Processing center virtual debugging and process optimization method based on digital twinning

Abstract

The invention relates to the technical field of intelligent manufacturing and industrial digitization, in particular to a processing center virtual debugging and process optimization method based on digital twinning, which comprises the steps of acquiring an equipment running state feature set, generating a running state adaptation result, optimizing an operation adjustment parameter set, forming an operation correction matching result and executing an adjustment result. According to the method, the coordination and consistency of the debugging process are optimized by analyzing key variable distribution, the adaptability of operation instruction points and the triggering frequency, and finally process optimization is achieved. The method can improve the virtual debugging efficiency of the machining center and enhance the process execution precision and stability.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing and industrial digitalization technology, and in particular to a virtual debugging and process optimization method for a machining center based on digital twins. Background Art

[0002] Digital twin technology, a key tool in intelligent manufacturing, demonstrates significant advantages in the virtual commissioning and process optimization of machining centers by establishing a real-time mapping between virtual models and physical entities. Its core components include virtual modeling, real-time data acquisition and analysis, dynamic simulation, and the development of process optimization strategies. By comprehensively simulating and predicting the operating status of machining centers, it can improve production efficiency, shorten commissioning cycles, and reduce manufacturing costs. In recent years, with the development of the Industrial Internet of Things and big data technologies, the application of digital twins in the manufacturing industry has steadily deepened, becoming a key technical support for the commissioning and optimization of machining centers in complex, multi-tasking environments. Digital twin-based approaches for virtual commissioning and process optimization of machining centers involve multi-level modeling and simulation, from the device to the system level, encompassing aspects such as logical interlocking between devices, process parameter adjustment, and dynamic optimization. Specifically, a high-precision digital twin model is constructed to virtually reproduce the entire process flow of the machining center, and dynamic adjustments are achieved through the integration of real-time feedback mechanisms. Furthermore, data analysis and simulation tools are used to deeply optimize the process flow to support diverse machining needs. In addition, we also focus on universal design so that the model can adapt to the needs of different processing scenarios and further improve debugging efficiency and process accuracy.

[0003] Existing technical solutions have certain limitations in practical application. For example, Publication No. CN114777295B, a virtual commissioning system and method for data center cold source group control, proposes a virtual commissioning solution for cold source systems, relying primarily on the collaborative work of a dynamic simulation module, a logic control module, and a real-time database. However, this solution focuses on specific application scenarios for cold source systems, and its versatility is relatively limited, making it difficult to directly meet the needs of complex multi-tasking environments in machining centers. Furthermore, the solution's support for logical interlocking between devices and process flow optimization still needs improvement, especially when addressing diverse process requirements. Another technical solution, Publication No. CN108121216B, proposes a virtual commissioning method for automated workshops based on a digital factory. By developing a communication plug-in, it enables external applications to drive the actions of virtual simulation models. This solution focuses on workshop-level layout design and production process optimization, but lacks in-depth exploration of specific process optimization within the machining center. Furthermore, the solution's capabilities for real-time feedback and dynamic adjustment of key process parameters are relatively weak, potentially failing to fully meet the requirements of high-precision machining.

[0004] These issues demonstrate that existing virtual commissioning and process optimization technologies still have room for improvement in terms of versatility, in-depth process optimization, dynamic adjustment capabilities, and support for complex multi-tasking environments. Therefore, this paper aims to provide a digital twin-based virtual commissioning and process optimization method for machining centers. By constructing a high-precision digital twin model, this method enables virtual commissioning and dynamic optimization of the entire process flow, thereby better meeting the requirements of modern intelligent manufacturing for efficient and precise machining. Summary of the Invention

[0005] In order to solve the technical problems existing in the prior art, the embodiment of the present invention provides a virtual debugging and process optimization method for a machining center based on digital twins. The technical solution is as follows: A virtual debugging and process optimization method for a machining center based on digital twins includes the following steps: S1: Obtain the variation range of equipment operating parameters in adjacent time series under the operating status of the physical equipment of the machining center, identify key variables, calculate the distribution characteristics of key variables under different working conditions, analyze the change rate of the area where the distribution characteristic fluctuation exceeds the set threshold, and generate the machining center operating status feature set; S2: Based on the machining center operation state feature set, identifying the operation instruction points input by the user in the virtual debugging interface, analyzing the distribution relationship between the operation instruction points and the operation state feature set, evaluating the adaptability of the operation instruction point scope and the operation state feature, and forming an operation state adaptation result; S3: extracting the motion trajectory of the operation instruction point in the operation state adaptation result, analyzing the continuity of the operation state characteristics within the action range of the operation instruction point, calculating the frequency of instruction triggering in the continuous operation area, and generating an operation adjustment parameter set; S4: Calculate the frequency of repeated triggering of the operation instruction point based on the operation adjustment parameter set, analyze the operation consistency during the virtual debugging process, identify the position offset of adjacent trigger points and calibrate the trigger intensity change trend, extract the numerical range within the trigger intensity fluctuation range, and form an operation correction matching result.

[0006] As a further solution of the present invention, the machining center operation status feature set includes the distribution of key variables, regional change rate, and operation parameter difference analysis results; the operation status adaptation results include the operation instruction point position, range of action, adaptability evaluation results, and operation status characteristic morphology; the operation adjustment parameter set includes the operation instruction point motion trajectory, trigger frequency, operation status continuity, and coordination adjustment; the operation correction matching results include the trigger frequency, trigger point position offset, trigger intensity numerical sequence change trend, offset range, and numerical fluctuation range.

[0007] As a further solution of the present invention, the steps for obtaining the machining center operation status feature set are: S101: Obtain a time series arrangement of the operating status data of the physical equipment in the machining center, analyze the change range of the equipment operating parameters in adjacent time series, calculate the parameter change of each operating parameter relative to the surrounding time points, classify the operating status according to the parameter change, identify the state point where the parameter change exceeds the set change threshold, mark its time node, and establish a parameter change state point set; S102: Based on the parameter change state point set, calculate the distribution characteristics of key variables in the state point set, count the number of key variables in each state point set and their parameter means, obtain the volatility of the distribution characteristics of the key variables in each state point set, and screen the state point sets whose volatility exceeds a set volatility threshold to obtain a distribution characteristic mutation point set; S103: Based on the distribution feature mutation point set, calculate the regional change rate within each state point set, analyze the distribution characteristics of key variables in the state point set, count the variable connectivity and shape characteristic values ​​within the state point set, calculate the regional change rate, and establish the machining center operation state feature set.

[0008] As a further solution of the present invention, the steps for obtaining the operating state adaptation result are: S201: Based on the machining center operation state feature set, identifying the operation instruction point input by the user in the virtual debugging interface, obtaining the time node data of the operation instruction point, detecting the state point set where the operation instruction point is located, counting the change range of the operation parameters around the operation instruction point, screening the state point set with significant change range, and establishing the operation instruction point parameter change set; S202: Based on the parameter change set of the operation instruction point, analyzing the distribution relationship between the operation instruction point and the operating state feature set, calculating the variation range within the distribution range of the operating parameters around the operation instruction point, evaluating the adaptability of the operating instruction point range and the operating state feature, and establishing the adaptability distribution of the operation instruction point; S203: Based on the adaptability distribution of the operation instruction points, screen the target state point set whose adaptability meets the operation requirements, extract the best adaptation state point set where the operation instruction points are located according to the adaptability distribution of the operation instruction points, and establish the operation state adaptation result.

[0009] As a further solution of the present invention, the step of obtaining the operation adjustment parameter set is: S301: Based on the operation state adaptation result, extract the motion trajectory of the operation instruction point, obtain the time node data of the continuous operation instruction points, calculate the trajectory path according to the time series, count the time distribution of the operation instruction points, and establish the operation trajectory path set; S302: Based on the operation trajectory path set, analyze the continuity of the operating state characteristics within the scope of the operation instruction point, calculate the trigger frequency in the continuous operation area, count the number of repeated triggers of each operation instruction point within a specific time window, and filter the operation areas whose trigger frequency exceeds a set threshold to establish a high-frequency operation area set; S303: Based on the operation high-frequency area set, optimize the coordination of the operation process, calculate the time relationship between the operation instruction points, adjust the operation spacing and time interval, adjust the adaptability weight according to the timing relationship of the operation, and generate an operation adjustment parameter set.

[0010] As a further solution of the present invention, the steps of obtaining the operation correction matching result are: S401: Based on the operation adjustment parameter set, calculate the repetitive triggering frequency of the operation instruction point, obtain time series data of the operation instruction point, count the number of repetitive triggering of the same operation instruction point within a set time window, calculate the unit time triggering frequency of each operation instruction point, and establish the operation instruction point triggering frequency distribution; S402: Based on the trigger frequency distribution of the operation instruction points, analyzing the trigger consistency between the operation instruction points, calculating the position offsets of adjacent triggered operation instruction points, screening a range where the offsets do not exceed a set offset threshold, obtaining an operation instruction point offset ratio distribution, screening a range where the offset ratios are lower than a set threshold, and establishing an operation instruction point offset screening result; S403: Based on the operation instruction point offset screening result, calculate the change trend of the trigger intensity value sequence of the trigger record within the offset range, analyze the trigger intensity fluctuation range, screen the value range that meets the fluctuation range, and finally generate the operation correction matching result.

[0011] As a further embodiment of the present invention, the method further comprises: S5: calling the interval time of the operation trigger in the operation correction matching result, calling the time distribution rate to analyze the continuity change of the operation trigger, calculating the trigger repetition rate in the operation triggering process, optimizing the triggering method of the operation mode according to the repetition rate, and adjusting the order of operation execution to generate the virtual debugging and process optimization execution adjustment results of the machining center; The machining center virtual debugging and process optimization execution adjustment results include operation trigger interval time, time distribution rate, trigger repetition rate, operation mode optimization, and execution sequence adjustment.

[0012] As a further solution of the present invention, the steps for obtaining the adjustment results of the virtual debugging and process optimization execution of the machining center are as follows: S501: Call the operation trigger interval in the operation correction matching result, obtain the timestamp data of each operation instruction point, count the time difference between adjacent operation instruction points, and calculate the number of operation triggers per unit time. Organize these data into a time series, analyze the interval distribution of operation triggers, filter out high-frequency trigger intervals below a set threshold, and establish an operation time interval distribution data set; S502: Analyze the continuity change of the operation trigger based on the operation time interval distribution data set, calculate the repetition rate of the trigger during the operation trigger process, filter the high repetition trigger area according to the repetition rate, adjust the operation mode triggering method, and establish the operation trigger mode optimization result; S503: Based on the optimization result of the operation trigger mode, the operation execution sequence is adjusted, the response logic between consecutive operation instruction points is optimized, operations suitable for parallel execution are screened, and the sequence of associated operation instruction points is optimized, and finally the virtual debugging and process optimization execution adjustment results of the machining center are generated.

[0013] As a further solution of the present invention, the machining center operation status feature set collects spindle speed, feed speed, and cutting force as operation parameters through sensors and arranges them in a time series form.

[0014] As a further solution of the present invention, the operation instruction point is determined by the coordinate position input through the virtual debugging interface, and its scope of action is determined by the variation range of the surrounding operating parameters.

[0015] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: In the present invention, the amplitude of changes in equipment operating parameters is analyzed through time series, the fluctuation area of ​​distribution characteristics of key variables is identified, and the adaptability of operation instruction points and operation status characteristics is quantitatively evaluated to establish a dynamic mapping relationship between instruction action range and parameter changes. The operation coordination is optimized based on the trigger frequency to achieve precise matching of instruction triggering and equipment response during the debugging process. The trend of change in trigger intensity is calibrated by calibrating the position offset, the numerical interval is extracted to correct the matching result, and redundant operations in multi-task scenarios are eliminated. Combined with the analysis of the distribution rate and repetition rate of the trigger interval time, the operation trigger sequence and execution mode are reconstructed to form a closed-loop optimization mechanism, enhance the real-time performance of dynamic adjustment of process parameters, reduce the dependence of the debugging process on manual experience, and improve the equipment operation stability and resource scheduling efficiency under complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of the method of the present invention; Figure 2 This is a flow chart for obtaining a machining center operating status feature set according to the present invention; Figure 3 This is a flow chart for obtaining the operating state adaptation result of the present invention; Figure 4 A flow chart for obtaining a parameter set for adjusting the operation of the present invention; Figure 5 A flowchart for obtaining the corrected matching results for the present invention; Figure 6 This is a flow chart for obtaining adjustment results of the virtual debugging and process optimization execution of the machining center of the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0018] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0019] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0020] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0021] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0022] See also Figure 1 The present invention provides a technical solution: a virtual debugging and process optimization method for a machining center based on digital twins, comprising the following steps: S1: Obtain the time series arrangement of the operating status data of the physical equipment of the machining center, analyze the variation range of the equipment operating parameters in adjacent time series, identify key variables, calculate the distribution characteristics of key variables under different working conditions, analyze the change rate of the area where the distribution characteristic fluctuation exceeds the set threshold, and generate the machining center operating status feature set; S2: Based on the machining center's operating status feature set, identify the operation command points entered by the user in the virtual debugging interface, analyze the distribution relationship between the operation command points and the operating status feature set, calculate the change in the equipment operating parameters within the operating range of the operation command points, evaluate the adaptability of the operating range of the operation command points and the operating status features, and form an operating status adaptation result; S3: Extract the motion trajectory of the operation instruction point in the operation state adaptation result, analyze the continuity of the operation state characteristics within the scope of the operation instruction point, calculate the frequency of instruction triggering in the continuous operation area, optimize the coordination of the operation process based on the triggering frequency, and generate an operation adjustment parameter set; S4: Based on the operation adjustment parameter set, the frequency of repeated triggering of the operation instruction point is calculated, the operation consistency during virtual debugging is analyzed, the position offset of adjacent trigger points is identified, the trigger intensity change trend within the offset range is calibrated based on the position offset, and the value interval within the trigger intensity fluctuation range is extracted to form the operation correction matching result; S5: Call the operation correction matching result to find the interval time of the operation trigger, call the time distribution rate to analyze the continuity change of the operation trigger, calculate the trigger repetition rate during the operation triggering process, optimize the triggering method of the operation mode according to the repetition rate, and adjust the order of operation execution to generate the virtual debugging and process optimization execution adjustment results of the machining center.

[0023] The operating status feature set of the machining center includes the distribution of key variables, regional change rate, and operating parameter difference analysis results; the operating status adaptation results include the operation instruction point position, scope of action, adaptability evaluation results, and operating status feature morphology; the operation adjustment parameter set includes the operation instruction point motion trajectory, trigger frequency, operating status continuity, and coordination adjustment; the operation correction matching results include trigger frequency, trigger point position offset, trigger intensity numerical sequence change trend, offset range, and numerical fluctuation range; the virtual debugging and process optimization execution adjustment results of the machining center include operation trigger interval time, time distribution rate, trigger repetition rate, operation mode optimization, and execution sequence adjustment.

[0024] See also Figure 2 ,The steps to obtain the machining center operation status feature set are: S101: Obtain a time series arrangement of the operating status data of the physical equipment in the machining center, analyze the change range of the equipment operating parameters in adjacent time series, calculate the parameter change of each operating parameter relative to the surrounding time points, classify the operating status according to the parameter change, identify the state point where the parameter change exceeds the set change threshold, mark its time node, and establish a parameter change state point set; First, the operating parameters of the CNC machining center, such as spindle speed, feed speed, cutting force, and spindle temperature, are monitored in real time. Assuming that data is collected every 0.1 seconds, time series data is formed. For example, at the time point At time 1, the spindle speed is 8000 rpm, the feed rate is 1200 mm / min, the cutting force is 500 N, the spindle temperature is 35 degrees Celsius, and at time 2, the spindle speed is 8000 rpm, the feed rate is 1200 mm / min, the cutting force is 500 N, and the spindle temperature is 35 degrees Celsius. At 10 seconds, the spindle speed is 8010 rpm, the feed rate is 1205 mm / min, the cutting force is 503 N, and the spindle temperature is 35.1 degrees Celsius. When calculating the parameter changes of each operating parameter relative to the surrounding time points, the sliding window method is used, and the window size is set to 3 time points, that is, the current time point Compared with the previous time point , the next time point For comparison, for the spindle speed parameter, the time point The spindle speed change is RPM, time point The spindle speed change is RPM, when classifying the operating status according to the parameter change, set the change threshold To distinguish between stable state, small fluctuation state and drastic change state, for example, when the spindle speed changes RPM is considered stable. RPM is judged as a small fluctuation state. When the spindle speed exceeds the set change threshold, it is judged as a drastic change state. When the parameter change exceeds the set change threshold, the state point is identified and its time node is marked. The spindle speed change threshold is set to 20 rpm. When the spindle speed is at the time point The change from 8030 rpm to 8055 rpm is 25 rpm, which exceeds the set threshold of 20 rpm. Therefore, the time point is marked as a parameter change state point, thereby establishing a parameter change state point set. For example, for the spindle speed, the parameter change state point set includes the time point 、 Each time point corresponds to a dramatic change in the spindle speed. For the feed rate, the parameter change state point set includes the time point 、 Etc., each time point corresponds to a drastic change in feed speed.

[0025] S102: Based on the parameter change state point set, calculate the distribution characteristics of key variables in the state point set, count the number of key variables in each state point set and their parameter means, obtain the volatility of the distribution characteristics of the key variables in each state point set, and select the state point set whose volatility exceeds the set volatility threshold to obtain the distribution characteristic mutation point set; For the established parameter change state point set, such as the spindle speed change state point set, the statistics of each key variable within it are calculated. The key variables are defined as parameters that have a significant impact on the stability of the machining process, including spindle speed, feed rate, cutting force, and spindle temperature. For example, for the spindle speed change state point set, the distribution characteristics of the spindle speed are statistically analyzed, including mean, variance, skewness, and kurtosis, to reflect its numerical distribution around a specific time point. When the number of key variables and their parameter means in each state point set are statistically analyzed, for example, in the spindle speed change state point set In time The average spindle speed is 8050 rpm, at time The average spindle speed is 7500 rpm, at time The average spindle speed is 8100 rpm. When obtaining the volatility of the key variable distribution characteristics of each state point set, the volatility is calculated using the following formula: in, Indicates the state point concentration The parameter values ​​of the key variables, represents the parameter mean of the key variables in the state point concentration, It represents the number of key variables in the state point concentration. The significance of the formula is to quantify the fluctuation degree of the distribution characteristics by calculating the average relative deviation between the parameter value of the key variable and the mean. The larger the value, the more severe the fluctuation. Assume that in a certain parameter change state point concentration, the parameter values ​​of the spindle speed are RPM, then , RPM, fluctuation rate The reference content of volatility setting is determined based on historical data analysis and expert experience, which aims to capture significant changes in parameter distribution and set volatility thresholds. The rationality of this is verified by the fact that in the actual machining process, when the fluctuation rate exceeds this value, the machining quality deteriorates or the equipment is abnormal, and multiple sets of machining experiments are carried out in the test environment. By comparing and analyzing the machining results under different threshold settings, the effectiveness of the 2% threshold in accurately identifying mutation points is verified. When the fluctuation rate exceeds this threshold, it is considered that the state point set has a distribution feature mutation. The state point set with a fluctuation rate exceeding the set fluctuation rate threshold is screened to obtain the distribution feature mutation point set. For example, if the spindle speed fluctuation rate calculated above is 3.24%, which exceeds the 2% fluctuation rate threshold, the spindle speed change state point set is identified as the distribution feature mutation point set, so as to identify all mutation point sets that meet the conditions.

[0026] S103: Based on the distribution feature mutation point set, calculate the regional change rate within each state point set, analyze the distribution characteristics of key variables in the state point set, count the variable connectivity and shape characteristic values ​​within the state point set, calculate the regional change rate, and establish the machining center operation state feature set; For example, for the mutation point set of spindle speed, we analyze the distribution characteristics of key variables in the state point set. The distribution characteristics here refer to the data distribution form of the spindle speed in the mutation point set, including the value range, central tendency and dispersion degree. By calculating the coefficient of variation, for example, the coefficient of variation ,in is the standard deviation of the spindle speed, is the mean of the spindle speed. Assume that within a certain set of spindle speed mutation points, the standard deviation of the spindle speed is 150 rpm, average is 8000 rpm, then the coefficient of variation is When counting the variable connectivity and shape eigenvalues ​​in the state point set, variable connectivity refers to whether there is a significant correlation between different key variables (such as spindle speed and feed speed) in the mutation point set. The Pearson correlation coefficient is calculated. For example, the correlation coefficient between spindle speed and feed rate is 0.8, indicating a strong positive correlation between the two. The shape eigenvalue refers to the asymmetry and sharpness of the distribution of key variables, such as skewness and kurtosis. Assuming that the skewness of the spindle speed is 0.5 and the kurtosis is 3.2, when calculating the regional change rate, the calculation formula of the regional change rate is as follows: in, 、 、 is the weight coefficient, which is used to adjust the impact of different features on the regional change rate. The reference content of the weight coefficient setting is based on expert experience and historical data analysis. For example, , , ,The rationality of these weight coefficients lies in that they balance the importance of ,the coefficient of variation, correlation and shape characteristics in evaluating ,the regional change rate, and through the analysis of historical fault data, ,it is found that when the weight coefficients are set in this way, ,the operating status changes associated with equipment abnormalities can be more accurately ,identified. For example, when the coefficient of variation is , correlation coefficient , when the skewness is 0.5 and the kurtosis is 3.2, the regional change rate Through the above calculations, the operating status feature set of the machining center can be established. This feature set contains information such as the regional change rate of each mutation point set, the distribution characteristics of key variables, variable connectivity, and shape eigenvalues. For example, the operating status feature set can be represented as a series of high-dimensional vectors, each of which represents a specific operating status feature, including its regional change rate value of 0.4575, as well as the mean, variance, skewness, and kurtosis of the spindle speed.

[0027] See also Figure 3 , the steps to obtain the running status adaptation results are: S201: Based on the machining center operation state feature set, identify the operation instruction point input by the user in the virtual debugging interface, obtain the time node data of the operation instruction point, detect the state point set where the operation instruction point is located, count the change range of the operation parameters around the operation instruction point, select the state point set with significant change range, and establish the operation instruction point parameter change set; The user clicks the "Start Spindle" button in the virtual debugging interface. The operation instruction point is recognized. When obtaining the time node data of the operation instruction point, the system records the timestamp of the user clicking the "Start Spindle" button. For example, the timestamp is , when detecting the state point set where the operation instruction point is located, according to At a time point, find the parameter change state point closest to the time point in the parameter change state point set. For example, Located in the state point set where the spindle speed changes dramatically from static to high-speed startup, when counting the change range of the operating parameters around the operation instruction point, set a time window, for example, the operation instruction point time As the center, it extends 2 seconds forward and backward, that is, the time window is In this time window, the difference between the maximum and minimum values ​​of the operating parameters such as spindle speed, feed rate, cutting force, and spindle temperature is monitored. For example, the spindle speed increases from 0 rpm to 8000 rpm within the window, with a change range of 8000 rpm, and the feed rate increases from 0 mm / min to 1200 mm / min, with a change range of 1200 mm / min. When screening the state point set with significant change range, a significant change threshold is set. For example, the spindle speed change range threshold is 5000 rpm. minutes, the feed speed change amplitude threshold is 800 mm / min. When the spindle speed change amplitude of 8000 rpm exceeds the threshold of 5000 rpm, and the feed speed change amplitude of 1200 mm / min exceeds the threshold of 800 mm / min, it is determined that the change amplitude of the state point set is significant, and an operation instruction point parameter change set is established. For example, the operation instruction point parameter change set includes a significant state point set corresponding to the "start spindle" instruction, which records the drastic change amplitudes of the spindle speed and feed speed.

[0028] S202: Based on the parameter change set of the operation instruction point, analyze the distribution relationship between the operation instruction point and the operating state feature set, calculate the variation range within the distribution range of the operating parameters around the operation instruction point, evaluate the adaptability of the operating instruction point range and the operating state feature, and establish the adaptability distribution of the operation instruction point; The parameter change set of the operation instruction point is matched with the operation status feature set of the machining center. For example, the parameter change set of the operation instruction point includes the spindle speed change amplitude of 8000 rpm caused by the "start spindle" instruction. The features close to this amplitude range are searched in the operation status feature set. For example, there is a feature in the operation status feature set that describes the speed change range from 0 to 8000 rpm when the spindle is started, which shows that there is a strong distribution relationship between the two. When calculating the change amplitude within the distribution range of the operation parameters around the operation instruction point, for example, for the "start spindle" instruction, the operating parameters within its scope of action mainly include the spindle speed and motor current. , monitor the real-time value of the spindle speed, record its rising process from 0 to 8000 rpm, and calculate the maximum change gradient in the process. For example, it rises from 1000 rpm to 5000 rpm in 0.5 seconds, and the change gradient is 8000 rpm / second. When evaluating the adaptability of the operating instruction point's scope of action and the operating status characteristics, set an adaptability index. This index is measured by comparing the Euclidean distance between the actual parameter change amplitude within the operating instruction point's scope of action and the expected parameter change amplitude in the operating status feature set. For example, the smaller the Euclidean distance, the higher the adaptability. The quantitative standard for adaptability evaluation is that if the Euclidean distance is less than the preset threshold, , then the adaptability is considered good, if it is greater than or equal to The adaptability is poor. Assuming that under the "start spindle" operation, the actual measured spindle speed change range is 8000 rpm, while the running state feature is concentrated, the ideal spindle speed change range corresponding to this operation is 7950 rpm, then the Euclidean distance is , this value is less than the threshold of 100, indicating good adaptability. Based on this, the adaptability distribution of the operation instruction point is established. This distribution records the adaptability score of each operation instruction point and the operating parameter change characteristics within its scope of action. For example, the adaptability score of the "start spindle" operation instruction point is 95 points (out of 100 points).

[0029] S203: Based on the adaptability distribution of the operation instruction points, a target state point set whose adaptability meets the operation requirements is screened, and based on the adaptability distribution of the operation instruction points, the best adaptation state point set where the operation instruction points are located is extracted to establish an operation state adaptation result; From the adaptability distribution of the operation instruction points, select those operation instruction points whose adaptability scores are higher than the preset minimum adaptability threshold. For example, if the minimum adaptability threshold is set to 80 points and the adaptability score of the "Start Spindle" instruction is 95 points, it is screened as an instruction that meets the operation requirements. Based on the adaptability distribution of the operation instruction points, when extracting the best-fit state point set where the operation instruction point is located, for each screened operation instruction point, identify the state point set with the highest adaptability score corresponding to it in the operating state feature set. For example, the best-fit state point set corresponding to the "Start Spindle" instruction is the normal increase process of the spindle speed and motor current when the spindle starts, and its adaptability score reaches 95 points. In this way, an operating state adaptation result is established, which contains a series of operation instruction points and their corresponding best-fit state point sets. For example, the operating state adaptation result includes the "Start Spindle" instruction and its associated "Spindle Normal Start" state point set, as well as the "Feed Start" instruction and its associated "Feed Axis Normal Operation" state point set.

[0030] See also Figure 4 , the steps to obtain the operation adjustment parameter set are: S301: Based on the running state adaptation result, the motion trajectory of the operation instruction point is extracted, the time node data of the continuous operation instruction points are obtained, the trajectory path is calculated according to the time series, the time distribution of the operation instruction points is counted, and the operation trajectory path set is established; From the acquired operation status adaptation result, extract the "Start spindle" instruction, followed by the "Start feed" instruction, and then the "Start cutting" instruction. The execution order of these instructions in the virtual debugging interface is obtained. When obtaining the time node data of the continuous operation instruction points, the timestamp of each operation instruction point is recorded. For example, the timestamp of "Start spindle" is The timestamp of "Start Feed" is The timestamp of "Start cutting" is When calculating the trajectory path according to the time series, these timestamps are arranged in ascending order to form the execution sequence of the operation instructions. For example, the sequence is "start the spindle" Start Feed “Start cutting”, when counting the time distribution of the operation instruction points, the time interval between each instruction point and the next instruction point is analyzed, for example, Second, seconds, establish an operation trajectory path set. For example, the operation trajectory path set contains multiple operation trajectories, each trajectory consists of a series of ordered operation instruction points and their time intervals. For example, a trajectory path is , the time interval is .

[0031] S302: Based on the operation trajectory path set, analyze the continuity of the operating state characteristics within the scope of the operation instruction point, calculate the trigger frequency in the continuous operation area, count the number of repeated triggers of each operation instruction point within a specific time window, and filter the operation areas with a trigger frequency exceeding a set threshold to establish a high-frequency operation area set; Centrally "Start Spindle" for operating path Start Feed For the path of “start cutting”, within the time window of each operation instruction point, the operation status feature set is checked to evaluate its stability during the switching process of the operation instruction point. For example, the operation status feature value (such as the regional change rate) is observed to see whether it remains within the preset stable range during the process from spindle speed startup to stabilization and feed axis startup to stabilization. If the regional change rate does not fluctuate violently during this process, it is considered that the continuity is good. When calculating the trigger frequency within the continuous operation area, a continuous operation area is defined. For example, in a specific processing task, the spindle startup to cutting completion becomes a continuous operation area, and the statistics of the area are counted. The number of times each operation instruction point in the domain (such as "Start Spindle", "Start Feed", and "Start Cutting") is triggered. For example, in a complete machining process, "Start Spindle" is triggered once, "Start Feed" is triggered once, and "Start Cutting" is triggered once. When counting the number of repeated triggers of each operation instruction point within a specific time window, set a time window, for example, 10 minutes. During virtual debugging, if the "Start Spindle" instruction is clicked repeatedly by the user 3 times within 10 minutes, the number of repeated triggers is 3. When filtering operation areas whose trigger frequency exceeds the set threshold, set the trigger frequency threshold. , which is used to identify high-frequency operation areas. Its rationale lies in that high-frequency operation areas usually indicate the uncertainty of users in the debugging process or potential bottlenecks in the operation process. For example, if "start spindle" is triggered 3 times in a certain continuous operation area, exceeding the threshold 2, then the operation area is identified as a high-frequency operation area. In this way, a high-frequency operation area set is established, which includes the operation instruction points with high triggering frequency within a specific time window and the continuous operation area where they are located.

[0032] S303: Based on the high-frequency operation area set, optimize the coordination of the operation process, calculate the time relationship between the operation instruction points, adjust the operation spacing and time interval, adjust the adaptability weight according to the temporal relationship of the operations, and generate an operation adjustment parameter set; For the set of high-frequency operation areas, for example, if the "start spindle" operation is identified as a high-frequency area, it indicates that the user may have repeatedly tried to start the spindle, or there is a problem in the startup process. When calculating the time relationship between the operation instruction points, the average time interval and standard deviation between the operation instruction points in the high-frequency area are analyzed. For example, if the repeated trigger intervals of "start spindle" are 1.5 seconds, 1.2 seconds, and 1.8 seconds, respectively, the average interval is 1.5 seconds. When adjusting the operation spacing and time interval, the goal is to make the operation process smoother and more efficient. For example, the ideal time interval between "start spindle" and "start feed" is adjusted from 1.5 seconds to 1.0 second to reduce the waiting time, based on the timing relationship of the operations. When adjusting the adaptability weight, higher adaptability weights are assigned to operations on the critical path, such as from "Start spindle" to "Start cutting", to ensure the priority and execution quality of these operations. For example, in the adaptability distribution of operation instruction points, the adaptability weights of "Start spindle" and "Start cutting" are adjusted from the default 1.0 to 1.2 to reflect their importance in the optimization process, and an operation adjustment parameter set is generated. The set includes information such as the optimized operation instruction point time interval, operation spacing, and adjusted adaptability weight. For example, the operation adjustment parameter set includes a new time interval of 1.0 second between "Start spindle" and "Start feed" and an adjusted adaptability weight of 1.2.

[0033] See also Figure 5 , the steps to obtain the corrected matching results are: S401: Based on the operation adjustment parameter set, calculate the re-triggering frequency of the operation instruction point, obtain the time series data of the operation instruction point, count the number of re-triggering times of the same operation instruction point within the set time window, calculate the unit time triggering frequency of each operation instruction point, and establish the triggering frequency distribution of the operation instruction point; For example, the operation adjustment parameter set includes a new time interval for the "Start Spindle" operation. When acquiring the time series data of the operation instruction points, during the virtual debugging process, the timestamp of each time the "Start Spindle" operation is triggered by the user is recorded, for example, , , , when counting the number of repeated triggers of the same operation instruction point within the set time window, set a time window of 10 seconds. The "Start Spindle" command is triggered 3 times within 10 seconds. When calculating the unit time trigger frequency of each operation command point, the unit is times / second. For example, if the "Start Spindle" command is triggered 3 times within 10 seconds, its unit time trigger frequency is times / second, establish the trigger frequency distribution of the operation instruction point, which records the repeated trigger frequency of each operation instruction point in different time windows. For example, in the trigger frequency distribution of the operation instruction point, the trigger frequency of "start spindle" is 0.3 times / second, and the trigger frequency of "emergency stop" is 0.05 times / second.

[0034] S402: Based on the trigger frequency distribution of the operation instruction points, the trigger consistency between the operation instruction points is analyzed, the position offsets of adjacent triggered operation instruction points are calculated, and a range of offsets not exceeding a set offset threshold is screened. The operation instruction point offset ratio distribution is obtained, and a range of offset ratios below a set threshold is screened to establish an operation instruction point offset screening result. For the two adjacent operations "Start spindle" and "Start feed" in the trigger frequency distribution of the operation instruction points, if the trigger frequency of "Start spindle" is 0.3 times / second and the trigger frequency of "Start feed" is 0.25 times / second, the trigger frequency difference between them is calculated to determine their consistency. When calculating the position offset of adjacent trigger operation instruction points, in the virtual debugging interface, the coordinate difference of the mouse click position when the user executes adjacent operation instruction points (for example, clicking the "Start spindle" and "Start feed" buttons in sequence) is monitored. For example, the screen coordinates of the first click of "Start spindle" are , click "Start Feed" screen coordinates , when clicked for the second time, "Start Spindle" is , "Start Feed" is , calculate the relative position offset of each click. For example, the relative position offset vector of the first click "Start Spindle" and "Start Feed" is , the relative position offset vector of the second click is , when the screening offset does not exceed the range of the set offset threshold, set the position offset threshold The rationality of this threshold is that it takes into account the slight deviation of user operations but excludes significant misoperations. For example, if the Euclidean distance of the relative position offset vector of two clicks is , is less than the threshold of 5 pixels, then its position offset is considered to be within the acceptable range. When obtaining the offset ratio distribution of the operation instruction point, the offset ratio is defined as the ratio between the actual position offset and the preset ideal position offset. For example, the ideal position offset is , the actual offset is , the offset ratio is 1, and by conducting multiple debugging experiments on different operators and collecting their operation data, it is verified that the threshold of 5 pixels can effectively distinguish skilled operations from unskilled operations. When screening the range where the offset ratio is lower than the set threshold, the offset ratio threshold is set to 1.1. For example, if the offset ratio of an operation instruction point is 1.05, which is lower than the threshold of 1.1, it is screened out, thereby establishing the operation instruction point offset screening result, which includes all operation instruction points whose position offset and offset ratio meet the requirements.

[0035] S403: Based on the operation instruction point offset screening result, calculate the change trend of the trigger intensity value sequence of the trigger record within the offset range, analyze the trigger intensity fluctuation range, screen the value range that meets the fluctuation range, and finally generate the operation correction matching result; For the filtered operation instruction points, such as the "start spindle" instruction, during the virtual debugging process, record the corresponding trigger strength value each time the instruction is triggered. The trigger strength value can be click pressure, key duration, etc. For example, the first click strength is 1.2 units, the second is 1.1 units, and the third is 1.3 units, forming a trigger strength value sequence. When analyzing the trigger intensity fluctuation range, calculate the mean and standard deviation of the value sequence and define a fluctuation range as [mean - k*standard deviation, mean + k*standard deviation], where k is a coefficient. For example, set The setting reference of this coefficient is based on the statistical analysis of user operation habits, aiming to capture the normal fluctuation range of user operation intensity. By analyzing a large amount of user operation data, it is found that when k=1.5, abnormal operations can be effectively identified. For example, if the average value is 1.2 and the standard deviation is 0.1, the fluctuation range is When filtering the value interval that meets the fluctuation range, all trigger intensity values ​​fall within All records within the interval are retained, and the operation correction matching result is finally generated. The result includes the filtered and analyzed operation instruction points and their corresponding trigger intensity fluctuation intervals. For example, the operation correction matching result includes the "start spindle" instruction, and its trigger intensity fluctuation interval is .

[0036] See also Figure 6 ,The steps for obtaining the adjustment results of the machining center virtual debugging and process optimization execution are: S501: Call the operation trigger interval in the operation correction matching result, obtain the timestamp data of each operation instruction point, count the time difference between adjacent operation instruction points, and calculate the number of operation triggers per unit time. Organize these data into a time series, analyze the interval distribution of operation triggers, filter out high-frequency trigger intervals below the set threshold, and establish an operation time interval distribution data set; For example, for the "Start Spindle" instruction, the trigger intensity fluctuation range has been determined, and the timestamp of each trigger is recorded. When obtaining the timestamp data of each operation instruction point, for example, the continuous trigger timestamp of "Start Spindle" is , , , when counting the time difference between adjacent operation instruction points, calculate Second, When calculating the number of operation triggers per unit time, the unit time is set to 1 second. If a command is triggered 2 times within 1 second, the number of triggers per unit time is 2 times / second. When these data are sorted into a time series, a time interval sequence is formed. When analyzing the interval time distribution of the operation trigger, calculate the mean, standard deviation and frequency distribution of the time interval series. For example, the average interval is 1.821 seconds and the standard deviation is 0.401 seconds. When filtering out the high-frequency trigger interval below the set threshold, set the high-frequency trigger time interval threshold. The threshold is determined based on observation and statistical analysis of skilled operators' behavior. Its rationality lies in the fact that intervals below this threshold usually indicate that the operation is too fast or unnatural. For example, if the time interval of 1.420 seconds is lower than the threshold of 1.5 seconds, the trigger is marked as a high-frequency trigger. By conducting multiple debugging experiments with different operators, collecting their operation data, and performing statistical analysis on the time intervals, it is verified that the 1.5-second threshold can effectively distinguish between normal operations and high-frequency operations. An operation time interval distribution dataset is established, which contains all identified high-frequency trigger time intervals and their corresponding operation instruction points, as shown in Table 1.

[0037] Table 1: Operation time interval distribution dataset As shown in Table 1, the dataset records the trigger intervals of different operation instructions and whether they are judged as high-frequency triggers.

[0038] S502: Based on the operation time interval distribution data set, analyze the continuity change of the operation trigger, calculate the repetition rate of the trigger during the operation trigger process, filter the high repetition trigger area according to the repetition rate, adjust the operation mode triggering method, and establish the operation trigger mode optimization result; For the high-frequency trigger intervals of "Start Spindle" and "Start Feed," analyze their continuous occurrence in the time series. If multiple high-frequency triggers occur continuously within a short period of time, it indicates a problem with the operation continuity. When calculating the repetition rate of the trigger during the operation triggering process, the repetition rate calculation formula is as follows: The number of repeated triggers of a specific operation instruction refers to the number of times the same operation instruction is repeatedly executed by the user within a certain time window. The total number of triggers of the operation instruction refers to the total number of times the operation instruction is executed during the entire debugging process. Assume that in a certain debugging process, the "Start Spindle" instruction is triggered a total of 10 times, of which 3 are repeated triggers (that is, clicked again within a short period of time). The repetition rate is , when filtering high repetition trigger areas based on repetition rate, set the repetition rate threshold This threshold is designed to identify operations that users frequently repeat. The rationale is that an excessively high repetition rate may indicate inefficient user operations or uncertainty about the results of operations. Analysis of virtual debugging logs shows that operations with a repetition rate exceeding 20% ​​are often associated with inefficient debugging or operational errors. For example, if the repetition rate of "starting the spindle" is 30%, which exceeds the 20% threshold, the "starting the spindle" operation is identified as a high-repetition trigger area. When adjusting the trigger mode of the operation mode, for operations in the high-repetition trigger area, such as "starting the spindle", its trigger mode is adjusted from a single click to a long press for 2 seconds to reduce false touches and improve operation accuracy. An operation trigger mode optimization result is established, which includes the optimized operation trigger mode. For example, the operation trigger mode optimization result includes a new trigger mode for the "starting the spindle" instruction: long press for 2 seconds.

[0039] S503: Based on the optimization results of the operation trigger mode, the operation execution sequence is adjusted, the response logic between consecutive operation instruction points is optimized, operations suitable for parallel execution are screened, and the sequence of associated operation instruction points is optimized, ultimately generating the virtual commissioning and process optimization execution adjustment results of the machining center; According to the optimization results of the operation trigger mode, for example, the operation mode of "start spindle" has been adjusted to long press trigger. During the virtual debugging process, based on the actual processing process and equipment linkage relationship, the execution order of the operation instructions is adjusted. For example, the original order may be "start spindle" Start Coolant "Start feed", after optimization, if "Start coolant" can be executed in parallel during the spindle startup process, it can be adjusted to "Start spindle" (long press) and "Start coolant" in parallel to shorten the overall processing time, and optimize the response logic between continuous operation instruction points. For example, after "Start spindle" is completed, the system needs to detect whether the spindle speed reaches the set value before proceeding to the next operation "Start feed". By adjusting the response logic, the judgment conditions for spindle speed detection are relaxed, or some instructions are pre-loaded to reduce response delays. When screening operations suitable for parallel execution, analyze whether there is resource competition or logical dependence between different operation instructions. If two operations are not If key resources are shared and there is no logical dependency, they can be executed in parallel. For example, "starting the chip conveyor" and "starting the workpiece clamping" can be executed in parallel. When the order of the associated operation instruction points is optimized, for example, "starting the spindle" and "starting the coolant" are set as parallel operations, and it is ensured that the spindle has reached a stable speed before "starting the feed", the final result of the adjustment of the virtual debugging and process optimization execution of the machining center is generated. The result includes the complete optimized operation execution sequence, the adjusted operation response logic and the combination of operations executed in parallel. For example, the adjustment result of the virtual debugging and process optimization execution of the machining center includes: the operation sequence is adjusted to "(starting the spindle and starting the coolant in parallel)" Start Feed "Start cutting", and detailed description of the triggering method and response logic of each operation.

[0040] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A virtual debugging and process optimization method for a machining center based on digital twins, characterized in that: The following steps are involved: S1: Obtain the variation range of equipment operating parameters in adjacent time series under the operating status of the physical equipment of the machining center, identify key variables, calculate the distribution characteristics of key variables under different working conditions, analyze the change rate of the area where the distribution characteristic fluctuation exceeds the set threshold, and generate the machining center operating status feature set; S2: Based on the machining center operation state feature set, identifying the operation instruction points input by the user in the virtual debugging interface, analyzing the distribution relationship between the operation instruction points and the operation state feature set, evaluating the adaptability of the operation instruction point scope and the operation state feature, and forming an operation state adaptation result; S3: extracting the motion trajectory of the operation instruction point in the operation state adaptation result, analyzing the continuity of the operation state characteristics within the action range of the operation instruction point, calculating the frequency of instruction triggering in the continuous operation area, and generating an operation adjustment parameter set; S4: Calculate the frequency of repeated triggering of the operation instruction point based on the operation adjustment parameter set, analyze the operation consistency during the virtual debugging process, identify the position offset of adjacent trigger points and calibrate the trigger intensity change trend, extract the numerical range within the trigger intensity fluctuation range, and form an operation correction matching result.

2. The method for virtual debugging and process optimization of a machining center based on digital twin according to claim 1, characterized in that: The machining center operation state feature set includes key variable distribution, regional change rate, and operation parameter difference analysis results; the operation state adaptation result includes the operation instruction point position, scope of action, adaptability evaluation result, and operation state feature form; The operation adjustment parameter set includes the operation instruction point motion trajectory, trigger frequency, operation state continuity, and coordination adjustment; The operation correction matching result includes the trigger frequency, the trigger point position offset, the trigger intensity value sequence change trend, the offset range, and the value fluctuation range.

3. The method for virtual debugging and process optimization of a machining center based on digital twin according to claim 1, characterized in that: The steps for obtaining the machining center operation status feature set are as follows: S101: Obtain a time series arrangement of the operating status data of the physical equipment in the machining center, analyze the change range of the equipment operating parameters in adjacent time series, calculate the parameter change of each operating parameter relative to the surrounding time points, classify the operating status according to the parameter change, identify the state point where the parameter change exceeds the set change threshold, mark its time node, and establish a parameter change state point set; S102: Based on the parameter change state point set, calculate the distribution characteristics of key variables in the state point set, count the number of key variables in each state point set and their parameter means, obtain the volatility of the distribution characteristics of the key variables in each state point set, and screen the state point sets whose volatility exceeds a set volatility threshold to obtain a distribution characteristic mutation point set; S103: Based on the distribution feature mutation point set, calculate the regional change rate within each state point set, analyze the distribution characteristics of key variables in the state point set, count the variable connectivity and shape characteristic values ​​within the state point set, calculate the regional change rate, and establish the machining center operation state feature set.

4. The method for virtual debugging and process optimization of a machining center based on digital twinning according to claim 1, characterized in that: The steps for obtaining the running status adaptation result are: S201: Based on the machining center operation state feature set, identifying the operation instruction point input by the user in the virtual debugging interface, obtaining the time node data of the operation instruction point, detecting the state point set where the operation instruction point is located, counting the change range of the operation parameters around the operation instruction point, screening the state point set with significant change range, and establishing the operation instruction point parameter change set; S202: Based on the parameter change set of the operation instruction point, analyzing the distribution relationship between the operation instruction point and the operating state feature set, calculating the variation range within the distribution range of the operating parameters around the operation instruction point, evaluating the adaptability of the operating instruction point range and the operating state feature, and establishing the adaptability distribution of the operation instruction point; S203: Based on the adaptability distribution of the operation instruction points, screen the target state point set whose adaptability meets the operation requirements, extract the best adaptation state point set where the operation instruction points are located according to the adaptability distribution of the operation instruction points, and establish the operation state adaptation result.

5. The method for virtual debugging and process optimization of a machining center based on digital twin according to claim 1, characterized in that: The steps for obtaining the operation adjustment parameter set are: S301: Based on the operation state adaptation result, extract the motion trajectory of the operation instruction point, obtain the time node data of the continuous operation instruction points, calculate the trajectory path according to the time series, count the time distribution of the operation instruction points, and establish the operation trajectory path set; S302: Based on the operation trajectory path set, analyze the continuity of the operating state characteristics within the scope of the operation instruction point, calculate the trigger frequency in the continuous operation area, count the number of repeated triggers of each operation instruction point within a specific time window, and filter the operation areas whose trigger frequency exceeds a set threshold to establish a high-frequency operation area set; S303: Based on the operation high-frequency area set, optimize the coordination of the operation process, calculate the time relationship between the operation instruction points, adjust the operation spacing and time interval, adjust the adaptability weight according to the timing relationship of the operation, and generate an operation adjustment parameter set.

6. The method for virtual debugging and process optimization of a machining center based on digital twin according to claim 1, characterized in that: The steps for obtaining the operation correction matching result are: S401: Based on the operation adjustment parameter set, calculate the repetitive triggering frequency of the operation instruction point, obtain time series data of the operation instruction point, count the number of repetitive triggering of the same operation instruction point within a set time window, calculate the unit time triggering frequency of each operation instruction point, and establish the operation instruction point triggering frequency distribution; S402: Based on the trigger frequency distribution of the operation instruction points, analyzing the trigger consistency between the operation instruction points, calculating the position offsets of adjacent triggered operation instruction points, screening a range where the offsets do not exceed a set offset threshold, obtaining an operation instruction point offset ratio distribution, screening a range where the offset ratios are lower than a set threshold, and establishing an operation instruction point offset screening result; S403: Based on the operation instruction point offset screening result, calculate the change trend of the trigger intensity value sequence of the trigger record within the offset range, analyze the trigger intensity fluctuation range, screen the value range that meets the fluctuation range, and finally generate the operation correction matching result.

7. The method for virtual debugging and process optimization of a machining center based on digital twin according to claim 1, characterized in that: The method further comprises: S5: calling the interval time of the operation trigger in the operation correction matching result, calling the time distribution rate to analyze the continuity change of the operation trigger, calculating the trigger repetition rate in the operation triggering process, optimizing the triggering method of the operation mode according to the repetition rate, and adjusting the order of operation execution to generate the virtual debugging and process optimization execution adjustment results of the machining center; The machining center virtual debugging and process optimization execution adjustment results include operation trigger interval time, time distribution rate, trigger repetition rate, operation mode optimization, and execution sequence adjustment.

8. The method for virtual debugging and process optimization of a machining center based on digital twin according to claim 7, characterized in that: The steps for obtaining the adjustment results of the virtual debugging and process optimization execution of the machining center are as follows: S501: Call the operation trigger interval in the operation correction matching result, obtain the timestamp data of each operation instruction point, count the time difference between adjacent operation instruction points, and calculate the number of operation triggers per unit time. Organize these data into a time series, analyze the interval distribution of operation triggers, filter out high-frequency trigger intervals below a set threshold, and establish an operation time interval distribution data set; S502: Analyze the continuity change of the operation trigger based on the operation time interval distribution data set, calculate the repetition rate of the trigger during the operation trigger process, filter the high repetition trigger area according to the repetition rate, adjust the operation mode triggering method, and establish the operation trigger mode optimization result; S503: Based on the optimization result of the operation trigger mode, the operation execution sequence is adjusted, the response logic between consecutive operation instruction points is optimized, operations suitable for parallel execution are screened, and the sequence of associated operation instruction points is optimized, and finally the virtual debugging and process optimization execution adjustment results of the machining center are generated.

9. The method for virtual debugging and process optimization of a machining center based on digital twin according to claim 1, characterized in that: The machining center operation status feature set collects spindle speed, feed speed, and cutting force as operation parameters through sensors and arranges them in a time series form.

10. The method for virtual debugging and process optimization of a machining center based on digital twin according to claim 1, characterized in that: The operation instruction point is determined by the coordinate position input through the virtual debugging interface, and its scope of action is determined by the variation range of the surrounding operating parameters.

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