A material characteristic detection system based on pulse current

Abstract

The application discloses a kind of material characteristic detection systems based on pulse current, it is related to material identification field.The system includes high-voltage pulse generation module, multidimensional synchronous data acquisition module, data processing module and man-machine interaction module.High-voltage pulse generation module can be switched by three flexible boosting ways, to apply voltage to the material to be tested in square array point by point, with detection efficiency and low damage detection.Multidimensional synchronous data acquisition module synchronously collects voltage, current, emission spectrum and environmental parameters, to provide comprehensive data for accurate material identification.Data processing module is preprocessed, matched with standard library, converted region secondary detection and data fusion, to generate high-precision material distribution heat map, and quantify the area ratio of each material.Human-computer interaction module realizes visual operation and result display.The system realizes material micro-damage, rapid, accurate detection and global distribution analysis, to meet the efficient supervision needs of customs port on-site inspection and other scenarios.

Description

Technical Field

[0001] This invention relates to the field of materials identification, and in particular to a material characteristic detection system based on pulse current. Background Technology

[0002] In on-site inspection scenarios such as customs ports and cross-border logistics hubs, material feature detection must simultaneously meet the dual core requirements of rapid screening and accurate identification. It must shorten the testing time for each batch and avoid customs delays under the rhythm of high-volume and efficient cargo flow, while accurately identifying prohibited materials, adulterated components, or materials that do not meet standards.

[0003] Existing material feature detection technologies have many limitations and cannot simultaneously meet the requirements of low-damage, accurate, efficient and global distribution analysis, thus exhibiting significant limitations.

[0004] Destructive testing methods, such as chemical titration and metallographic sectioning, can achieve precise component analysis, but they have long testing cycles and are complex to operate. They are completely unsuitable for finished products, valuable materials, or on-site rapid inspection scenarios, and cannot match the pace of efficient cross-border material flow.

[0005] Non-destructive testing techniques, such as infrared spectroscopy and Raman spectroscopy, can achieve component analysis, but they are easily affected by the surface condition of the sample and the detection range is limited to local points, which cannot reflect the overall composition distribution of the material and poses a risk of misjudgment of "local compliance but overall adulteration". X-ray fluorescence detection can shorten the detection time, but it has problems such as insufficient detection accuracy and low sensitivity to light elements, making it difficult to meet the accurate identification requirements in high-demand scenarios. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a low-destruction, high-precision material feature detection system.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0008] A material feature detection system based on pulsed current, the key features of which include a high-voltage pulse generation module, a multi-dimensional synchronous data acquisition module, a data processing module and a human-computer interaction module;

[0009] The high-voltage pulse generation module applies voltage to the material under test in various boosting methods and monitors the current change in real time. It judges the breakdown state by the rate of current change and cuts off the output voltage when breakdown occurs.

[0010] The multi-dimensional synchronous data acquisition module collects information on voltage, current, material temperature, emission spectrum, and ambient temperature and humidity of the material under test during the voltage boosting process.

[0011] The data processing module constructs a material standard library, which includes the emission spectrum, breakdown voltage, corresponding environmental parameters, and corresponding thickness of various materials.

[0012] Then, the data collected by the multi-dimensional synchronous data acquisition module is compared with the data under the corresponding environmental parameters in the material standard library to determine the material information of the material to be tested; by combining the material information output by the data processing module with the interpolation algorithm, a material distribution heat map is generated to obtain the material distribution information of various materials;

[0013] The human-computer interaction module displays the material information and material distribution heat map of the material under test.

[0014] Preferably, the high-voltage pulse generation module energizes the material under test using multiple voltage boosting methods, including linear voltage boosting, step voltage boosting, and fuzzy PI adaptive voltage boosting.

[0015] In the linear boost mode, the voltage is gradually increased according to a preset boost rate; in the step boost mode, a pulse voltage with a preset increment is used to apply voltage to the material under test; in the fuzzy PI adaptive boost mode, the boost rate is dynamically adjusted according to the current increase.

[0016] The first detection step size is used as the spacing between adjacent detection points for array-type power supply.

[0017] Preferably, the data processing module preprocesses the data, reduces noise in the voltage signal, corrects the ambient temperature and humidity data, and standardizes the data.

[0018] Then, the standardized data and the testing points are correlated to obtain the testing point data set.

[0019] Preferably, the data processing module imports the detection point data group, matches the environmental benchmark with the environmental data, and compares the breakdown voltage data of the detection point with the thickness of the material at the detection point. At the same time, it calculates the thermal response characteristic parameters of the material under test by combining the power-on time, real-time voltage data, current data, and the difference between the ambient temperature and the material temperature at the detection point, and then initially screens out the first candidate material group that meets the conditions.

[0020] Then, the emission spectrum characteristic peaks of the detection point are matched with the standard emission spectrum of the first candidate material group; if the similarity between the standard emission spectrum of the first candidate material and the emission spectrum characteristic peaks of the detection point is lower than a preset threshold, the candidate material is excluded and the next material is matched again.

[0021] By combining voltage matching degree and emission spectrum matching degree, the confidence level is calculated. If the confidence level is higher than the predetermined standard, the material information is output. If the confidence level is lower than the predetermined standard, it is marked as pending verification and the material information and the information pending verification are output.

[0022] Preferably, the data processing module integrates the coordinate information and material determination results of all detection points to construct a coordinate-material mapping table; and uses an inverse distance weighted interpolation algorithm to interpolate the material and breakdown voltage data of discrete detection points to generate a continuous first material distribution heat map.

[0023] Furthermore, based on the first material distribution heatmap, a second identification is performed in the material transition region;

[0024] The rule for determining the conversion area is that when the material types of adjacent detection points are different, or when the confidence level of the material at the same detection point is pending verification, it is marked as a conversion area.

[0025] The coordinate data of the conversion area is sent back to the high-voltage pulse generation module for a second power-on, and at the same time, the multi-dimensional synchronous data acquisition module performs a second data acquisition.

[0026] The second power-on of the conversion region is performed in a matrix manner with a second detection step size smaller than the first detection step size between the detection points in the first identification.

[0027] After the second power-on, the acquired data is also preprocessed to update the coordinate-material mapping table constructed in the first identification. Then, the inverse distance weight interpolation algorithm is used to combine the first material distribution heat map to generate the second material distribution heat map. Finally, the second material distribution heat map and the detection point data of each detection point are combined and output to the human-computer interaction module.

[0028] The beneficial effects of adopting the above technical solution are as follows:

[0029] This invention employs a high-voltage pulse generation module to immediately cut off the voltage when the material breaks down, thus avoiding permanent damage to the material. Three voltage boosting methods can be flexibly switched to adapt to different scenarios such as unknown materials and high-precision detection. At the same time, it combines multi-dimensional data acquisition and spectral feature matching to improve the accuracy of material identification and reduce the damage to the material under test.

[0030] In this invention, the data processing module generates a high-precision material distribution heat map by converting the region for secondary detection and data fusion. It accurately quantifies the boundaries of each material, clearly presents the heterogeneous regions and boundaries inside the material, provides comprehensive data support for material quality assessment, and improves the accuracy of material identification. Attached Figure Description

[0031] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0032] Figure 1 This is a schematic diagram of the structure of a material feature detection system based on pulse current proposed in this invention;

[0033] Figure 2 These are the characteristic emission spectra of the lambskins used to construct the standard library in the embodiments of this invention;

[0034] Figure 3 This is the characteristic emission spectrum of the white cardboard used to construct the standard library in the embodiments of this invention. Detailed Implementation

[0035] To make the above-mentioned objectives, features, and advantages of the present invention more apparent and understandable, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] The purpose of this invention is to overcome the shortcomings of the prior art and provide a material feature detection system based on pulse current. Through a high-voltage pulse generation module, a multi-dimensional synchronous data acquisition module, a data processing module, and a human-computer interaction module, it can achieve accurate and efficient detection of materials. Compared with traditional destructive testing, this invention only causes instantaneous breakdown at the microscale, without affecting the macroscopic integrity and usability of the material. At the same time, it accurately analyzes the internal material distribution of the material, providing a comprehensive and reliable technical solution for material testing.

[0037] A material feature detection system based on pulsed current, such as Figure 1 The system includes a high-voltage pulse generation module, a multi-dimensional synchronous data acquisition module, a data processing module, and a human-machine interaction module. These modules work together to form a closed-loop detection process of "pulse excitation - signal acquisition - data processing - result output." Because it uses voltage breakdown for information acquisition, this system is suitable for identifying insulating and semi-insulating materials such as leather, fur, polymer materials, wood products, natural materials, and composite materials.

[0038] I. High-voltage pulse generation module

[0039] The high-voltage pulse generation module is the core excitation unit of this system. Its main function is to provide pulse currents of various modes to the material under test, induce local controllable breakdown of the material, and ensure that the breakdown process does not cause permanent damage to the material, thus providing a stable and reliable excitation source for multi-dimensional signal acquisition.

[0040] (I) Multiple boosting methods design

[0041] The module adopts three boost modes: linear boost, step boost, and fuzzy PI adaptive boost. It can flexibly select or automatically switch according to the characteristics of the material to be tested, the detection accuracy requirements, and the detection scenario, to ensure that different types of materials can be effectively broken down, while ensuring the accuracy and efficiency of the detection.

[0042] Linear boost mode: In this mode, the voltage is continuously and smoothly increased step by step according to a preset boost rate. Its core function is to quickly screen the breakdown voltage range of materials. It is suitable for preliminary testing scenarios with high requirements for testing efficiency, and can obtain the approximate breakdown characteristics of materials in a short time, providing a reference for subsequent accurate testing. The advantage of the linear boost mode is that the boost process is continuous and uninterrupted, which can avoid instantaneous overload damage to materials caused by voltage abrupt changes. At the same time, it can quickly cover a wide voltage range, making it suitable for preliminary preliminary testing of unknown materials. In practical use, considering both efficiency and accuracy, the preset boost rate is generally 2-10kV / s.

[0043] Stepped voltage boost mode: In this mode, the module releases voltage to the object under test using pulse voltages in preset increments. After each pulse voltage release, a certain time interval is maintained before releasing the next voltage level. Its main function is to accurately capture the breakdown threshold of the material, suitable for scenarios with high detection accuracy requirements. The voltage increment of the stepped voltage boost is 0.2-2kV, and the interval time is 50-500ms. By stepping up the voltage and reserving the interval time, it can be ensured that the material's response signal can be fully collected and recorded after each voltage application, avoiding missed detection or distortion of the breakdown signal due to excessively rapid voltage changes, and improving the accuracy of breakdown voltage detection.

[0044] Fuzzy PI Adaptive Boost Mode: This mode combines the advantages of fuzzy control and proportional-integral (PI) control, dynamically adjusting the boost rate based on the real-time current signal increase acquired by the multi-dimensional synchronous data acquisition module. When no significant change in the current signal is detected, it indicates that the material is not yet close to the breakdown state, and the boost rate can be appropriately increased to improve detection efficiency. When a slight increase in the current signal is detected, it indicates that the material is close to the breakdown threshold, and the boost rate needs to be reduced to more precisely approximate the breakdown voltage. When a sudden increase in current exceeds the preset threshold, it is determined that the material has broken down, and the output voltage is immediately cut off to avoid continuous discharge causing damage to the material. The core function of the fuzzy PI adaptive boost mode is to achieve intelligent adaptive adjustment of the boost process, balancing detection efficiency and accuracy, while effectively protecting the material and detection equipment. It is suitable for the detection of various materials with unknown or complex properties.

[0045] In actual testing, before breakdown, the rapid approach voltage rises at a rate of 200 V / s.

[0046] Current monitoring: The initial leakage current is about 2 μA, caused by leakage and polarization current on the surface of the material under test. At this time, the rate of change of current is close to 0.

[0047] When the voltage rises to 900 V, the current slowly increases from 2 μA to 5 μA, with a change rate of about 1.0 μA / s, which is in the small increase range. At this point, the boost rate will be reduced to 50 V / s.

[0048] When the voltage slowly rises to 1.86 kV, the current jumps instantaneously from 50 μA to more than 2 mA, with a rate of change greater than 200 μA / s. At this point, the system stops high-voltage output, the high voltage is quickly shut off and discharged, and the breakdown voltage is recorded as 1.86 kV.

[0049] (ii) Array-type power control

[0050] When the high-voltage pulse generation module detects the material under test, it applies power in a square array, using the first detection step size as the spacing between adjacent detection points. The first detection step size is set according to the detection range, detection efficiency, and preliminary detection accuracy requirements.

[0051] The purpose of array-type energization is to achieve full-area coverage detection of the material under test, ensuring that the detection points are evenly distributed on the material surface. This avoids deviations in material distribution analysis caused by uneven distribution of detection points, laying the foundation for the subsequent generation of a comprehensive and accurate material distribution heat map. Through array-type energization, breakdown response signals from different regions of the material can be systematically acquired, reflecting the overall characteristics and regional differences of the material.

[0052] (III) Breakdown Protection Mechanism

[0053] When the multi-dimensional synchronous data acquisition module detects a sudden increase in current exceeding a preset threshold, the high-voltage pulse generation module immediately cuts off the output voltage. The core function of this protection mechanism is to prevent continuous high-voltage pulse input from causing material burn-out, carbonization, or structural damage after material breakdown, thus ensuring the minimal damage during the testing process; at the same time, it prevents continuous discharge from causing overcurrent damage to the testing equipment, ensuring the safe and stable operation of the equipment.

[0054] II. Multi-dimensional synchronous data acquisition module

[0055] The multi-dimensional synchronous data acquisition module is a key unit for acquiring material characteristic information. Its core function is to simultaneously acquire the voltage, current, emission spectrum, and environmental information of the material under test during the voltage boosting and breakdown process of the high-voltage pulse generation module, providing comprehensive, synchronous, and reliable raw data support for the data processing module.

[0056] The design of each acquisition channel is as follows:

[0057] Voltage signal acquisition: A high-voltage probe with a range of 0-50kV is used to acquire voltage signals during the voltage boost and breakdown processes, ensuring accurate capture of the voltage change waveform at the moment of breakdown and obtaining key parameters such as the breakdown voltage threshold. The voltage signal directly reflects the insulation characteristics of the material and is one of the core parameters for material identification.

[0058] Current signal acquisition: Current signals are acquired using a micro-current sampling device, recording dynamic characteristics such as peak current and rate of change of current. The current signal reflects the movement characteristics of charge carriers during material breakdown, determining whether the material has broken down.

[0059] Emission spectral signal acquisition: The emission spectral signal generated when the material breaks down is acquired by using a fiber optic probe in conjunction with a grating spectrometer. When the material breaks down, a high-temperature plasma is formed, and different elements or functional groups emit spectral characteristic peaks of specific wavelengths. The emission spectral signal is a "fingerprint feature" for identifying the material composition and type, enabling accurate determination of the elemental composition of the material.

[0060] Environmental information acquisition: Temperature and humidity data of the detection environment are collected by temperature and humidity sensors. The environmental information is used to correct the interference of environmental factors on the material testing results, and to ensure the consistency and reliability of the test results under different environmental conditions.

[0061] III. Data Processing Module

[0062] The data processing module is the intelligent core unit of this system. Its core function is to preprocess, analyze, match and fuse the raw data collected by the multi-dimensional synchronous data acquisition module, build a material standard library, realize material identification, secondary detection of conversion area and generation of material distribution heat map, and finally output accurate detection results.

[0063] (a) Data preprocessing

[0064] Data preprocessing is a fundamental step in ensuring the accuracy of test results. It removes noise and interference from the raw data, corrects for the influence of environmental factors, and standardizes the data to provide high-quality data for subsequent material matching and analysis. The specific steps are as follows:

[0065] Signal denoising: Wavelet threshold denoising algorithm is used to process voltage and current signals to eliminate power frequency interference, circuit noise and electromagnetic interference caused by high voltage pulses, and retain the true transient characteristics at the moment of breakdown; baseline correction and smoothing are performed on the emission spectrum signal to eliminate the influence of ambient light and instrument noise, and highlight the contour and intensity of characteristic element peaks.

[0066] Environmental and Temperature Correction: The system utilizes collected environmental temperature and humidity data, along with temperature signals, to specifically correct the breakdown voltage data. Temperature correction employs a linear correction algorithm, using 25℃ as the standard temperature. The breakdown voltage is adjusted according to a preset correction coefficient based on the difference between the actual measured temperature and the standard temperature. Humidity correction addresses high-humidity environments by using exponential or linear correction algorithms to eliminate the impact of moisture absorption on insulation properties and ensures the corrected voltage data matches the environmental baseline in the material standard library.

[0067] In practice, different parameters are used depending on the temperature and humidity characteristics of different materials.

[0068] Temperature correction: A linear correction algorithm is used, with a temperature coefficient α = 0.005℃, meaning that for every 1℃ deviation of the temperature from the standard, the breakdown voltage changes by 0.5%.

[0069] Humidity correction: To address the strong hygroscopic properties of leather, a linear correction algorithm is used with a correction factor of 0.008, meaning that for every 1% deviation of the relative humidity from the standard, the breakdown voltage changes by 0.8%.

[0070] Furthermore, by combining real-time voltage and current data with the difference between the ambient temperature and the material temperature at the detection point, thermal response characteristic indices are calculated, such as the equivalent specific heat capacity. The formula for calculating the equivalent specific heat capacity is: C=Q / (mΔT), where C is the specific heat capacity, Q is the electrical energy consumed, m is the mass of the heated area, and ΔT is the temperature change of the material under energy input.

[0071] Data standardization: The preprocessed voltage, current, emission spectrum and environmental data are standardized in format and dimension, and the coordinate information of each detection point is associated to generate a detection point data group of "coordinate-multi-dimensional parameters". This ensures the consistency and comparability of the data and provides standardized data units for subsequent material matching, mapping table construction and heat map generation.

[0072] (II) Construction and Maintenance of Material Standard Library

[0073] The material standard library is the core foundation for achieving accurate material identification. Its function is to store the characteristic parameters and environmental benchmark data of various known materials, providing a reference for the comparison and matching of test data.

[0074] The core data dimensions of the standard library are as follows: Each standard data entry contains three elements: "material type - characteristic parameters - environmental benchmark". The material type covers common test objects; the characteristic parameters include the material's emission spectrum characteristics, breakdown voltage range, peak current range, specific heat capacity, and other core identification parameters under standard conditions; the environmental benchmark records the correction values ​​of characteristic parameters under different environmental temperatures and humidity levels, supporting accurate matching of cross-environment data.

[0075] The standard library is dynamically updated: It supports importing standard data for new materials through the human-computer interaction module. The system automatically completes data format adaptation, classification and storage and index construction without manual modification of the underlying program, and is easy to expand. The original standard parameters are regularly optimized and adjusted based on laboratory calibration data and actual test feedback data to correct parameter deviations, improve the accuracy of material matching and ensure the timeliness and reliability of the standard library.

[0076] (III) Material Information Determination

[0077] Material information determination is the core function of the data processing module. It involves comparing the pre-processed detection point data set with a material standard library across multiple dimensions, calculating the confidence level using voltage matching and emission spectrum matching, and ultimately outputting accurate material information. The specific steps are as follows:

[0078] Environmental benchmark matching: The environmental temperature and humidity data in the test point data group are compared with the environmental benchmarks in the material standard library. The standard environmental parameters and corresponding characteristic parameter correction values ​​that are closest to the current test environment are selected to ensure that subsequent comparisons are carried out under the same environmental benchmarks and to eliminate matching errors caused by environmental differences.

[0079] Preliminary voltage screening: The corrected breakdown voltage is extracted from the test point data set and compared with the breakdown voltage range of each material in the material standard library under the corresponding environmental benchmark. Materials with breakdown voltages within this range are selected to form the first candidate material group. The purpose of preliminary voltage screening is to quickly narrow down the material matching range, eliminate obviously mismatched materials, and improve the efficiency of subsequent matching. Specifically, the breakdown voltage is corrected based on the material thickness at the test points. At typical thicknesses, the breakdown voltage and thickness have a linear relationship; therefore, a linear method is used to correct the breakdown voltage for thickness to match the first candidate material group.

[0080] Emission spectrum matching: Extract the characteristic peaks of the emission spectrum from the detection point data set and compare them one by one with the standard emission spectra of each material in the first candidate material group. If the similarity between the characteristic peaks of the standard emission spectrum of a candidate material and the emission spectrum of the detection point is lower than a preset threshold, the candidate material is excluded, and the matching continues to the next material; if the similarity is higher than the preset threshold, the candidate material is retained and proceeds to the confidence calculation stage. The purpose of accurate emission spectrum matching is to use spectral "fingerprint features" to achieve accurate material differentiation and avoid misjudgment caused by overlapping breakdown voltage ranges of different materials; in the embodiment, such as... Figure 2-3 To obtain the spectra of lambskin and white cardboard using a spectrometer and to construct a standard library, it was found that the characteristic peak wavelengths and characteristic peak intensities of the two materials are significantly different.

[0081] Specifically, the similarity of emission spectra is quantified through wavelength matching and intensity matching, calculated using the following formula:

[0082] Wavelength matching score formula:

[0083]

[0084] in The first point in the emission spectrum of the detection point The actual measured wavelength of each characteristic peak; Candidate materials in the standard library The theoretical wavelength of each characteristic peak; : No. The maximum permissible wavelength deviation of each characteristic peak is adjusted according to the material's spectral resolution requirements.

[0085] when When the wavelength score of a single peak is calculated according to the formula, if it exceeds the allowable deviation, the score of the single peak is 0, and the wavelength of the characteristic peak is directly determined to be mismatched.

[0086] Strength matching score formula:

[0087]

[0088] in The first point in the emission spectrum of the detection point The actual measured intensity of each characteristic peak; Candidate materials in the standard library The normal intensity range of each characteristic peak; : No. The standard intensity average of each characteristic peak; when the detected intensity is within the standard range, the single peak intensity score is calculated based on the deviation rate from the standard average value; if it exceeds the range, the single peak score is 0, and the intensity of the characteristic peak is directly determined to be mismatched.

[0089] Confidence Calculation and Output: Combining voltage matching and emission spectrum matching, a weighted algorithm is used to calculate the material determination confidence level, with weights evenly distributed. Voltage matching is scored based on the deviation of the breakdown voltage from the standard range; the smaller the deviation, the higher the score. Specifically, S = 100 × max(0, 1 − k * D), where S is the voltage matching score, D is the deviation (the ratio of the difference between the breakdown voltage and the standard range boundary to the standard voltage range boundary), and K is the penalty coefficient, set according to industry standards. Emission spectrum matching uses wavelength matching and intensity matching scores as standards; higher scores indicate higher similarity. The emission spectrum matching is represented by the average of the two scores. When the confidence level is higher than the predetermined standard, the final material information is output; when the confidence level is lower than the predetermined standard, it is marked as "pending verification," and preliminary material information and a "pending verification" prompt are output to provide a basis for subsequent secondary testing. The confidence level, as a measure of the system's accuracy, can be adjusted according to the actual application environment requirements. The purpose of confidence calculation is to quantify the reliability of material determination, ensure the credibility of the output results, and identify low-confidence detection points to support the accurate location of material conversion areas.

[0090] (iv) Material distribution analysis and secondary testing

[0091] The core function of material distribution analysis is to generate a material distribution heatmap by integrating the coordinates of all detection points and the material determination results. This accurately identifies material transition areas and performs secondary detection, ultimately outputting comprehensive and accurate material distribution information. The specific steps are as follows:

[0092] Coordinate-Material Mapping Table Construction: Integrating the coordinate information of all detection points from the first detection with the material judgment results (including material information with confidence levels higher than the established standard and information marked as "to be verified"), a three-dimensional "coordinate-material-confidence" mapping table is constructed. This mapping table is the basic data structure for material distribution analysis, clearly recording the location of each detection point and its corresponding material information, providing data support for subsequent heatmap generation and conversion area identification.

[0093] The first material distribution heatmap is generated using an inverse distance weighted interpolation algorithm to interpolate the material and breakdown voltage data of discrete detection points in the 3D mapping table. The core principle of this algorithm is that the attribute value of an unknown point is obtained by a weighted average of the attribute values ​​of its surrounding known points. The weight is inversely proportional to the distance from the unknown point to the known points; the closer the distance, the greater the weight. This algorithm converts discrete detection point data into a continuous material distribution image, generating the first material distribution heatmap. Different material types are marked with different colors in the heatmap, and the color intensity helps to represent the differences in breakdown voltage, visually displaying the overall material distribution trend and providing a visual basis for preliminary judgment of material homogeneity and heterogeneous regions.

[0094] Material transition region identification: Based on the first material distribution heatmap and 3D mapping table, material transition regions are automatically identified. The rules for determining transition regions are as follows: when adjacent detection points (horizontal and vertical) have different material types, it indicates the existence of a material boundary in the region, and it is marked as a transition region; when the material confidence level of the same detection point is "to be verified," it indicates that the reliability of the material determination at that point is insufficient, and it may be in a material transition region, and it is also marked as a transition region. The purpose of transition region identification is to accurately locate areas in the material where the material changes or the determination is uncertain, providing a clear target range for secondary detection and avoiding problems such as blurred material boundaries or missed detections caused by a large step size in the first detection.

[0095] Secondary detection parameter adaptation and data acquisition: The identified transition region coordinate data is fed back to the high-voltage pulse generation module to initiate the second detection. To improve the detection accuracy of the transition region, the second detection uses a smaller step size than the first detection step size for array-type energization, increasing the detection point density in the transition region and ensuring accurate capture of material gradient details and boundary positions. Simultaneously, the multi-dimensional synchronous data acquisition module initiates the second data acquisition, maintaining consistent acquisition parameters with the first to ensure data comparability. To address the potential mixing characteristics of the material in the transition region, parameters such as spectral integration time can be appropriately adjusted to improve the sensitivity of characteristic peak identification.

[0096] Data fusion and generation of the second material distribution heatmap: After undergoing the same data preprocessing procedure as the first detection, the data acquired in the second detection is used to update the "coordinate-material-confidence" 3D mapping table constructed in the first detection, replacing the original low-confidence or erroneous data in the transformation area. Subsequently, an inverse distance weighted interpolation algorithm is used to combine the global data of the first material distribution heatmap with the accurate data of the transformation area from the second detection to generate the second material distribution heatmap. The second material distribution heatmap retains the advantage of full coverage from the first detection while improving the detection accuracy of the transformation area through the second detection. The material boundaries are clearer and the distribution is more accurate, which can accurately reflect the material distribution inside the material.

[0097] Material area ratio calculation: Based on the second material distribution heat map, the image recognition algorithm is used to accurately segment different material regions and count the pixel area of ​​each material region; combined with the actual physical size of the detection range and the pixel density of the heat map, the pixel area is converted into the actual area; then the area ratio of each material is calculated, and the quantitative results are output to provide data support for the overall material quality assessment.

[0098] IV. Human-Computer Interaction Module

[0099] The human-computer interaction module is the core unit for information exchange between the system and the user. Its core function is to provide users with a convenient operating interface, while intuitively displaying the detection process and results, and realizing the storage, query and export of detection data.

[0100] The test results are displayed visually, including: a second material distribution heatmap, with different material areas marked by distinct colors and clearly visible material boundaries; detailed data sets for each test point, including raw and preprocessed data such as voltage, current, temperature, and emission spectrum; material determination results, including material type and confidence level for each area; statistical data on the area proportion of each material; and the location of the converted area and comparison results of the secondary test. Users can switch between different display contents through the interface to fully understand the test results.

[0101] The alarm and prompt function ensures that, in actual use, when equipment failure, abnormal test data, or generally low material confidence occurs during the testing process, the module immediately triggers an audible and visual alarm and displays specific alarm information on the interface, thus ensuring the smooth progress of the testing process and the reliability of the test results.

[0102] V. System Collaboration Workflow

[0103] The overall collaborative workflow of the pulsed current-based material feature detection system of this invention is as follows:

[0104] Test preparation: The user fixes the material to be tested onto the sample clamping device, sets the test parameters (such as the boosting method, first test step size, confidence threshold, etc.) through the human-computer interaction module, and starts the test process.

[0105] First test: The high-voltage pulse generation module energizes the material under test in a square array according to the set voltage boosting method and the first test step size; the multi-dimensional synchronous data acquisition module synchronously collects voltage, current, temperature, emission spectrum and environmental information, and transmits the raw data to the data processing module.

[0106] First data processing: The data processing module preprocesses the raw data, constructs a coordinate-material mapping table, compares it with the material standard library, generates the first material distribution heat map, and identifies material conversion areas.

[0107] Second detection: The data processing module feeds back the coordinates of the conversion area to the high-voltage pulse generation module, which then performs a matrix-style energization of the conversion area with a second detection step size; the multi-dimensional synchronous data acquisition module simultaneously collects the secondary detection data and transmits it to the data processing module.

[0108] Data fusion and result generation: The data processing module preprocesses the secondary detection data, updates the coordinate-material mapping table, merges the first and second detection data to generate a second material distribution heat map, and calculates the area ratio of each material.

[0109] Results display and storage: The human-computer interaction module displays information such as the heat map of the second material distribution, material determination results, and area ratio, generates a test report, and completes the test.

[0110] In practical applications, material testing of lambskin and white cardboard is conducted.

[0111] First, set up the testing environment and testing objects. Lambskin sample: size 20cm×15cm, thickness 2.52mm, surface free of stains and damage, cut into a regular rectangle; white cardboard sample: size 20cm×15cm, thickness 2.45mm, uncoated.

[0112] Testing environment temperature: 25℃; Ambient humidity: 50%RH; Environmental correction factor: Temperature and humidity are within the standard range, no additional correction is required.

[0113] II. Testing Process

[0114] As shown in Table 1, a fuzzy PI adaptive boost was used for the lambskin sample. The initial boost rate was 5 kV / s. When the current change rate was <0.05 A / ms, the boost rate was increased to 7.5 kV / s. When the current change rate was ≥0.10 A / ms, it was reduced to 1.5 kV / s. When the sample broke down, the current change rate was 2.5 A / ms, which immediately triggered the power-off protection. The measured breakdown voltage was 8.14 kV.

[0115] For the white cardboard sample, a linear voltage increase method was used with a voltage increase rate of 4 kV / s. The current change rate at the time of sample breakdown was 2.3 A / ms, and the breakdown voltage was measured to be 12.05 kV.

[0116] Table 1. Permeability data of lambskin and white cardboard

[0117] Detection object Breakdown voltage Peak current Material temperature after breakdown Lambskin 8.14kV 0.89A 32.9℃ White cardstock 12.05kV 1.12A 30.2℃

[0118] In the emission spectrum of lambskin, the characteristic spectral line for sulfur (S) in the disulfide bonds of lambskin keratin is 257.5 nm. Keratin contains numerous peptide bonds (-CO-NH-), and the emission line for nitrogen (N) in the sample is 746.8 nm. CN bonds show high intensity at 388.30 nm and 419.70 nm. The addition of CaCO3 filler to white cardboard results in Ca peaks at 393.3 nm and 396.745 nm becoming core characteristics. Lambskin contains no artificial additives; its elemental composition is determined by its biological structure. Precise material identification can be achieved through differential matching of spectral characteristic peaks.

[0119] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A material feature detection system based on pulsed current, characterized in that, It includes a high-voltage pulse generation module, a multi-dimensional synchronous data acquisition module, a data processing module, and a human-computer interaction module; The high-voltage pulse generation module applies voltage to the material under test in various boosting methods and monitors the current change in real time. It judges the breakdown state by the rate of current change and cuts off the output voltage when breakdown occurs. The multi-dimensional synchronous data acquisition module collects information on voltage, current, material temperature, emission spectrum, and ambient temperature and humidity of the material under test during the voltage boosting process. The data processing module constructs a material standard library, which includes the emission spectrum, breakdown voltage, corresponding environmental parameters, and corresponding thickness of various materials. Then, the data collected by the multi-dimensional synchronous data acquisition module is compared with the data under the corresponding environmental parameters in the material standard library to determine the material information of the material to be tested; by combining the material information output by the data processing module with the interpolation algorithm, a material distribution heat map is generated to obtain the material distribution information of various materials; The human-computer interaction module displays the material information and material distribution heat map of the material under test.

2. The material feature detection system based on pulsed current according to claim 1, characterized in that, The high-voltage pulse generation module powers the material under test using various boosting methods, including linear boosting, step boosting, and fuzzy PI adaptive boosting. In the linear boost mode, the voltage is gradually increased according to a preset boost rate; in the step boost mode, a pulse voltage with a preset increment is used to apply voltage to the material under test; in the fuzzy PI adaptive boost mode, the boost rate is dynamically adjusted according to the current increase. The first detection step size is used as the spacing between adjacent detection points for array-type power supply.

3. The material feature detection system based on pulsed current according to claim 1, characterized in that, The data processing module preprocesses the data, reduces noise in the voltage signal, corrects the ambient temperature and humidity data, and standardizes the data. Then, the standardized data and the testing points are correlated to obtain the testing point data set.

4. The material feature detection system based on pulsed current according to claim 1, characterized in that, The data processing module imports the detection point data group, matches the environmental data with the environmental benchmark, and compares the breakdown voltage data of the detection point with the thickness of the material at the detection point. At the same time, it calculates the thermal response characteristic parameters of the material under test by combining the power-on time, real-time voltage data, current data, and the difference between the ambient temperature and the material temperature at the detection point, and then initially screens out the first candidate material group that meets the conditions. Then, the emission spectrum characteristic peaks of the detection point are matched with the standard emission spectrum of the first candidate material group; if the similarity between the standard emission spectrum of the first candidate material and the emission spectrum characteristic peaks of the detection point is lower than a preset threshold, the candidate material is excluded and the next material is matched again. By combining voltage matching degree and emission spectrum matching degree, the confidence level is calculated. If the confidence level is higher than the predetermined standard, the material information is output. If the confidence level is lower than the predetermined standard, it is marked as pending verification and the material information and the information pending verification are output.

5. The material feature detection system based on pulsed current according to claim 1, characterized in that, The data processing module integrates the coordinate information and material determination results of all detection points to construct a coordinate-material mapping table; it uses an inverse distance weighted interpolation algorithm to interpolate the material and breakdown voltage data of discrete detection points to generate a continuous first material distribution heat map. Furthermore, based on the first material distribution heatmap, a second identification is performed in the material transition region; The rule for determining the conversion area is that when the material types of adjacent detection points are different, or when the confidence level of the material at the same detection point is pending verification, it is marked as a conversion area. The coordinate data of the conversion area is sent back to the high-voltage pulse generation module for a second power-on, and at the same time, the multi-dimensional synchronous data acquisition module performs a second data acquisition. The second power-on of the conversion region is performed in a matrix manner with a second detection step size smaller than the first detection step size between the detection points in the first identification. After the second power-on, the acquired data is also preprocessed to update the coordinate-material mapping table constructed in the first identification. Then, the inverse distance weight interpolation algorithm is used to combine the first material distribution heat map to generate the second material distribution heat map. Finally, the second material distribution heat map and the detection point data of each detection point are combined and output to the human-computer interaction module.

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