Cutting quality real-time monitoring method, electronic equipment and storage medium

Abstract

The invention discloses a cutting quality real-time monitoring method, electronic equipment and a storage medium, and relates to the technical field of laser cutting, and the method comprises the following steps: monitoring target laser perforation cutting through a high-precision sensor, obtaining a feedback signal when target laser is in initial contact with a material, analyzing the feedback signal according to a pre-cutting quality evaluation strategy, and determining the cutting quality of the material according to the feedback signal; obtaining a first cutting quality coefficient; dynamically monitoring a cutting signal time sequence through an intelligent monitor, extracting cutting feature information, and inputting the cutting feature information into the cutting quality evaluation model to obtain a second cutting quality coefficient; finally, the first cutting quality coefficient and the second cutting quality coefficient are averaged, and a target cutting quality index is calculated and used for representing the cutting quality condition. The technical problems that in the prior art, the laser cutting quality monitoring means is single, and real-time dynamic evaluation cannot be achieved are solved, and the technical effects of real-time dynamic monitoring and accurate quantitative evaluation of the laser cutting quality are achieved.

Description

Technical Field

[0001] The present application relates to the technical field of laser cutting, and particularly to a method for real-time monitoring of cutting quality, an electronic device, and a storage medium. Background Art

[0002] With the wide application of laser processing technology, laser cutting, as an efficient and precise processing method, has been widely used in industries such as automotive, aviation, and electronics. However, existing laser cutting quality monitoring technologies usually rely on single static monitoring means, such as single-parameter monitoring of laser power, gas pressure, etc., and it is difficult to comprehensively characterize the adaptability of materials before cutting and the dynamic quality characteristics during the cutting process, resulting in defects (such as rough cut edges, incomplete perforation, or uneven cutting) that occur during the cutting process being difficult to detect and adjust in a timely manner. Especially in the case of complex workpiece materials or high-speed cutting tasks, the existing technologies cannot effectively monitor and evaluate the entire process before, during, and after cutting, and also lack the ability to dynamically quantify and predict cutting quality. Therefore, how to achieve real-time dynamic monitoring and accurate evaluation of laser cutting quality has become a technical problem that urgently needs to be solved in the field of laser processing.

[0003] In the related technologies at the present stage, there are technical problems such as single laser cutting quality monitoring means and inability to perform real-time dynamic evaluation. Summary of the Invention

[0004] The present application solves the technical problems in the prior art that the laser cutting quality monitoring means are single and cannot perform real-time dynamic evaluation by providing a method for real-time monitoring of cutting quality, an electronic device, and a storage medium.

[0005] The present application provides a method for real-time monitoring of cutting quality, including:

[0006] Monitoring the target laser perforation cutting through a high-precision sensor to obtain a contact point feedback signal, where the contact point feedback signal refers to the feedback signal when the target laser in the target laser perforation cutting initially contacts the target material; retrieving a pre-cutting quality evaluation strategy to evaluate and analyze the contact point feedback signal to obtain a first cutting quality coefficient; dynamically monitoring through an intelligent monitor to obtain the cutting signal time series of the target laser perforation cutting, and acquiring the cutting feature information of the cutting signal time series; using the cutting feature information as input data of a cutting-in-process quality evaluation model, and obtaining output data through the cutting-in-process quality evaluation model, where the output data includes a second cutting quality coefficient; taking the average value of the first cutting quality coefficient and the second cutting quality coefficient as the target cutting quality index, where the target cutting quality index is used to characterize the cutting quality situation of the target laser perforation cutting.

[0007] The present application also provides an electronic device, including:

[0008] A memory for storing executable instructions; a processor for implementing a real-time monitoring method for cutting quality when executing the executable instructions stored in the memory.

[0009] This application also provides a computer-readable storage medium, including:

[0010] A computer program is stored thereon, and when the program is executed by a processor, a real-time monitoring method for cutting quality is implemented.

[0011] It is intended to propose a real-time monitoring method for cutting quality, an electronic device, and a storage medium through this application. First, a high-precision sensor is used to monitor the target laser perforation cutting, obtain the feedback signal when the target laser first contacts the material, and analyze the feedback signal according to the pre-cut quality evaluation strategy to obtain the first cutting quality coefficient; the intelligent monitor dynamically monitors the cutting signal timing sequence, extracts the cutting feature information, and inputs it into the in-cut quality evaluation model to obtain the second cutting quality coefficient; finally, the average value of the first and second cutting quality coefficients is taken to calculate the target cutting quality index, which is used to characterize the cutting quality situation, achieving the technical effect of realizing real-time dynamic monitoring and precise quantitative evaluation of laser cutting quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the operations described above or below do not necessarily need to be executed precisely in sequence. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more steps can be removed from these processes.

[0013] Figure 1 It is a schematic flowchart of a real-time monitoring method for cutting quality provided by an embodiment of the present application;

[0014] Figure 2 It is a schematic flowchart of the cutting signal timing sequence construction of a real-time monitoring method for cutting quality provided by an embodiment of the present application;

[0015] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The above description is only an overview of the technical solutions of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the following specifically describes the embodiments of this application.

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following will further describe this application in detail with reference to the accompanying drawings. The described embodiments should not be construed as limitations on this application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this application.

[0018] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first / second" involved are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0019] An embodiment of this application provides a method for real-time monitoring of cutting quality, as Figure 1 shown, the method includes:

[0020] Step S100, monitoring the target laser piercing cutting through a high-precision sensor to obtain a contact point feedback signal, where the contact point feedback signal refers to the feedback signal when the target laser in the target laser piercing cutting initially contacts the target material. Specifically, the process of target laser piercing cutting is monitored in real time through a high-precision sensor, and the feedback signal when the laser initially contacts the material is obtained. This signal reflects the state of laser energy transfer and surface interaction of the material. The feedback signals collected by the sensor include laser reflection intensity, material heating temperature change, vibration response, etc. After filtering, denoising, and feature extraction, key parameters such as focus deviation and temperature change rate are generated. These parameters are used to evaluate whether the laser power, focus position, and auxiliary gas pressure meet the preset requirements. At the same time, by comparing with the cutting conditions, real-time adjustment of the laser power, focus position, and auxiliary gas pressure is achieved, optimizing the initial cutting conditions to ensure cutting stability and accuracy. Finally, this feedback signal generates a first cutting quality coefficient, providing a key reference basis for quality assessment before cutting and subsequent process monitoring.

[0021] Step S200: Retrieve the pre-cut quality assessment strategy to evaluate and analyze the contact point feedback signal, and obtain the first cutting quality coefficient. Specifically, by retrieving the pre-cut quality assessment strategy, analyze the contact point feedback signal to identify the predetermined pre-cut factors involved, such as laser power, focal position deviation, gas pressure, gas purity, and laser cutting distance. Correspond the feedback signal with each factor one by one and extract relevant parameters, such as the stability of laser power, the focal shift amount, the instantaneous value of the auxiliary gas pressure, etc. Then, perform variant weighting processing on these parameters, assign different weights according to the material characteristics and actual working conditions to highlight the influence of key factors. Through comprehensive calculation, generate the first cutting quality coefficient, which is used to characterize the quality level of the cutting initial conditions. The closer the quality coefficient is to the ideal state, the more sufficient the pre-cut preparation is; if the value is low, it indicates that there may be problems such as focal shift, power fluctuation, or abnormal gas parameters. Finally, this coefficient is used in the cutting control system to realize real-time optimization of cutting parameters, providing stable and superior initial conditions for the subsequent cutting process.

[0022] In a possible implementation, retrieve the pre-cut quality assessment strategy to evaluate and analyze the contact point feedback signal, and obtain the first cutting quality coefficient. Step S200 further includes step S210: Read the predetermined pre-cut factors in the pre-cut quality assessment strategy. Specifically, first parse the pre-cut quality assessment strategy and extract the predetermined pre-cut factors, including key parameters such as laser power, laser beam focal position deviation, gas pressure, gas purity, and laser cutting distance. These factors define the target value range of the cutting operation. For example, the laser power should be between 85% - 95%, the focal position deviation should not exceed ±0.2 mm, and the gas purity should be higher than 98.5%. Subsequently, the system matches these target values with the actual parameters of the cutting equipment, calibrates the laser power, adjusts the focal position, detects the gas pressure and purity, and confirms the laser cutting distance by calling the equipment module to ensure that the equipment state meets the requirements of the assessment strategy. At the same time, the extracted factor target values are structured and stored as a standardized factor list, providing a basis for subsequent data analysis and real-time monitoring, and dynamically adjusting the target range in combination with the cutting task to enhance the adaptability of the strategy. Finally, these factor target values serve as the input data for subsequent evaluation and analysis, supporting the generation of the first cutting quality coefficient and laying a foundation for cutting quality monitoring.

[0023] Step S220: Traverse the predetermined pre - cutting factors in the contact point feedback signal to obtain pre - cutting factor parameters. Specifically, the system analyzes the contact point feedback signal through a high - precision sensor. It sequentially extracts the predetermined pre - cutting factor parameters, including laser power, laser beam focus position deviation, gas pressure, gas purity, and laser cutting distance, etc. First, the system extracts the laser energy data in the feedback signal, calculates the deviation between the actual power value and the predetermined power value, and marks the abnormal interval. Then, it analyzes the laser beam focus position, quantifies the degree of focus deviation through a positioning algorithm, and generates corresponding parameters. Subsequently, the system analyzes the gas pressure and purity, extracts the actual parameter values in combination with gas flow rate, density, and spectral characteristics, and screens out non - compliant situations by comparing with the predetermined thresholds. Finally, the system measures the laser cutting distance to confirm whether the straight - line distance between the laser and the material meets the requirements. Through traversal analysis, the system converts each factor in the contact point feedback signal into specific pre - cutting factor parameters, generates structured data, including actual values, deviation values, and abnormal marks, providing a reliable basis for subsequent cutting quality evaluation and real - time control.

[0024] Step S230: Perform mutation - weighted processing on the pre - cutting factor parameters to obtain the first cutting quality coefficient. Specifically, after obtaining the pre - cutting factor parameters, the system calculates the first cutting quality coefficient through mutation - weighted processing. First, according to the predetermined pre - cutting quality evaluation strategy, initial weights are assigned to each factor (such as laser power, focus position deviation, gas pressure, gas purity, laser cutting distance), and mutation analysis is carried out based on the deviation between the actual value and the theoretical standard value to dynamically adjust the weights of each factor. The weight of the factor with a large mutation degree increases to highlight its impact on the evaluation result, while the weight of the factor with a small mutation degree decreases accordingly. Subsequently, the adjusted factor weights are weighted with the corresponding parameter values, and the cumulative weighted total value is obtained. The result is converted into a value within the range of 0 to 1 through normalization processing to generate the first cutting quality coefficient. This coefficient intuitively reflects the overall quality level of the pre - cutting parameters, providing a scientific basis for the quality evaluation of the subsequent cutting process.

[0025] Step S240, wherein the predetermined pre-cutting factors include laser power, laser beam focus position deviation, gas pressure, gas purity, and laser cutting distance, and the laser cutting distance refers to the straight-line distance between the target laser and the target material. Specifically, in the pre-cutting quality assessment, the predetermined pre-cutting factors include laser power, laser beam focus position deviation, gas pressure, gas purity, and laser cutting distance, and the factors are crucial to the cutting quality. Laser power determines the efficiency of the material melting due to heat. Insufficient power will result in incomplete cutting, while too high power may cause overburning. Laser beam focus position deviation affects cutting accuracy, and focus deviation will result in increased or uneven incision width. Gas pressure affects cutting speed and slag removal effect. Insufficient pressure may cause slag, while too high pressure will destroy the cutting texture. Gas purity has a significant impact on chemical reactions. Low purity may generate impurities or reduce the protection effect. Laser cutting distance directly affects laser energy distribution and cutting accuracy. Too large a distance will result in energy attenuation, while too small a distance may damage the equipment. The system collects these factor data through high-precision sensors and compares them with preset standard values to comprehensively evaluate the pre-cutting status, providing an accurate basis for subsequent quality optimization and calculation of the first cutting quality coefficient.

[0026] In step S300, the cutting signal timing of the target laser perforation cut is dynamically monitored by an intelligent monitor, and cutting characteristic information of the cutting signal timing is obtained. Specifically, during the laser perforation cutting process, the intelligent monitor dynamically monitors the cutting signal timing, collecting and recording various signal data in real time, such as laser power changes, plasma light radiation intensity, and arc signal strength, to fully reflect the dynamic changes from the start to the end of the cutting process. The intelligent monitor, equipped with a built-in high-precision sensor and data processing module, analyzes and processes the collected signal timing data to extract key cutting characteristic information, such as cutting speed trends, laser power output fluctuations, plasma intensity peaks, and cutting width signal stability. This characteristic information is optimized through filtering algorithms to remove noise and time series analysis to capture signal patterns, ensuring its accuracy and representativeness. Multidimensional data is then simultaneously analyzed to form a comprehensive cutting characteristic description. Ultimately, the extracted characteristic information is output in a standardized form as a key input to the subsequent cutting quality assessment model, providing a basis for real-time cutting quality assessment and process parameter optimization, effectively improving the quality and efficiency of laser perforation cutting.

[0027] In one possible implementation, Figure 2As described above, the cutting signal timing of the target laser piercing and cutting is dynamically monitored by the intelligent monitor, and the cutting feature information of the cutting signal timing is obtained. Step S300 further includes step S310 of monitoring the target laser piercing and cutting through the plasma sensor in the intelligent monitor to obtain the first plasma intensity at the first moment. Specifically, during the laser piercing and cutting process, the intelligent monitor uses the plasma sensor to monitor the plasma radiation signal generated when the laser first contacts the target material in real time, and obtains the plasma intensity at the first moment. The spectral signal captured by the sensor is converted into numerical data through photoelectric conversion, representing the thermal effect and energy transfer efficiency of the interaction between the laser and the material. This signal undergoes anti-interference transmission and filtering processing to ensure data integrity and accuracy, and is recorded in the cutting signal database. The intelligent monitor further analyzes the plasma intensity value to determine whether it is within a reasonable range. If it deviates from the normal value, it indicates problems with the laser power, gas assistance, or the material surface. This value serves as the initial data point of the cutting signal timing, providing a crucial basis for subsequent cutting state monitoring and quality assessment.

[0028] Step S320: Extract the cutting control device group in the intelligent monitor, and monitor the predetermined cutting control indicators of the target laser piercing and cutting at the first moment through the cutting control device group to obtain the first cutting control parameter group. Specifically, during the target laser piercing and cutting process, it is crucial to extract the cutting control device group in the intelligent monitor and conduct real-time monitoring. The cutting control device group includes multiple components such as a laser power controller, a motion platform controller, an auxiliary gas pressure regulator, and an optical focus adjustment system. These devices collect the key control indicators of the cutting through coordinated work. At the first moment, each component records data such as laser power, cutting path movement speed, auxiliary gas pressure and flow rate, and laser focus position deviation, and transmits the collected monitoring data to the intelligent monitor through a communication network. After the monitor processes the data, it generates the first cutting control parameter group, which contains real-time values, time stamps, and association information with the cutting state, providing an accurate data basis for subsequent cutting speed calculation and dynamic adjustment to ensure the efficiency and stability of the cutting process.

[0029] Step S330: Read the predetermined relative weight allocation of the predetermined cutting control metrics. Specifically, in the target laser piercing and cutting process, reading the predetermined relative weight allocation of the predetermined cutting control metrics is an important step in optimizing cutting quality. First, according to the different influences of indicators such as laser power, material type, cut width, defocus amount, and assist gas pressure, the relative weights of each indicator are determined through experimental data, industry standards, and process requirements. For example, high-precision cutting may place more emphasis on laser power and focus position, while thick plate cutting has a higher weight for assist gas pressure. Then, the system extracts relevant records from the historical cutting database, analyzes the cutting quality under different conditions, uses algorithms such as multi-factor regression analysis or principal component analysis to generate preliminary weight values, and adjusts them in combination with the optimization goal to form the final weight allocation scheme. Finally, these weights are embedded in the subsequent calculation of cutting control parameters. By calculating key parameters such as cutting speed through weighted calculation, the influence of different indicators on cutting quality is comprehensively reflected, so as to ensure that the evaluation and optimization are more in line with the actual working conditions and improve cutting efficiency and quality consistency.

[0030] Step S340: Perform weighted calculation on the first cutting control parameter group according to the predetermined relative weight allocation to obtain the first cutting speed. Specifically, in the real-time monitoring of the target laser piercing and cutting, performing weighted calculation on the first cutting control parameter group according to the predetermined relative weight allocation to determine the first cutting speed is a key step. The system first obtains the cutting control parameter group at the first moment in real time through the cutting control equipment group, including parameter values such as laser power, material type, cut width, defocus amount, and assist gas pressure. Subsequently, the pre-set weight allocation is extracted according to the cutting process requirements, and the weight value is set according to the influence degree of each parameter on cutting quality. For example, thin plate cutting may pay more attention to laser power and focus position, while thick plate cutting focuses more on assist gas pressure and material type. By multiplying each parameter value by the corresponding weight and accumulating, the comprehensive first cutting speed is calculated. After the calculation is completed, the system verifies the result to ensure that the speed is within a reasonable range. If an abnormality is found, an alarm will be triggered and the weight or parameter value will be recalibrated. Finally, the first cutting speed synthesizes the influence of each cutting control indicator, providing accurate and reliable data support for subsequent cutting quality evaluation and optimization.

[0031] Step S350, weight the first plasma intensity and the first cutting speed to obtain a first cutting index. Specifically, during the real-time monitoring of cutting quality, calculating the first cutting index by weighting the first plasma intensity and the first cutting speed is one of the key steps. The system first obtains the plasma intensity value at the first moment from the plasma sensor. This value reflects the energy release characteristics of the interaction between the laser and the material and is closely related to the melting and penetration efficiency of the material. At the same time, the system calculates the first cutting speed according to the cutting control parameter set and the preset weight distribution. This speed synthesizes multi-dimensional influencing factors such as laser power, kerf width, and material type. Subsequently, according to the cutting process requirements and material characteristics, the system sets weight coefficients for the plasma intensity and the cutting speed and comprehensively calculates them into the first cutting index through a weighting formula. This index can not only reflect the current cutting efficiency and quality but also be used for subsequent timing analysis and process optimization. Finally, the system verifies and records the cutting index to ensure it is within a reasonable range. If an abnormality is found, an alarm is triggered to adjust the parameters, and the data is stored in the database to provide a basis for optimizing the subsequent cutting process.

[0032] Step S360, establish the cutting signal time series according to the first corresponding relationship between the first cutting index and the first moment. Specifically, during the real-time monitoring of cutting quality, establishing the cutting signal time series according to the corresponding relationship between the first cutting index and the first moment is a key step. The system first binds the calculated first cutting index to its corresponding first moment timestamp to form data points reflecting the cutting state at a specific time. Subsequently, with time as the main axis, the system concatenates multiple data points to construct a complete cutting signal time series curve, with time as the horizontal axis and the cutting index as the vertical axis, dynamically showing the change trend of cutting quality. During this process, the system real-time verifies the integrity and accuracy of the data, detects and corrects abnormal fluctuations or discontinuous points to ensure the reliability of the time series curve. The finally generated cutting signal time series is stored in the monitoring system, which not only provides a basis for real-time monitoring but also serves as a data foundation for subsequent analysis and process optimization, used to identify the reasons for quality fluctuations, adjust cutting parameters, and improve cutting quality and stability.

[0033] In one possible implementation, the cutting control device group in the intelligent monitor is extracted, and the predetermined cutting control index of the target laser perforation cutting at the first moment is monitored by the cutting control device group to obtain a first cutting control parameter group. Step S320 further includes step S321, and the predetermined cutting control index includes laser power, material type, kerf width, defocus amount and auxiliary gas pressure. Specifically, the predetermined cutting control index includes laser power, material type, kerf width, defocus amount and auxiliary gas pressure, and these indicators constitute a multi-dimensional dynamic control system for the cutting process. Laser power directly affects the laser energy transfer efficiency and the material melting effect. The system ensures that the power is within the target range through real-time monitoring and adjustment to avoid overburning or incomplete cutting. The material type determines the matching of the cutting parameters. The system adjusts the laser wavelength, power and cutting speed according to the material characteristics to ensure process adaptability. The kerf width is used as an accuracy indicator. Through real-time monitoring and comparison with the target value, the cutting path or power is dynamically adjusted to ensure dimensional accuracy. Defocus affects laser energy concentration, which in turn affects cutting depth and surface quality. The system monitors the laser focal position and adjusts defocus to maintain optimal cutting conditions. Assist gas pressure regulates the flow and pressure of the injected gas to remove slag, cool the cutting area, and prevent oxidation, thereby improving cutting efficiency and cut quality. Real-time monitoring and optimization of these indicators provide a solid foundation for establishing cutting signal timing and evaluating cut quality.

[0034] In one possible implementation, the predetermined relative weight distribution of the predetermined cutting control index is read, and step S330 further includes step S331, extracting the first historical cutting record from the historical laser cutting database. Specifically, in order to extract the first historical cutting record from the historical laser cutting database, a connection is first established with the database through a data extraction module to access complete historical data including cutting process parameters, equipment settings, process monitoring data, and cutting quality results. Then, extraction rules are formulated based on screening conditions (such as time period, material type, or equipment model), and cutting records that meet the requirements are filtered out through query statements or data interfaces. Subsequently, data cleaning is performed to remove missing values, outliers, or entries with inconsistent formats to ensure data integrity and accuracy. The cleaned data is classified and organized, and the cutting control parameters (such as laser power, cutting speed, incision width, etc.) are structured and stored, and timestamps or task identifiers are attached to improve traceability. Finally, the organized records are output in a standardized format (such as CSV or JSON files) or stored in a temporary database to provide high-quality data support for subsequent cutting parameter analysis and model optimization.

[0035] Step S332, traverse the first historical cutting record based on the predetermined cutting control index to obtain the first historical cutting control parameter group. Specifically, in order to traverse the first historical cutting record based on the predetermined cutting control index and obtain the first historical cutting control parameter group, first clarify the indicators such as laser power, material type, incision width, defocus amount and auxiliary gas pressure, and match these indicators with the corresponding fields in the historical records. Scan the historical records one by one through a traversal algorithm or query program, extract the parameter values related to the predetermined indicators, and store them according to the indicator classification. At the same time, preprocess the extracted data to remove outliers and fill in missing values to ensure data integrity and accuracy. During the traversal process, attach timestamps, material batches and other information for further analysis. Finally, the extracted control parameters are organized into a structured first historical cutting control parameter group, laying a data foundation for subsequent correlation analysis and cutting quality optimization.

[0036] Step S333, a many-to-one correlation analysis is performed on the first historical cutting control parameter group and the first historical cutting speed in the first historical cutting record to obtain a first correlation analysis result. Specifically, in order to perform a many-to-one correlation analysis on the first historical cutting control parameter group and the cutting speed in the first historical cutting record, the control parameter data (such as laser power, material type, incision width, defocus amount and auxiliary gas pressure) and the corresponding cutting speed in the historical database are first cleaned, and outliers and missing items are removed to ensure data integrity. Subsequently, a correlation analysis method (such as Pearson correlation coefficient, multiple linear regression, etc.) is selected, and the control parameter group is used as an independent variable and the cutting speed is used as a dependent variable. A mathematical model is constructed to calculate the correlation coefficient or regression coefficient between each control parameter and the cutting speed, and the degree of influence of each parameter on the cutting speed is quantified. In the analysis, the accuracy and stability of the model are verified by cross-validation and residual evaluation, and the influence weight and contribution of each parameter are sorted out to generate the first correlation analysis result, which provides a reference basis for cutting control optimization.

[0037] Step S334: Analyze the first correlation analysis result and determine the predetermined relative weight allocation of the predetermined cutting control indicators. Specifically, after analyzing the first correlation analysis result, by interpreting the correlation coefficients between each cutting control parameter (such as laser power, material type, kerf width, defocus amount, and assist gas pressure) and the cutting speed, quantify the influence degree of each parameter on the cutting speed, sort these data, and determine the main and secondary influencing parameters. Combining the actual controllability of the parameters, equipment limitations, and process requirements, allocate corresponding weights. For example, assign higher weights to parameters with high correlation such as laser power, and lower weights to parameters with low correlation. Subsequently, standardize the weights through weighted calculation or normalization processing so that their sum is 1 (or 100%). To verify the rationality of the weights, apply them to historical cutting cases for simulation evaluation, and compare the coincidence degree between the simulation results and the actual effects, and adjust the weights if necessary. Finally, through multiple rounds of optimization, generate a set of scientific and reasonable weight allocation schemes for the predetermined cutting control indicators, providing an accurate basis for cutting quality evaluation.

[0038] In a possible implementation manner, establish the cutting signal time sequence according to the first corresponding relationship between the first cutting index and the first moment. Step S360 further includes step S361: Establish the plasma intensity time sequence according to the second corresponding relationship between the first moment and the first plasma intensity. Specifically, through the plasma sensor in the intelligent monitor, collect the first plasma intensity at the first moment during the laser piercing and cutting process, and record the corresponding relationship between this time point and the intensity value as the basic data for constructing the plasma intensity time sequence. Subsequently, continuously collect the plasma intensity values at multiple time points in different stages of cutting, and arrange these data in chronological order to form a complete plasma intensity time series. During this process, perform data cleaning processing, including removing outliers, filling in missing data, and using a smoothing algorithm to optimize the intensity fluctuations, ensuring the authenticity and integrity of the time sequence data. By establishing a second corresponding relationship model between the plasma intensity and time, use a mathematical fitting method to describe the intensity change trend in different stages of cutting, and finally generate time sequence data that can reflect the dynamic characteristics of the plasma signal, providing a reliable basis for the quality evaluation and parameter optimization of the laser cutting process.

[0039] Step S362, establish a cutting speed time series based on the third correspondence between the first moment and the first cutting speed. Specifically, during the laser cutting process, the first cutting speed value is collected and recorded at the first moment by the cutting control equipment group, and a third correspondence is established between the time point and the corresponding cutting speed value, which is used as the initial data of the cutting speed time series. Subsequently, the cutting speed data of multiple time points are continuously collected in real time, and a preliminary cutting speed time series is generated in chronological order. The data is cleaned and preprocessed, outliers are removed, missing data is supplemented, and a smoothing algorithm is used to eliminate high-frequency noise. The data is trend-fitted in combination with cutting process parameters (such as laser power, material type, incision width, etc.) to construct a complete cutting speed time series, accurately reflecting the dynamics of speed changes during the cutting process, providing a reliable basis for quality assessment and parameter optimization, and supporting real-time monitoring and analysis of the cutting process.

[0040] Step S363, obtain the target prediction moment, and analyze the plasma intensity time series in turn to obtain the predicted plasma intensity at the target prediction moment, and analyze the cutting speed time series to obtain the predicted cutting speed at the target prediction moment. Specifically, in the real-time monitoring of laser cutting quality, in order to predict the key cutting parameters at the target prediction moment, the target prediction moment is first determined, and this moment can be dynamically generated according to the current cutting process or real-time condition changes. Subsequently, by analyzing the established plasma intensity time series, the historical plasma intensity data is fitted and trend analyzed using a time series model, and the predicted plasma intensity at the target prediction moment is extrapolated. At the same time, relying on the cutting speed time series, historical cutting speed data is analyzed using methods such as linear regression or polynomial fitting to capture its dynamic change pattern and predict the cutting speed value at the target moment. Finally, the predicted plasma intensity and cutting speed values are combined to provide a basis for the prediction and optimization of cutting quality, while supporting intelligent regulation and abnormal warning, and improving the stability and accuracy of the cutting process.

[0041] Step S364, weight the predicted plasma intensity and the predicted cutting speed to obtain a predicted cutting index. Specifically, during the laser cutting quality prediction process, the predicted cutting index is obtained by weighted calculation of the predicted plasma intensity and the predicted cutting speed. First, the predicted plasma intensity and the predicted cutting speed are extracted, which respectively reflect the energy release characteristics between the laser and the material and the dynamic performance of the cutting head movement speed. Then, weights are set for these two parameters according to the cutting process objectives. The weight allocation needs to combine actual requirements. For example, when emphasizing efficiency, the weight of the cutting speed is higher; when focusing on precision, the weight of the plasma intensity is dominant. Subsequently, these two parameters are normalized to eliminate the dimension difference, and they are weighted and summed through the weight factor, and finally the predicted cutting index is calculated. As a comprehensive evaluation index, this index can characterize the overall performance of the cutting process in real time. The higher the index, the better the cutting quality and efficiency; if the index is low, it indicates that parameters such as laser power, cutting speed, or auxiliary gas pressure need to be optimized, providing a reliable basis for the optimization of the cutting process and the improvement of quality.

[0042] Step S365, analyze the predicted cutting feature information of the predicted cutting signal time series through the in-cut quality evaluation model to obtain a predicted cutting quality coefficient. Specifically, in the real-time monitoring of laser cutting quality, in order to obtain the predicted cutting index, it is necessary to weight the predicted plasma intensity and the predicted cutting speed at the target prediction moment. First, weight values are set according to the importance of the two to the cutting quality, and the predicted plasma intensity and the predicted cutting speed are standardized to ensure that the data dimensions are consistent and avoid affecting the calculation accuracy due to differences. Subsequently, the standardized predicted plasma intensity and the predicted cutting speed are respectively multiplied by the corresponding weight values, and are weighted and calculated according to the formula: predicted cutting index = weight 1 × predicted plasma intensity + weight 2 × predicted cutting speed predicted cutting index = weight 1\times predicted plasma intensity + weight 2\times predicted cutting speed predicted cutting index = weight 1 × predicted plasma intensity + weight 2 × predicted cutting speed, where the sum of the weight values is 1. Finally, the predicted cutting index, as a comprehensive evaluation index, reflects the comprehensive influence of the plasma characteristics and the cutting speed at the target prediction moment on the cutting performance, provides a basis for cutting quality evaluation and parameter optimization, and supports the early identification of quality problems and the adjustment of cutting strategies, improving cutting efficiency and quality stability.

[0043] Step S366: Take the average of the first cutting quality coefficient and the predicted cutting quality coefficient as the predicted cutting quality index, where the predicted cutting quality index is used to characterize the predicted cutting quality of the target laser piercing cutting at the target prediction moment. Specifically, in order to characterize the overall cutting quality of the target laser piercing cutting at the prediction moment, it is necessary to calculate the predicted cutting quality index. First, obtain the first cutting quality coefficient and the predicted cutting quality coefficient. Among them, the first cutting quality coefficient reflects the influence of pre-cutting factors (such as laser power, focal position deviation, gas pressure, etc.) on the initial quality state, and the predicted cutting quality coefficient is output by the in-cutting quality assessment model based on features such as plasma intensity time series and cutting speed time series, reflecting the dynamic quality state at the prediction moment. Then, take the average of the two as the predicted cutting quality index. The formula is: Predicted cutting quality index = (First cutting quality coefficient + Predicted cutting quality coefficient) / 2. This calculation combines the dual influences on quality before and during cutting, ensuring coverage of the dynamic characteristics of the entire cutting process. Finally, the predicted cutting quality index, as a comprehensive indicator, is used to intuitively reflect the cutting quality level, judge whether it meets the expected quality requirements, and at the same time provide data support for parameter adjustment and process optimization, improving the stability and efficiency of cutting quality.

[0044] Step S400: Use the cutting feature information as the input data of the in-cutting quality assessment model, and obtain the output data through the in-cutting quality assessment model, where the output data includes the second cutting quality coefficient. Specifically, the cutting feature information consists of time-domain features and frequency-domain features extracted from the cutting signal time series data, such as parameters like plasma intensity, cutting speed, and heat input fluctuation, which can comprehensively reflect the dynamic changes and real-time state during the cutting process. Input the sorted feature information into the in-cutting quality assessment model. This model is built by combining machine learning techniques and historical cutting data, and conducts in-depth learning and comprehensive analysis on the relationship between the input features and cutting quality, such as identifying whether the plasma intensity fluctuation is too large and whether the cutting speed matches the material type. The model calculates these influencing factors through internal algorithms to generate output data, including the second cutting quality coefficient, which quantitatively describes the real-time quality situation during the cutting process, such as edge quality, cutting depth consistency, and material melting degree. The second cutting quality coefficient, as a key measurement index for the dynamic quality of the cutting process, provides data support for subsequent quality judgment, parameter optimization, and process adjustment, ensuring a more precise and efficient cutting process.

[0045] In a possible implementation, the cutting feature information is used as the input data of the in-cut quality evaluation model, and output data is obtained through the in-cut quality evaluation model. The output data includes a second cutting quality coefficient. Step S400 further includes step S410 of obtaining an in-cut specimen, and the in-cut specimen has an identifier of a specimen cutting data group. Specifically, in the process of cutting quality evaluation, obtaining an in-cut specimen is a key step for extracting and analyzing the actual data of the cutting process. An in-cut specimen is a target material sample selected during the laser cutting process, and an independent specimen cutting data group identifier is assigned to facilitate tracking and analysis. The data group identifier records the physical properties, cutting parameters, equipment information, and process conditions of the specimen, ensuring that the data throughout the cutting process is bound to the specific specimen. The system selects specimens and generates identifiers according to preset rules or real-time conditions, which are used to store the time points, environmental conditions, and cutting parameters of the cutting process, and at the same time support comparison with other specimens or standard cutting results. By obtaining specimens and data identifiers, it provides basic data support for extracting signal features, evaluating cutting quality, and optimizing the process, and lays a reliable basis for tracing abnormal situations.

[0046] Step S420 is to sequentially collect the specimen time-domain features and specimen frequency-domain features of the specimen cutting signal sequence in the specimen cutting data group and form specimen cutting feature information. Specifically, in the process of cutting quality evaluation, the signal sequence of the specimen cutting data group is sequentially collected and the time-domain features and frequency-domain features are extracted to form complete specimen cutting feature information. In time-domain analysis, by calculating parameters such as the amplitude, peak value, mean value, and standard deviation of the signal, the time-varying trend during the cutting process is captured, such as the stability of the laser output and the vibration during the cutting process. Subsequently, the signal is transformed into the frequency domain through Fourier transform, and features such as frequency components, main frequency peak values, and frequency band energy distributions are extracted to identify possible high-frequency interference or low-frequency abnormal phenomena during the cutting process. The extracted time-domain and frequency-domain features are uniformly formatted and then integrated into structured cutting feature information, providing comprehensive and accurate input data for the subsequent in-cut quality evaluation model, and at the same time providing important support for optimizing the cutting process and problem diagnosis.

[0047] Step S430: Train the cutting quality evaluation model during cutting based on the machine learning principle using the sample cutting feature information and the sample cutting quality coefficient in the sample cutting data set. Specifically, construct a cutting quality evaluation model based on the machine learning principle. First, use the sample cutting feature information and the cutting quality coefficient in the sample cutting data set as training data. Take the time-domain features (such as amplitude, mean value) and frequency-domain features (such as main frequency, spectral energy distribution) of the sample signal as the input variables of the model, and the cutting quality coefficient as the output target. Optimize the input data set through steps such as data preprocessing for noise reduction and standardization, and select a suitable data-driven algorithm (such as neural network or random forest) for modeling. During the training process, adjust the model parameters through an iterative optimization algorithm to minimize the prediction error, and combine cross-validation to improve the generalization ability of the model. After the training is completed, use independent verification data to evaluate the model performance, and optimize the model parameters based on error analysis and determination coefficient to ensure that the model can accurately predict the cutting quality coefficient. The finally obtained model can process the cutting feature information in real time, output a high-precision cutting quality coefficient, provide data support for the dynamic monitoring of the cutting process and process optimization, and effectively improve the cutting efficiency and quality stability.

[0048] Step S500: Take the mean of the first cutting quality coefficient and the second cutting quality coefficient as the target cutting quality index, where the target cutting quality index is used to characterize the cutting quality of the target laser perforation cutting. Specifically, the target cutting quality index is calculated by integrating the mean of the first cutting quality coefficient and the second cutting quality coefficient, and is used to comprehensively characterize the overall quality of the target laser perforation cutting. The first cutting quality coefficient is obtained through the analysis of the contact point feedback signal based on the pre-cutting evaluation strategy, reflecting the influence of factors such as laser power, focus deviation, and gas pressure before cutting on the cutting quality; the second cutting quality coefficient is obtained by dynamically monitoring the cutting signal time series through an intelligent monitor, extracting the cutting feature information and calculating it with the cutting quality evaluation model during cutting, reflecting the real-time influence of the actual operation parameters during the cutting process on the quality. By calculating the mean to fuse the quality information before and after cutting, ensure that the calculation result is accurate and reliable, and at the same time provide a scientific basis for cutting process optimization. The target cutting quality index not only clearly reflects the overall performance of the cutting quality, but also indicates potential abnormalities during the cutting process, providing an important reference for subsequent adjustment of process parameters and improvement of equipment, thereby improving the cutting efficiency and the stability of the finished product quality.

[0049] In a possible implementation, the mean value of the first cutting quality coefficient and the second cutting quality coefficient is taken as the target cutting quality index, where the target cutting quality index is used to characterize the cutting quality of the target laser perforation cutting. Step S500 further includes step S510 of monitoring the target laser perforation cutting through a backlight reflection sensor to obtain the time series of the backlight reflection optical signal. Specifically, the target laser perforation cutting is monitored through the backlight reflection sensor to capture in real time the optical signal reflected from the surface of the target material during the cutting process and form a complete time series of the backlight reflection optical signal. The sensor is installed in the cutting device and can highly sensitively sense the change in the intensity of the reflected light generated when the laser interacts with the material. These changes are affected by the laser power, cutting speed, cutting depth, and material properties. The sensor continuously records the intensity of the reflected light at each moment at a high sampling rate and transmits the data to the processing unit in real time. The data processing unit removes noise and corrects anomalies from the signal to ensure the authenticity and accuracy of the data, and arranges and generates the time series of the backlight reflection optical signal in chronological order. This time series records the dynamic changes in the intensity of the reflected light and provides basic data support for analyzing the cutting quality, identifying cutting anomalies, and optimizing process parameters, thereby realizing the real-time monitoring and dynamic adjustment of the cutting process and improving the cutting accuracy and efficiency.

[0050] Step S520, extracting the target backlight reflection optical signal corresponding to the target moment in the time series of the backlight reflection optical signal and obtaining the intensity of the target backlight reflection optical signal corresponding to the target backlight reflection optical signal. Specifically, when analyzing the time series of the backlight reflection optical signal, the target moment is first extracted from the recorded data, and the target moment is determined according to the cutting process requirements or specific events (such as the initial contact between the laser and the material or a key time point). By parsing the time series of the optical signal, the backlight reflection optical signal corresponding to the target moment is accurately located, and the complete signal characteristics are extracted. During the extraction process, the system automatically filters out noise interference or abnormal data to ensure the accuracy of the signal. Subsequently, the intensity value of the target backlight reflection optical signal is further parsed and obtained to reflect the interaction state between the laser and the material at the target moment, such as the change in the reflection performance of the material surface or the instantaneous influence of the laser power. The extracted signal intensity not only provides an important basis for the real-time evaluation of the cutting quality but also provides data support for the optimization and adjustment decision of the cutting process, thereby improving the cutting accuracy and efficiency.

[0051] Step S530: If the intensity of the target backlight reflection optical signal is less than the predetermined perforation detection threshold, determine the target total cutting duration in combination with the target time. Specifically, during the cutting process, by comparing the intensity of the target backlight reflection optical signal with the predetermined perforation detection threshold, the perforation state is accurately judged. When it is detected that the intensity of the backlight reflection optical signal is lower than the threshold, the system determines that the laser has completed or is close to completing the perforation operation, and calculates the duration from the start of cutting to the time when the signal intensity is lower than the threshold in combination with the target time (the initial time point when the laser starts to act on the material), so as to determine the target total cutting duration. The whole process relies on real-time monitoring and continuous sampling to ensure the integrity and accuracy of the signal data. At the same time, the signal fluctuations are smoothed by the dynamic data analysis module to eliminate environmental interference and noise. The determination of the target total cutting duration not only provides a key time reference for cutting quality evaluation, but also lays an important data foundation for process optimization and efficiency analysis.

[0052] Step S540: Obtain the target cutting fitness according to the target total cutting duration and the target cutting quality index. Specifically, during the cutting quality evaluation process, the target cutting fitness is calculated by comprehensively analyzing the target total cutting duration and the target cutting quality index. The target total cutting duration reflects the material penetration efficiency and process stability. A shorter duration indicates high cutting efficiency, while a longer duration may indicate equipment performance problems or complex material characteristics; the target cutting quality index comprehensively quantifies multi-dimensional cutting effects such as cutting accuracy and edge smoothness. After inputting the two parameters into the fitness evaluation model, weighted calculation is performed according to the preset weights. Among them, the cutting duration associated with efficiency is given a negative weight, and the cutting quality index related to accuracy is given a positive weight, and finally the target cutting fitness value is generated. A high fitness score indicates good cutting efficiency and quality and reasonable process settings; if the score is low, the laser power, cutting speed, or auxiliary gas parameters, etc. need to be adjusted, and the equipment and material processes need to be optimized to improve the fitness level. The fitness result can not only evaluate the current process, but also provide an optimization reference for subsequent tasks.

[0053] In the embodiment of the present application, high-precision sensors are used to monitor the target laser perforation cutting, obtain the feedback signal when the target laser initially contacts the material, and analyze the feedback signal according to the pre-cutting quality evaluation strategy to obtain the first cutting quality coefficient; the cutting signal timing is dynamically monitored by an intelligent monitor, the cutting feature information is extracted and input into the in-cutting quality evaluation model to obtain the second cutting quality coefficient; finally, the first and second cutting quality coefficients are averaged to calculate the target cutting quality index, which is used to characterize the cutting quality situation, achieving the technical effect of realizing real-time dynamic monitoring and accurate quantitative evaluation of laser cutting quality.

[0054] Based on the foregoing embodiments, the embodiments of the present application further provide an electronic device and a computer-readable storage medium. A computer program is stored in the computer-readable storage medium. When the computer program is executed by a processor of the electronic device, the method described in any of the previous embodiments can be implemented.

[0055] Figure 3 FIG. 4 is a schematic structural diagram of an electronic device provided by an embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 3 The electronic device shown is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present invention. The electronic device is presented in the form of a general-purpose computing device, and its components may include, but are not limited to, an input device 301, a processor 302, a memory 303, and an output device 304. Among them, the processor 302 may be one or more; the memory 303 may include a computer-readable medium and at least one program product. The program product has a set (at least one) of program modules, and these program modules are configured to execute the functions of the embodiments of the present application.

[0056] The memory 303 shown in the embodiments of the present invention may adopt any combination of one or more computer-readable media; the computer-readable storage medium may be, but is not limited to, infrared rays, semiconductor systems, devices or components, or any combination of the above, for storing software programs, computer-executable programs, and modules, such as program instructions / modules corresponding to a real-time cutting quality monitoring method in the embodiments of the present invention. The processor 302 executes various functional applications and data processing of the computer device by running the software programs, instructions, and modules stored in the memory 303, that is, implements the above-mentioned real-time cutting quality monitoring method.

[0057] The above specific implementation manners do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A real-time monitoring method for cutting quality, characterized in that, Including: Monitoring the target laser piercing and cutting through a high-precision sensor to obtain a contact point feedback signal, where the contact point feedback signal refers to the feedback signal when the target laser initially contacts the target material during the target laser piercing and cutting; Retrieving the pre-cut quality assessment strategy to evaluate and analyze the contact point feedback signal to obtain a first cutting quality coefficient; Dynamically monitoring the cutting signal sequence of the target laser piercing and cutting through an intelligent monitor and obtaining the cutting feature information of the cutting signal sequence; Using the cutting feature information as input data for the in-cut quality assessment model and obtaining output data through the in-cut quality assessment model, where the output data includes a second cutting quality coefficient; Taking the average of the first cutting quality coefficient and the second cutting quality coefficient as the target cutting quality index, where the target cutting quality index is used to characterize the cutting quality of the target laser piercing and cutting.

2. The method according to claim 1, wherein Retrieving the pre-cut quality assessment strategy to evaluate and analyze the contact point feedback signal to obtain a first cutting quality coefficient, including: Reading the predetermined pre-cut factors in the pre-cut quality assessment strategy; Traversing the predetermined pre-cut factors in the contact point feedback signal to obtain pre-cut factor parameters; Performing mutation weighting on the pre-cut factor parameters to obtain the first cutting quality coefficient; Where the predetermined pre-cut factors include laser power, laser beam focus position deviation, gas pressure, gas purity, and laser cutting distance, and the laser cutting distance refers to the straight-line distance between the target laser and the target material.

3. The method according to claim 1, characterized in that Dynamically monitoring the cutting signal sequence of the target laser piercing and cutting through an intelligent monitor and obtaining the cutting feature information of the cutting signal sequence, including: Monitoring the target laser piercing and cutting through the plasma sensor in the intelligent monitor to obtain the first plasma intensity at the first moment; Extracting the cutting control device group in the intelligent monitor and monitoring the predetermined cutting control indicators of the target laser piercing and cutting at the first moment through the cutting control device group to obtain a first cutting control parameter group; Reading the predetermined relative weight distribution of the predetermined cutting control indicators; Performing weighted calculation on the first cutting control parameter group according to the predetermined relative weight distribution to obtain a first cutting speed; Weighting the first plasma intensity and the first cutting speed to obtain a first cutting index; Establishing the cutting signal sequence according to the first cutting index and the first correspondence relationship at the first moment.

4. The method according to claim 3, wherein The predetermined cutting control indicators include laser power, material type, cut width, defocus amount, and auxiliary gas pressure.

5. The method according to claim 3, wherein Reading the predetermined relative weight distribution of the predetermined cutting control indicators, including: Extracting the first historical cutting record in the historical laser cutting database; Traversing the first historical cutting record based on the predetermined cutting control indicators to obtain a first historical cutting control parameter group; Perform a one-to-many correlation analysis on the first historical cutting control parameter group and the first historical cutting speed in the first historical cutting record to obtain a first correlation analysis result; Analyze the first correlation analysis result and determine the predetermined relative weight assignment of the predetermined cutting control index.

6. The method according to claim 1, wherein Use the cutting feature information as input data for the in-cut quality evaluation model, and obtain output data through the in-cut quality evaluation model, where the output data includes a second cutting quality coefficient, including: Obtain an in-cut specimen, and the in-cut specimen has an identifier of a specimen cutting data group; Successively collect the specimen time-domain characteristics and specimen frequency-domain characteristics of the specimen cutting signal timing in the specimen cutting data group, and form specimen cutting feature information; Based on the principle of machine learning, train the specimen cutting feature information and the specimen cutting quality coefficient in the specimen cutting data group to obtain the in-cut quality evaluation model.

7. The method according to claim 3, wherein Further include: Establish a plasma intensity time series according to the second correspondence between the first moment and the first plasma intensity; Establish a cutting speed time series according to the third correspondence between the first moment and the first cutting speed; Obtain a target prediction moment, and successively analyze the plasma intensity time series to obtain the predicted plasma intensity at the target prediction moment and analyze the cutting speed time series to obtain the predicted cutting speed at the target prediction moment; Weight the predicted plasma intensity and the predicted cutting speed to obtain a predicted cutting index; Analyze the predicted cutting feature information of the predicted cutting signal time series through the in-cut quality evaluation model to obtain a predicted cutting quality coefficient; Take the average of the first cutting quality coefficient and the predicted cutting quality coefficient as the predicted cutting quality index, where the predicted cutting quality index is used to characterize the predicted cutting quality situation of the target laser perforation cutting at the target prediction moment.

8. The method according to claim 1, wherein Further include: Monitor the target laser perforation cutting through a backlight reflection sensor to obtain a backlight reflection light signal time series; Extract the target backlight reflection light signal corresponding to the target moment in the backlight reflection light signal time series, and obtain the target backlight reflection light signal intensity corresponding to the target backlight reflection light signal; If the target backlight reflection light signal intensity is less than a predetermined perforation detection threshold, determine the target total cutting duration in combination with the target moment; Obtain a target cutting fitness according to the target total cutting duration and the target cutting quality index.

9. An electronic device, characterized in that, The electronic device includes: A memory for storing executable instructions; A processor for implementing a method for real-time monitoring of cutting quality according to any one of claims 1 to 8 when executing the executable instructions stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements a method for real-time monitoring of cutting quality according to any one of claims 1-8.

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