A machining center virtual debugging and process optimization method based on digital twinning

Abstract

The present application relates to the technical field of intelligent manufacturing and industrial digitization, in particular to a machining center virtual debugging and process optimization method based on digital twinning, which comprises obtaining a set of equipment operating state characteristics, generating an operating state adaptation result, optimizing a set of operation adjustment parameters, forming an operation correction matching result, and executing an adjustment result. By analyzing the distribution of key variables, the adaptability of operation instruction points, and the triggering frequency, the present application optimizes the coordination and consistency of the debugging process, and ultimately realizes process optimization. This method can improve the efficiency of machining center virtual debugging, and enhance the execution accuracy and stability of the process.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent manufacturing and industrial digitization, and particularly relates to a machining center virtual debugging and process optimization method based on digital twinning. BACKGROUND

[0002] As an important tool in the field of intelligent manufacturing, digital twinning technology exhibits significant advantages in machining center virtual debugging and process optimization by constructing real-time mapping between virtual models and physical entities. Its core content includes virtual modeling, real-time data acquisition and analysis, dynamic simulation, and the formulation of process optimization strategies. It can improve production efficiency, shorten debugging cycles, and reduce manufacturing costs through comprehensive simulation and prediction of machining center operating conditions. In recent years, with the development of industrial Internet of Things and big data technology, the application of digital twinning in manufacturing has gradually deepened, especially in the demand for machining center debugging and optimization in complex multi-task environments, becoming a key technical support. Among them, the machining center virtual debugging and process optimization method based on digital twinning involves multi-level modeling and simulation from the device level to the system level, covering device interlocking, process parameter adjustment, and dynamic optimization. Specifically, by constructing a high-precision digital twinning model, the entire process flow of the machining center is virtually restored, and dynamic adjustment is achieved through real-time feedback mechanisms. At the same time, data analysis and simulation tools are used to deeply optimize the process flow to support diversified processing needs. In addition, attention is paid to general design, making the model adaptable to different processing scenarios, further improving debugging efficiency and process precision.

[0003] The existing technical solutions have certain limitations in actual application. For example, a data center cold source group control virtual debugging system and method with publication number CN114777295B proposes a virtual debugging scheme for the cold source system, mainly relying on the cooperative work of dynamic simulation modules, logic control modules, and real-time databases. However, this scheme focuses on the specific application scenario of the cold source system, and its versatility is limited, making it difficult to directly meet the needs of complex multi-task environments in machining centers. At the same time, the support capability of this scheme in device interlocking and process flow optimization still needs to be strengthened, especially when dealing with diversified process demands. Another technical solution with publication number CN108121216B proposes an automatic workshop virtual debugging method based on a digital factory, which realizes the driving of virtual simulation model actions by external application programs through the development of communication plug-ins. This scheme focuses on workshop-level layout design and production process optimization, but lacks in-depth exploration in the specific process optimization level within the machining center. In addition, the real-time feedback and dynamic adjustment capability of this scheme for key process parameters is relatively weak, which may not fully meet the requirements of high-precision machining.

[0004] The above problems show that the existing virtual debugging and process optimization technology still has room for improvement in terms of universality, process depth optimization, dynamic adjustment capability, and support for complex multitasking environment. Therefore, the present application aims to provide a machining center virtual debugging and process optimization method based on digital twinning, which realizes virtual debugging and dynamic optimization of the whole process by constructing a high-precision digital twinning model, thereby better meeting the demand for efficient and accurate machining in modern intelligent manufacturing. SUMMARY

[0005] In order to solve the technical problems existing in the prior art, the embodiment of the present application provides a machining center virtual debugging and process optimization method based on digital twinning. The technical solution is as follows:

[0006] A machining center virtual debugging and process optimization method based on digital twinning, comprising the following steps:

[0007] S1: Obtain the variation amplitude of the equipment operation parameters in the adjacent time sequence under the running state of the machining center physical equipment, identify the key variables, calculate the distribution characteristics of the key variables under different working conditions, analyze the regional change rate of the distribution characteristics fluctuation exceeding the set threshold, and generate the machining center running state feature set;

[0008] S2: According to the machining center running state feature set, identify the operation instruction points input by the user in the virtual debugging interface, analyze the distribution relationship between the operation instruction points and the running state feature set, evaluate the adaptability of the operation instruction point action range and the running state feature, and form the running state adaptation result;

[0009] S3: Extract the motion trajectory of the operation instruction points in the running state adaptation result, analyze the continuity of the running state features in the operation instruction point action range, calculate the frequency of instruction triggering in the continuous operation area, and generate the operation adjustment parameter set;

[0010] S4: Calculate the frequency of repeated triggering of the operation instruction points based on the operation adjustment parameter set, analyze the operation consistency in the virtual debugging process, identify the position offset of the adjacent triggering points and calibrate the triggering intensity change trend, extract the numerical interval within the triggering intensity fluctuation range, and form the operation correction matching result.

[0011] As a further scheme of the present application, the machining center running state feature set includes key variable distribution, regional change rate and running parameter difference analysis result; the running state adaptation result includes operation instruction point position, action range, adaptability evaluation result and running state feature form; the operation adjustment parameter set includes operation instruction point motion trajectory, triggering frequency, running state continuity and coordination adjustment; and the operation correction matching result includes triggering frequency, triggering point position offset, triggering intensity numerical sequence change trend, offset range and numerical fluctuation interval.

[0012] As a further scheme of the present application, the acquisition step of the machining center running state feature set is:

[0013] S101: Acquire the time sequence arrangement of the machining center physical equipment running state data, analyze the change amplitude of the equipment running parameters in the adjacent time sequence, calculate the parameter change amount of each running parameter relative to the surrounding time point, classify the running state according to the parameter change amount, identify the state points whose parameter change amount exceeds the set change threshold and mark their time nodes, and establish the parameter change state point set;

[0014] S102: Based on the parameter change state point set, calculate the distribution characteristics of the key variables in the state point set, count the number of key variables in each state point set and their parameter mean value, obtain the fluctuation rate of the key variable distribution characteristics of each state point set, and screen the state point set whose fluctuation rate exceeds the set fluctuation rate threshold to obtain the distribution feature mutation point set;

[0015] S103: Based on the distribution feature mutation point set, calculate the area change rate in each state point set, analyze the distribution characteristics of the key variables in the state point set, count the variable connectivity and shape characteristic value in the state point set, calculate the area change rate, and establish the machining center running state feature set.

[0016] As a further scheme of the present application, the acquisition step of the running state adaptation result is:

[0017] S201: Based on the machining center running state feature set, identify the operation instruction point input by the user in the virtual debugging interface, acquire the time node data of the operation instruction point, detect the state point set where the operation instruction point is located, count the change amplitude of the running parameters around the operation instruction point, screen the state point set whose change amplitude is significant, and establish the operation instruction point parameter change set;

[0018] S202: Based on the operation instruction point parameter change set, analyze the distribution relationship between the operation instruction point and the running state feature set, calculate the change amplitude in the distribution range of the running parameters around the operation instruction point, evaluate the adaptability of the operation instruction point action range to the running state feature, and establish the operation instruction point adaptability distribution;

[0019] S203: Based on the operation instruction point adaptability distribution, screen the target state point set whose adaptability meets the operation requirement, extract the best adaptation state point set where the operation instruction point is located according to the operation instruction point adaptability distribution, and establish the running state adaptation result.

[0020] As a further scheme of the present application, the acquisition step of the operation adjustment parameter set is:

[0021] S301: Extract the motion trajectory of the operation instruction point based on the running state adaptation result, obtain the time node data of the continuous operation instruction point, calculate the trajectory path in time sequence, count the time distribution of the operation instruction point, and establish the operation trajectory path set;

[0022] S302: Based on the operation trajectory path set, analyze the continuity of the running state characteristics in the operation instruction point action range, calculate the trigger frequency in the continuous operation area, count the number of repeated triggers of each operation instruction point in a specific time window, and filter the operation area with trigger frequency exceeding the set threshold, to establish the operation high-frequency area set;

[0023] 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 distance and time interval, adjust the adaptability weight according to the time sequence relationship of the operation, and generate the operation adjustment parameter set.

[0024] As a further scheme of the present application, the operation correction matching result acquisition step is:

[0025] S401: Based on the operation adjustment parameter set, calculate the repeated trigger frequency of the operation instruction point, obtain the time sequence data of the operation instruction point, count the number of repeated triggers of the same operation instruction point in a set time window, calculate the trigger frequency per unit time of each operation instruction point, and establish the operation instruction point trigger frequency distribution;

[0026] S402: Based on the operation instruction point trigger frequency distribution, analyze the trigger consistency between the operation instruction points, calculate the position offset of the adjacent trigger operation instruction points, filter the range with offset not exceeding the set offset threshold, obtain the operation instruction point offset ratio distribution, and filter the range with offset ratio lower than the set threshold, to establish the operation instruction point offset screening result;

[0027] S403: Based on the operation instruction point offset screening result, calculate the change trend of the trigger strength numerical sequence of the trigger record in the offset range, analyze the trigger strength fluctuation interval, filter the numerical interval meeting the fluctuation range, and finally generate the operation correction matching result.

[0028] As a further scheme of the present application, the method further comprises:

[0029] S5: Call the interval time of the operation trigger in the operation correction matching result, analyze the continuity change of the operation trigger according to the time distribution rate, calculate the trigger repetition rate in the operation trigger process, optimize the trigger mode of the operation mode according to the repetition rate, adjust the order of operation execution, and generate the machining center virtual debugging and process optimization execution adjustment result;

[0030] The machining center virtual debugging and process optimization execution adjustment result comprises operation trigger interval time, time distribution rate, trigger repetition rate, operation mode optimization and execution sequence adjustment.

[0031] As a further scheme of the present application, the machining center virtual debugging and process optimization execution adjustment result acquisition step is:

[0032] S501: call the operation trigger interval time in the operation correction matching result, acquire the time stamp data of each operation instruction point, count the time difference of adjacent operation instruction points, and calculate the operation trigger frequency per unit time, arrange these data into a time sequence, analyze the interval time distribution of operation trigger, screen out the high-frequency trigger interval below the set threshold, and establish the operation time interval distribution data set;

[0033] S502: based on the operation time interval distribution data set, analyze the continuity change of operation trigger, calculate the repetition rate of trigger in the operation trigger process, screen the high-repetition trigger area according to the repetition rate, adjust the operation mode trigger mode, and establish the operation trigger mode optimization result;

[0034] S503: based on the operation trigger mode optimization result, adjust the operation execution sequence, optimize the response logic between consecutive operation instruction points, screen the operation suitable for parallel execution, and optimize the sequence of associated operation instruction points, and finally generate the machining center virtual debugging and process optimization execution adjustment result.

[0035] As a further scheme of the present application, the machining center running state feature set collects the spindle speed, feed speed and cutting force as running parameters through a sensor, and arranges them in a time sequence form.

[0036] As a further scheme of the present application, the coordinate position of the operation instruction point is determined through the virtual debugging interface input, and the range of action is determined by the change amplitude of the surrounding running parameters.

[0037] The technical scheme provided by the embodiment of the present application has at least the following beneficial effects:

[0038] In the present application, the amplitude of the change of the equipment operation parameter is analyzed by time series analysis, the fluctuation area of the distribution characteristics of the key variable is identified, the adaptability of the operation instruction point and the operation state characteristics is quantitatively evaluated, the dynamic mapping relationship between the instruction action range and the parameter change is established, the operation coordination is optimized based on the trigger frequency, the precise matching of the instruction triggering and the equipment response in the debugging process is realized, the change trend of the trigger intensity is extracted by the calibration position offset, the numerical interval is modified, the matching result is modified, the redundant operation in the multi-task scene is eliminated, the operation trigger sequence and the execution mode are reconstructed by combining the trigger interval time distribution rate and the repetition rate analysis, the closed-loop optimization mechanism is formed, the real-time performance of the dynamic adjustment of the process parameters is enhanced, the dependence on artificial experience in the debugging process is reduced, and the equipment operation stability and the resource scheduling efficiency under complex working conditions are improved. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 The method flowchart of the present application is shown in the figure.

[0040] Figure 2 The acquisition flowchart of the operation state characteristic set of the machining center of the present application is shown in the figure.

[0041] Figure 3 The acquisition flowchart of the operation state adaptation result of the present application is shown in the figure.

[0042] Figure 4 The acquisition flowchart of the operation adjustment parameter set of the present application is shown in the figure.

[0043] Figure 5 The acquisition flowchart of the operation modification matching result of the present application is shown in the figure.

[0044] Figure 6 The acquisition flowchart of the machining center virtual debugging and process optimization execution adjustment result of the present application is shown in the figure. DETAILED DESCRIPTION

[0045] The technical solutions in the present application will be described below with reference to the accompanying drawings.

[0046] In the embodiments of the present application, the words such as "example", "for example" are used to represent an example, illustration or description. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "example" is intended to present the concept in a specific manner. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0047] In the embodiments of the present application, "image" and "picture" can be used interchangeably, and it should be pointed out that the meanings expressed are consistent when the distinction is not emphasized.

[0048] In the embodiments of the present application, sometimes the subscript such as W1 can be written in the form of non-subscript such as W1, and the meanings expressed are consistent when the distinction is not emphasized.

[0049] In order to make the technical problems, technical solutions and advantages to be solved by the present application more clear, the following will be described in detail in conjunction with the drawings and specific embodiments.

[0050] Please refer to Figure 1 The present application provides a technical solution: a machining center virtual debugging and process optimization method based on digital twinning, comprising the following steps:

[0051] S1: Obtain the time sequence arrangement of the machining center physical device running state data, analyze the change amplitude of the device running parameters in the adjacent time sequence, identify the key variables, calculate the distribution characteristics of the key variables under different working conditions, analyze the area change rate of the distribution characteristics fluctuation exceeding the set threshold, and generate the machining center running state feature set;

[0052] S2: According to the machining center running state feature set, identify the operation instruction points input by the user in the virtual debugging interface, analyze the distribution relationship between the operation instruction points and the running state feature set, calculate the change amount of the device running parameters in the operation instruction point action range, evaluate the adaptability of the operation instruction point action range and the running state feature, and form the running state adaptation result;

[0053] S3: Extract the motion trajectory of the operation instruction point in the running state adaptation result, analyze the continuity of the running state feature in the operation instruction point action range, calculate the frequency of instruction triggering in the continuous operation area, optimize the coordination of the operation process according to the triggering frequency, and generate the operation adjustment parameter set;

[0054] S4: Based on the operation adjustment parameter set, calculate the frequency of repeated triggering of the operation instruction point, analyze the operation consistency in the virtual debugging process, identify the position offset of the adjacent triggering points, determine the triggering intensity change trend in the offset range according to the position offset, extract the numerical interval within the triggering intensity fluctuation range, and form the operation correction matching result;

[0055] S5: calling operation correction matching result operation trigger interval, calling time distribution rate analysis operation trigger continuity change, calculating trigger repetition rate in operation trigger process, optimizing operation mode trigger mode according to repetition rate, and adjusting operation execution sequence, generating machining center virtual debugging and process optimization execution adjustment result.

[0056] The machining center running state feature set includes key variable distribution, area change rate and running parameter difference analysis result; the running state adaptation result includes operation instruction point position, action range, adaptability evaluation result and running state feature form; the operation adjustment parameter set includes operation instruction point motion trajectory, trigger frequency, running state continuity and coordination adjustment; the operation correction matching result includes trigger frequency, trigger point position offset, trigger strength numerical sequence change trend, offset range and numerical fluctuation interval; the machining center virtual debugging and process optimization execution adjustment result includes operation trigger interval, time distribution rate, trigger repetition rate, operation mode optimization and execution sequence adjustment.

[0057] Please refer to Figure 2 The acquisition step of the machining center running state feature set is:

[0058] S101: acquire time sequence arrangement of machining center physical equipment running state data, analyze change amplitude of equipment running parameters in adjacent time sequence, calculate parameter change amount of each running parameter relative to surrounding time points, classify running state according to parameter change amount, identify state points with parameter change amount exceeding set change threshold and mark their time nodes, and establish parameter change state point set;

[0059] First, the running parameters of numerical control machining center such as spindle speed, feed speed, cutting force and spindle temperature are monitored in real time. It is assumed that data is collected every 0.1 second to form time sequence data. For example, at time point , the spindle speed is 8000 rpm, the feed speed is 1200 mm / min, the cutting force is 500 N, and the spindle temperature is 35°C. At time point , the spindle speed is 8010 rpm, the feed speed is 1205 mm / min, the cutting force is 503 N, and the spindle temperature is 35.1°C. When calculating the parameter change amount of each running parameter relative to the surrounding time points, the sliding window method is used, and the window size is set to 3 time points, i.e. the current time point , the previous time point , and the next time point . For the spindle speed parameter, the spindle speed change amount at time point is rpm, and the spindle speed change amount at time point is When classifying operating status based on parameter changes per minute, a threshold for the change amount is set. To distinguish between stable states, small fluctuation states, and drastic change states, for example, when the spindle speed changes... A speed of 100 revolutions per minute is considered a stable state. When the speed is rpm, it is judged to be in a state of slight fluctuation. A change in spindle speed is considered a drastic change at a certain speed (rpm). The system identifies and marks the point in time when the parameter change exceeds a set threshold. The threshold for spindle speed change is set to 20 rpm. When the spindle speed reaches the specified time point... The speed changed from 8030 rpm to 8055 rpm, a change of 25 rpm, which exceeds the set threshold of 20 rpm. Therefore, the time point... Each point is marked as a parameter change state point, and a set of parameter change state points is established accordingly. For example, for spindle speed, the set of parameter change state points includes time points. , Each time point corresponds to a drastic change in spindle speed. For feed rate, the set of parameter change state points includes time points. , Each point in time corresponds to a drastic change in the feed rate.

[0060] S102: Based on the set of state points with parameter changes, calculate the distribution characteristics of key variables in the set of state points, count the number of key variables and their mean values ​​in each set of state points, obtain the volatility of the distribution characteristics of key variables in each set of state points, filter the set of state points with volatility exceeding the set volatility threshold, and obtain the set of distribution characteristic mutation points.

[0061] For a set of parameter variation state points, such as a set of spindle speed variation state points, the statistics of each key variable within it are calculated. 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 a set of spindle speed variation state points, 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. The number of key variables and their mean values ​​are also statistically analyzed in each set of state points. For example, in the set of spindle speed variation state points... In the middle, time point The average spindle speed was 8050 rpm, at which point... The average spindle speed was 7500 rpm, at which point... The average spindle speed is 8100 rpm. When obtaining the volatility of the key variable distribution characteristics for each state point set, the volatility is calculated using the following formula: in, Represents the first state point in the set of state points. the parameter value of the key variable, representing the average value of the parameter of the key variable in the state point set, representing the number of key variables in the state point set, the meaning of the formula is to quantify the fluctuation degree of the distribution characteristics by calculating the average relative deviation of the parameter value and the average value of the key variable, the larger the value, the more violent the fluctuation, assuming that in a certain parameter change state point set, the parameter value of the main shaft speed is rpm, then , rpm, the fluctuation rate , the setting reference content of the fluctuation rate is determined based on historical data analysis and expert experience, which aims to capture significant changes in parameter distribution, and the fluctuation rate threshold is set to 2%, the rationality is verified by observing the decline in processing quality or abnormal phenomena of equipment when the fluctuation rate exceeds this value in the actual processing process, and by comparing and analyzing the processing results under different threshold settings through multiple processing experiments in the test environment, 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 characteristic mutation, the state point set with a fluctuation rate exceeding the set fluctuation rate threshold is screened, and the distribution characteristic mutation point set is obtained, for example, if the fluctuation rate of the main shaft speed calculated above is 3.24%, which exceeds the fluctuation rate threshold of 2%, then the main shaft speed change state point set is identified as the distribution characteristic mutation point set, and all mutation point sets that meet the conditions are identified in this way.

[0062] S103: Based on the distribution characteristic mutation point set, calculate the area change rate in each state point set, analyze the distribution characteristics of the key variables in the state point set, and calculate the variable connectivity and shape characteristic value in the state point set to establish the machining center running state characteristic set;

[0063] For example, the main shaft speed mutation point set, analyze the distribution characteristics of the key variables in the state point set, the distribution characteristics here refer to the data distribution form of the main shaft speed in the mutation point set, including the numerical range, the concentration trend and the dispersion degree, by calculating the coefficient of variation, for example, the coefficient of variation , wherein is the standard deviation of the main shaft speed, is the average value of the main shaft speed, assuming that in a certain main shaft speed mutation point set, the standard deviation of the main shaft speed is 150 rpm, and the average value is 8000 rpm, then the coefficient of variation is , when calculating the variable connectivity and shape characteristic value in the state point set, the variable connectivity refers to whether there is a significant correlation between different key variables (such as main shaft speed and feed speed) in the mutation point set, by calculating the Pearson correlation coefficient For example, the correlation coefficient between spindle speed and feed speed is 0.8, indicating a strong positive correlation between the two, and the shape feature value refers to the asymmetry and sharpness of the distribution of the key variable, such as skewness and kurtosis. Assuming the skewness of the spindle speed is 0.5 and the kurtosis is 3.2, the calculation formula for the region change rate is as follows: wherein, , , is a weight coefficient, used to adjust the influence of different features on the region change rate. The setting of the weight coefficient is based on expert experience and historical data analysis. For example, set , , The rationality of these weight coefficients lies in that they balance the importance of the coefficient of variation, correlation, and shape feature in evaluating the region change rate, and through analysis of historical failure data, it is found that when the weight coefficients are set in this way, the operating state changes related to device abnormalities can be more accurately identified. For example, when the coefficient of variation , the correlation coefficient , the skewness is 0.5, and the kurtosis is 3.2, the region change rate Through the above calculation, a machining center operating state feature set can be established, which contains information such as the region change rate of each mutation point set, the distribution features of the key variables, the variable connectivity, and the shape feature values. For example, the operating state feature set can be represented as a series of high-dimensional vectors, each vector representing a specific operating state feature, including its region change rate value 0.4575, as well as the mean, variance, skewness, and kurtosis of the spindle speed.

[0064] Please refer to Figure 3 The steps for obtaining the operating state adaptation result are as follows:

[0065] S201: Based on the machining center operating state feature set, identify the user input operation instruction point 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 amplitude of the operating parameters around the operation instruction point, filter the state point set with significant change amplitude, and establish the operation instruction point parameter change set;

[0066] When the user clicks the "start spindle" button in the virtual debugging interface, the operation instruction point is identified. 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 the time point, find the parameter change state point closest to the time point in the parameter change state point set, for example, it is found that When the variation range of the operating parameter around the operating instruction point is determined, a time window is set, for example, the time window is 2 seconds before and after the operating instruction point time The maximum and minimum values of the operating parameters such as the spindle speed, the feed speed, the cutting force, and the spindle temperature in the time window are monitored, for example, the spindle speed rises from 0 rpm to 8000 rpm in the window, the variation range is 8000 rpm, the feed speed rises from 0 mm / min to 1200 mm / min, the variation range is 1200 mm / min, when the state point set with a significant variation range is screened, a significant variation threshold is set, for example, the spindle speed variation range threshold is 5000 rpm, the feed speed variation range threshold is 800 mm / min, when the spindle speed variation range 8000 rpm exceeds the threshold of 5000 rpm, and the feed speed variation range 1200 mm / min exceeds the threshold of 800 mm / min, it is determined that the variation range of the state point set is significant, and the operating instruction point parameter variation set is established, for example, the operating instruction point parameter variation set contains the significant state point set corresponding to the “start spindle” instruction, which records the significant variation range of the spindle speed and the feed speed.

[0067] S202: Based on the operating instruction point parameter variation set, the distribution relationship between the operating instruction point and the operating state feature set is analyzed, the variation range in the operating parameter distribution range around the operating instruction point is calculated, the adaptability of the operating instruction point action range and the operating state feature is evaluated, and the operating instruction point adaptability distribution is established.

[0068] ​The operation instruction point parameter change set is matched with the machining center running state feature set. For example, the spindle speed change amplitude caused by the "start spindle" instruction in the operation instruction point parameter change set is 8000 rpm, and a feature close to this amplitude range is searched in the running state feature set, for example, a feature in the running state feature set describes the change range of the spindle speed from 0 to 8000 rpm when the spindle is started, which indicates that there is a strong distribution relationship between the two. When calculating the change amplitude in the distribution range of the running parameters around the operation instruction point, for example, for the "start spindle" instruction, the running parameters in its action range mainly include the spindle speed and the motor current. The real-time value of the spindle speed is monitored, the rising process from 0 to 8000 rpm is recorded, and the maximum change gradient in the process is calculated, for example, from 1000 rpm to 5000 rpm in 0.5 seconds, the change gradient is 8000 rpm / s. When evaluating the adaptability of the operation instruction point action range and the running state feature, an adaptability index is set, which measures the Euclidean distance between the actual parameter change amplitude in the operation instruction point action range and the expected parameter change amplitude in the running state feature set. For example, the smaller the Euclidean distance, the higher the adaptability. The quantitative standard of adaptability evaluation is that if the Euclidean distance is less than a preset threshold , it is considered that the adaptability is good, and 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 amplitude is 8000 rpm, and in the running state feature set, the ideal spindle speed change amplitude corresponding to this operation is 7950 rpm, the Euclidean distance is , which is less than the threshold value 100, indicating that the adaptability is good. In this way, the operation instruction point adaptability distribution is established, which records the adaptability score of each operation instruction point and the running parameter change feature in its action range, for example, the adaptability score of the "start spindle" operation instruction point is 95 points (full score 100 points).

[0069] S203: Based on the operation instruction point adaptability distribution, a target state point set with adaptability meeting the operation requirements is screened out. According to the operation instruction point adaptability distribution, the best adaptive state point set where the operation instruction point is located is extracted, and the running state adaptability result is established.

[0070] From the operation instruction point adaptability distribution, select those operation instruction points with adaptability scores higher than the preset minimum adaptability threshold, for example, set the minimum adaptability threshold to 80 points, if the adaptability score of the "start main shaft" instruction is 95 points, it is screened as an instruction meeting the operation requirements, according to the operation instruction point adaptability distribution, when extracting the optimal adaptability state point set where the operation instruction point is located, for each screened operation instruction point, identify the state point set corresponding to it in the running state feature set with the highest adaptability score, for example, the optimal adaptability state point set corresponding to the "start main shaft" instruction is the normal rising process of the main shaft speed and motor current when the main shaft is started, and its adaptability score reaches 95 points, thereby establishing a running state adaptability result, which contains a series of operation instruction points and the optimal adaptability state point sets corresponding to them, for example, the running state adaptability result contains the "start main shaft" instruction and its associated "main shaft normal start" state point set, and the "feed start" instruction and its associated "feed shaft normal running" state point set.

[0071] Please refer to Figure 4 , the operation adjustment parameter set acquisition step is:

[0072] S301: Based on the running state adaptability result, extract the motion trajectory of the operation instruction point, acquire the time node data of the continuous operation instruction points, sort the trajectory path according to the time sequence, calculate the trajectory path, and establish the operation trajectory path set;

[0073] From the acquired running state adaptability result, extract the "start main shaft" instruction, followed by the "start feed" instruction, and then the "start cutting" instruction, the execution sequence of these instructions in the virtual debugging interface, when acquiring the time node data of the continuous operation instruction points, record the time stamp of the execution of each operation instruction point, for example, the time stamp of "start main shaft" is , the time stamp of "start feed" is , and the time stamp of "start cutting" is , when sorting the trajectory path according to the time sequence, arrange these time stamps in ascending order to form the execution sequence of the operation instructions, for example, the sequence is "start main shaft" "start feed" "start cutting", when analyzing the time interval between each instruction point and the next instruction point, for example, seconds, seconds, establish the operation trajectory path set, for example, the operation trajectory path set contains multiple operation trajectories, each trajectory is composed of a series of ordered operation instruction points and their time intervals, for example, a trajectory path is , and the time interval is .

[0074] S302: Based on the operation trajectory path set, analyze the continuity of the running state characteristics within the operation instruction point action range, 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 area with a trigger frequency exceeding the set threshold to establish the operation high-frequency area set;

[0075] For the "start spindle" path in the operation trajectory path set "Start feed" The "start cutting" path, within the time window of each operation instruction point action, checks the running state characteristics set and evaluates its smoothness during the operation instruction point switching process. For example, observe whether the running state characteristic values (such as area change rate) remain within the preset stable range during the process of starting the spindle to stable and starting the feed axis to stable. If the area change rate does not fluctuate sharply during this process, it is considered to have good continuity. When calculating the trigger frequency in the continuous operation area, define a continuous operation area, for example, in a specific machining task, the spindle starts to complete cutting as a continuous operation area. Count the number of times each operation instruction point (such as "start spindle", "start feed", "start cutting") is triggered in this area. 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 3 times within 10 minutes, its repeated trigger number is 3. When filtering the operation area with a trigger frequency exceeding the set threshold, set the trigger frequency threshold to identify high-frequency operation areas, which is reasonable because high-frequency operation areas usually indicate user uncertainty during debugging or potential bottlenecks in the operation process. For example, if "start spindle" is triggered 3 times in a continuous operation area, exceeding the threshold of 2, the operation area is identified as an operation high-frequency area, thereby establishing the operation high-frequency area set, which contains operation instruction points with high trigger frequencies within a specific time window and their continuous operation areas.

[0076] S303: Based on the operation high-frequency area set, optimize the coordination of the operation process, calculate the time relationship between operation instruction points, adjust the operation distance and time interval, adjust the adaptability weight according to the operation timing relationship, and generate the operation adjustment parameter set;

[0077] For high-frequency operation regions, such as "start spindle" being identified as a high-frequency region, indicating that the user may repeatedly attempt to start the spindle or that there are problems with the startup process, when calculating the time relationship between operation command points, the average time interval and standard deviation between operation command points within the high-frequency region are analyzed. For example, if the repeated trigger intervals for "start spindle" are 1.5 seconds, 1.2 seconds, and 1.8 seconds, with an average interval of 1.5 seconds, the goal when adjusting the operation spacing and time interval is to make the operation process smoother and more efficient. For example, the ideal time interval between "start spindle" and "start feed" could be adjusted from 1.5 seconds to 1.0 second to reduce waiting time, based on the timing relationship of the operations. When adjusting the adaptability weights, operations on the critical path, such as from "start spindle" to "start cutting," are assigned higher adaptability weights to ensure the priority and execution quality of these operations. For example, in the adaptability distribution of operation command 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. An operation adjustment parameter set is generated, which contains information such as the optimized operation command point time interval, operation spacing, and adjusted adaptability weights. For example, the operation adjustment parameter set includes a new time interval of 1.0 seconds between "start spindle" and "start feed," and an adjusted adaptability weight of 1.2.

[0078] Please see Figure 5 The steps for obtaining the corrected matching results are as follows:

[0079] S401: Based on the operation adjustment parameter set, calculate the repetitive triggering frequency of operation command points, obtain the time series data of operation command points, count the number of repetitive triggers of the same operation command point within a set time window, calculate the unit time triggering frequency of each operation command point, and establish the operation command point triggering frequency distribution.

[0080] For example, if the operation adjustment parameter set includes a new time interval for the "start spindle" operation, when acquiring the time series data of the operation command points, during virtual debugging, the timestamp of each time the "start spindle" operation is triggered by the user is recorded. , , When counting the number of times the same operation command is repeatedly triggered within a set time window, a time window of 10 seconds is set. Within 10 seconds, the "Start Spindle" command was triggered 3 times. When calculating the trigger frequency per unit time for each operation command point, the unit is times per second. For example, if the "Start Spindle" command is triggered 3 times within 10 seconds, its trigger frequency per unit time is... The operation instruction point trigger frequency distribution records the repetition trigger frequency of each operation instruction point in different time windows, for example, the trigger frequency of "start spindle" is 0.3 times per second, and the trigger frequency of "emergency stop" is 0.05 times per second.

[0081] S402: Based on the operation instruction point trigger frequency distribution, analyze the trigger consistency between the operation instruction points, calculate the position offset of the adjacent trigger operation instruction points, filter the range whose offset does not exceed the set offset threshold, obtain the operation instruction point offset ratio distribution, and filter the range whose offset ratio is lower than the set threshold, and establish the operation instruction point offset screening result;

[0082] For the two adjacent operations of "start spindle" and "start feed" in the operation instruction point trigger frequency distribution, if the trigger frequency of "start spindle" is 0.3 times per second and the trigger frequency of "start feed" is 0.25 times per second, the trigger frequency difference between them is calculated to judge their consistency. When calculating the position offset of the adjacent trigger operation instruction points, in the virtual debugging interface, monitor the coordinate difference of the mouse click position when the user executes the adjacent operation instruction points (for example, clicks the "start spindle" and "start feed" buttons in turn). For example, the screen coordinates of the first click of "start spindle" are , the screen coordinates of the click of "start feed" are , the second click of "start spindle" is , and the second click of "start feed" is , the relative position offset of each click is calculated, for example, the relative position offset vector of the first click of "start spindle" and "start feed" is , the relative position offset vector of the second click is , when the offset is filtered to be within the set offset threshold, the position offset threshold is set to pixels, which is reasonable because the threshold takes into account the subtle deviation of user operation, but excludes significant misoperation, for example, if the Euclidean distance of the relative position offset vector of the two clicks is , less than the threshold of 5 pixels, it is considered that the position offset is within the acceptable range. When obtaining the operation instruction point offset ratio distribution, 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 , and the actual offset is The offset ratio is 1. Through multiple debugging experiments with different operators and the collection of their operation data, it was verified that the threshold of 5 pixels can effectively distinguish between skilled and unskilled operators. When filtering out offset ratios below the set threshold, the offset ratio threshold is set to 1.1. For example, if the offset ratio of an operation command point is 1.05, which is lower than the threshold of 1.1, it will be filtered out. This establishes the operation command point offset filtering result, which includes all operation command points whose position offset and offset ratio meet the requirements.

[0083] S403: Based on the operation command point offset filtering results, calculate the changing trend of the trigger intensity value sequence of the trigger record within the offset range, analyze the trigger intensity fluctuation range, filter the value range that meets the fluctuation range, and finally generate the operation correction matching result;

[0084] For selected operation command points, such as the "start spindle" command, during virtual debugging, the corresponding trigger intensity value is recorded each time the command is triggered. The trigger intensity value can be the click pressure, button duration, etc. For example, the first click intensity is 1.2 units, the second is 1.1 units, and the third is 1.3 units, forming a trigger intensity value sequence. When analyzing the fluctuation range of trigger intensity, calculate the mean and standard deviation of the numerical sequence, and define a fluctuation range as [mean - k * standard deviation, mean + k * standard deviation], where k is a coefficient, for example, set as... The coefficient is set based on statistical analysis of user operating habits, aiming to capture the normal fluctuation range of user operation intensity. Analysis of a large amount of user operation data shows that when k=1.5, abnormal operations can be effectively identified. For example, with a mean of 1.2 and a standard deviation of 0.1, the fluctuation range is... When filtering for numerical ranges that match the fluctuation range, all trigger strength values ​​fall within the range of... All records within the specified interval are retained, ultimately generating an operation correction matching result. This result contains the filtered and analyzed operation command points and their corresponding trigger intensity fluctuation ranges. For example, if the operation correction matching result contains the "start spindle" command, its trigger intensity fluctuation range is... .

[0085] Please see Figure 6 The steps for obtaining the results of virtual debugging and process optimization adjustments in a machining center are as follows:

[0086] S501: Call the operation correction operation trigger interval in the matching result, get the timestamp data of each operation instruction point, count the time difference of adjacent operation instruction points, and calculate the operation trigger frequency per unit time, organize these data into time series, analyze the interval time distribution of operation trigger, filter out the high-frequency trigger interval below the set threshold, and establish the operation time interval distribution data set;

[0087] For example, for the "start spindle" instruction, the trigger intensity fluctuation interval 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 timestamps of "start spindle" are , , When calculating the time difference of adjacent operation instruction points, calculate seconds, seconds, and calculate the operation trigger frequency per unit time, set the unit time as 1 second, if a certain instruction triggers 2 times in 1 second, the unit time trigger frequency is 2 times / second, when organizing these data into time series, form a time interval sequence When analyzing the interval time distribution of operation trigger, calculate the mean, standard deviation and frequency distribution of the time interval sequence, for example, the average interval is 1.821 seconds, 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 seconds, the threshold is determined according to the observation and statistical analysis of the behavior of skilled operators, its rationality lies in that the interval below this threshold usually indicates that the operation is too fast or unnatural, for example, the time interval 1.420 seconds is lower than the threshold of 1.5 seconds, then the trigger is marked as high-frequency trigger, through multiple debugging experiments of different operators, collect their operation data, and statistically analyze the time interval, it is verified that the threshold of 1.5 seconds can effectively distinguish normal operation from high-frequency operation, establish the operation time interval distribution data set, which contains all the identified high-frequency trigger time intervals and the corresponding operation instruction points, as shown in Table 1.

[0088] Table 1: Operation time interval distribution data set

[0089]

[0090] As shown in Table 1, the data set records the trigger interval of different operation instructions and whether it is judged as high-frequency trigger.

[0091] S502: Based on the operation time interval distribution data set, analyze the continuity change of operation trigger, calculate the repetition rate of trigger in operation trigger process, filter high-repetition trigger area according to repetition rate, and adjust operation mode trigger method, establish operation trigger mode optimization result;

[0092] For the high-frequency trigger interval of "start spindle" and "start feed", analyze the continuous occurrence in time series, if multiple high-frequency triggers occur continuously in a short time, it indicates that the operation continuity problem exists, when calculating the repetition rate of operation trigger, the calculation formula of repetition rate is as follows: Wherein, the repetition trigger times of specific operation instruction refers to the number of times that the same operation instruction is repeatedly executed by the user within a certain time window, the total trigger times of the operation instruction refers to the total number of times that the operation instruction is executed in the entire debugging process, assuming that in a certain debugging process, the "start spindle" instruction is triggered a total of 10 times, of which 3 times are repeated triggers (i.e. clicking again in a short time), then its repetition rate is According to the repetition rate to screen high-repetition trigger area, set the repetition rate threshold The threshold aims to identify operations that are frequently repeated by users, which is reasonable because a high repetition rate may indicate low user operation efficiency or uncertainty about the operation result, and through analysis of the virtual debugging log, it is found that operations with a repetition rate of more than 20% are often related to low debugging efficiency or operation errors, for example, if the repetition rate of "start spindle" is 30%, which exceeds the threshold of 20%, then the "start spindle" operation is identified as a high-repetition trigger area, and when adjusting the operation mode trigger method, for operations in the high-repetition trigger area, such as "start spindle", the trigger method is adjusted from single click to long press 2 seconds to trigger, in order to reduce the misoperation and improve the operation accuracy, the operation trigger mode optimization result is established, which contains the new trigger method of the operation trigger mode optimization result, for example, the new trigger method of the "start spindle" instruction in the operation trigger mode optimization result is long press 2 seconds.

[0093] S503: Based on the operation trigger mode optimization result, adjust the operation execution sequence, optimize the response logic between consecutive operation instruction points, screen operations suitable for parallel execution, and optimize the order of associated operation instruction points, finally generate the machining center virtual debugging and process optimization execution adjustment result;

[0094] According to the operation trigger mode optimization result, for example, the operation method of "start spindle" has been adjusted to long press trigger, in the virtual debugging process, based on the actual machining process flow and equipment linkage relationship, the execution order of operation instruction is adjusted, for example, the original order may be "start spindle" "start coolant" "start spindle", if "start coolant" can be executed in parallel with "start spindle" during the spindle start-up process, it can be adjusted to execute in parallel with "start spindle" (long press) and "start coolant" to shorten the overall machining time, optimize the response logic between consecutive operation 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 condition of spindle speed detection is relaxed, or part of the instructions is preloaded to reduce the response delay, screening operations suitable for parallel execution, analyzing whether there is resource competition or logical dependence between different operation instructions, if two operations do not share critical resources and have no logical dependence, they can be executed in parallel, for example, "start chip removal machine" and "start workpiece clamping" can be executed in parallel, and the associated operation points are optimized in sequence, for example, "start spindle" and "start coolant" are set to operate in parallel, and ensure that the spindle has reached a stable speed before "start feed", finally generate machining center virtual debugging and process optimization execution adjustment results, which include complete optimized operation execution sequence, adjusted operation response logic and parallel operation combination, for example, machining center virtual debugging and process optimization execution adjustment results include: operation sequence adjustment is "(start spindle in parallel with start coolant)" "start feed" "start cutting", and details the triggering method and response logic of each operation.

[0095] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any skilled person in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for virtual debugging and process optimization of machining centers based on digital twins, characterized in that, Includes the following steps: S1: Obtain the variation range of equipment operating parameters in adjacent time series under the physical equipment operating status of the machining center, identify key variables, calculate the distribution characteristics of key variables under different operating conditions, analyze the change rate of the area where the distribution characteristics fluctuate beyond the set threshold, and generate a feature set of the machining center operating status. S2: Based on the machining center's operating status feature set, identify the user-inputted operation command points in the virtual debugging interface, analyze the distribution relationship between the operation command points and the operating status feature set, evaluate the adaptability of the operation command point's scope of action to the operating status features, and form an operating status adaptation result. The steps for obtaining the running state adaptation result are as follows: S201: Based on the operating status feature set of the machining center, identify the operation command points input by the user in the virtual debugging interface, obtain the time node data of the operation command points, detect the status point set where the operation command points are located, count the change range of the operating parameters around the operation command points, filter the status point set with significant change range, and establish the parameter change set of the operation command points. S202: Based on the set of changes in operation command point parameters, analyze the distribution relationship between operation command points and the set of operating state features, calculate the variation range of operating parameters around the operation command point, evaluate the adaptability of the operation command point's range of action to the operating state features, and establish the adaptability distribution of operation command points. S203: Based on the adaptability distribution of the operation command points, filter the target state point set that meets the operation requirements, extract the best adaptable state point set where the operation command points are located according to the adaptability distribution of the operation command points, and establish the running state adaptability result. S3: Extract the motion trajectory of the operation command point in the running state adaptation result, analyze the continuity of the running state characteristics within the range of the operation command point, calculate the frequency of command triggering within the continuous operation area, and generate a set of operation adjustment parameters. S4: Calculate the frequency of repeated triggering of operation command points based on the operation adjustment parameter set, analyze the consistency of operation during virtual debugging, identify the positional offset of adjacent trigger points and mark the trend of trigger intensity change, extract the numerical range within the trigger intensity fluctuation range, and form the operation correction matching result; The steps for obtaining the modified matching result are as follows: S401: Based on the operation adjustment parameter set, calculate the repetition frequency of the operation command point, obtain the time series data of the operation command point, count the number of times the same operation command point is repeatedly triggered within a set time window, calculate the unit time trigger frequency of each operation command point, and establish the operation command point trigger frequency distribution. S402: Based on the trigger frequency distribution of the operation command points, analyze the trigger consistency between operation command points, calculate the position offset of adjacent triggered operation command points, filter the range where the offset does not exceed the set offset threshold, obtain the operation command point offset ratio distribution, and filter the range where the offset ratio is lower than the set threshold to establish the operation command point offset filtering result. S403: Based on the operation command point offset filtering results, calculate the changing trend of the trigger intensity value sequence of the trigger record within the offset range, analyze the trigger intensity fluctuation range, filter the value range that meets the fluctuation range, and finally generate the operation correction matching result.

2. The method for virtual debugging and process optimization of machining centers based on digital twins according to claim 1, characterized in that: The machining center's operational status feature set includes the distribution of key variables, regional change rates, and operational parameter difference analysis results; the operational status adaptation results include the location of operation command points, the scope of action, adaptation evaluation results, and operational status feature morphology. The set of operation adjustment parameters includes the movement trajectory of operation command points, trigger frequency, and adjustments to the continuity and coordination of the running state. The operation correction matching results include trigger frequency, trigger point position offset, trigger intensity numerical sequence change trend, offset range, and numerical fluctuation range.

3. The method for virtual debugging and process optimization of machining centers based on digital twins according to claim 1, characterized in that: The steps for obtaining the machining center's operating status feature set are as follows: S101: Obtain the time series arrangement of the physical equipment operation status data of the processing center, analyze the change range of equipment operation parameters in adjacent time series, calculate the parameter change of each operation parameter relative to the surrounding time points, classify the operation status according to the parameter change, identify the status points where the parameter change exceeds the set change threshold and mark their time nodes, and establish a set of parameter change status points. S102: Based on the set of state points with parameter changes, calculate the distribution characteristics of key variables in the set of state points, count the number of key variables and their average values ​​in each set of state points, obtain the volatility of the distribution characteristics of key variables in each set of state points, filter the set of state points with volatility exceeding the set volatility threshold, and obtain the set of distribution characteristic mutation points. S103: Based on the set of abrupt change points of the distribution characteristics, calculate the regional change rate within each state point set, analyze the distribution characteristics of key variables in the state point set, statistically analyze the connectivity and shape characteristic values ​​of variables in the state point set, calculate the regional change rate, and establish a processing center operation state characteristic set.

4. The method for virtual debugging and process optimization of machining centers based on digital twins according to claim 1, characterized in that: The steps for obtaining the operation adjustment parameter set are as follows: S301: Based on the running state adaptation result, extract the motion trajectory of the operation command point, obtain the time node data of the continuous operation command points, sort the trajectory path according to the time sequence, statistically analyze the time distribution of the operation command points, and establish an operation trajectory path set. S302: Based on the operation trajectory path set, analyze the continuity of the running state characteristics within the range of operation command points, calculate the trigger frequency in the continuous operation area, count the number of repeated triggers of each operation command point within a specific time window, and filter the operation areas with trigger frequencies exceeding a set threshold to establish a set of high-frequency operation areas. S303: Based on the high-frequency region set of operations, optimize the coordination of the operation process, calculate the time relationship between operation command points, adjust the operation spacing and time interval, adjust the adaptability weight according to the time sequence relationship of operations, and generate an operation adjustment parameter set.

5. The method for virtual debugging and process optimization of machining centers based on digital twins according to claim 1, characterized in that, The method further includes: S5: Call the operation correction matching result to determine the interval time of operation triggering, call the time distribution rate to analyze the continuous change of operation triggering, calculate the trigger repetition rate in the operation triggering process, optimize the triggering method of the operation mode based on the repetition rate, adjust the order of operation execution, and generate the virtual debugging and process optimization execution adjustment results of the machining center. The results of the virtual debugging and process optimization of the machining center include operation trigger interval time, time distribution rate, trigger repetition rate, operation mode optimization, and execution sequence adjustment.

6. The method for virtual debugging and process optimization of machining centers based on digital twins according to claim 5, characterized in that: The steps for obtaining the results of the virtual debugging and process optimization of the machining center are as follows: S501: Call the operation trigger interval time in the operation correction matching result, obtain the timestamp data of each operation instruction point, count the time difference between adjacent operation instruction points, calculate the number of operation triggers per unit time, organize these data into a time series, analyze the distribution of operation trigger interval time, filter out the high-frequency trigger interval below the set threshold, and establish an operation time interval distribution dataset. S502: Based on the operation time interval distribution dataset, analyze the continuous change of operation triggering, calculate the repetition rate of triggering during operation triggering, filter high repetition triggering areas according to the repetition rate, adjust the operation mode triggering method, and establish operation triggering mode optimization results; S503: Based on the optimization results of the operation triggering mode, adjust the operation execution order, optimize the response logic between consecutive operation command points, select operations suitable for parallel execution, optimize the order of related operation command points, and finally generate the virtual debugging and process optimization execution adjustment results of the machining center.

7. The method for virtual debugging and process optimization of machining centers based on digital twins according to claim 1, characterized in that: The machining center's operating status feature set uses sensors to collect spindle speed, feed rate, and cutting force as operating parameters, and arranges them in a time series format.

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

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