Intelligent Methods, Systems and Equipment for Chemical Composition Analysis and Performance Prediction of Die Steel

Abstract

The present invention relates to the technical field of component analysis, and particularly to an intelligent method, system and device for chemical composition analysis and performance prediction of die steel. By laser-exciting die steel to obtain plasma and analyzing its spectral signals, the present invention can detect elements at extremely low concentrations and can also analyze multiple elements in a sample simultaneously. Compared with the chemical titration method, it not only saves time but also improves the analysis efficiency. By comparing multiple characteristic spectral lines, elements can be identified more accurately. Using multiple spectral lines can improve the detection sensitivity for low-concentration elements. The data of multiple characteristic spectral lines can be used for more precise quantitative analysis to provide more reliable element concentration information. By establishing a concentration-signal intensity relationship model by obtaining the spectral signal intensity at multiple preset concentrations, the spectral signal intensity can be more precisely converted into an element concentration, the complex spectral data can be converted into an intuitive concentration value, and the accuracy and reliability of quantitative analysis can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of component analysis, and particularly to an intelligent method, system and device for analyzing the chemical composition and predicting the performance of die steel. Background Art

[0002] Die steel is the steel used to manufacture cold stamping dies and die-casting dies. Dies are the main processing tools in machinery manufacturing, radio instruments, motors, electrical appliances, industrial production, etc. The quality of dies directly affects the quality of the pressure processing technology, the precision and output of products, and the production cost. Since cold stamping dies need to withstand impact loads, and nickel and manganese can increase toughness, die steel with a certain content of nickel and manganese is suitable for manufacturing cold stamping dies. While die-casting dies have high wear resistance requirements, and high carbon content and chromium, molybdenum can provide high hardness and strength. Therefore, die steel with high carbon content and a certain content of chromium and molybdenum is suitable for manufacturing die-casting dies.

[0003] Currently, the main method for analyzing the chemical composition of die steel is chemical titration. Its principle is to cut and crush the sample into small particles and dissolve them in an acidic medium to an appropriate concentration, and then select a titrant for titration detection and analysis. However, the above method requires selecting a suitable titrant according to the analyzed elemental composition. Multiple elemental compositions may require selecting multiple titrants for multiple detections, which is time-consuming and laborious. Therefore, it is necessary to study an intelligent method for analyzing the chemical composition and predicting the performance of die steel to analyze the chemical composition of die steel, and then predict and evaluate the die performance, so as to facilitate selecting whether to use it for manufacturing cold stamping dies or die-casting dies according to its performance. Summary of the Invention

[0004] The main object of the present invention is to provide an intelligent method for analyzing the chemical composition and predicting the performance of die steel, aiming to solve the technical problems in the prior art.

[0005] The present invention proposes an intelligent method for analyzing the chemical composition and predicting the performance of die steel, including:

[0006] Exciting the die steel by laser to obtain plasma;

[0007] Obtaining a plurality of spectral signals of the plasma, and obtaining a spectrogram according to the plurality of spectral signals;

[0008] Obtaining a plurality of characteristic spectral lines according to the spectrogram, and obtaining the corresponding actual wavelength according to each characteristic spectral line;

[0009] Comparing each actual wavelength with the elemental characteristic spectral line database to obtain the element corresponding to the actual wavelength;

[0010] Obtain a concentration-signal intensity curve graph, and obtain the actual concentration value corresponding to each element according to the actual wavelength and the concentration-signal intensity curve graph;

[0011] Predict the performance characteristic evaluation value of the die steel according to the actual concentration values of multiple elements, and classify the use of the die steel according to the performance characteristic evaluation value, wherein the performance characteristic evaluation value includes a wear resistance evaluation value and a toughness performance evaluation value.

[0012] Preferably, the step of obtaining a spectrogram according to the multiple spectral signals includes:

[0013] Use a spectrometer to convert each of the spectral signals into an initial electrical signal, and perform amplification processing on each of the initial electrical signals to obtain a corresponding analog electrical signal;

[0014] Use an analog-to-digital converter to obtain the voltage values of each analog electrical signal at multiple preset time points, and extract the maximum voltage value as the maximum input voltage;

[0015] Obtain the quantization level number of the analog-to-digital converter, and calculate the quantization step according to the ratio of the maximum input voltage to the quantization level number;

[0016] Calculate the corresponding discrete quantization level index according to the ratio of each voltage value to the quantization step;

[0017] Use a spectrometer to plot the multiple discrete quantization level indexes to obtain a spectrogram.

[0018] Preferably, the step of using a spectrometer to plot the multiple discrete quantization level indexes to obtain a spectrogram includes:

[0019] Obtain the minimum wavelength and the maximum wavelength of the spectrometer, and calculate the wavelength step according to the minimum wavelength, the maximum wavelength and the discrete quantization level index, wherein the calculation formula is:

[0020]

[0021] wherein, B(C) represents the wavelength step, Z(D) represents the maximum wavelength, Z(X) represents the minimum wavelength, and L(J) represents the quantization level number;

[0022] Calculate the corresponding wavelength according to the wavelength step, the minimum wavelength and the multiple discrete quantization level indexes, wherein the calculation formula is:

[0023]

[0024] wherein, A represents the wavelength, B(C) represents the wavelength step, Z(X) represents the minimum wavelength, and L(S) nrepresents the nth discrete quantization level index, where n represents the sequence number of the discrete quantization level index, and m represents the number of discrete quantization level indices;

[0025] Convert each of the discrete quantization level indices into a corresponding signal intensity;

[0026] Establish a spectral graph coordinate axis with wavelength as the X-axis and signal intensity as the Y-axis, and plot each signal intensity and the corresponding wavelength of each spectral signal on the spectral graph coordinate axis by a broken line to obtain a spectral graph.

[0027] Preferably, the step of obtaining a plurality of characteristic spectral lines according to the spectral graph and obtaining the corresponding actual wavelength according to each of the characteristic spectral lines includes:

[0028] Extract a plurality of peaks of each broken line in the spectral graph to obtain a plurality of characteristic spectral lines;

[0029] Sort the plurality of characteristic spectral lines in ascending order to obtain a spectral line sorting table;

[0030] Select the characteristic spectral line ranked first from the spectral line sorting table as the detection spectral line;

[0031] Obtain the corresponding actual wavelength from the spectral graph according to the detection spectral line.

[0032] Preferably, the step of obtaining a concentration-signal intensity curve graph and obtaining the actual concentration value corresponding to each element according to the actual wavelength and the concentration-signal intensity curve graph includes:

[0033] Obtain the spectral signal intensities of the element corresponding to the actual wavelength at a plurality of preset concentrations;

[0034] Establish a concentration-signal intensity coordinate axis with the preset concentration as the X-axis and the spectral signal intensity as the Y-axis;

[0035] Mark each preset concentration and the corresponding spectral signal intensity as connection points on the concentration-signal intensity coordinate axis;

[0036] Connect the plurality of connection points by a curve to obtain a concentration-signal intensity curve graph;

[0037] Obtain the curve slope of the concentration-signal intensity curve graph, and obtain the actual signal intensity according to the product of the curve slope and the actual wavelength;

[0038] Match the actual signal intensity with the concentration-signal intensity curve graph to obtain the actual concentration value of each element.

[0039] Preferably, the step of predicting the performance characteristic evaluation value of die steel according to the actual concentration values of multiple elements and classifying the use of die steel according to the performance characteristic evaluation value includes:

[0040] Obtain the weight coefficient of the actual concentration value of each element, and perform weighted calculation according to the actual concentration value of each element and the corresponding weight coefficient to obtain the total performance score of the die steel;

[0041] Judge whether the total performance score is less than the preset performance score;

[0042] If the total performance score is less than the preset performance score, it is determined that the performance of the die steel is poor;

[0043] If the total performance score is not less than the preset performance score, obtain the standard concentration range of each element;

[0044] Judge whether the number of actual concentration values of multiple elements not within the corresponding standard concentration range is greater than the preset number;

[0045] If the number of actual concentration values of multiple elements not within the corresponding standard concentration range is greater than the preset number, it is determined that the performance of the die steel is poor;

[0046] If the number of actual concentration values of multiple elements not within the corresponding standard concentration range is not greater than the preset number, it is determined that the performance of the die steel is good, and extract the actual concentration values of carbon, chromium, molybdenum, nickel and manganese elements in the die steel;

[0047] Calculate the wear resistance evaluation value according to the actual concentration values of the carbon, chromium and molybdenum elements and their corresponding weight coefficients, where the calculation formula is:

[0048] N(X) = C * α + Cr * β + Mo * γ;

[0049] Among them, N(X) represents the wear resistance evaluation value, C represents the carbon element, α represents the weight coefficient of the carbon element, Cr represents the chromium element, β represents the weight coefficient of the chromium element, Mo represents the molybdenum element, and γ represents the weight coefficient of the molybdenum element;

[0050] Judge whether the wear resistance evaluation value is greater than the first preset value;

[0051] If the wear resistance evaluation value is greater than the first preset value, it is determined that the die steel is suitable for manufacturing die-casting dies;

[0052] Calculate the toughness performance evaluation value according to the actual concentration values of the nickel and manganese elements and their corresponding weight coefficients, where the calculation formula is:

[0053] R(X) = Ni * δ + Mn * ε;

[0054] Among them, R(X) represents the toughness performance evaluation value, Ni represents nickel element, δ represents the weight coefficient of nickel element, Mn represents manganese element, and ε represents the weight coefficient of manganese element;

[0055] Judge whether the toughness performance evaluation value is greater than the second preset value;

[0056] If the wear resistance evaluation value is greater than the second preset value, it is determined that the die steel is suitable for manufacturing cold stamping dies.

[0057] This application also provides an intelligent system for die steel chemical composition analysis and performance prediction, including:

[0058] A processing module for exciting the die steel by laser to obtain plasma;

[0059] A first acquisition module for acquiring a plurality of spectral signals of the plasma and obtaining a spectrogram according to the plurality of spectral signals;

[0060] A second acquisition module for acquiring a plurality of characteristic spectral lines according to the spectrogram and obtaining the corresponding actual wavelength according to each characteristic spectral line;

[0061] A comparison module for comparing each actual wavelength with an element characteristic spectral line database to obtain the element corresponding to the actual wavelength;

[0062] A third acquisition module for acquiring a concentration-signal intensity curve graph and obtaining the actual concentration value corresponding to each element according to the actual wavelength and the concentration-signal intensity curve graph;

[0063] A prediction module for predicting the performance characteristic evaluation value of the die steel according to the actual concentration values of multiple elements and classifying the use of the die steel according to the performance characteristic evaluation value, wherein the performance characteristic evaluation value includes a wear resistance evaluation value and a toughness performance evaluation value.

[0064] Preferably, the first acquisition module includes:

[0065] A conversion unit for converting each spectral signal into an initial electrical signal by using a spectrometer and performing amplification processing on each initial electrical signal to obtain a corresponding analog electrical signal;

[0066] An extraction unit for obtaining the voltage values of each analog electrical signal at multiple preset time points by using an analog-to-digital converter and extracting the maximum voltage value as the maximum input voltage;

[0067] A first calculation unit for obtaining the quantization level number of the analog-to-digital converter and calculating the quantization step according to the ratio of the maximum input voltage to the quantization level number;

[0068] A second calculation unit, configured to calculate corresponding discrete quantization level indexes according to the ratio of each of the voltage values to the quantization step size;

[0069] A plotting unit, configured to plot the plurality of discrete quantization level indexes by using a spectrometer to obtain a spectrogram.

[0070] The present invention also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the intelligent method for analyzing the chemical composition and predicting the performance of the die steel are implemented.

[0071] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the intelligent method for analyzing the chemical composition and predicting the performance of the die steel are implemented.

[0072] The beneficial effects of the present invention are as follows: By exciting the die steel with a laser to obtain a plasma and analyzing its spectral signals, the present invention can detect elements with extremely low concentrations, and can also analyze multiple elements in the sample simultaneously, including metal elements and non-metal elements. Compared with the chemical titration method, it not only saves time, but also improves the analysis efficiency. By comparing multiple characteristic spectral lines, elements can be identified more accurately, reducing the possibility of misidentification. Using multiple spectral lines can improve the detection sensitivity for low-concentration elements. The data of multiple characteristic spectral lines can be used for more accurate quantitative analysis, providing more reliable element concentration information. By obtaining the spectral signal intensity at multiple preset concentrations, an accurate concentration-signal intensity relationship model can be established, so as to convert the spectral signal intensity into the element concentration more precisely. Using the concentration-signal intensity curve graph can convert complex spectral data into intuitive concentration values, which can improve the accuracy and reliability of quantitative analysis. Determining the performance of the die steel according to the actual concentration value of each element, the concentration of different elements has a direct impact on the performance of the die steel. Accurately measuring these concentrations can predict the performance of the die steel more precisely, and can provide a detailed performance evaluation based on the actual concentration of the chemical composition, which is more accurate than relying solely on experience or standard specifications. Description of the Drawings

[0073] Figure 1 It is a schematic flowchart of the method according to an embodiment of the present invention.

[0074] Figure 2 It is a schematic structural diagram of the system according to an embodiment of the present invention.

[0075] Figure 3 It is a schematic internal structure diagram of the computer device according to an embodiment of the present application.

[0076] Figure 4 It is a schematic diagram of the spectrogram according to an embodiment of the present application.

[0077] The realization, functional characteristics, and advantages of the object of the present invention will be further described in conjunction with embodiments with reference to the accompanying drawings. Specific embodiments

[0078] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0079] As Figures 1-4 shown, the present application provides an intelligent method for analyzing the chemical composition and predicting the performance of die steel, including:

[0080] S1. Excite the die steel by laser to obtain plasma;

[0081] S2. Obtain a plurality of spectral signals of the plasma, and obtain a spectrogram according to the plurality of spectral signals;

[0082] S3. Obtain a plurality of characteristic spectral lines according to the spectrogram, and obtain the corresponding actual wavelength according to each characteristic spectral line;

[0083] S4. Compare each actual wavelength with the element characteristic spectral line database to obtain the element corresponding to the actual wavelength;

[0084] S5. Obtain a concentration-signal intensity curve, and obtain the actual concentration value corresponding to each element according to the actual wavelength and the concentration-signal intensity curve;

[0085] S6. Predict the performance characteristic evaluation value of the die steel according to the actual concentration values of a plurality of elements, and classify the use of the die steel according to the performance characteristic evaluation value, where the performance characteristic evaluation value includes a wear resistance evaluation value and a toughness performance evaluation value.

[0086] As described in the above steps S1 - S6, die steel is the steel used to manufacture molds such as injection molds, cold stamping dies, hot forging dies, die - casting dies, etc. Molds are the main processing tools in machinery manufacturing, radio instruments, motors, electrical appliances, industrial production, etc. The quality of the mold directly affects the quality of the pressure processing technology, the precision, output and production cost of the product. Therefore, it is necessary to analyze the chemical composition of the die steel to predict and evaluate the mold performance. At present, the main method for analyzing the chemical composition of die steel is the chemical titration method. Its principle is to cut and crush the sample into small particles and then dissolve it in an acidic medium to an appropriate concentration, and then select a titrant for titration detection and analysis. However, the above - mentioned method requires selecting a suitable titrant according to the analyzed element components. For multiple element components, multiple titrants may need to be selected for multiple detections, which is time - consuming and laborious. In the present invention, the die steel is excited by a laser to obtain a plasma, and a spectrogram is obtained based on multiple spectral signals of the plasma. Among them, the plasma is a gaseous substance composed of free electrons and charged ions. The free electrons in the plasma collide with atoms or molecules and generate characteristic spectra. Through these spectral signals, the chemical composition of the die steel can be analyzed. By using an appropriate excitation source, such as an arc, plasma or laser, it is ensured that all elements in the sample can be fully excited. Since the spectral signals generated by the plasma are very strong and highly sensitive, by exciting the die steel with a laser to obtain a plasma and analyzing its spectral signals, elements with extremely low concentrations can be detected, and multiple elements in the sample, including metal elements and non - metal elements, can be analyzed simultaneously. Compared with the chemical titration method, it not only saves time but also improves the analysis efficiency. For example, as Figure 4As shown, there are four complete broken lines in the spectrogram, and each broken line represents a different element. Multiple characteristic spectral lines of each element are obtained from the spectrogram, and the actual wavelength of each element is obtained based on the characteristic spectral lines. Then, the element corresponding to the actual wavelength is obtained by comparing each actual wavelength with the element characteristic spectral line database. By comparing multiple characteristic spectral lines, elements can be identified more accurately, reducing the possibility of misidentification. Using multiple spectral lines can improve the detection sensitivity for low-concentration elements because the intensities of different spectral lines may vary. Comprehensive analysis can enhance the detection ability. The data of multiple characteristic spectral lines can be used for more precise quantitative analysis, providing more reliable element concentration information. Moreover, since the characteristic spectral lines of each element have significant intensities at specific wavelengths, different from background noise, by selecting multiple characteristic spectral lines, the element signal and noise can be effectively distinguished. Therefore, by comparing multiple characteristic spectral lines, the interference of background noise on element identification can be reduced, improving the accuracy of the results. By obtaining the spectral signal intensities of the element corresponding to the actual wavelength at multiple preset concentrations and establishing a concentration-signal intensity curve graph based on the multiple spectral signal intensities and the corresponding preset concentrations of the element corresponding to the actual wavelength, and then obtaining the actual concentration value of each element according to the actual wavelength and the concentration-signal intensity curve graph. By obtaining the spectral signal intensities at multiple preset concentrations, an accurate concentration-signal intensity relationship model can be established, thus converting the spectral signal intensity into the element concentration more precisely. Using the concentration-signal intensity curve graph can convert complex spectral data into intuitive concentration values, improving the accuracy and reliability of quantitative analysis. The performance of die steel is determined according to the actual concentration value of each element. The concentrations of different elements have a direct impact on the performance of die steel (such as hardness, strength, wear resistance, etc.). Accurately measuring these concentrations can more precisely predict the performance of die steel, and a detailed performance evaluation can be provided based on the actual concentrations of chemical components, which is more accurate than relying solely on experience or standard specifications.

[0087] In one embodiment, step S2 of obtaining the spectrogram according to multiple of the spectral signals includes:

[0088] S21. The step of obtaining the spectrogram according to multiple of the spectral signals includes:

[0089] S22. Using a spectrometer to convert each of the spectral signals into an initial electrical signal and performing amplification processing on each of the initial electrical signals to obtain a corresponding analog electrical signal;

[0090] S23. Using an analog-to-digital converter to obtain the voltage values of each analog electrical signal at multiple preset time points and extracting the maximum voltage value as the maximum input voltage;

[0091] S24. Obtain the number of quantization levels of the analog-to-digital converter, and obtain the quantization step according to the ratio of the maximum input voltage to the number of quantization levels, where the calculation formula is:

[0092]

[0093] where L(C) represents the quantization step, D(Z) represents the maximum input voltage, and L(J) represents the number of quantization levels;

[0094] S25. Calculate the corresponding discrete quantization level index according to the ratio of each voltage value to the quantization step, where the calculation formula is:

[0095]

[0096] where L(S) i represents the i-th discrete quantization level index, D(Y) i represents the i-th voltage value, L(B) represents the quantization step, and i represents the serial number of the voltage value;

[0097] Use a spectrometer to plot the multiple discrete quantization level indices to obtain a spectrogram.

[0098] As described in the above steps S21 - S25, the present invention converts each spectral signal into an initial electrical signal by using a spectrometer, and amplifies each initial electrical signal to obtain a corresponding analog electrical signal. By amplifying the initial electrical signal, the intensity of weak spectral signals can be increased, enabling even low - concentration elements to be detected. After signal amplification, background noise and interference can be better overcome, and clearer spectral data can be obtained. The amplification process allows accurate acquisition of spectral data even in complex samples, especially suitable for the analysis of samples with multiple elements such as die steel. By amplifying the signal, measurement fluctuations caused by equipment fluctuations or environmental changes can be reduced, which helps improve the resolution and accuracy of the signal, thus facilitating more accurate identification and quantification of elemental compositions in the subsequent process. By using an analog - to - digital converter to obtain the voltage values of each analog electrical signal at multiple preset time points, and extracting the maximum voltage value as the maximum input voltage, by obtaining the quantization level number of the analog - to - digital converter, and obtaining the quantization step size through the ratio of the maximum input voltage to the quantization level number. Here, the quantization step size refers to the minimum unit or interval between each quantization level during the quantization process. In an analog - to - digital converter, the quantization level number represents the number of different amplitude levels used when the signal is discretized. Since the quantization step size determines the resolution of the data, a smaller step size can provide more precise voltage measurements, thus more accurately reflecting the spectral signal intensity. Through high - resolution quantization, weak signals can be better analyzed, and the detection ability for low - concentration elements can be improved. By means of an accurate quantization step size, errors in the analog - to - digital signal conversion process can be reduced, and the reliability of the data can be improved. After calculating the quantization step size, the voltage value of the analog electrical signal can be measured more precisely, thereby improving the measurement accuracy of the elemental concentration, which helps more accurate and consistent analysis of the chemical elemental composition of die steel in the subsequent process. By calculating the ratio of each voltage value to the quantization step size, the corresponding discrete quantization level index is calculated. Here, the discrete quantization level index refers to the index value used to identify the discretized data level during the quantization process. And by using a spectrometer to plot multiple discrete quantization level indexes, a spectrogram is obtained. Since the spectrogram can intuitively display the signal intensity of each discrete quantization level, facilitating the identification of spectral characteristics of different elements, and the discrete quantization level index can improve the spectral resolution, helping to identify and distinguish the spectral peaks of different elements in the chemical composition. Through accurate quantization level indexes, the signal peaks in the spectrogram can be more precisely located, thereby improving the measurement accuracy of the elemental concentration. Each quantization level on the spectrogram corresponds to a specific signal intensity, which can be used for quantitative analysis, facilitating the subsequent estimation of the concentration of each element in die steel. Moreover, discretizing the voltage values and plotting the spectrogram can reduce the influence of background noise and improve the accuracy of data processing. Among them, after calculating the corresponding discrete quantization level index according to the ratio of each voltage value to the quantization step size, since the calculated discrete quantization level index may not be an integer, that is, at this time, the discrete quantization level index does not exactly fall on the boundary of the quantization level.Therefore, it is necessary to round the discrete quantization level index at this time to obtain the discrete quantization level index in integer form. For example, when the maximum input voltage is 5V, a 12-bit analog-to-digital converter has a total of 4096 quantization levels. At this time, the quantization step size is calculated according to the formula. When the second voltage value is 2.7V, the second discrete quantization level index is calculated according to the formula at this time. Since 2213.11 is not an integer, 2213.11 needs to be rounded to 2213. Then the actual discrete quantization level index at this time should be 2213. It should be noted that the numbers 5, 12, 2213, 2213.11, etc. appearing above are all examples for clearly explaining the embodiments and are not limited uniquely here.

[0099] In one embodiment, the step S25 of using a spectrometer to plot the plurality of discrete quantization level indices to obtain a spectrogram includes:

[0100] S251. Obtain the minimum wavelength and the maximum wavelength of the spectrometer, and calculate the wavelength step size according to the minimum wavelength, the maximum wavelength, and the discrete quantization level index. The calculation formula is:

[0101]

[0102] where B(C) represents the wavelength step size, z(D) represents the maximum wavelength, Z(X) represents the minimum wavelength, and L(J) represents the number of quantization levels;

[0103] S252. Calculate the corresponding wavelengths according to the wavelength step size, the minimum wavelength, and the plurality of discrete quantization level indices. The calculation formula is:

[0104]

[0105] where A represents the wavelength, B(C) represents the wavelength step size, Z(X) represents the minimum wavelength, and L(S) n represents the nth discrete quantization level index, n represents the serial number of the discrete quantization level index, and m represents the number of discrete quantization level indices;

[0106] S253. Convert each of the discrete quantization level indices into the corresponding signal intensity;

[0107] S254. Establish a spectrogram coordinate axis with the wavelength as the X-axis and the signal intensity as the Y-axis, and plot each signal intensity and the corresponding wavelength of each spectral signal on the spectrogram coordinate axis through a broken line to obtain a spectrogram.

[0108] As described in the above steps S251 - S254, the present invention calculates the wavelength step by obtaining the minimum wavelength and the maximum wavelength of the spectrometer and based on the minimum wavelength, the maximum wavelength, and the discrete quantization level index. Here, the wavelength step refers to the wavelength interval selected when measuring spectral data in spectral analysis. By calculating the wavelength step, it can ensure uniform sampling between the minimum wavelength and the maximum wavelength, reduce data errors caused by non-uniform sampling covering the entire spectral range, so as to comprehensively analyze all elements in the die steel. The wavelength step calculated according to the discrete quantization level index can provide appropriate resolution, help identify and analyze subtle differences in the spectrum, and improve data accuracy. Then, the corresponding wavelengths are calculated through the wavelength step, the minimum wavelength, and each discrete quantization level index. Each wavelength calculated through the wavelength step, the minimum wavelength, and multiple discrete quantization level indexes corresponds to a specific position on the spectrogram, which can accurately calibrate each sampling point in the spectral data and improve the positioning accuracy of the wavelength. By calculating the wavelengths of each quantization level, the spectral characteristics at each wavelength can be clearly presented on the spectrogram, which helps to identify and distinguish subtle spectral peaks. Systematically calculating wavelengths according to the wavelength step and the quantization level index can ensure the consistency of data processing. Precise wavelength calculation helps to clearly distinguish different element characteristics in the spectrum and reduce the influence of spectral line overlap on the analysis results. By converting each discrete quantization level index into the corresponding signal intensity and establishing the spectrogram coordinate axes with the wavelength as the X-axis and the signal intensity as the Y-axis, the signal intensity of each spectral signal and the corresponding wavelength are plotted as a line on the spectrogram coordinate axes to obtain the spectrogram. Here, the wavelength and signal intensity of each signal can be organized into a feature vector to form a matrix, where each row represents the feature vector of a signal and each column represents different features (wavelength and signal intensity). At the same time, to avoid the influence of different feature scales, the wavelength and signal intensity can be standardized. A common method is to scale the features to the same range or standardize them to a standard normal distribution with a mean of 0 and a standard deviation of 1. Then, a suitable clustering algorithm (such as K-means clustering or hierarchical clustering) is selected, so that multiple spectral signals can be classified according to different elements, and then plotted as a line to form a spectrogram. The spectrogram can visually display the signal intensity of each wavelength, making the spectral characteristics of the elements clear at a glance, which helps to identify and analyze the characteristic peaks of different chemical elements. The peak positions on the spectrogram correspond to the characteristic wavelengths of different elements, helping to accurately identify and distinguish various elements present in the die steel. The concentration of each element can be quantitatively estimated through the signal intensity, providing detailed information about the composition of the die steel. Therefore, by converting the discrete quantization level index into the signal intensity and plotting the spectrogram, the spectral characteristics can be clearly displayed, the element peaks can be accurately identified, quantitative analysis can be supported, data analysis and interpretation can be optimized, and complex samples can be comprehensively covered, so as to obtain more accurate and comprehensive results when analyzing the chemical element composition of the die steel.

[0109] In one embodiment, step S3 of obtaining a plurality of characteristic spectral lines according to the spectrogram and obtaining the corresponding actual wavelength according to each characteristic spectral line includes:

[0110] S31. Extract a plurality of peaks of each broken line in the spectrogram to obtain a plurality of characteristic spectral lines;

[0111] S32. Sort the plurality of characteristic spectral lines in ascending order to obtain a spectral line sorting table;

[0112] S33. Select the characteristic spectral line ranked first in the spectral line sorting table as the detection spectral line;

[0113] S34. Obtain the corresponding actual wavelength from the spectrogram according to the detection spectral line.

[0114] As described in steps S31 - S34 above, the present invention obtains a plurality of characteristic spectral lines by extracting a plurality of peaks of each element broken line in the spectrogram, then sorts the plurality of characteristic spectral lines in ascending order to obtain a spectral line sorting table, selects the characteristic spectral line ranked first in the spectral line sorting table as the detection spectral line, and then obtains the actual wavelength of each element from the spectrogram according to the detection spectral line. Among them, the spectral line refers to the peak of the signal intensity at the wavelength shown in the spectrum. Selecting the characteristic spectral line ranked first in the sorting table as the detection spectral line helps to determine the most representative feature in the spectrogram, thereby improving the recognition accuracy of the actual wavelength of each element. The sorted characteristic spectral lines can help to preferentially analyze the most significant element signals, reduce the dependence on insignificant or noise - interfering spectral lines, optimize the effectiveness of element detection. By extracting the most significant characteristic spectral line, the actual wavelength of each element can be determined more accurately, which helps to improve the quantitative analysis accuracy of the element concentration. Clearly selecting the detection spectral line can be used as the most characteristic reference point in the spectrogram to help better interpret the spectral data and improve the interpretability of the results.

[0115] In one embodiment, step S5 of obtaining a concentration - signal intensity curve graph and obtaining the actual concentration value corresponding to each element according to the actual wavelength and the concentration - signal intensity curve graph includes:

[0116] S51. Obtain the spectral signal intensities of the element corresponding to the actual wavelength at a plurality of preset concentrations;

[0117] S52. Establish a concentration - signal intensity coordinate axis with the preset concentration as the X - axis and the spectral signal intensity as the Y - axis;

[0118] S53. Mark each preset concentration and the corresponding spectral signal intensity as connection points on the concentration - signal intensity coordinate axis;

[0119] S54. Connect multiple said connection points through a curve to obtain a concentration-signal intensity curve graph;

[0120] S55. Obtain the curve slope of the concentration-signal intensity curve graph, and obtain the actual signal intensity according to the product of the curve slope and the actual wavelength;

[0121] S56. Match the actual signal intensity with the concentration-signal intensity curve graph to obtain the actual concentration value of each element.

[0122] As described in the above steps S51 - S56, the present invention establishes a concentration-signal intensity coordinate axis with the preset concentration as the X-axis and the spectral signal intensity as the Y-axis, and then marks each preset concentration and the corresponding spectral signal intensity as connection points on the concentration-signal intensity coordinate axis. Connect multiple said connection points through a curve to obtain a concentration-signal intensity curve graph. The concentration-signal intensity curve graph can establish a quantitative relationship between the element concentration and the spectral signal intensity, so that the actual concentration of the element in the sample can be directly deduced according to the spectral signal intensity. By drawing the concentration-signal intensity curve graph, the relationship between the concentration and the signal intensity can be identified, which helps to analyze and interpret the spectral data more accurately. The concentration-signal intensity curve graph can clearly show the trend of the signal intensity changing with the concentration. By drawing the concentration-signal intensity curve graph, the change of the signal intensity at different concentrations can be evaluated. The curve graph can be used for quantitative analysis. By fitting the curve and determining the relationship formula of the concentration-signal intensity, the concentration of the element in the sample can be accurately calculated. By obtaining the curve slope of the concentration-signal intensity curve graph and obtaining the actual signal intensity according to the product of the curve slope and the actual wavelength, and by matching the actual signal intensity with the concentration-signal intensity curve graph to obtain the actual concentration value of each element. By obtaining the actual signal intensity through the product of the curve slope and the actual wavelength and combining with the matching of the concentration-signal intensity curve graph, the actual concentration of the element can be accurately determined, improving the accuracy of quantitative analysis. Using the slope of the curve to calculate the actual signal intensity helps to improve the accuracy of the measurement method and ensure the reliability of the result. Through the calculation of the slope and the actual wavelength, matching the spectral signal intensity with the concentration graph can clearly explain the relationship between the signal intensity and the actual concentration, enhancing the interpretability of the data. Moreover, by calculating the actual signal intensity through the curve slope, the error caused by the noise or interference of the spectrogram can be reduced, improving the accuracy of the concentration measurement.

[0123] In one embodiment, the step S6 of predicting the performance characteristic evaluation value of the die steel according to the actual concentration values of multiple elements and classifying the use of the die steel according to the performance characteristic evaluation value includes:

[0124] S61. Obtain the weight coefficients of the actual concentration values of each element, and perform weighted calculation based on the actual concentration values of each element and the corresponding weight coefficients to obtain the total performance score of the die steel;

[0125] S62. Determine whether the total performance score is less than the preset performance score;

[0126] If the total performance score is less than the preset performance score, it is determined that the performance of the die steel is poor;

[0127] If the total performance score is not less than the preset performance score, obtain the standard concentration range of each element;

[0128] S63. Determine whether the number of actual concentration values of multiple elements that are not within the corresponding standard concentration ranges is greater than the preset number;

[0129] If the number of actual concentration values of multiple elements that are not within the corresponding standard concentration ranges is greater than the preset number, it is determined that the performance of the die steel is poor;

[0130] If the number of actual concentration values of multiple elements that are not within the corresponding standard concentration ranges is not greater than the preset number, it is determined that the performance of the die steel is good, and extract the actual concentration values of carbon, chromium, molybdenum, nickel, and manganese elements in the die steel;

[0131] S64. Calculate the wear resistance evaluation value according to the actual concentration values of carbon, chromium, and molybdenum elements and their corresponding weight coefficients, where the calculation formula is:

[0132] N(X) = C * α + Cr * β + Mo * γ;

[0133] Among them, N(X) represents the wear resistance evaluation value, C represents the carbon element, α represents the weight coefficient of the carbon element, Cr represents the chromium element, β represents the weight coefficient of the chromium element, Mo represents the molybdenum element, and γ represents the weight coefficient of the molybdenum element;

[0134] S65. Determine whether the wear resistance evaluation value is greater than the first preset value;

[0135] If the wear resistance evaluation value is greater than the first preset value, it is determined that the die steel is suitable for manufacturing die-casting dies:

[0136] S66. Calculate the toughness performance evaluation value according to the actual concentration values of nickel and manganese elements and their corresponding weight coefficients, where the calculation formula is:

[0137] R(X) = Ni * δ + Mn * ε;

[0138] Among them, R(X) represents the toughness performance evaluation value, Ni represents nickel element, δ represents the weight coefficient of nickel element, Mn represents manganese element, and ε represents the weight coefficient of manganese element;

[0139] S67. Determine whether the toughness performance evaluation value is greater than a second preset value;

[0140] If the wear resistance evaluation value is greater than the second preset value, it is determined that the die steel is suitable for manufacturing cold stamping dies.

[0141] As described in the above steps S61 - S67, the present invention calculates the total performance score of the die steel through weighted calculation based on the actual concentration value of each element and the corresponding weight coefficient, and determines whether the total performance score is less than the preset performance score. If the total performance score is less than the preset performance score, it is determined that the performance of the die steel is poor. Otherwise, further judgment and prediction are carried out by judging the number of actual concentration values of multiple elements that are not within the corresponding standard concentration intervals. By calculating the score through specific mathematical formulas, a quantitative evaluation index can be provided, which is more operable than simply relying on whether the elements are within the standard range. Through the total performance score, the performance of the die steel in actual applications can be predicted more accurately. By comparing with the preset performance score, it can be directly judged whether the die steel meets specific performance requirements. In the case where the total performance score is qualified, by checking the number of elements within the standard concentration intervals, the quality of the die steel can be further verified, which helps to analyze whether the die steel is suitable for manufacturing cold stamping dies or die-casting dies. If the number of actual concentration values of multiple elements that are not within the corresponding standard concentration intervals is greater than the preset number, it is determined that the performance of the die steel is poor. Otherwise, it is determined that the performance of the die steel is good, and the actual concentration values of carbon, chromium, molybdenum, nickel, and manganese elements in the die steel are extracted. Then, the wear resistance evaluation value is calculated based on the actual concentration values of carbon, chromium, and molybdenum elements and their corresponding weight coefficients, and the toughness performance evaluation value is calculated based on the actual concentration values of nickel and manganese elements and their corresponding weight coefficients. Then, by judging whether the wear resistance evaluation value is greater than the first preset value and whether the toughness performance evaluation value is greater than the second preset value, if the wear resistance evaluation value is greater than the first preset value, it is determined that the die steel is suitable for manufacturing die-casting dies. If the wear resistance evaluation value is greater than the second preset value, it is determined that the die steel is suitable for manufacturing cold stamping dies. By calculating the wear resistance and toughness performance evaluation values respectively and using the preset wear resistance and toughness performance values as standards, it can be clearly judged whether the die steel meets specific performance requirements. Deciding the applicable type of the die steel (such as die-casting die or cold stamping die) according to the wear resistance evaluation value and the toughness performance evaluation value can ensure that the steel performs excellently in a specific application environment, improve the pertinence of the use of die steel. Conducting a detailed performance evaluation before deciding to use can discover potential problems in advance. If the number of actual concentration values of multiple elements that are not within the corresponding standard concentration intervals is greater than the preset number, it indicates that there may be a risk of poor performance, which helps to avoid selecting die steel with insufficient performance and reduce the risks brought by insufficient die steel performance.

[0142] This application also provides an intelligent system for die steel chemical composition analysis and performance prediction, including:

[0143] A processing module for exciting the die steel by laser to obtain plasma;

[0144] The first acquisition module is used to acquire a plurality of spectral signals of the plasma and obtain a spectrogram based on the plurality of spectral signals;

[0145] The second acquisition module is used to acquire a plurality of characteristic spectral lines based on the spectrogram and obtain the corresponding actual wavelength for each characteristic spectral line;

[0146] The comparison module is used to compare each actual wavelength with an elemental characteristic spectral line database to obtain the element corresponding to the actual wavelength;

[0147] The third acquisition module is used to acquire a concentration-signal intensity curve graph and obtain the actual concentration value corresponding to each element based on the actual wavelength and the concentration-signal intensity curve graph;

[0148] The prediction module is used to predict the performance characteristic evaluation value of die steel based on the actual concentration values of multiple elements and classify the use of die steel according to the performance characteristic evaluation value, where the performance characteristic evaluation value includes a wear resistance evaluation value and a toughness performance evaluation value.

[0149] In one embodiment, the first acquisition module includes:

[0150] The first acquisition module includes:

[0151] The conversion unit is used to convert each spectral signal into an initial electrical signal by using a spectrometer and perform amplification processing on each initial electrical signal to obtain a corresponding analog electrical signal;

[0152] The extraction unit is used to obtain the voltage values of each analog electrical signal at multiple preset time points by using an analog-to-digital converter and extract the maximum voltage value as the maximum input voltage;

[0153] The first calculation unit is used to obtain the quantization level number of the analog-to-digital converter and calculate the quantization step size according to the ratio of the maximum input voltage to the quantization level number;

[0154] The second calculation unit is used to calculate the corresponding discrete quantization level index according to the ratio of each voltage value to the quantization step size;

[0155] The drawing unit is used to draw a spectrogram by using a spectrometer for the multiple discrete quantization level indexes.

[0156] The present invention also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the intelligent method for analyzing the chemical composition and predicting the performance of die steel are implemented.

[0157] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the intelligent method for analyzing the chemical composition and predicting the performance of the die steel are implemented.

[0158] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided in the present application and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0159] It should be noted that in this article, the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, apparatus, article, or method including a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such a process, apparatus, article, or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, apparatus, article, or method including that element.

[0160] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structural or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be included in the patent protection scope of the present invention by the same token.

Claims

1. An intelligent method for chemical composition analysis and performance prediction of die steel, characterized in that Including: Performing an excitation treatment on die steel by laser to obtain a plasma; Obtaining a plurality of spectral signals of the plasma and obtaining a spectrogram based on the plurality of spectral signals; Obtaining a plurality of characteristic spectral lines according to the spectrogram and obtaining the corresponding actual wavelength according to each characteristic spectral line; Comparing each actual wavelength with an element characteristic spectral line database to obtain the element corresponding to the actual wavelength; Obtaining a concentration-signal intensity curve graph and obtaining the actual concentration value corresponding to each element according to the actual wavelength and the concentration-signal intensity curve graph; Obtaining the weight coefficient of each element's actual concentration value and performing a weighted calculation according to the actual concentration value of each element and the corresponding weight coefficient to obtain the total performance score of the die steel; Judging whether the total performance score is less than a preset performance score; If the total performance score is less than the preset performance score, it is determined that the performance of the die steel is poor; If the total performance score is not less than the preset performance score, obtaining the standard concentration range of each element; Judging whether the number of actual concentration values of multiple elements not within the corresponding standard concentration range is greater than a preset number; If the number of actual concentration values of multiple elements not within the corresponding standard concentration range is greater than the preset number, it is determined that the performance of the die steel is poor; If the number of actual concentration values of multiple elements not within the corresponding standard concentration range is not greater than the preset number, it is determined that the performance of the die steel is good, and the actual concentration values of carbon, chromium, molybdenum, nickel, and manganese elements in the die steel are extracted; Calculating a wear resistance evaluation value according to the actual concentration values of the carbon, chromium, and molybdenum elements and their corresponding weight coefficients, where the calculation formula is: ； Wherein, N(X) represents the wear resistance evaluation value, C represents the actual concentration value of the carbon element, α represents the weight coefficient of the carbon element, Cr represents the actual concentration value of the chromium element, β represents the weight coefficient of the chromium element, Mo represents the actual concentration value of the molybdenum element, and γ represents the weight coefficient of the molybdenum element; Judging whether the wear resistance evaluation value is greater than a first preset value; If the wear resistance evaluation value is greater than the first preset value, it is determined that the die steel is suitable for manufacturing die-casting molds; Calculating a toughness performance evaluation value according to the actual concentration values of the nickel and manganese elements and their corresponding weight coefficients, where the calculation formula is: ； Wherein, R(X) represents the toughness performance evaluation value, Ni represents the actual concentration value of the nickel element, δ represents the weight coefficient of the nickel element, Mn represents the actual concentration value of the manganese element, and ε represents the weight coefficient of the manganese element; Judging whether the toughness performance evaluation value is greater than a second preset value; If the toughness performance evaluation value is greater than the second preset value, it is determined that the die steel is suitable for manufacturing cold stamping dies.

2. The intelligent method for analyzing the chemical composition and predicting the properties of die steel according to claim 1, characterized in that The step of obtaining a spectrogram according to the plurality of spectral signals includes: Converting each spectral signal into an initial electrical signal by a spectrometer and performing an amplification process on each initial electrical signal to obtain a corresponding analog electrical signal; Obtaining the voltage values of each analog electrical signal at a plurality of preset time points by an analog-to-digital converter and extracting the maximum voltage value as the maximum input voltage; Obtain the number of quantization levels of the analog-to-digital converter, and calculate the quantization step according to the ratio of the maximum input voltage to the number of quantization levels; Calculate the corresponding discrete quantization level index according to the ratio of each of the voltage values to the quantization step; Use a spectrometer to plot multiple of the discrete quantization level indices to obtain a spectrogram.

3. The intelligent method for analyzing the chemical composition and predicting the properties of die steel according to claim 2, characterized in that, The step of using a spectrometer to plot multiple of the discrete quantization level indices to obtain a spectrogram includes: Obtain the minimum wavelength and the maximum wavelength of the spectrometer, and calculate the wavelength step according to the minimum wavelength, the maximum wavelength, and the discrete quantization level index, where the calculation formula is: ； Where, B(C) represents the wavelength step, Z(D) represents the maximum wavelength, Z(X) represents the minimum wavelength, and L(J) represents the number of quantization levels; Calculate the corresponding wavelength according to the wavelength step, the minimum wavelength, and multiple discrete quantization level indices, where the calculation formula is: ； Wherein, A represents the wavelength, B(C) represents the wavelength step size, Z(X) represents the minimum wavelength, L(S) n represents the index of the nth discrete quantization level, n represents the serial number of the discrete quantization level index, and m represents the number of discrete quantization level indices; Convert each of the discrete quantization level indices into a corresponding signal intensity; Establish a spectrogram coordinate axis with the wavelength as the X-axis and the signal intensity as the Y-axis, and plot each signal intensity and the corresponding wavelength of each spectral signal on the spectrogram coordinate axis through a broken line to obtain a spectrogram.

4. The intelligent method for analyzing the chemical composition and predicting the properties of die steel according to claim 1, wherein The step of obtaining multiple characteristic spectral lines according to the spectrogram and obtaining the corresponding actual wavelength according to each of the characteristic spectral lines includes: Extract multiple peaks of each broken line in the spectrogram to obtain multiple characteristic spectral lines, where the characteristic spectral line refers to the peak of the signal intensity at the wavelength shown in the spectrum; Sort multiple of the characteristic spectral lines in ascending order to obtain a spectral line sorting table; Select the characteristic spectral line ranked first from the spectral line sorting table as the detection spectral line; Obtain the corresponding actual wavelength from the spectrogram according to the detection spectral line.

5. An intelligent system for chemical composition analysis and performance prediction of die steel, characterized in that, Includes: A processing module for exciting die steel by laser to obtain plasma; A first acquisition module for acquiring multiple spectral signals of the plasma and obtaining a spectrogram according to multiple of the spectral signals; A second acquisition module for obtaining multiple characteristic spectral lines according to the spectrogram and obtaining the corresponding actual wavelength according to each of the characteristic spectral lines; A comparison module for comparing each of the actual wavelengths with an element characteristic spectral line database to obtain the element corresponding to the actual wavelength; A third acquisition module for obtaining a concentration-signal intensity curve graph and obtaining the actual concentration value corresponding to each element according to the actual wavelength and the concentration-signal intensity curve graph; A prediction module for obtaining the weight coefficient of the actual concentration value of each element, and performing weighted calculation according to the actual concentration value of each element and the corresponding weight coefficient to obtain the total performance score of the die steel; Judge whether the total performance score is less than a preset performance score; If the total performance score is less than the preset performance score, it is determined that the performance of the die steel is poor; If the total performance score is not less than the preset performance score, obtain the standard concentration range of each element; Judge whether the number of actual concentration values of multiple elements not within the corresponding standard concentration range is greater than a preset number; If the number of actual concentration values of multiple elements that are not within the corresponding standard concentration intervals is greater than a preset number, it is determined that the performance of the die steel is poor; If the number of actual concentration values of multiple elements that are not within the corresponding standard concentration intervals is not greater than a preset number, it is determined that the performance of the die steel is good, and the actual concentration values of carbon, chromium, molybdenum, nickel, and manganese elements in the die steel are extracted; According to the actual concentration values of the carbon, chromium, and molybdenum elements and their corresponding weight coefficients, a wear resistance evaluation value is calculated, where the calculation formula is: ； Wherein, N(X) represents the wear resistance evaluation value, C represents the actual concentration value of the carbon element, α represents the weight coefficient of the carbon element, Cr represents the actual concentration value of the chromium element, β represents the weight coefficient of the chromium element, Mo represents the actual concentration value of the molybdenum element, and γ represents the weight coefficient of the molybdenum element; Judge whether the wear resistance evaluation value is greater than a first preset value; If the wear resistance evaluation value is greater than the first preset value, it is determined that the die steel is suitable for manufacturing die-casting dies; According to the actual concentration values of the nickel and manganese elements and their corresponding weight coefficients, a toughness performance evaluation value is calculated, where the calculation formula is: ； Wherein, R(X) represents the toughness performance evaluation value, Ni represents the actual concentration value of the nickel element, δ represents the weight coefficient of the nickel element, Mn represents the actual concentration value of the manganese element, and ε represents the weight coefficient of the manganese element; Judge whether the toughness performance evaluation value is greater than a second preset value; If the toughness performance evaluation value is greater than the second preset value, it is determined that the die steel is suitable for manufacturing cold stamping dies.

6. The intelligent system for die steel chemical composition analysis and performance prediction according to claim 5, characterized in that, The first acquisition module includes: A conversion unit for converting each of the spectral signals into an initial electrical signal by using a spectrometer and performing amplification processing on each of the initial electrical signals to obtain a corresponding analog electrical signal; An extraction unit for obtaining the voltage values of each analog electrical signal at multiple preset time points by using an analog-to-digital converter and extracting the maximum voltage value as the maximum input voltage; A first calculation unit for obtaining the quantization level number of the analog-to-digital converter and calculating a quantization step according to the ratio of the maximum input voltage to the quantization level number; A second calculation unit for calculating a corresponding discrete quantization level index according to the ratio of each voltage value to the quantization step; A drawing unit for drawing a spectrogram by using a spectrometer for multiple of the discrete quantization level indexes.

7. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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