the sample morphological feature data from the sample scan data;

[0042] Perform preliminary regional division on the sample scan data according to the sample morphological feature data to obtain the first sample division data;

[0043] Perform morphological clustering calculation according to the first sample division data to obtain the sample division clustering data;

[0044] Construct a local connection graph according to the first sample division data and the sample division clustering data to obtain the sample division graph data;

[0045] Perform fine division on the sample division graph data according to the sample division clustering data to obtain the second sample division data;

[0046] Perform feature detection on the second sample division data to obtain the sample division feature data;

[0047] Perform weighted screening on the second sample division data according to the sample division feature data to obtain the local regional division data;

[0048] Perform local reference plane fitting on the local regional division data to obtain the local reference plane fitting data;

[0049] Perform local plane error correction according to the local reference plane fitting data to obtain the reference plane smoothing correction data;

[0050] Perform local datum plane fusion based on the datum plane smoothing correction data to obtain the first datum plane data.

[0051] In the present invention, by extracting the morphological features of the sample scanning data, the minute morphological changes on the surface of the sample can be carefully captured, thereby providing rich detailed information for the datum plane reconstruction. The detailed processing helps to eliminate the measurement errors caused by the different minute morphologies on the sample surface and ensures the accuracy of the reconstruction result. By performing preliminary regional division on the scanning data and conducting clustering calculations according to the morphological features of the sample, the nature of different regions on the sample surface can be automatically distinguished. The local morphological features of each region are clearly reflected, avoiding the errors caused by global assumptions in the datum plane reconstruction, thereby improving the accuracy. By adopting the preliminary regional division and clustering algorithm based on morphological features, the heterogeneity in shape and structure on the sample surface can be automatically identified. Whether the sample surface is flat or has complex curved surfaces, the system can flexibly perform the division to ensure the applicability and flexibility of the datum plane reconstruction method. The system can finely adjust and fit the morphology of each local region to ensure that the entire sample surface can be accurately reconstructed. Through the automated feature detection and weighted screening steps, the manual intervention can be greatly reduced and the work efficiency can be improved. The generation of the sample division feature data and the weighted screening data is the result of the execution of the automated system, greatly reducing the influence of manual errors and saving time and labor costs at the same time. By constructing a local connection graph for the sample division data and the clustering data, the data can be processed more structurally. At the same time, the deep-level data connection relationships in the data can be reflected, such as the similarity correlation and distance correlation of adjacent data. Through local datum plane fitting and smoothing correction, the influence of local errors on the overall datum plane reconstruction can be further eliminated, ensuring the stability and accuracy of the datum plane data.

[0052] Preferably, the minimum convex hull reconstruction is specifically as follows:

[0053] Perform point cloud screening and reduction on the sample scanning data to obtain the sample screening data;

[0054] Perform point cloud space optimization based on the sample screening data to obtain the point cloud optimization data;

[0055] Construct an initial convex hull set based on the point cloud optimization data to obtain the preliminary convex hull data;

[0056] Perform local adjustment on the preliminary convex hull data to obtain the convex hull boundary optimization data;

[0057] Perform surface reconstruction on the convex hull boundary optimization data to obtain the convex hull optimization data;

[0058] Perform full-local fusion based on the preliminary convex hull data and the convex hull optimization data to obtain the second datum plane data.

[0059] In the present invention, by performing point cloud screening and reduction on the sample scan data, noise data and irrelevant points can be removed, and the most representative and effective point data can be retained. This can effectively reduce the redundancy of the point cloud data, reduce the computational burden, and improve the processing accuracy at the same time. Point cloud space optimization is achieved through precise spatial distribution analysis and adjustment to ensure the optimal spatial distribution of the point cloud data, eliminating deviations and redundancies. By constructing preliminary convex hull data based on the optimized point cloud data, the basic shape framework of the sample can be quickly obtained at an early stage, laying a foundation for fine optimization. The local adjustment step can better adapt to the local changes of the sample surface through fine adjustment of the convex hull boundary. Especially when dealing with samples with complex shapes, it can ensure the optimization of local accuracy within the global range. By globally fusing the preliminary convex hull data with the optimized convex hull boundary, the global consistency of the data can be further improved, and the deviations between local data can be eliminated. The convex hull data after global fusion is more accurate, stable, and can reflect the true geometric features of the entire sample.

[0060] Preferably, step S2 is specifically as follows:

[0061] Perform region calibration according to the two-dimensional thickness distribution map data to obtain region calibration data;

[0062] Calculate the thickness variance of the region calibration data to obtain region variance data;

[0063] Calculate the thickness non-uniformity according to the region variance data to obtain thickness non-uniformity data;

[0064] Detect the critical region of the thickness non-uniformity data to obtain critical region data;

[0065] Integrate the thickness non-uniformity data and the critical region data to obtain thickness uniformity data.

[0066] In the present invention, by performing region calibration on the two-dimensional thickness distribution map data, the thickness characteristics of different regions can be identified, providing a clear regional division for subsequent uniformity analysis. The calibration data can accurately reflect the thickness differences in different regions, ensuring the accuracy and reliability of the calculation. Calculating the variance of the thickness within the region can effectively quantify the degree of dispersion of the thickness within each region. Variance is a standard index for describing data changes and can reflect the thickness stability of the local region, helping to quantitatively analyze the uniformity of the entire sample. Calculating the thickness non-uniformity based on the region variance data to further evaluate the thickness change trend of each region can effectively reveal the fluctuations in the surface thickness. By detecting the critical region of the thickness non-uniformity data, regions with significant thickness changes can be automatically identified. These regions often correspond to potential problems in the production process, such as raw material quality problems, improper equipment calibration, or irregular operations.

[0067] Preferably, step S3 is specifically as follows:

[0068] Formulate a sample screening rule based on the thickness uniformity data to obtain sample screening rule data;

[0069] Score the samples according to the sample screening rule data to obtain sample score data;

[0070] Classify and sort the sample score data to obtain sample classification and sorting data;

[0071] Perform Monte Carlo simulation performance prediction according to the sample classification data to obtain sample performance distribution simulation data;

[0072] Mark critical samples according to the sample performance distribution simulation data to obtain data for selecting tensile strength test samples.

[0073] In the present invention, by formulating a scientific and reasonable screening rule based on the thickness uniformity data, the systematicness and standardization of the screening process are ensured. The screening rule can clarify which samples meet the requirements of the tensile strength test, avoid the interference of human subjective factors, and improve the reliability of the screening results. The formulation of the screening rule not only relies on experience but also combines data analysis. By using the quantitative results of the thickness uniformity data to determine the key screening parameters, it ensures that the screening process better meets the actual performance requirements of the samples. By scoring and sorting the samples, the samples can be prioritized according to certain criteria. The sorting not only considers the thickness uniformity but also combines other performance parameters, providing a basis for the selection of the tensile strength test, avoiding unnecessary test work, and improving the utilization efficiency of test resources. The Monte Carlo simulation is based on the known thickness data and other factors affecting performance, providing the statistical distribution of the sample performance, and being able to predict the performance of different samples in the actual tensile strength test, helping to select the samples that best represent the overall performance. Marking critical samples according to the performance simulation results can ensure that the test resources are used for the most representative samples with the most likely occurrence of extreme performance, avoiding a large number of unnecessary tests, concentrating on testing the samples that best reflect the true performance of the samples, and improving the test efficiency.

[0074] Preferably, step S4 is specifically as follows:

[0075] Perform tensile strength test operations on the tensile strength test sample selection data through a tensile strength test device to obtain stress-time series data;

[0076] Perform curve fitting on the stress-time series data to obtain stress-strain curve data;

[0077] Calculate the strength extreme value according to the stress-strain curve data to obtain strength extreme value data;

[0078] Tensile strength evaluation is carried out according to the stress-strain curve data and the strength extreme value data to obtain the tensile strength test data.

[0079] In the present invention, the stress change of the sample during the stretching process is accurately recorded by the tensile strength test equipment, and the generated stress-time series data provides detailed time-series data support for the analysis. The stress-time series data is transformed into stress-strain curve data through curve fitting. The fitting not only improves the smoothness and accuracy of the data, but also can effectively remove noise. By calculating the strength extreme values of the stress-strain curve, key performance indicators such as the fracture point and yield point of the material under extreme stress can be obtained. Through comprehensive analysis by combining the strength extreme value data and the stress-strain curve data, not only the single tensile strength value of the material can be understood, but also the performance of the material under different stress states, such as yield strength, tensile strength, elastic modulus and other key physical performance indicators, can be comprehensively grasped. The tensile strength evaluation results provide data basis for the problems existing in the production process. For example, if the test data shows that there are abnormalities in the stress-strain curve performance of some batches of materials, the production line can immediately make adjustments to optimize the material ratio and production process to ensure production consistency and quality.

[0080] Preferably, the present application also provides a wire insulation layer measurement sample system for performing the wire insulation layer measurement sample method as described above. The wire insulation layer measurement sample system includes:

[0081] A two-dimensional thickness distribution map generation module for scanning and measuring the insulation layer thickness of the numbered sample by using a laser thickness gauge to generate two-dimensional thickness distribution map data;

[0082] A thickness uniformity analysis module for performing thickness uniformity analysis according to the two-dimensional thickness distribution map data to obtain thickness uniformity data;

[0083] A tensile strength test sample selection module for selecting tensile strength test samples according to the thickness uniformity data to obtain tensile strength test sample selection data;

[0084] A tensile strength test module for performing tensile strength tests according to the tensile strength test sample selection data to obtain tensile strength test data;

[0085] A wire insulation layer performance evaluation module for evaluating the wire insulation layer performance according to the two-dimensional thickness distribution map data, the thickness uniformity data and the tensile strength test data to obtain wire insulation layer performance evaluation data for assisting the operation of wire insulation layer sample measurement.

[0086] The beneficial effects of the present invention are as follows: The thickness of the insulating layer of the sample is scanned by a laser thickness gauge to generate high-precision two-dimensional thickness distribution map data. This non-contact detection technology based on laser measurement can accurately capture the thickness changes of the insulating layer, avoid the errors generated by traditional contact measurement, and is not affected by surface microstructures or foreign objects, ensuring the accuracy of the data. By analyzing the thickness uniformity of the two-dimensional thickness distribution map data, the thickness uniformity of the wire insulating layer can be detected in real time, and potential weak links can be found. Especially for wire products in large-scale production, unqualified products can be efficiently screened out, and the production process can be adjusted in a timely manner to ensure the quality stability of each wire. By screening samples according to the thickness uniformity data for tensile strength testing, and then comprehensively evaluating the performance by combining the thickness distribution and strength data, the multi-dimensional performance evaluation can comprehensively reflect the quality of the wire insulating layer, especially its performance in actual use. The tensile strength test can provide the performance of the material under stress conditions, including its breaking strength, ductility and other characteristics under various extreme conditions, so as to provide data support for the reliability in applications. Through data-based analysis and evaluation, quality problems occurring in the production process, such as uneven thickness and insufficient tensile strength, can be identified in real time, and production process parameters can be adjusted in a timely manner, such as changing the coating thickness, changing the material selection, adjusting the production speed, etc., to ensure the quality stability of each batch of products from the source. Combining the data on the selection of tensile strength test samples and the thickness uniformity data can intelligently screen out representative samples for in-depth testing, thereby reducing unnecessary test expenses and time consumption. During large-scale production, it can not only reduce the errors caused by manual operations, but also greatly shorten the test cycle and improve the overall operation efficiency of the production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Other features, objects, and advantages of the present application will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings:

[0088] Figure 1 The flowchart showing the steps of a method for measuring a wire insulating layer sample in an embodiment is shown;

[0089] Figure 2 The flowchart showing the steps of a method for generating a two-dimensional thickness distribution map in an embodiment is shown;

[0090] Figure 3 The flowchart showing the steps of a method for thickness uniformity analysis in an embodiment is shown;

[0091] Figure 4 The flowchart showing the steps of a method for selecting tensile strength test samples in an embodiment is shown;

[0092] Figure 5The flowchart of the steps of a tensile strength test method according to an embodiment is shown. Detailed implementation manners

[0093] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative efforts belong to the scope of protection of the present invention.

[0094] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0095] It should be understood that although terms such as "first" and "second" may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0096] Please refer to Figures 1 to 5 , this application provides a method for measuring a sample of a wire insulation layer, including the following steps:

[0097] Step S1: Use a laser thickness gauge to scan and measure the thickness of the insulation layer of the numbered sample to generate two-dimensional thickness distribution map data;

[0098] Specifically, a laser thickness gauge is used. It irradiates the surface of the insulation layer with a laser beam, and the reflected laser signal is received by a receiver. The distance from the surface to the instrument is calculated based on the time delay of the laser beam (time-of-flight principle) or the change in the intensity of the reflected light. The sample to be measured is placed within the scanning range of the laser thickness gauge. The laser thickness gauge scans along the surface of the sample and automatically records the thickness information of each point to generate a two-dimensional thickness distribution map. During scanning, the resolution of the instrument is set to a certain value to ensure that the thickness measurement accuracy of each point meets the requirements. Through the calculation function of the laser thickness gauge, the thickness information of each point is generated into two-dimensional thickness distribution map data, and the data form is a matrix, and each element of the matrix represents the thickness of the corresponding point.

[0099] Step S2: Perform thickness uniformity analysis based on the two-dimensional thickness distribution map data to obtain thickness uniformity data;

[0100] Specifically, process the obtained two-dimensional thickness distribution map data. First, remove noise and smooth the data to ensure the accuracy of the thickness measurement values. Smooth the thickness data through a moving average filter (such as a 3x3 or 5x5 window). Calculate the thickness uniformity of the entire sample, and quantify the thickness variation through statistical methods, including calculating the standard deviation, coefficient of variation, etc. of the thickness. If the standard deviation is small, it indicates that the thickness distribution is relatively uniform; if the standard deviation is large, it indicates that there is a situation of non-uniform thickness. At this time, further analyze the position and size of the non-uniform area.

[0101] Step S3: Select tensile strength test samples based on the thickness uniformity data to obtain tensile strength test sample selection data;

[0102] Specifically, based on the thickness uniformity data obtained in Step S2, select the areas with relatively poor thickness uniformity as the candidate areas for tensile strength testing. Generally, select the areas with a large thickness standard deviation for subsequent testing. According to the results of the uniformity analysis, determine the size and position of the test area. If there are some areas where the thickness is significantly too low or too high, select these areas as test samples. When selecting test samples, it is necessary to determine a certain number of test points according to the total area of the sample and the detection requirements. Select k test points to ensure that the test points cover different areas of the entire sample.

[0103] Step S4: Perform tensile strength tests based on the tensile strength test sample selection data to obtain tensile strength test data;

[0104] Specifically, cut out samples with specific dimensions from the test samples selected in Step S3 to ensure that the boundaries of the samples meet the requirements of tensile testing. Perform tensile strength tests through a standard tensile testing instrument. The tensile testing instrument generally includes a loading device, with the sample fixed at both ends, and the tensile strength of the sample is obtained through the stress-strain curve generated by stretching. Record the stress-strain curve (applied tensile force divided by the cross-sectional area) during the test. By measuring the maximum tensile force of the sample, the tensile strength of the sample is obtained. Record the measured tensile strength value, and this value can be compared with the thickness data to analyze the influence of thickness on the performance of the wire insulation layer.

[0105] Step S5: Evaluate the performance of the wire insulation layer based on the two-dimensional thickness distribution map data, thickness uniformity data, and tensile strength test data to obtain wire insulation layer performance evaluation data for assisting the measurement operation of the wire insulation layer sample.

[0106] Specifically, performance evaluation is carried out based on two-dimensional thickness distribution data, thickness uniformity data, and tensile strength test data. Based on various performance indicators of the insulating layer (such as thickness uniformity, tensile strength, etc.), a performance score is obtained through weighted average method, regression analysis, or other data fusion methods. According to the performance evaluation data, the evaluation results of the insulating layer are output, providing decision-making support for production and quality control.

[0107] Preferably, step S1 is specifically as follows:

[0108] Step S11: Perform spatial positioning and optical axis calibration on the laser thickness gauge to generate laser positioning data;

[0109] Specifically, the spatial positioning of the laser thickness gauge is carried out by setting its positioning system. The common positioning method is through three-dimensional coordinate measurement technology to ensure that the laser thickness gauge can accurately track the spatial position of the sample during scanning. The coordinate system of the laser thickness gauge is calibrated to a specific reference point on the sample surface to ensure that the data collected during scanning conforms to the actual three-dimensional geometry of the sample surface. To ensure that the laser beam is perpendicular to the sample surface and avoid measurement errors caused by the tilt of the optical axis, the optical axis of the laser thickness gauge needs to be calibrated. By calibrating the calibration target plate and the mirror, it is detected whether the laser beam is completely perpendicular, and the position of the laser is finely adjusted to ensure the consistency of the emission angle and the reception angle of the laser beam. The error correction of the optical axis is carried out using precision optical equipment. Based on the spatial positioning and optical axis calibration, a series of reference data based on the initial position of the laser are generated, which is called laser positioning data. This data records all the laser emission and reception positions in the measurement area.

[0110] Step S12: Reconstruct the reference plane of the sample through the laser thickness gauge according to the laser positioning data to obtain reference plane data;

[0111] Specifically, during the initial scanning stage of the sample by the laser thickness gauge, the flattest area is selected as the reference plane. The reference plane is determined by calculating the average height value of multiple measurement points. Based on the laser positioning data, a reference reference plane is reconstructed through the thickness data collected from multiple measurement points. The goal is to obtain a standardized plane so that the thickness measurement data can be offset-corrected relative to this reference plane to eliminate the errors caused by the unevenness of the sample surface. At the same time, the least squares method is used to fit the reference plane.

[0112] Step S13: Perform multi-point sampling of the thickness through the laser thickness gauge according to the reference plane data to obtain the preliminary thickness sampling matrix data;

[0113] Specifically, on the reference plane, the laser thickness gauge will scan along different paths. To obtain the global information of the sample thickness, multiple evenly distributed points are selected on the sample surface for thickness sampling. The laser makes multiple measurements at each sampling point to ensure the reliability of the data. According to the position distribution on the sample surface, a series of thickness data are obtained. These data are organized into a matrix, and each element of the matrix represents the thickness value at the corresponding position.

[0114] Step S14: Perform thickness distribution interpolation calculation based on the thickness preliminary sampling matrix data to obtain thickness distribution interpolation data;

[0115] Specifically, to achieve a relatively smooth thickness distribution map, an interpolation algorithm is used to interpolate the data between the sampling points. The interpolation method is linear interpolation, quadratic interpolation, or high-order interpolation, and the specific selection depends on the smoothness of the sample surface and the required interpolation accuracy.

[0116] Specifically, extract the smoothness feature and the change degree feature from the thickness preliminary sampling matrix data to obtain the preliminary sampling smoothness feature data and the preliminary sampling change degree feature data respectively; perform weighted calculation based on the preliminary sampling smoothness feature data and the preliminary sampling change degree feature data to obtain the preliminary sampling irregularity degree data (such as ), where is the preliminary sampling irregularity degree data, is the weight coefficient of the preliminary sampling smoothness feature data, is the preliminary sampling smoothness feature data, is the weight coefficient of the preliminary sampling change degree feature data, is the preliminary sampling change degree feature data; when it is determined that the preliminary sampling irregularity degree data is greater than the preset first irregularity degree threshold data, perform linear interpolation on the thickness preliminary sampling matrix data to obtain thickness distribution interpolation data; when it is determined that the preliminary sampling irregularity degree data is less than or equal to the preset first irregularity degree threshold data and greater than the preset second irregularity degree threshold data, perform linear interpolation on the thickness preliminary sampling matrix data to obtain thickness distribution interpolation data; when it is determined that the preliminary sampling irregularity degree data is less than or equal to the preset second irregularity degree threshold data, perform linear interpolation on the thickness preliminary sampling matrix data to obtain thickness distribution interpolation data, where the preset first irregularity degree threshold data is greater than the preset second irregularity degree threshold data.

[0117] Step S15: Generate a dynamic distribution map based on the thickness distribution interpolation data to obtain two-dimensional thickness distribution map data.

[0118] Specifically, based on the interpolated data, an image processing technique is used to generate a two-dimensional thickness distribution map. First, the interpolated data is converted into grayscale values or color values to represent the thickness. Thicker regions can be represented by darker colors, and thinner regions can be represented by lighter colors, or the color mapping relationship can be adjusted according to actual needs. To more intuitively display the thickness distribution, parameters such as the color scale and contrast can be dynamically adjusted when generating the image, making the thickness changes in the image more obvious. By setting appropriate thresholds, some regions with larger thickness changes can be highlighted.

[0119] Preferably, the spatial positioning and optical axis calibration are specifically as follows:

[0120] By using the inertial sensor integrated in the laser thickness gauge and an external vision sensor, the spatial position data of the device is captured in real time;

[0121] Specifically, the laser thickness gauge integrates an inertial measurement unit (IMU), which includes sensors such as an accelerometer, a gyroscope, and a magnetometer. The accelerometer can measure the linear acceleration of the device, the gyroscope can measure the angular velocity of the device, and the magnetometer can be used to calibrate the direction. By fusing the data of these sensors, the spatial position data of the device is captured in real time, specifically including the displacement (X, Y, Z) and rotation angles (angles around the X, Y, Z axes) of the device. The external vision sensor is a camera or a lidar (LiDAR), which is used to obtain the position of the device in three-dimensional space through image recognition or laser scanning. By calibrating the position of the calibration points or known objects (such as reference points or rulers) in the visual image, the position coordinates of the device relative to the reference point are calculated. Combining these visual data with the inertial data, the precise spatial position of the device can be obtained. By combining the data of the inertial sensor and the external vision sensor, the position and attitude information of the device are updated in real time. Through data fusion technology, the spatial position data of the device is obtained in real time, ensuring high-precision and high-reliability measurement.

[0122] Based on the spatial position data, attitude calculation is performed to obtain the device attitude data;

[0123] Specifically, the attitude refers to the angle and direction of the device in space, represented by Euler angles (roll, pitch, yaw angles) or quaternions. According to the linear acceleration and angular velocity data collected by the inertial sensors (accelerometer and gyroscope), the angle change of the device is obtained through integration, and then the attitude of the device is calculated to obtain the device attitude data, that is, the rotation angle of the device relative to the initial position, including three rotation angles (pitch, roll, yaw).

[0124] Based on the device attitude data, attitude error analysis is performed to obtain the attitude error data;

[0125] Specifically, there are error sources in attitude calculation, such as sensor noise, sensor accuracy limitations, integration errors, etc. Error analysis needs to compare the data of inertial sensors with the data of external sensors (such as vision sensors or reference points) to determine the deviation of the device's attitude. By comparing the difference between the calculated attitude data and the actual position of the external vision sensor or known reference point, the attitude error is calculated. Error analysis can be carried out in the following way: compare the angle and position of the device relative to the reference point provided by the external vision sensor with the attitude calculated by the inertial sensor to obtain the error. Use techniques such as Kalman filtering to integrate the data of inertial sensors and vision sensors to minimize the error. Obtain the error data of the device's attitude, including the deviation of the device's attitude estimation at each measurement moment, such as the errors of pitch angle, roll angle, and yaw angle.

[0126] Perform attitude calibration based on the attitude error data to obtain attitude calibration data;

[0127] Specifically, according to the calculated attitude error data, the attitude of the device is corrected through reverse compensation or calibration algorithms. Methods such as proportional-integral-derivative (PID) control algorithm, least squares method, and Kalman filtering are used to correct the attitude error. If the attitude error is calculated, the error data can be compared and the attitude of the device can be adjusted appropriately. The calibrated attitude data will more accurately reflect the true attitude of the device, reducing errors caused by sensor errors or external interference.

[0128] Align the laser optical axis with the attitude calibration data to obtain optical axis alignment data;

[0129] Specifically, laser optical axis alignment is to ensure that the laser beam of the laser measuring instrument is in the same direction as the physical structure of the device (such as the laser emitter). The laser optical axis is adjusted by comparing the actual attitude of the device with the actual emission direction of the laser (which can be measured by the angle of the reflection point or target point). Through the attitude calibration data, the angle deviation between the laser emission axis and the device installation axis is calculated, and then alignment adjustment is carried out. By rotating the laser or adjusting its emission angle, the laser optical axis is made to be consistent with the actual attitude of the device. The calibration data of the laser optical axis is calculated to ensure that the emission direction of the laser matches the actual movement direction of the device, minimizing the error.

[0130] Perform error correction based on the optical axis alignment data to obtain error correction data;

[0131] Specifically, after the laser optical axis is aligned, there are still errors caused by equipment attitude changes, external factor interferences, etc. Further error correction is required to ensure the measurement accuracy of the laser thickness gauge under different attitudes. According to the optical axis alignment data, the accuracy of the laser beam is optimized by correcting the deviation. By correcting and compensating the laser measurement results, the beam error caused by the equipment attitude change is eliminated. The obtained error correction data represents the error correction values of the laser measurement instrument under different equipment attitudes after calibration. These data are used for subsequent laser positioning.

[0132] Laser positioning data is generated based on the spatial position data, attitude calibration data, optical axis alignment data, and error correction data to obtain the laser positioning data.

[0133] Specifically, by combining the spatial position data, attitude calibration data, optical axis alignment data, and error correction data, through a mathematical model (such as calculating the weights of the spatial position data and attitude calibration data based on the error correction data to obtain the corrected data, and combining the corrected data with the optical axis alignment data), the actual position, attitude, and optical axis direction of the laser and the measurement results are comprehensively calculated to generate the laser positioning data. Ensure that the laser measurement results have high precision and can accurately reflect the actual spatial position of the equipment.

[0134] Preferably, the reconstruction of the sample reference plane is specifically as follows:

[0135] The sample is preliminarily scanned by the laser thickness gauge according to the laser positioning data to obtain the sample scan data;

[0136] Specifically, based on the previous laser positioning data, the laser thickness gauge performs a preliminary scan along the sample surface. The sample surface is measured at multiple points by the laser beam to obtain the coordinates and thickness data of each measurement point. The position of each scan point is defined by the three-dimensional coordinate system of the laser, and the laser dynamically adjusts the scan path according to the laser positioning data to ensure that all important areas of the sample are covered. Through the scan, a set of three-dimensional point data on the sample surface is obtained, and the scan data is preprocessed, including noise filtering and missing data supplementation, to ensure the accuracy and integrity of the scan data. The high-frequency noise is removed using a smoothing filter algorithm, or the missing scan data is filled by an interpolation method.

[0137] Local reference plane reconstruction is performed based on the sample scan data to obtain the first reference plane data;

[0138] Specifically, the local datum plane refers to the datum plane obtained by fitting the datum plane of each local area by selecting a small area on the sample surface. For each local area, the least squares method is used to perform plane fitting on the scanned data to obtain the datum plane of that area. The selection of the local area is achieved through grid division or point-based clustering methods (such as DBSCAN). After performing plane fitting on each local area, a local datum plane dataset is obtained. All local datum planes are merged to form multiple datum plane segments of the sample, and the first datum plane data is obtained.

[0139] The minimum convex hull reconstruction is performed based on the sample scanned data to obtain the second datum plane data;

[0140] Specifically, the minimum convex hull is the smallest convex polyhedron that encloses the set of sample scanned data points, and it can better represent the shape of the sample surface. By performing convex hull calculation on all scanned data points, a convex surface that encloses these points is obtained, providing a global reference datum plane that covers the entire sample surface. The convex hull algorithm is used to calculate the minimum convex hull of these points. The specific implementation method is to find a set of points such that all scanned points are located inside the convex hull formed by these points. The calculation method can be achieved by calculating the boundary planes of the point set, that is, finding a set of planes that completely enclose all scanned points. After calculating the minimum convex hull, a polyhedron containing multiple patches is obtained. By converting these patches into a smooth datum plane, the second datum plane data is obtained. The patches of the convex hull are smoothed using a triangular mesh or plane fitting algorithm to form a continuous datum plane suitable for further analysis.

[0141] Shape feature fusion is performed on the first datum plane data and the second datum plane data to obtain the datum plane data;

[0142] Specifically, to obtain a more accurate datum plane, the local datum plane and the global datum plane need to be combined. The local datum plane (obtained by least squares fitting) accurately describes the shape features of a small area, while the global datum plane (obtained by minimum convex hull reconstruction) can describe the shape of the entire sample. By combining the advantages of both, a more accurate and robust datum plane data can be obtained. Weighted fusion is performed according to the locality of the data. For example, in the flat area of the sample, more reliance can be placed on the local datum plane; while in the area with large curvature changes, more reliance can be placed on the global datum plane. It is dynamically adjusted according to the characteristics of the sample surface. For example, different weights can be selected according to the curvature of the local area: more weight of the local datum plane is given to the area with smaller curvature (i.e., the flat area), and more weight of the global datum plane is given to the area with larger curvature (i.e., the curved surface area). After fusion, an accurate datum plane data that combines global and local features is obtained.

[0143] Among them, the preliminary scanning of the sample is specifically as follows:

[0144] The surface of the sample is scanned by a laser thickness gauge to obtain the point cloud data of the sample surface;

[0145] Specifically, the laser thickness gauge scans the surface of the sample multiple times with laser beams, and the reflection of each laser beam generates a measurement point. Each measurement point contains three-dimensional coordinates and thickness values. During scanning, the device sequentially scans the surface of the sample according to a preset path to generate a point cloud data set containing thousands to millions of points.

[0146] Particle iterative filtering is performed on the point cloud data of the sample surface to obtain scanned noise reduction data;

[0147] Specifically, during the process of point cloud noise reduction, each point is regarded as a particle, and the weights of these particles are iteratively optimized to remove unstable or noisy points. At initialization, a particle is assigned to each point, with the position being each coordinate in the point cloud, and the initial weights are set to be equal. Based on the assumption of the smoothness of the sample surface shape, the weights of the particles are iteratively optimized. That is, the similarity of each particle is evaluated, and the particle with a larger weight indicates a better match to the real surface. The weight of a particle can be determined by calculating the deviation between the neighboring points and the current point. Resampling is performed to retain the particles with higher weights and remove the particles with lower weights, and the estimated values of the particles are iteratively updated. This process is continued until the noise in the point cloud is effectively removed to obtain the noise-reduced point cloud data.

[0148] KD tree region division is performed based on the scanned noise reduction data to obtain scanned division data;

[0149] Specifically, the KD tree (K-Dimensional Tree) is a spatial partitioning data structure used to quickly find neighboring points in the point cloud. By constructing a KD tree, efficient region division and nearest neighbor search of the point cloud can be performed. Specifically, the KD tree divides the point cloud data of the sample surface into several small regions, and the boundaries of these small regions are adaptively divided according to the distribution of the point cloud. For the noise-reduced point cloud data set, a suitable dimension (such as x, y, or z) is selected for division. Initially, the dimension with the largest variance is selected for division so that each division can effectively split the point cloud data as much as possible. Based on the selected dimension, the point cloud is divided into two parts according to the median of that dimension, and subtrees are recursively constructed for each subset until a certain stopping criterion is reached (for example, the number of points in each leaf node is less than a certain threshold). The constructed KD tree can be used to efficiently query the neighborhood of the point cloud data, as well as perform region division and nearest neighbor queries. Through the KD tree, the point cloud is divided into multiple small regions, and the points within each region are relatively close in spatial position. These regions can be represented by the nodes of the KD tree.

[0150] Voxelization and resampling are performed on the scanned division data to obtain sample resampling data;

[0151] Specifically, voxelization is the conversion of continuous three-dimensional point cloud data into a discrete voxel grid, where each voxel represents a small region in space. The voxelization process can be achieved by defining a voxel size in the point cloud data, and each voxel contains several points. Voxelization helps reduce the data volume and improve the processing efficiency. Select the size of the voxel to determine the size of each voxel in the x, y, and z directions. For each region in the scanned partition data, its space is divided into several voxel grids of size V. The points within each voxel are marked as the representative points of the voxel (which can be the average position or centroid of all points within the voxel). After voxelization, the data is resampled by aggregating the points within each voxel into a representative point, reducing the number of points and ensuring that the representative point can effectively express the point cloud characteristics within the voxel. The resampled point of each voxel is the average position or geometric center of the points within the voxel.

[0152] Perform spatial coordinate alignment based on the sample scan data and the sample resampled data to obtain the sample scan data.

[0153] Specifically, based on the sample resampled data, further perform coordinate alignment with the scanned noise-reduced data. Ensure that data from different sources can be compared and further analyzed in a unified coordinate system. Use the least squares method to achieve the rigid registration of the two sets of data. Select a reference point set (for example, the points in the sample resampled data) as the target for alignment. Use rigid transformations (including translation and rotation) to adjust the scanned noise-reduced data to align it as closely as possible with the sample resampled data to obtain the sample scan data.

[0154] Preferably, the local reference plane reconstruction is specifically as follows:

[0155] Extract the sample morphological feature data from the sample scan data;

[0156] Specifically, extract morphological features from the sample scan data, mainly including the curvature, normal vector, surface smoothness, etc. of the sample surface. Use the geometric properties of the point cloud data to describe the local morphological features of the sample surface. These features help identify flat regions, curved regions, and other defect regions on the surface. Calculate the curvature value through the neighborhood of each point. The normal vector of each point is estimated through the covariance matrix of the neighborhood points. Fit the neighborhood points by the least squares method to obtain the normal vector. The obtained morphological feature data includes the curvature and normal vector information of each point, as well as other geometric features, which can help identify the characteristics of the sample surface region.

[0157] Perform a preliminary regional division on the sample scan data according to the sample morphological feature data to obtain the first sample division data;

[0158] Specifically, the surface of the sample is preliminarily divided into regions according to the morphological feature data. A common method is to divide the regions by the curvature threshold or the difference in normal vectors. The region with a smaller curvature can be regarded as a flat region, while the region with a larger curvature is regarded as a curved region. The region with a large difference in normal vectors indicates the presence of protrusions or depressions on the surface. Each point in the scanned data is classified into different regions according to its curvature and normal vector information. Curvature thresholds and normal vector difference thresholds can be set for the division. If both are less than the two thresholds, they are regarded as the same region.

[0159] Perform morphological clustering calculations based on the first sample division data to obtain sample division clustering data;

[0160] Specifically, perform clustering calculations on the first sample division data to further refine the region division. Common clustering methods include density-based clustering algorithms (such as DBSCAN) or K-means clustering. Clustering is performed according to the distribution density and morphological features of the points within the region. For each region, calculate the density of the points within the region. The Euclidean distance metric can be used to calculate the distance between points. If the distance between two points is less than a preset threshold, they are considered to belong to the same cluster. Evaluate the clustering results to ensure that each cluster represents a connected region. Through clustering, new division data is obtained, and each cluster represents a region with similar morphological features.

[0161] Construct a local connection graph based on the first sample division data and the sample division clustering data to obtain sample division graph data;

[0162] Specifically, construct a local connection graph according to the sample division clustering data. The nodes of this graph represent the regions or points in the sample, and the edges represent the connection relationships between different regions. For each pair of regions or clusters, calculate the distance or morphological similarity between them. If the distance between two regions is less than a certain threshold, they are considered to be connected in space. Use the adjacency matrix of the graph to represent these connection relationships, and the weights of the edges can be weighted according to the distance or morphological similarity between the regions. A graph containing several nodes and edges is obtained, where the nodes represent regions or clusters, and the edges represent the connection relationships.

[0163] Perform a fine division on the sample division graph data according to the sample division clustering data to obtain the second sample division data;

[0164] Specifically, based on the local connection graph, the graph is further refined by a graph partitioning algorithm (such as spectral clustering or graph cut) to divide the graph into several smaller subgraphs according to the connection strength of the graph. Calculate the weight of each edge, and the weight of the edge represents the connection strength or the similarity of regions. Use the graph partitioning algorithm to divide the graph into several subgraphs, and each subgraph represents a smaller region in the sample. The data after fine partitioning contains multiple small regions, and the morphological features of each region are more consistent and more distinct from other regions.

[0165] Perform feature detection on the second sample partition data to obtain sample partition feature data;

[0166] Specifically, for each region in the second sample partition data, perform feature detection, mainly detecting the flatness, curvature, difference in normal vectors, etc. of the region. Calculate geometric features such as the average curvature and maximum curvature of each region. Calculate the change in the normal vector of each region and analyze the smoothness of the region surface. According to the extracted features, classify the regions into flat regions, curved regions, etc. Through feature detection, obtain the morphological feature data of each region, including curvature, change in normal vector, flatness, etc.

[0167] Specifically, more importantly, perform local region feature extraction on the second sample partition data to obtain local region feature data; perform pyramid convolution according to the local region feature data to obtain local region multi-scale feature data; perform high-dimensional feature mapping according to the local region multi-scale feature data to obtain preliminary sample partition feature data; calculate local fuzzy parameters for the local region feature data to obtain local fuzzy data; perform Gaussian blur on the local region feature data according to the local fuzzy data to obtain region Gaussian blur data; perform local merging according to the region Gaussian blur data and the preliminary sample partition feature data to obtain local merged data; generate local feature descriptors for the local merged data to obtain sample partition feature data.

[0168] According to the second sample partitioning data, local region partitioning and feature extraction are performed on the sample. Each local region represents a specific detailed area on the sample surface, and these regions have different geometric shapes and texture features. Based on the data collected by the scanning device, according to the point cloud data of each local region, local surface features are extracted, including information such as surface normal, curvature, and texture. After obtaining the local region feature data, further processing is carried out through the pyramid convolution method. The pyramid convolution uses convolution kernels of different scales to perform convolution operations on the local regions of the sample at different resolutions, thereby obtaining multi-scale feature representations. It can effectively capture the details of the local regions while maintaining the overall features of larger-scale regions. The generated local region multi-scale feature data expresses the local region features at multiple levels at different scales. After obtaining the local region multi-scale feature data, these multi-scale features are transformed into a higher-dimensional space representation through high-dimensional feature mapping. Using high-dimensional mapping techniques, the original multi-scale feature vectors are projected into a higher-dimensional space to better capture the potential patterns and structures hidden in the local regions, such as based on polynomial mapping or Fourier transform mapping, to map low-dimensional data to a high-dimensional space, and the local region feature data is transformed into a data representation in the high-dimensional space through the mapping function. After high-dimensional feature mapping, the local region feature data is blurred to enhance its smoothness. The fuzzy parameters of each local region are calculated, and these parameters are determined according to the feature distribution within the local region, such as the uniformity of the texture, the smoothness of the edges, etc., and are dynamically adjusted according to the changes on the sample surface to generate local fuzzy data. After obtaining the local fuzzy data, Gaussian blurring is performed on the local region features using these data. Gaussian blurring is a smoothing operation that can effectively remove noise in the data while retaining the main feature information. Based on the previously obtained fuzzy parameters, compared with conventional Gaussian blurring, through polynomial mapping or Gaussian radial basis function (RBF) mapping, non-linear relationships in the data can be captured in the high-dimensional space. For complex and hidden structures in the data (such as non-linear geometric shapes and textures between samples), high-dimensional mapping can better express them. By applying Gaussian blurring to the local region feature data, the local details in the image can be further smoothed, and the interference of high-frequency noise can be reduced. The obtained regional Gaussian blurring data will more accurately reflect the smooth characteristics and main structures of the local regions. After obtaining the regional Gaussian blurring data and the preliminary sample partitioning feature data, the merging process of local regions is carried out. The local merging process integrates the local region feature data and the Gaussian blurring data. Through weighted merging or fusion strategies, various information is assembled into a feature description. The merged data represents the fine structure and smooth characteristics of the local region, generating local merged data. Through the generation of local feature descriptors, the key features of each sample are extracted, accurately representing the local geometric shape, texture, and other important features of the sample, and obtaining the sample partitioning feature data.

[0169] Perform weighted screening on the second sample division data according to the sample division characteristic data to obtain local area division data;

[0170] Specifically, according to the extracted characteristic data, assign weights to each area. Select representative areas through weighted screening, and remove those abnormal or non-compliant areas. Set weights for each area according to characteristic data (such as curvature, flatness). Eliminate areas with lower weights and retain areas with higher weights. Through weighted screening, local area division data is obtained, and these areas represent different local characteristics of the sample surface.

[0171] Perform local reference plane fitting on the local area division data to obtain local reference plane fitting data;

[0172] Specifically, perform reference plane fitting on the data of each local area, and use the least squares method to fit a plane or a quadratic surface. Fit a plane or a surface to the point set of each local area, assuming that the local area surface can be approximated by a plane or a surface. Use the least squares method to optimize the fitting result and minimize the fitting error. The reference plane obtained by fitting each local area is the local reference plane of that area, and the result obtained is the local reference plane data.

[0173] Perform local plane error correction according to the local reference plane fitting data to obtain reference plane smoothing correction data;

[0174] Specifically, after the local reference plane fitting is completed, local plane errors caused by measurement errors, noise, or equipment instability will occur. These errors cause local protrusions or depressions on the reference plane, affecting the overall flatness. To solve this problem, smooth correction is performed on the reference plane. Use data smoothing techniques to eliminate these local errors. Locally smooth the fitted reference plane using the weighted least squares method or the moving average method. Alternatively, perform local plane error correction through polynomial interpolation. For example, use a quadratic or cubic polynomial to fit the error data of the local area for surface smoothing and correction.

[0175] Perform local reference plane fusion according to the reference plane smoothing correction data to obtain the first reference plane data.

[0176] Specifically, after error correction is completed in each local area, fuse adjacent reference plane areas through weighted averaging or interpolation methods. After all local areas have been corrected, fuse these local reference planes through a global model. Use a global optimization algorithm (such as the least squares method) to find the best global reference plane. This process takes into account the smooth transition between each local reference plane and minimizes the seam error between regions.

[0177] Preferably, the minimum convex hull reconstruction is specifically as follows:

[0178] Perform point cloud screening and reduction on the sample scan data to obtain sample screening data;

[0179] Specifically, the point cloud data contains a large number of redundant or irrelevant points. Therefore, screening and reduction are required to improve processing efficiency. The screening methods are based on distance thresholds, density, or geometric features (such as curvature, normal vector), etc. Use a distance-based screening method to remove outliers. For example, if the distance from a certain point to its neighboring points is greater than the set threshold, then this point is considered a noise point. Or, calculate the neighborhood density of each point in the point cloud, and remove the low-density regions by setting a density threshold to ensure that the retained points are representative of the sample surface. Or, by calculating the curvature of each point, points with too large curvature (such as sharp or curved parts) can be selected to be retained, and points in flat areas can be removed to reduce the computational complexity. After screening, sample screening data is obtained.

[0180] Perform point cloud space optimization based on the sample screening data to obtain point cloud optimized data;

[0181] Specifically, point cloud optimization aims to make the point cloud data better represent the sample surface by removing redundant points and adjusting the distribution of points. Common optimization methods include grid-based homogenization, coordinate deviation correction, etc. Adopt the grid method, set a suitable grid size, and divide the point cloud into several small units (grids). Select representative points within each unit, and choose the centroid or the nearest point as the representative. Adjust the deviation points in the point cloud, and use the least squares method to perform global translation and rotation on the point cloud so that the overall point cloud is closer to the actual shape of the sample. After the preliminary optimization, perform several iterations, each time adjusting the position and distribution of points to reduce the excessive deviation or uneven density between points in the point cloud, thereby obtaining point cloud optimized data.

[0182] Construct an initial set of convex hulls based on the point cloud optimized data to obtain preliminary convex hull data;

[0183] Specifically, the convex hull is the smallest convex polyhedron that encloses all points of the point cloud. By constructing the convex hull of the point cloud data, the outer boundary of the point cloud is obtained. The initial convex hull is constructed using methods such as the Wiener enclosure method, Quickhull algorithm, etc. First, select a set of points as the preliminary boundary by calculating the boundary region of the point cloud and selecting the points on the boundary. Based on the preliminary boundary points, use a convex hull algorithm (such as Quickhull) to calculate the minimum convex hull of these points. By solving the boundary points of the plane or polyhedron, determine the convex hull shape of the point cloud. Calculate the volume and number of faces of the preliminary convex hull. If the preliminary result does not meet the expectations, re-screen the boundary points and recalculate the convex hull. Obtain the preliminary convex hull data, which contains the boundary points and faces of the convex hull, represented as a set of convex hull shapes.

[0184] Perform local adjustment on the preliminary convex hull data to obtain optimized convex hull boundary data;

[0185] Specifically, the preliminary convex hull often cannot perfectly fit the sample surface, with overfitting or uneven boundaries. The local adjustment aims to correct the boundary of the convex hull according to the actual shape of the sample to make it more conform to the surface characteristics. Smooth the convex hull boundary by weighted least squares method, so that the boundary is no longer too sharp or irregular, and maintain the smoothness of the boundary. Refine each face of the convex hull, and adjust the position of the convex hull face using the morphological features of the point cloud (such as normal vector, curvature) to make the convex hull fit the actual surface better. For the corners and irregular parts of the convex hull, use local optimization methods to adjust the positions of the boundary points and reduce overfitting. Through the above adjustment process, the optimized convex hull boundary data is obtained, which includes the optimized and adjusted boundaries and faces.

[0186] Perform surface reconstruction on the optimized convex hull boundary data to obtain optimized convex hull data;

[0187] Specifically, based on the optimized convex hull boundary, perform surface reconstruction to obtain a more accurate sample surface model. Use NURBS (Non-Uniform Rational B-Spline) or other surface fitting methods to generate a smooth surface. First, through the optimized convex hull boundary, use the polygon mesh algorithm to construct a surface mesh, which represents the general shape of the sample surface. Adopt fitting methods such as the least squares method, and calculate the fitted plane or surface based on the point cloud data and the boundary mesh. Optimize the smoothness and accuracy of the surface by adjusting the weights and fitting errors. Make fine corrections to the fitted surface to ensure that the surface can fit the sample surface well in each local area. The obtained surface is the optimized convex hull data, which represents the shape of the sample and fits the actual surface more accurately.

[0188] Perform global-local fusion based on the preliminary convex hull data and the optimized convex hull data to obtain the second reference plane data.

[0189] Specifically, fuse the preliminary convex hull and the optimized convex hull data to ensure that the reference plane can adapt to the overall shape and local details simultaneously within the global scope. Between the preliminary convex hull and the optimized data, apply methods such as weighted average and surface smoothing, so that the finally fused surface can have a smooth transition and be accurately matched with the point cloud data. By adjusting the weights in the fusion process, enhance the local details of the sample surface, so that the reference plane maintains consistency globally while retaining details in the local area. The fused data is the second reference plane data, which combines the data of the preliminary convex hull and the optimized convex hull to form a complete and smooth reference plane.

[0190] Preferably, step S2 is specifically as follows:

[0191] Step S21: performing regional calibration according to the two-dimensional thickness distribution map data to obtain regional calibration data;

[0192] Specifically, the purpose of regional calibration is to divide the two-dimensional thickness distribution map data into several regions, each region represents a different regional feature of the sample surface, which is convenient for analysis and calculation. Regional calibration is implemented based on threshold segmentation, region growing or clustering algorithm. Based on the range of thickness values, the points in the two-dimensional thickness distribution map data are divided into several regions according to different thickness values. For example, set an upper and lower limit of thickness, and all points whose thickness values fall within this interval are classified into the same region. Alternatively, if the thickness distribution map is more complex, a density-based clustering method is used to divide points with similar thickness and adjacent positions into one region. When clustering, the distance between points and the thickness difference can be used as the basis for calculation. Alternatively, the boundaries of each region are determined by continuity checks, such as using edge detection methods to ensure that the boundaries of each region are clear.

[0193] Step S22: Calculate the thickness variance of the regional calibration data to obtain regional variance data;

[0194] Specifically, for each region, the variance of its thickness data is calculated. The variance can reflect the degree of thickness dispersion in the region. The larger the variance, the more drastic the thickness variation in the region; the smaller the variance, the more uniform the thickness in the region. For each region, the variance is calculated. The variance of each region indicates the degree of thickness fluctuation in the region, and the variance data of all regions constitute the regional variance data.

[0195] Step S23: Calculate thickness non-uniformity according to the regional variance data to obtain thickness non-uniformity data;

[0196] Specifically, the overall thickness non-uniformity is calculated based on the variance data of each region, which helps to evaluate the stability and uniformity of the sample surface thickness. The thickness non-uniformity is obtained by calculating the weighted average or maximum value of the variance of all regions. Taking into account the different contributions of different regions to the overall non-uniformity, the regional variance is weighted averaged according to the area of the region or the number of thickness points to obtain the thickness non-uniformity data. Alternatively, if you only focus on the most non-uniform area in the sample, you can also select the area with the largest variance as the thickness non-uniformity index of the sample to obtain the thickness non-uniformity data. The overall thickness non-uniformity is calculated by the above method, which is a key indicator for evaluating the uniformity of the sample.

[0197] Step S24: performing critical area detection on the thickness non-uniformity data to obtain critical area data;

[0198] Specifically, the critical region refers to the region where the thickness fluctuation is severe or exceeds the preset tolerance range, and these regions affect the quality and performance of the sample. Therefore, the detection of the critical region is helpful for analysis. Set a critical threshold according to the actual requirements. If the thickness variance or non-uniformity of a certain region exceeds this threshold, then this region is considered a critical region. For each region, if its variance or thickness non-uniformity exceeds the critical threshold, it is marked as a critical region. Collect the marks of all detected critical regions to obtain critical region data.

[0199] Step S25: Integrate the thickness non-uniformity data and the critical region data to obtain thickness uniformity data.

[0200] Specifically, integrate the thickness non-uniformity data and the critical region data to obtain the thickness uniformity evaluation result. It will not only consider the non-uniformity of the overall thickness, but also need to consider the distribution of the critical region and its influence. By combining the thickness non-uniformity data and the critical region data, the uniformity of the sample can be comprehensively evaluated through a multi-dimensional index combination. For example, calculate the overall non-uniformity and mark out the critical regions. The thickness uniformity data uses the following comprehensive evaluation method: , where is the thickness uniformity evaluation value / thickness uniformity data, is the thickness non-uniformity data, is the number of critical regions, is the total area number of the sample. This formula considers the comprehensive influence of the thickness non-uniformity and the number of critical regions on the uniformity evaluation. Through the integration step, thickness uniformity data is obtained , which can be used for further sample quality analysis and optimization.

[0201] Preferably, step S3 is specifically:

[0202] Step S31: Formulate sample screening rules based on the thickness uniformity data to obtain sample screening rule data;

[0203] Specifically, by analyzing the thickness uniformity data, formulate sample screening rules. These rules are based on the uniformity data of the sample and evaluate which samples meet the requirements of the tensile strength test. The screening rules are based on factors such as thickness uniformity, maximum variance, and the size of the critical region. Extract key indicators from the thickness uniformity data, such as regional variance and overall non-uniformity . Set standard thresholds and , if the uniformity data of a certain sample exceeds these thresholds, then this sample is considered not to meet the requirements of the tensile strength test. For the data of the critical region, the maximum allowable number of critical regions , samples exceeding this quantity will also be excluded. The sample screening rules are represented by the following conditions: , , , and screening rule data is obtained. According to these screening conditions, all samples are divided into samples that meet the rules and samples that do not meet the rules, and the data of the samples that meet the rules is saved as sample screening rule data.

[0204] Step S32: Score the samples according to the sample screening rule data to obtain sample score data;

[0205] Specifically, based on the thickness uniformity data and the screening rule data, a score is assigned to each sample. The basis for scoring is the degree of uniformity of the sample. Samples with higher scores have better thickness uniformity and are suitable for tensile strength testing. Weights are assigned to different uniformity indicators. For example, thickness non-uniformity and regional variance can set weight coefficients according to actual requirements and , and the number of critical regions can also affect the score. The score of each sample can be obtained through the following weighted calculation formula: , where and are the uniformity data of sample , , and are weight coefficients, is the number of critical regions of sample . By scoring all samples, sample score data S={S1,S2,…,Sn} is obtained, where is the score of sample .

[0206] Step S33: Classify and sort the sample score data to obtain sample classification and sorting data;

[0207] Specifically, classify and sort the samples according to the score data. Samples can be divided into several categories. For example, high-quality samples, general samples, and unqualified samples. High-quality samples are preferentially selected for tensile strength testing. According to the range of the score, the samples are divided into different categories. For example, the score range is set as: high-quality samples , general samples: , unqualified samples: , is a high score, is a low score. All samples are sorted according to the score ​​​Sort from high to low, giving priority to samples with higher scores. After sorting, the sample classification and sorting data is obtained. ,in( ) represents the sample By classifying and sorting all samples, we can get the sample classification and sorting data.

[0208] Step S34: performing Monte Carlo simulation performance prediction based on the sample classification data to obtain sample performance distribution simulation data;

[0209] Specifically, the Monte Carlo simulation method is used to predict the tensile strength of the sample. Monte Carlo simulation simulates the performance distribution of the sample by random sampling, considering the effect of thickness uniformity on tensile strength. According to the thickness uniformity data of the sample, the relationship between tensile strength and thickness uniformity is defined. The tensile strength F is the product of uniformity U and variance Function, calculated as: ,in, , and is the regression coefficient, obtained by fitting the experimental data. Using the Monte Carlo method, a certain number of samples are randomly selected and the uniformity data is used to and Calculate the tensile strength of each sample , and generate the tensile strength distribution of the sample through multiple sampling simulations. Get performance distribution simulation data: Through the simulation process, get the tensile strength distribution data of the sample, reflecting the performance fluctuation of the sample under different uniformity conditions.

[0210] Step S35: Mark key samples according to the sample performance distribution simulation data to obtain tensile strength test sample selection data.

[0211] Specifically, based on the tensile strength distribution obtained by Monte Carlo simulation, key samples that show strong performance or meet test requirements are marked. A performance threshold is set. If the tensile strength prediction value of a sample is higher than the threshold , then the sample is considered to be a key sample. Greater than The samples are marked as key samples. The sample selection data for tensile strength test is obtained. After screening and marking, the sample data that meets the tensile strength test requirements is finally obtained.

[0212] Preferably, step S4 is specifically:

[0213] Step S41: performing a tensile strength test operation through a tensile strength test device according to the tensile strength test sample selection data to obtain stress time series data;

[0214] Specifically, after selecting a sample that meets the requirements, it is clamped onto the tensile strength testing equipment. The tensile strength testing equipment will perform tensile loading according to the shape and material properties of the sample, and record in real time the changes in stress and time of the sample during the tensile process. The sample data selected from the previous steps will include information such as its dimensions, shape, and material, and this information will be used to configure the tensile testing equipment. In the tensile testing equipment, the sample is gradually stretched, and the equipment records in real time the displacement changes of the sample and the applied force. Stress and strain are calculated based on the force and displacement data. The testing equipment will record in real time the changes in stress and time, forming stress-time series data.

[0215] Step S42: Perform curve fitting on the stress-time series data to obtain stress-strain curve data;

[0216] Specifically, the stress-time series data is converted into stress-strain data, and a stress-strain curve is obtained through fitting. Curve fitting is to convert the time series data into a function of strain, and perform smoothing and fitting to obtain a more accurate stress-strain relationship. To convert from stress-time series to stress-strain data, the displacement data recorded by the testing equipment is used to calculate the strain at each moment, thereby obtaining stress-strain data. In order to obtain a smooth stress-strain curve, the least squares method is used to fit the stress-strain data. For example, a quadratic or cubic polynomial can be used to fit the data to obtain a continuous stress-strain curve. Through fitting, a continuous stress-strain curve is obtained.

[0217] Step S43: Calculate the strength extreme value based on the stress-strain curve data to obtain strength extreme value data;

[0218] Specifically, the strength extreme value refers to the maximum stress point on the stress-strain curve, that is, the maximum load that the material can bear when being pulled. This point corresponds to the critical point before the sample ruptures. Using the fitted stress-strain curve, numerical methods (such as the derivative method) are used to calculate the derivative of the stress-strain curve and find its maximum point. The curve is , first calculate its first derivative , where , , is the constant term of the curve derivative. By solving the equation where the first derivative is equal to zero, the extreme point of the curve is obtained. The stress value corresponding to the extreme point is the strength extreme value, denoted as or (yield strength). For example, in the above fitted curve, the stress at the extreme point can be obtained by substituting the strain value corresponding to the extreme value into the polynomial fitting formula, obtaining the strength extreme value data. The strength extreme value is a key indicator for each sample, representing the maximum tensile strength of the sample.

[0219] Step S44: Conduct tensile strength evaluation based on the stress-strain curve data and strength extreme value data to obtain tensile strength test data.

[0220] Specifically, combining the stress-strain curve and strength extreme value data, evaluate the overall tensile strength performance of the sample. The evaluation is mainly based on the maximum stress value of the sample during the tensile process, as well as the elastic and plastic characteristics of the sample. The tensile strength takes the maximum stress value in the stress-strain curve as the tensile strength of the material. If there are multiple stress-strain curves, calculate the maximum stress value of each curve and take the maximum value as the tensile strength evaluation result. When considering multiple performance indicators, use the weighted method to comprehensively combine data such as stress extreme values, elastic modulus, and fracture strain to give the tensile strength evaluation. For example, the comprehensive evaluation formula is: Where, is the elastic modulus of the sample, is the maximum strain of the sample, , , are the weight coefficients. Through the tensile strength evaluation of all samples, a set of tensile strength test data can be obtained, including the maximum stress value of each sample and other evaluation indicators.

[0221] Preferably, the present application also provides a wire insulation layer measurement sample system for performing the wire insulation layer measurement sample method described above. The wire insulation layer measurement sample system includes:

[0222] A two-dimensional thickness distribution map generation module for scanning and measuring the thickness of the insulation layer of the numbered sample using a laser thickness gauge to generate two-dimensional thickness distribution map data;

[0223] A thickness uniformity analysis module for performing thickness uniformity analysis based on the two-dimensional thickness distribution map data to obtain thickness uniformity data;

[0224] A tensile strength test sample selection module for selecting tensile strength test samples based on the thickness uniformity data to obtain tensile strength test sample selection data;

[0225] A tensile strength test module for performing tensile strength tests based on the tensile strength test sample selection data to obtain tensile strength test data;

[0226] A wire insulation layer performance evaluation module for evaluating the performance of the wire insulation layer based on the two-dimensional thickness distribution map data, thickness uniformity data, and tensile strength test data to obtain wire insulation layer performance evaluation data for assisting in the measurement operation of the wire insulation layer sample.

[0227] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended application documents rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0228] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A method for measuring a sample of a wire insulation layer, characterized in that, Including the following steps: Step S1: Use a laser thickness gauge to scan and measure the thickness of the insulation layer of the numbered samples, generating two-dimensional thickness distribution map data; Step S2: Conduct thickness uniformity analysis based on the two-dimensional thickness distribution map data to obtain thickness uniformity data; Step S3: Select tensile strength test samples according to the thickness uniformity data to obtain tensile strength test sample selection data; Step S4: Conduct a tensile strength test according to the tensile strength test sample selection data to obtain tensile strength test data; Step S5: Evaluate the performance of the wire insulation layer based on the two-dimensional thickness distribution map data, thickness uniformity data, and tensile strength test data to obtain wire insulation layer performance evaluation data for assisting the measurement operation of the wire insulation layer samples; Specifically, Step S1 is as follows: Perform spatial positioning and optical axis calibration on the laser thickness gauge to generate laser positioning data; Reconstruct the sample reference plane through the laser thickness gauge according to the laser positioning data to obtain reference plane data; Perform multi-point thickness sampling through the laser thickness gauge according to the reference plane data to obtain preliminary thickness sampling matrix data; Conduct thickness distribution interpolation calculation based on the preliminary thickness sampling matrix data to obtain thickness distribution interpolation data; Generate a dynamic distribution map based on the thickness distribution interpolation data to obtain two-dimensional thickness distribution map data; Specifically, the reconstruction of the sample reference plane is as follows: Perform a preliminary scan of the sample through the laser thickness gauge according to the laser positioning data to obtain sample scan data; Reconstruct the local reference plane based on the sample scan data to obtain the first reference plane data; Reconstruct the minimum convex hull based on the sample scan data to obtain the second reference plane data; Fuse the shape features of the first reference plane data and the second reference plane data to obtain reference plane data; Specifically, the preliminary scan of the sample is as follows: Scan the surface of the sample through the laser thickness gauge to obtain sample surface point cloud data; Perform particle iterative filtering on the sample surface point cloud data to obtain scanned noise reduction data; Conduct KD tree region division based on the scanned noise reduction data to obtain scanned division data; Voxelize and resample the scanned division data to obtain sample resampled data; Align the spatial coordinates based on the sample scan data and the sample resampled data to obtain sample scan data; Specifically, the reconstruction of the local reference plane is as follows: Extract the sample morphological features from the sample scan data to obtain sample morphological feature data; Conduct a preliminary region division on the sample scan data according to the sample morphological feature data to obtain the first sample division data; Conduct morphological clustering calculation based on the first sample division data to obtain sample division clustering data; Construct a local connection graph based on the first sample division data and the sample division clustering data to obtain sample division graph data; Conduct a fine division on the sample division graph data according to the sample division clustering data to obtain the second sample division data; Conduct feature detection on the second sample division data to obtain sample division feature data; Perform weighted screening on the second sample division data according to the sample division feature data to obtain local region division data; Fit the local reference plane to the local region division data to obtain local reference plane fitting data; Perform local plane error correction based on the data fitted to the local reference plane to obtain smoothed reference plane correction data; Perform local reference plane fusion based on the smoothed reference plane correction data to obtain first reference plane data; Among them, the feature detection is specifically as follows: Extract local region feature data from the data of the second sample division; perform pyramid convolution based on the local region feature data to obtain local region multi-scale feature data; perform high-dimensional feature mapping based on the local region multi-scale feature data to obtain preliminary sample division feature data; calculate local fuzzy parameters for the local region feature data to obtain local fuzzy data; perform Gaussian blur on the local region feature data according to the local fuzzy data to obtain region Gaussian blur data; perform local merging based on the region Gaussian blur data and the preliminary sample division feature data to obtain local merged data; generate local feature descriptors for the local merged data to obtain sample division feature data.

2. The method according to claim 1, characterized in that, Among them, the spatial positioning and optical axis calibration are specifically as follows: Real-time capture the spatial position data of the device by using the inertial sensor and the external vision sensor integrated in the laser thickness gauge; Perform attitude calculation based on the spatial position data to obtain device attitude data; Perform attitude error analysis based on the device attitude data to obtain attitude error data; Perform attitude calibration based on the attitude error data to obtain attitude calibration data; Perform laser optical axis alignment on the attitude calibration data to obtain optical axis alignment data; Perform error correction based on the optical axis alignment data to obtain error correction data; Generate laser positioning data based on the spatial position data, attitude calibration data, optical axis alignment data, and error correction data.

3. The method according to claim 1, characterized in that Among them, the minimum convex hull reconstruction is specifically as follows: Perform point cloud screening and reduction on the sample scan data to obtain sample screening data; Perform point cloud spatial optimization based on the sample screening data to obtain point cloud optimized data; Construct the initial set of convex hulls based on the point cloud optimized data to obtain preliminary convex hull data; Perform local adjustment on the preliminary convex hull data to obtain convex hull boundary optimized data; Perform surface reconstruction on the convex hull boundary optimized data to obtain convex hull optimized data; Perform full local fusion based on the preliminary convex hull data and the convex hull optimized data to obtain second reference plane data.

4. The method according to claim 1, wherein Step S2 is specifically as follows: Perform region calibration based on the two-dimensional thickness distribution map data to obtain region calibration data; Calculate the thickness variance of the region calibration data to obtain region variance data; Calculate the thickness non-uniformity based on the region variance data to obtain thickness non-uniformity data; Perform critical region detection on the thickness non-uniformity data to obtain critical region data; Integrate the thickness non-uniformity data and the critical region data to obtain thickness uniformity data.

5. The method according to claim 1, characterized in that, Step S3 is specifically as follows: Formulate sample screening rules based on the thickness uniformity data to obtain sample screening rule data; Score the samples according to the sample screening rule data to obtain sample score data; Classify and sort the sample score data to obtain sample classification and sorting data; Perform Monte Carlo simulation performance prediction based on the sample classification data to obtain sample performance distribution simulation data; Key samples are marked according to the simulated data of the sample performance distribution to obtain the data for selecting the tensile strength test samples.

6. The method according to claim 1, characterized in that, Specifically, step S4 is as follows: Tensile strength test operations are performed on the tensile strength test equipment according to the data for selecting the tensile strength test samples to obtain the stress-time series data; Curve fitting is performed on the stress-time series data to obtain the stress-strain curve data; Strength extreme value calculations are performed based on the stress-strain curve data to obtain the strength extreme value data; Tensile strength evaluations are performed based on the stress-strain curve data and the strength extreme value data to obtain the tensile strength test data.

7. A wire insulation layer measurement sample system, characterized in that, For implementing the method for measuring samples of the wire insulation layer as described in claim 1, the system for measuring samples of the wire insulation layer includes: A two-dimensional thickness distribution map generation module, which is used to scan and measure the thickness of the insulation layer of the numbered samples by using a laser thickness gauge to generate two-dimensional thickness distribution map data; A thickness uniformity analysis module, which is used to perform thickness uniformity analysis based on the two-dimensional thickness distribution map data to obtain thickness uniformity data; A tensile strength test sample selection module, which is used to select tensile strength test samples based on the thickness uniformity data to obtain the data for selecting the tensile strength test samples; A tensile strength test module, which is used to perform tensile strength tests based on the data for selecting the tensile strength test samples to obtain the tensile strength test data; A wire insulation layer performance evaluation module, which is used to perform wire insulation layer performance evaluations based on the two-dimensional thickness distribution map data, the thickness uniformity data, and the tensile strength test data to obtain wire insulation layer performance evaluation data for assisting in the operation of measuring wire insulation layer samples.

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