High-integration-level variable-focus laser engraving control method and system

Abstract

The invention relates to the technical field of the manufacturing industry, and discloses a high-integration variable-focus laser engraving control method and system, and the method comprises the steps: carrying out the high-frequency data collection of the surface of a workpiece through a sensor array, and obtaining an initial surface state data set; and analyzing surface tiny differences by using the feature extraction model, and determining a dynamic trend distribution diagram. A real-time monitoring technique is employed to track the surface change, and focus position correction is triggered when it is detected that the height fluctuation exceeds a threshold. And adjusting the focus position according to a deviation correction algorithm, and driving an execution mechanism to carry out real-time correction. And the processing effect is analyzed and evaluated through data comparison, a parameter optimization instruction is generated, and the dynamic adjustment control model is called to finely adjust operation parameters. And finally outputting a processing quality evaluation result, and generating a complete log file. Real-time precision monitoring and dynamic adjustment in the complex surface machining process are achieved, the machining precision and stability are improved, and the high-end manufacturing standard requirement is met.

Description

Technical Field

[0001] The present invention relates to the technical field of manufacturing, and in particular to a highly integrated variable-focus laser engraving control method and system. Background Art

[0002] In modern manufacturing, improving workpiece surface machining accuracy is crucial to product quality and performance, and research in this area is directly related to the efficiency and reliability of industrial production. However, current mainstream machining methods often struggle to achieve high-precision real-time monitoring and dynamic adjustment when faced with complex surface variations. They suffer from slow response speeds and insufficient precision, resulting in unstable machining results and failing to meet the demands of high-end manufacturing.

[0003] In existing technologies, the core challenges faced in this field mainly focus on how to achieve real-time monitoring and precise control of the workpiece surface. First, due to the small and complex changes in the workpiece surface during the machining process, traditional monitoring methods have difficulty capturing these subtle differences, resulting in a lag in information acquisition. This lag further leads to the inability of the machining equipment to adjust parameters in a timely manner, causing the focus position to deviate from actual requirements. The deviation in the focus position directly affects the stability of the machining accuracy, especially in scenarios requiring micro-nano-level precision. This problem is particularly prominent. These interlocking technical difficulties make it extremely difficult to achieve optimal machining results in a dynamic environment.

[0004] Therefore, how to monitor the tiny changes on the workpiece surface in real time during the machining process and ensure the precise matching of the focus position through high-precision step control and dynamic adjustment technology has become a key issue that needs to be solved in this study. Summary of the Invention

[0005] The present invention provides a highly integrated variable-focus laser engraving control method and system to ensure precise matching of slight changes in the workpiece surface.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a highly integrated variable-focus laser engraving control method, comprising: Obtaining an initial surface state data set of workpiece surface changes and obtaining a dynamic trend distribution diagram; Obtaining the current focus position deviation value and the adjusted focus position coordinate value according to the dynamic trend distribution diagram to drive the actuator of the processing equipment to perform real-time position correction and determine whether the correction meets the preset accuracy standard; If the correction is successful, the fluctuation of the processing effect is evaluated. If the fluctuation value is found to exceed the preset fluctuation threshold, a parameter optimization instruction is generated; Fine-tune the operating parameters of the processing equipment according to the parameter optimization instructions, obtain optimized processing effect records, combine historical processing data and current surface conditions to determine whether the requirements of high-end manufacturing standards are met, and output the final processing quality evaluation results; A complete log file of the machining process is generated according to the final machining quality evaluation result, and iterative reference information is determined through key data points in the complete log file.

[0007] Preferably, obtaining an initial surface state data set of workpiece surface changes includes: The workpiece surface is collected at high frequency by a sensor array, and a multi-point synchronous scanning method is used to obtain data on the topographic features of each tiny area. The data includes surface height, roughness value and local curvature, and a preliminary surface state record is obtained; Classifying and arranging the topographical features of each micro-region according to the preliminary surface state record, establishing a feature matrix according to the surface height, the roughness value and the local curvature, and determining the feature distribution of each micro-region; If the roughness value of one of the micro areas in the feature matrix exceeds a preset roughness threshold, the surface height and local curvature of the one of the micro areas are corrected to obtain corrected feature data; The corrected feature data is compared with the initial data set, and the topographic feature information of one of the micro-regions is integrated to generate a complete description of the workpiece surface state, thereby obtaining a final surface state data set.

[0008] Preferably, obtaining the dynamic trend distribution graph includes: The height fluctuation and curvature change are obtained based on the initial surface state data set, and the fluctuation amplitude and change rate of each small area are recorded to obtain a preliminary set of difference features; Matching the preliminary difference feature set with a pre-established feature extraction template to obtain the surface state variation range of each area; If the height fluctuation or curvature change of one of the regions within the surface state change range exceeds a preset threshold, the surface state of the one of the regions is corrected, and a local feature of the dynamic trend is obtained from the corrected data; A dynamic trend distribution diagram is generated based on the local features, and data mapping is performed on surface differences and minor changes to obtain a complete distribution description of surface state changes.

[0009] Preferably, obtaining the current focus position deviation value according to the dynamic trend distribution graph includes: Based on the monitoring data of the dynamic trend distribution diagram, the instantaneous state of the surface change is continuously tracked and compared to obtain a change value of the height fluctuation, and determine whether the change value exceeds a preset change threshold range; If the change value of the height fluctuation exceeds a preset threshold range, a deviation detection is performed on the focus position to obtain a deviation value of the current focus position and obtain the deviation distribution status; Calculating correction parameters for the deviation distribution, updating the focus position in real time, and determining whether the corrected state meets a preset standard; If so, the instantaneous state data is mapped to the dynamic trend distribution map according to the corrected focus position, and the latest situation of the surface change is recorded to obtain updated map data.

[0010] Preferably, obtaining the adjusted focus position coordinate value includes: Compare the focus deviation data with the corresponding position coordinates point by point to obtain the specific situation of the deviation distribution and determine the key areas of focus deviation; According to the deviation data of the key area, combined with a preset deviation correction rule, the focus control parameter is dynamically adjusted to obtain an updated parameter value; If there is a difference between the updated parameter value and the actual requirement of the workpiece surface, the real-time updated focus position coordinates are compared with the actual requirement of the workpiece surface to obtain the final adjustment data and determine the latest state of the focus position.

[0011] Preferably, the step of performing real-time position correction on the actuator driving the processing equipment and determining whether the correction reaches a preset accuracy standard comprises: Verifying the focus coordinate value and the target position based on the recorded data of the focus coordinates, obtaining a specific condition of the deviation distribution, determining the key points of correction from the specific condition, and obtaining a correction data set; sending an adjustment instruction to the actuator according to the correction data set, obtaining the operating status of the actuator through a status monitoring tool during the adjustment, determining whether the preliminary position correction is completed based on the operating status, and obtaining status data after preliminary correction; If the status data after the preliminary correction is different from the accuracy standard, the status data is recorded through the feedback data collection tool, and a secondary verification is performed to determine whether the correction reaches the preset threshold and determine the correction compliance data; The focus coordinates are finally adjusted according to the correction compliance data to generate a correction signal, and a final operating state is obtained from the correction signal.

[0012] Preferably, if the correction is successful, the fluctuation of the processing effect is evaluated, and if it is found that the fluctuation value exceeds a preset fluctuation threshold, a parameter optimization instruction is generated, including: Classifying and processing the stability indicators in the correction signal to obtain core data of machining accuracy and obtain preliminary comparison results of the core data; According to the preliminary comparison results, the fluctuation of the processing accuracy is checked to obtain specific information of the fluctuation assessment and determine the detailed distribution of the fluctuation assessment; Based on the detailed distribution of the fluctuation assessment, the operating status of the processing process is monitored in real time, key adjustment points are obtained, and the optimization direction of the key adjustment points is determined; The instruction generation tool is used to refine the optimization direction to obtain the final parameter adjustment plan.

[0013] Preferably, the operating parameters of the processing equipment are fine-tuned according to the parameter optimization instruction to obtain a record of the optimized processing effect, and whether the requirements of high-end manufacturing standards are met is judged by combining historical processing data and current surface conditions, and a final processing quality evaluation result is output, including: Integrate the surface state of the complex surface with historical data according to a pre-established repository to obtain at least one key state indicator, compare the key state indicator with the processing effect record, and obtain initial data for state comparison; Correlating the comparison data with the performance of the processing quality based on the initial data of the state comparison, and adjusting the comparison parameters if the correlation result deviates from a preset correlation threshold to determine an intermediate result of the adaptability analysis; According to the intermediate results, the processing quality is compared with the standard value item by item, at least one mismatching point is obtained, and the deviation range of the point is determined; According to the judgment result of the deviation range, the mismatching points and the processing effect records are comprehensively sorted out, and the final report of the processing quality is output.

[0014] Preferably, generating a complete log file of the machining process according to the final machining quality assessment result, and determining iterative reference information through key data points in the complete log file include: Obtaining at least one key data point from the processing log according to a pre-established repository, and comparing and collating the key data point with the process record to obtain a complete data set of the processing process; Using the complete data set, the monitoring indicators are matched item by item with the actual values ​​in the processing log according to the requirements of real-time monitoring; If the matching result deviates from the preset matching threshold, relevant parameters are adjusted to determine the deviation range of the monitoring indicator; Based on the deviation range and in combination with the dynamic adjustment target, correlation analysis is performed on the adjustment parameter and the control performance, at least one mismatch point is obtained, and the specific impact degree of the mismatch point is determined; According to the determination result of the specific impact degree, the mismatch points and the results of the performance analysis are comprehensively processed to obtain a retrospective data report.

[0015] In a second aspect, the present invention provides a highly integrated variable-focus laser engraving control system, comprising: An acquisition module is used to obtain an initial surface state data set of workpiece surface changes and obtain a dynamic trend distribution diagram; a judgment module, configured to obtain a current focus position deviation value and an adjusted focus position coordinate value based on the dynamic trend distribution diagram, so as to drive an actuator of the processing equipment to perform real-time position correction and determine whether the correction meets a preset accuracy standard; A generation module is used to evaluate the fluctuation of the processing effect if the correction is successful, and generate parameter optimization instructions if the fluctuation value exceeds a preset fluctuation threshold; An output module is used to fine-tune the operating parameters of the processing equipment according to the parameter optimization instructions, obtain a record of the optimized processing effect, combine historical processing data and current surface conditions, determine whether the requirements of high-end manufacturing standards are met, and output a final processing quality evaluation result; The determination module is used to generate a complete log file of the processing process according to the final processing quality evaluation result, and determine iterative reference information through key data points in the complete log file.

[0016] Compared with the existing technology, the present invention provides a highly integrated variable-focus laser engraving control method and system, which performs high-frequency data acquisition on the workpiece surface through a sensor array to obtain an initial surface state data set. A feature extraction model is used to analyze small surface differences and determine a dynamic trend distribution map. Real-time monitoring technology is used to track surface changes, and when height fluctuations exceeding the threshold are detected, focus position correction is triggered. The focus position is adjusted according to the deviation correction algorithm, and the actuator is driven to perform real-time correction. The processing effect is evaluated through data comparison and analysis, parameter optimization instructions are generated, and the dynamic adjustment control model is called to fine-tune the operating parameters. Finally, the processing quality evaluation results are output and a complete log file is generated. The present invention realizes real-time monitoring and dynamic adjustment of precision in the complex surface processing process, improves processing accuracy and stability, and meets the requirements of high-end manufacturing standards. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a highly integrated variable-focus laser engraving control method provided by the first embodiment of the present invention; Figure 2 This is a schematic structural diagram of a highly integrated variable-focus laser engraving control system provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] Reference Figure 1 The first embodiment of the present invention provides a flow chart of a highly integrated variable-focus laser engraving control method, comprising the following steps: S11, acquiring an initial surface state data set of workpiece surface changes and obtaining a dynamic trend distribution diagram; S12, obtaining a current focus position deviation value and an adjusted focus position coordinate value according to the dynamic trend distribution diagram to drive an actuator of the processing equipment to perform real-time position correction, and determining whether the correction meets a preset accuracy standard; S13, if the calibration is successful, the fluctuation of the processing effect is evaluated, and if it is found that the fluctuation value exceeds the preset fluctuation threshold, a parameter optimization instruction is generated; S14, fine-tuning the operating parameters of the processing equipment according to the parameter optimization instruction, obtaining a record of the optimized processing effect, combining historical processing data and the current surface condition, determining whether the requirements of high-end manufacturing standards are met, and outputting a final processing quality evaluation result; S15 , generating a complete log file of the machining process according to the final machining quality evaluation result, and determining iterative reference information through key data points in the complete log file.

[0020] In step S11, an initial surface state data set of workpiece surface changes is acquired and a dynamic trend distribution diagram is obtained.

[0021] The step of obtaining an initial surface state data set of workpiece surface changes includes: The workpiece surface is collected at high frequency by a sensor array, and a multi-point synchronous scanning method is used to obtain data on the topographic features of each tiny area. The data includes surface height, roughness value and local curvature, and a preliminary surface state record is obtained; Classifying and arranging the topographical features of each micro-region according to the preliminary surface state record, establishing a feature matrix according to the surface height, the roughness value and the local curvature, and determining the feature distribution of each micro-region; If the roughness value of one of the micro areas in the feature matrix exceeds a preset roughness threshold, the surface height and local curvature of the one of the micro areas are corrected to obtain corrected feature data; The corrected feature data is compared with the initial data set, and the topographic feature information of one of the micro-regions is integrated to generate a complete description of the workpiece surface state, thereby obtaining a final surface state data set.

[0022] For example, when using a sensor array to collect high-frequency data on the surface of a workpiece, you can imagine a precision machining scenario where the workpiece is a metal plate and the sensor array scans the surface at a frequency of 1,000 times per second, covering a tiny area of ​​1 square centimeter. Up to 100 points are collected in each area to ensure high-density data coverage. This multi-point synchronous scanning method can capture subtle changes in the surface, such as tiny bumps or scratches, and provide reliable basic data for subsequent analysis. The surface height data obtained in this way may show that the height variation range of a certain area is between 0.01 mm and 0.05 mm, and the roughness value is expressed as Ra as 1.2 microns. The local curvature reflects the smoothness or mutation of the area.

[0023] Specifically, when processing data based on preliminary surface state records, data processing tools can be used to classify and organize the morphological features of each tiny area. Suppose the height data of a certain area shows obvious fluctuations, the roughness value Ra reaches 2.5 microns, which exceeds the preset threshold of 1.5 microns, and the local curvature shows a sharp change. At this time, when constructing the feature matrix, the area can be marked as an "abnormal area" and its feature distribution status recorded. This matrix processing helps to quickly locate the problem area and provide a basis for subsequent corrections. The technical effect brought about by this method is to improve the efficiency and accuracy of data analysis and avoid the tediousness of manual inspection one by one.

[0024] For example, when correcting areas with excessive roughness, the data smoothing tool can be used to adjust the surface height and local curvature. For example, if the height data for a particular area fluctuates significantly, after smoothing, the height variation is reduced to within 0.02 mm, and the local curvature also flattens. The corrected roughness value drops to 1.3 microns, meeting processing standards. This correction not only improves data accuracy but also provides a more reliable reference for subsequent processing, avoiding processing errors caused by surface defects and significantly improving product quality.

[0025] Specifically, during the data fusion stage, the corrected feature data can be compared with the initial dataset to integrate the topographical feature information of all tiny areas. For example, if a workpiece surface contains 1,000 tiny areas, fusion generates a complete surface state description, showing an overall roughness average of 1.1 microns, uniform height distribution, and no abnormal mutations in local curvature. This complete dataset can intuitively reflect the machining quality of the workpiece surface and provide an important basis for subsequent process optimization. Its beneficial effect is that data fusion not only retains the details of the original data, but also improves the overall consistency of the data through correction and integration, providing strong support for quality control.

[0026] For example, when determining whether an area meets machining standards, multi-dimensional criteria can be set, such as a roughness value of less than 1.5 microns and a height variation range of less than 0.03 mm. This method can quickly screen out unqualified areas and implement targeted treatment to ensure that the workpiece surface quality meets expectations. This multi-dimensional judgment method can effectively reduce the false positive rate and improve detection reliability.

[0027] The obtaining of the dynamic trend distribution graph includes: The height fluctuation and curvature change are obtained based on the initial surface state data set, and the fluctuation amplitude and change rate of each small area are recorded to obtain a preliminary set of difference features; Matching the preliminary difference feature set with a pre-established feature extraction template to obtain the surface state variation range of each area; If the height fluctuation or curvature change of one of the regions within the surface state change range exceeds a preset threshold, the surface state of the one of the regions is corrected, and a local feature of the dynamic trend is obtained from the corrected data; A dynamic trend distribution diagram is generated based on the local features, and data mapping is performed on surface differences and minor changes to obtain a complete distribution description of surface state changes.

[0028] For example, when processing data based on the initial surface state data set, you can first focus on the application principle of the data screening tool. The core of the data screening tool is to process complex surface differences and small changes in layers to separate the relevant data of height fluctuations and curvature changes. This hierarchical processing can classify the data according to different feature dimensions, such as fluctuation amplitude and change rate, to form a preliminary set of difference features. Suppose in a precision metal plate processing scenario, the surface height fluctuation amplitude of a certain area is recorded as 0.03 mm to 0.08 mm, and the curvature change rate shows a local mutation trend. The screening tool can quickly extract these key data and lay the foundation for subsequent analysis.

[0029] For example, when using data comparison tools for feature matching, a preliminary set of differential features can be compared with a pre-established feature extraction template to quantitatively analyze key value ranges and variation characteristics. For example, suppose the template sets a height fluctuation threshold of 0.05 mm and a curvature change rate threshold of a specific value. If the data in a certain area exceeds these thresholds, it will be marked as an area of ​​concern. This comparison method can accurately locate the range of surface condition changes, helping to quickly identify potential problem areas and improve the targeted nature of data processing.

[0030] For example, data interpolation tools are particularly important for areas exceeding thresholds. Interpolation tools correct the surface conditions of abnormal areas, filling in missing data or smoothing out unusual fluctuations to capture local characteristics of dynamic trends. For example, consider an area with large height fluctuations. After interpolation, the fluctuations are reduced to within 0.02 mm, and the dynamic trend shows a distribution that is more consistent with expectations. This correction method effectively improves data continuity and provides a more reliable basis for subsequent judgments.

[0031] For example, when generating dynamic trend distribution graphs, data visualization tools can map surface differences and subtle changes into intuitive graphics, forming a complete depiction of the distribution of surface state variations. For example, if a metal sheet's surface consists of multiple tiny regions, the graph generated by the visualization tool clearly shows that areas with large height fluctuations are concentrated at the edges, while areas with more gradual curvature changes are located in the center. This graph intuitively reflects the distribution of surface conditions, helping to quickly identify problem areas and develop targeted improvement measures. It also provides an important reference for evaluating processing quality.

[0032] For example, from an overall process perspective, the close integration of the aforementioned steps significantly improves the efficiency and reliability of surface condition analysis. Data screening ensures effective classification of raw data, comparison tools pinpoint problem areas, interpolation correction optimizes data quality, and visualization provides an intuitive basis for the final analysis. Each step revolves around the precise description of the surface condition, collectively supporting a comprehensive assessment of the workpiece's surface quality. This systematic approach not only improves data utilization but also provides strong support for subsequent optimization of machining processes.

[0033] In step S12, the current focus position deviation value and the adjusted focus position coordinate value are obtained according to the dynamic trend distribution diagram to drive the actuator of the processing equipment to perform real-time position correction and determine whether the correction meets the preset accuracy standard.

[0034] The obtaining of the current focus position deviation value according to the dynamic trend distribution graph includes: Based on the monitoring data of the dynamic trend distribution diagram, the instantaneous state of the surface change is continuously tracked and compared to obtain a change value of the height fluctuation, and determine whether the change value exceeds a preset change threshold range; If the change value of the height fluctuation exceeds a preset threshold range, a deviation detection is performed on the focus position to obtain a deviation value of the current focus position and obtain the deviation distribution status; Calculating correction parameters for the deviation distribution, updating the focus position in real time, and determining whether the corrected state meets a preset standard; If so, the instantaneous state data is mapped to the dynamic trend distribution map according to the corrected focus position, and the latest situation of the surface change is recorded to obtain updated map data.

[0035] For example, when using dynamic trend distribution graphs to monitor surface changes, one can first understand the principles of real-time monitoring tools. By continuously tracking the instantaneous state of the surface, real-time monitoring tools can capture subtle changes in height fluctuations. For example, in a precision sheet metal processing scenario, a monitoring tool records surface height data once per second and detects that a certain area fluctuates from 0.02 mm to 0.07 mm in a short period of time, significantly deviating from the normal range. This real-time data acquisition provides a timely basis for subsequent comparisons.

[0036] For example, for determining whether the height fluctuation value exceeds a preset threshold, a threshold range may be set to 0.05 mm.

[0037] In one possible implementation, if a fluctuation value in a monitored area reaches 0.06 mm, the system automatically marks that area as abnormal and generates a warning signal. This threshold comparison mechanism can quickly screen areas requiring attention, ensuring that problems are not overlooked.

[0038] For example, after triggering the correction signal generation tool, detecting focus deviation is crucial. Suppose the focus deviation in a certain area is detected to be 0.03 mm, with a localized distribution. Analyzing the deviation distribution can clarify the direction and focus of correction. This detection method lays the foundation for subsequent parameter adjustments.

[0039] For example, the role of deviation numerical processing tools in the calculation of correction parameters cannot be ignored.

[0040] In one possible implementation, the system calculates the parameter values ​​that need to be adjusted based on the 0.03 mm deviation and the surface characteristics, and generates correction instructions. This parameter calculation process ensures the accuracy of the correction and avoids over- or under-adjustment.

[0041] For example, the parameter adjustment tool's real-time update of the focus position is a crucial step in the entire process. Suppose, after adjustment, the focus position deviation is reduced from 0.03 mm to 0.01 mm, and the corrected state meets the expected standard. This real-time update method allows for timely correction of surface conditions and ensures stability during processing.

[0042] For example, the status update tool maps instantaneous status data onto a dynamic trend distribution graph, providing a visual representation of the latest surface changes. For example, if, after correction, the height fluctuation in a certain area stabilizes within 0.02 mm, the updated graph will indicate that the area has returned to normal. This recording method helps track surface condition trends over time.

[0043] For example, the updated map data can be further analyzed to determine the overall distribution of surface variations. For example, if the map shows that fluctuations in the edge region are still slightly higher than in the center, but remain within the threshold, this indicates that the corrective measures have been effective. This map update mechanism provides a valuable reference for subsequent process improvements, ensuring continuous optimization of surface quality.

[0044] The step of obtaining the adjusted focus position coordinate value includes: Compare the focus deviation data with the corresponding position coordinates point by point to obtain the specific situation of the deviation distribution and determine the key areas of focus deviation; According to the deviation data of the key area, combined with a preset deviation correction rule, the focus control parameter is dynamically adjusted to obtain an updated parameter value; If there is a difference between the updated parameter value and the actual requirement of the workpiece surface, the real-time updated focus position coordinates are compared with the actual requirement of the workpiece surface to obtain the final adjustment data and determine the latest state of the focus position.

[0045] For example, when using focus deviation data records for analysis, you can start with the basic principles of the deviation analysis tool. The deviation analysis tool can reveal the specific conditions of the deviation distribution in detail by comparing the position coordinates point by point. Suppose in a precision machining scenario, the focus position coordinates of the workpiece surface are recorded as a series of data points. The analysis tool finds that the deviation value of a certain area fluctuates between 0.04 mm and 0.08 mm, and is clearly concentrated at the edge of the workpiece. Through this point-by-point comparison method, the key areas of deviation distribution can be clearly located, providing an accurate basis for subsequent processing.

[0046] For example, it's particularly important for the parameter calculation tool to dynamically adjust deviation data in critical areas, combining it with pre-set deviation correction rules. For example, if the pre-set rules dictate that areas with deviations exceeding 0.05 mm require priority adjustment, the system will dynamically calculate the adjustment range for the control parameters based on the deviation data in the edge areas, generating the updated parameter values. This approach ensures a highly targeted adjustment process and avoids interference with normal areas.

[0047] For example, the use of condition monitoring tools is crucial for assessing the degree of match between updated parameter values ​​and the actual requirements of the workpiece surface. Suppose the adjusted parameter values ​​reduce the deviation to 0.02 mm, but certain areas of the workpiece surface require higher accuracy, within 0.01 mm. The condition monitoring tool compares the actual requirements with the current state to assess the degree of match and determine whether the adjustments meet the expected standards. This evaluation mechanism helps to promptly identify potential problems and ensure machining accuracy.

[0048] For example, based on the assessment results, the data mapping tool compares the real-time updated focus position coordinates with the actual requirements on the workpiece surface to obtain final adjustment data. If the mapping comparison reveals that the focus position at the edge area still needs to be fine-tuned by 0.01 mm, the system will generate final adjustment data based on the comparison results to determine the latest state of the focus position. This mapping method can intuitively reflect the gap between deviations and requirements, providing a reliable reference for subsequent process optimization.

[0049] For example, the collaborative working of various tools throughout the entire process demonstrates the advantages of technological integration. For example, from deviation analysis to parameter adjustment, condition assessment, and data mapping, each step is seamlessly integrated to ensure precise control of the focal position. This interconnected process not only improves surface finish consistency but also effectively reduces the defect rate caused by deviation, ensuring high-quality production.

[0050] For example, the analysis tool can delve deeper into the distribution of deviations from various perspectives. For example, if deviations are concentrated in edge areas, the system can further analyze the frequency and trend of these variations, revealing that fluctuations are primarily concentrated in the early stages of processing. This multi-dimensional analysis provides a more comprehensive understanding of the causes of deviations and supports the development of targeted correction strategies.

[0051] For example, when dynamically adjusting control parameters, the system can customize settings based on the workpiece material characteristics. For example, for a harder workpiece, the adjustment amplitude might be reduced to avoid overcorrection, while for a softer material, the adjustment amplitude might be slightly increased. This flexibility allows for better adaptation to different processing requirements and ensures optimal adjustment results.

[0052] The method of performing real-time position correction on the actuator of the driving processing equipment and determining whether the correction reaches a preset accuracy standard includes: Verifying the focus coordinate value and the target position based on the recorded data of the focus coordinates, obtaining a specific condition of the deviation distribution, determining the key points of correction from the specific condition, and obtaining a correction data set; sending an adjustment instruction to the actuator according to the correction data set, obtaining the operating status of the actuator through a status monitoring tool during the adjustment, determining whether the preliminary position correction is completed based on the operating status, and obtaining status data after preliminary correction; If the status data after the preliminary correction is different from the accuracy standard, the status data is recorded through the feedback data collection tool, and a secondary verification is performed to determine whether the correction reaches the preset threshold and determine the correction compliance data; The focus coordinates are finally adjusted according to the correction compliance data to generate a correction signal, and a final operating state is obtained from the correction signal.

[0053] For example, when verifying focus coordinate record data, one can start with the basic functions of the data comparison tool. By matching the recorded coordinate values ​​with the target positions one by one, the data comparison tool can clearly reveal the specific distribution of deviations. Suppose in a precision machining scenario, the focus coordinate data record shows that the deviation value of a certain area is between 0.05 mm and 0.1 mm, and is mainly concentrated in the center of the workpiece. Through this comparison method, the key points that need correction can be quickly identified, forming a data set of correction requirements, providing a basis for subsequent adjustments.

[0054] For example, the application of real-time actuation tools is particularly critical for data sets that require correction. Based on the deviation data, the real-time actuation tool sends adjustment instructions to the actuator to ensure that the focal position gradually approaches the target value. For example, for a deviation in the center area, the instruction requires the actuator to move the focal position upward by 0.03 mm. Simultaneously, the condition monitoring tool obtains the actuator's operating status in real time to determine whether the preliminary correction has been completed. The condition monitoring tool may indicate that the adjusted deviation has been reduced to 0.02 mm, generating preliminary correction status data and laying the foundation for subsequent verification.

[0055] For example, if the status data after initial calibration still differs from the accuracy standard, the feedback data collection tool becomes particularly useful. For example, if the accuracy standard requires a deviation of less than 0.015 mm, and the current status data is 0.02 mm, the feedback data collection tool will record this discrepancy in detail and perform secondary verification using a data comparison tool. This verification process may reveal that the deviation at certain points still exceeds the threshold, generating calibration compliance data to clearly indicate which areas require further adjustment. This secondary verification mechanism effectively improves calibration accuracy.

[0056] For example, during the final adjustment phase, the parameter update tool optimizes the focus coordinates based on the calibration compliance data and generates a calibration signal through the signal output tool. Assuming the final adjustment limits the deviation to within 0.01 mm, the signal output tool provides feedback on the final status, indicating that the calibration process is complete. This signal generation and status confirmation method ensures traceability throughout the calibration process, providing reliable support for subsequent processing tasks.

[0057] For example, from an overall process perspective, the synergy between the aforementioned tools demonstrates the advantages of technological integration. For example, from data verification to command issuance, to condition monitoring and final adjustment, each link is tightly linked to ensure precise control of the focus position. This interconnected design not only improves machining stability but also effectively reduces machining errors caused by deviations, ensuring high-quality production.

[0058] For example, analysis of calibration compliance data can be conducted from various perspectives. If secondary verification reveals that deviations are concentrated in a specific processing stage, the system can further analyze the cause of the deviation, potentially due to initial instability in the equipment. This multi-dimensional analysis helps develop more targeted calibration strategies and improve the adaptability of the overall process.

[0059] In step S13, if the correction is successful, the fluctuation of the processing effect is evaluated, and if it is found that the fluctuation value exceeds the preset fluctuation threshold, a parameter optimization instruction is generated.

[0060] If the correction is successful, the fluctuation of the processing effect is evaluated. If the fluctuation value exceeds the preset fluctuation threshold, a parameter optimization instruction is generated, including: Classifying and processing the stability indicators in the correction signal to obtain core data of machining accuracy and obtain preliminary comparison results of the core data; According to the preliminary comparison results, the fluctuation of the processing accuracy is checked to obtain specific information of the fluctuation assessment and determine the detailed distribution of the fluctuation assessment; Based on the detailed distribution of the fluctuation assessment, the operating status of the processing process is monitored in real time, key adjustment points are obtained, and the optimization direction of the key adjustment points is determined; The optimization direction is refined to obtain a final parameter adjustment solution.

[0061] For example, when processing recorded data from a calibration signal, a data extraction tool can be used to first classify the signal's stability indicators. The data extraction tool groups the signal data by time period or processing area, extracting key indicators such as signal strength and duration. For example, in a precision machining scenario, the recorded data shows that the signal stability indicator fluctuates between 0.02 and 0.05 over a certain period of time. Through classification, data segments with larger fluctuations can be quickly filtered out, forming core data for machining accuracy, providing a foundation for subsequent comparisons.

[0062] For example, based on the preliminary comparison results of core data, comparative analysis tools can be used to verify fluctuations in machining accuracy. The tool compares the core data against preset standard values ​​one by one, analyzing the specific time points and areas where fluctuations occurred. For example, if the comparison results show that fluctuations in a certain machining stage are concentrated at the edge of the workpiece, with a fluctuation value of 0.04, detailed fluctuation assessment information can be generated after verification, clarifying that the fluctuation distribution is concentrated in the initial stage of edge machining, providing a precise basis for subsequent adjustments.

[0063] For example, based on the detailed distribution of fluctuation assessments, parameter adjustment tools can be used to monitor the operating status of the machining process in real time. The tool continuously tracks the machine's operating parameters in different machining areas, recording key data such as speed and pressure. For example, if monitoring reveals that the machine's operating speed is too high when machining an edge area, potentially causing fluctuations, analysis can determine that the key adjustment point is the speed control parameter, and the optimization direction is to appropriately reduce the speed to minimize the impact of fluctuations.

[0064] For example, from an overall process perspective, the coordinated application of these tools can effectively improve machining stability. From signal data extraction to fluctuation verification, parameter monitoring, and command generation, each step is tightly integrated, ensuring continuous optimization of machining accuracy. Especially for fluctuation issues in edge areas, the collaborative use of multiple tools allows for rapid identification of the root cause and development of targeted solutions.

[0065] For example, in specific implementations, the distribution information from the fluctuation assessment can be supplemented with analysis from the perspective of the processing environment. If it is found that fluctuations in edge areas are related to changes in the processing environment's temperature, a temperature compensation mechanism can be incorporated into parameter adjustments to further reduce environmental interference. This multi-angle analysis ensures a comprehensive adjustment plan.

[0066] For example, when refining the command generation tool, the hardware limitations of the processing equipment can be taken into account. For example, if the equipment's speed adjustment range is limited, the tool will prioritize parameter values ​​within the feasible range when generating commands to ensure the solution is implemented. This careful consideration can effectively improve the practicality of the adjustment plan.

[0067] In step S14, the operating parameters of the processing equipment are fine-tuned according to the parameter optimization instructions to obtain an optimized processing effect record. Combined with the historical processing data and the current surface state, it is determined whether the requirements of high-end manufacturing standards are met, and the final processing quality evaluation result is output.

[0068] The operating parameters of the processing equipment are fine-tuned according to the parameter optimization instructions to obtain optimized processing effect records. Combined with historical processing data and current surface conditions, it is determined whether the requirements of high-end manufacturing standards are met and the final processing quality evaluation results are output, including: Integrate the surface state of the complex surface with historical data according to a pre-established repository to obtain at least one key state indicator, compare the key state indicator with the processing effect record, and obtain initial data for state comparison; Correlating the comparison data with the performance of the processing quality based on the initial data of the state comparison, and adjusting the comparison parameters if the correlation result deviates from a preset correlation threshold to determine an intermediate result of the adaptability analysis; According to the intermediate results, the processing quality is compared with the standard value item by item, at least one mismatching point is obtained, and the deviation range of the point is determined; According to the judgment result of the deviation range, the mismatching points and the processing effect records are comprehensively sorted out, and the final report of the processing quality is output.

[0069] For example, during the initial analysis of machining equipment operating parameters, tools can be called through pre-established instructions to systematically analyze the equipment's key operating data. In a precision machining scenario, the tool will focus on parameters such as the equipment's operating speed and machining pressure, analyzing potential issues that may affect machining accuracy. During analysis, the tool will segment the data, identifying areas where the operating speed is too high during certain time periods, and preliminarily determining that the speed parameter is a key point requiring fine-tuning. Speed ​​adjustments will then be prioritized based on the degree of impact.

[0070] For example, the dynamic adjustment tool uses preset thresholds for comparison analysis to determine the order in which key points are adjusted. For example, suppose the ideal threshold for the speed parameter is 30 to 45 units per minute, but actual operating data shows a speed of 48 units during a certain period, exceeding the upper threshold. Based on the comparison results, the tool automatically triggers a fine-tuning mechanism, gradually lowering the speed parameter to 42 units, creating the adjusted parameter combination. This approach enables timely response to abnormal data and ensures targeted parameter adjustments.

[0071] For example, after parameter adjustments, the real-time monitoring tool continuously collects feedback from the machining equipment, focusing on the stability of machining accuracy. For example, if the monitoring process reveals that the surface flatness of the machined surface improves from a fluctuation of 0.03 to 0.02 after the adjusted speed parameters are applied, indicating an improvement in stability, the tool will further analyze the degree of alignment between these indicators and pre-set standards to determine whether the current parameter combination meets expectations, providing data support for subsequent optimization.

[0072] For example, the entire process, from analyzing key points to determining the final solution path, demonstrates the systematic and dynamic nature of parameter adjustment. For example, during the operation of processing equipment, fine-tuning the speed parameters not only improved surface flatness but also indirectly reduced vibration. This feedback mechanism helps operators more accurately understand equipment status and improve process reliability.

[0073] For example, during implementation, monitoring of stability indicators can be supplemented with analysis based on real-time changes in the machining environment. For example, if slight fluctuations in machining accuracy are observed during certain periods, perhaps due to the influence of ambient humidity, the monitoring tool will record the relevant data to provide a reference for subsequent optimization. This multi-faceted data collection approach ensures the comprehensiveness and adaptability of the optimization plan.

[0074] In step S15 , a complete log file of the machining process is generated according to the final machining quality evaluation result, and iterative reference information is determined through key data points in the complete log file.

[0075] Generating a complete log file of the machining process according to the final machining quality assessment result, and determining iterative reference information through key data points in the complete log file, including: Obtaining at least one key data point from the processing log according to a pre-established repository, and comparing and collating the key data point with the process record to obtain a complete data set of the processing process; Using the complete data set, the monitoring indicators are matched item by item with the actual values ​​in the processing log according to the requirements of real-time monitoring; If the matching result deviates from the preset matching threshold, relevant parameters are adjusted to determine the deviation range of the monitoring indicator; Based on the deviation range and in combination with the dynamic adjustment target, correlation analysis is performed on the adjustment parameter and the control performance, at least one mismatch point is obtained, and the specific impact degree of the mismatch point is determined; According to the determination result of the specific impact degree, the mismatch points and the results of the performance analysis are comprehensively processed to obtain a retrospective data report.

[0076] For example, to assess quality, the output tool compiles mismatched points and machining results into a final report. Suppose the report indicates that roughness deviation is related to temperature fluctuations and equipment pressure settings during machining. It recommends optimizing the temperature control range to within ±2°C and adjusting pressure parameters. Through multi-dimensional analysis, the report not only reveals the root cause of the problem but also provides actionable suggestions for subsequent improvements. This comprehensive output effectively supports decision-making and improves the stability of machining quality.

[0077] For example, to achieve data retrospective results, a compilation tool can be used to combine mismatched points with performance analysis results to generate a retrospective data report. Suppose the report indicates that localized flatness deviations are related to ambient humidity during machining. It recommends strengthening humidity control during subsequent machining, maintaining it between 40% and 50%. Retrospective analysis can clearly trace the root cause of the problem, providing data support for corrective measures. This comprehensive approach not only identifies historical issues but also lays the foundation for future stable machining quality.

[0078] For example, from a dynamic adjustment perspective, the parameter optimization tool can also incorporate other environmental factors into its analysis of mismatched points. For example, if it finds that ventilation conditions in the machining area may also affect flatness, the report can recommend optimizing ventilation settings. This multi-dimensional analysis further enriches the comprehensiveness of the retrospective data, ensuring that quality assessments cover more potential influencing factors.

[0079] For example, in its implementation, the approach to each technical topic is closely centered around improving the quality of complex surface finishes. From data extraction to parameter adjustment and back-testing report generation, each step forms a closed loop, ensuring controllable and consistent machining processes. This rigorous logic benefits by enabling continuous improvement of machining processes and providing strong support for high-end manufacturing standards.

[0080] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

[0081] In summary, the present invention discloses a highly integrated variable-focus laser engraving control method and system, which performs high-frequency data acquisition on the workpiece surface through a sensor array to obtain an initial surface state data set. A feature extraction model is used to analyze slight surface differences and determine a dynamic trend distribution map. Real-time monitoring technology is used to track surface changes, and when height fluctuations exceeding a threshold are detected, focus position correction is triggered. The focus position is adjusted according to the deviation correction algorithm, and the actuator is driven to perform real-time correction. The processing effect is evaluated through data comparison and analysis, parameter optimization instructions are generated, and the dynamic adjustment control model is called to fine-tune the operating parameters. Finally, the processing quality evaluation results are output and a complete log file is generated. The present invention realizes real-time monitoring and dynamic adjustment of precision in the complex surface processing process, improves processing precision and stability, and meets the requirements of high-end manufacturing standards.

[0082] Reference Figure 2 The second embodiment of the present invention provides a highly integrated variable-focus laser engraving control system, comprising: An acquisition module 201 is used to acquire an initial surface state data set of workpiece surface changes and obtain a dynamic trend distribution diagram; A judgment module 202 is configured to obtain a current focus position deviation value and an adjusted focus position coordinate value based on the dynamic trend distribution diagram, so as to drive an actuator of the processing equipment to perform real-time position correction and determine whether the correction meets a preset accuracy standard; The generation module 203 is used to evaluate the fluctuation of the processing effect if the correction is successful, and generate a parameter optimization instruction if the fluctuation value exceeds a preset fluctuation threshold; Output module 204 is used to fine-tune the operating parameters of the processing equipment according to the parameter optimization instruction, obtain the optimized processing effect record, combine the historical processing data and the current surface condition, determine whether the requirements of high-end manufacturing standards are met, and output the final processing quality evaluation result; The determination module 205 is configured to generate a complete log file of the machining process according to the final machining quality evaluation result, and determine iterative reference information through key data points in the complete log file.

[0083] It should be noted that the highly integrated variable-focus laser engraving control system provided in the embodiment of the present invention is used to execute all the process steps of the highly integrated variable-focus laser engraving control method of the above embodiment. The working principles and beneficial effects of the two correspond one to one, and thus will not be described in detail.

[0084] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a highly integrated variable-focus laser engraving control program. When the processor executes the computer program, the steps of the aforementioned highly integrated variable-focus laser engraving control method embodiments are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the acquisition module.

[0085] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0086] The electronic device may be a computing device such as a desktop computer, notebook, PDA, or smart tablet. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the aforementioned components are merely examples of electronic devices and do not constitute a limitation of the electronic device. The electronic device may include more or fewer components than those described above, or a combination of certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, and the like.

[0087] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor is the control center of the electronic device and connects various parts of the entire electronic device using various interfaces and lines.

[0088] The memory can be used to store the computer programs and / or modules. The processor implements the various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and accessing the data stored in the memory. The memory may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function or an image playback function); the data storage area may store data generated based on the use of the mobile phone (such as audio data, a phone book, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0089] If the module / unit integrated into the electronic device is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content of the computer-readable medium can be appropriately increased or decreased based on the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, based on legislation and patent practice, computer-readable media does not include electric carrier signals and telecommunication signals.

[0090] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0091] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A highly integrated variable focus laser engraving control method, characterized in that: include: Obtaining an initial surface state data set of workpiece surface changes and obtaining a dynamic trend distribution diagram; Obtaining the current focus position deviation value and the adjusted focus position coordinate value according to the dynamic trend distribution diagram to drive the actuator of the processing equipment to perform real-time position correction and determine whether the correction meets the preset accuracy standard; If the correction is successful, the fluctuation of the processing effect is evaluated. If the fluctuation value is found to exceed the preset fluctuation threshold, a parameter optimization instruction is generated; Fine-tune the operating parameters of the processing equipment according to the parameter optimization instructions, obtain optimized processing effect records, combine historical processing data and current surface conditions to determine whether the requirements of high-end manufacturing standards are met, and output the final processing quality evaluation results; A complete log file of the machining process is generated according to the final machining quality evaluation result, and iterative reference information is determined through key data points in the complete log file.

2. The highly integrated variable focus laser engraving control method according to claim 1, characterized in that: The step of obtaining an initial surface state data set of workpiece surface changes includes: The workpiece surface is collected at high frequency by a sensor array, and a multi-point synchronous scanning method is used to obtain data on the topographic features of each tiny area. The data includes surface height, roughness value and local curvature, and a preliminary surface state record is obtained; Classifying and arranging the topographical features of each micro-region according to the preliminary surface state record, establishing a feature matrix according to the surface height, the roughness value and the local curvature, and determining the feature distribution of each micro-region; If the roughness value of one of the micro areas in the feature matrix exceeds a preset roughness threshold, the surface height and local curvature of the one of the micro areas are corrected to obtain corrected feature data; The corrected feature data is compared with the initial data set, and the topographic feature information of one of the micro-regions is integrated to generate a complete description of the workpiece surface state, thereby obtaining a final surface state data set.

3. The highly integrated variable focus laser engraving control method according to claim 2, characterized in that: The obtaining of the dynamic trend distribution graph includes: The height fluctuation and curvature change are obtained based on the initial surface state data set, and the fluctuation amplitude and change rate of each small area are recorded to obtain a preliminary set of difference features; Matching the preliminary difference feature set with a pre-established feature extraction template to obtain the surface state variation range of each area; If the height fluctuation or curvature change of one of the regions within the surface state change range exceeds a preset threshold, the surface state of the one of the regions is corrected, and a local feature of the dynamic trend is obtained from the corrected data; A dynamic trend distribution diagram is generated based on the local features, and data mapping is performed on surface differences and minor changes to obtain a complete distribution description of surface state changes.

4. The highly integrated variable focus laser engraving control method according to claim 1, characterized in that: The obtaining of the current focus position deviation value according to the dynamic trend distribution graph includes: Based on the monitoring data of the dynamic trend distribution diagram, the instantaneous state of the surface change is continuously tracked and compared to obtain a change value of the height fluctuation, and determine whether the change value exceeds a preset change threshold range; If the change value of the height fluctuation exceeds a preset threshold range, a deviation detection is performed on the focus position to obtain a deviation value of the current focus position and obtain a deviation distribution status; Calculating correction parameters for the deviation distribution, updating the focus position in real time, and determining whether the corrected state meets a preset standard; If so, the instantaneous state data is mapped to the dynamic trend distribution map according to the corrected focus position, and the latest situation of the surface change is recorded to obtain updated map data.

5. The highly integrated variable focus laser engraving control method according to claim 4, characterized in that: The step of obtaining the adjusted focus position coordinate value includes: Compare the focus deviation data with the corresponding position coordinates point by point to obtain the specific situation of the deviation distribution and determine the key areas of focus deviation; According to the deviation data of the key area, combined with the preset deviation correction rules, the focus control parameters are dynamically adjusted to obtain updated parameter values; If there is a difference between the updated parameter value and the actual requirement of the workpiece surface, the real-time updated focus position coordinates are compared with the actual requirement of the workpiece surface to obtain the final adjustment data and determine the latest state of the focus position.

6. The highly integrated variable focus laser engraving control method according to claim 5, characterized in that: The method of performing real-time position correction on the actuator of the driving processing equipment and determining whether the correction reaches a preset accuracy standard includes: Verifying the focus coordinate value and the target position based on the recorded data of the focus coordinates, obtaining a specific condition of the deviation distribution, determining the key points of correction from the specific condition, and obtaining a correction data set; sending an adjustment instruction to the actuator according to the correction data set, obtaining the operating status of the actuator through a status monitoring tool during the adjustment, determining whether the preliminary position correction is completed based on the operating status, and obtaining status data after preliminary correction; If the status data after the preliminary correction is different from the accuracy standard, the status data is recorded through the feedback data collection tool, and a secondary verification is performed to determine whether the correction reaches the preset threshold and determine the correction compliance data; The focus coordinates are finally adjusted according to the correction compliance data to generate a correction signal, and a final operating state is obtained from the correction signal.

7. The highly integrated variable focus laser engraving control method according to claim 1, characterized in that: If the correction is successful, the fluctuation of the processing effect is evaluated. If the fluctuation value exceeds the preset fluctuation threshold, a parameter optimization instruction is generated, including: Classifying and processing the stability indicators in the correction signal to obtain core data of the machining accuracy and obtaining a preliminary comparison result of the core data; According to the preliminary comparison results, the fluctuation of the processing accuracy is checked to obtain specific information of the fluctuation assessment and determine the detailed distribution of the fluctuation assessment; Based on the detailed distribution of the fluctuation assessment, the operating status of the processing process is monitored in real time, key adjustment points are obtained, and the optimization direction of the key adjustment points is determined; The optimization direction is refined to obtain a final parameter adjustment solution.

8. The highly integrated variable focus laser engraving control method according to claim 1, characterized in that: The operating parameters of the processing equipment are fine-tuned according to the parameter optimization instructions to obtain optimized processing effect records. Combined with historical processing data and current surface conditions, it is determined whether the requirements of high-end manufacturing standards are met and the final processing quality evaluation results are output, including: Integrate the surface state of the complex surface with historical data according to a pre-established repository to obtain at least one key state indicator, compare the key state indicator with the processing effect record, and obtain initial data for state comparison; Correlating the comparison data with the performance of the processing quality based on the initial data of the state comparison, and adjusting the comparison parameters if the correlation result deviates from a preset correlation threshold to determine an intermediate result of the adaptability analysis; According to the intermediate results, the processing quality is compared with the standard value item by item to obtain at least one mismatching point, and the deviation range of the mismatching point is determined; According to the judgment result of the deviation range, the mismatching points and the processing effect records are comprehensively sorted out, and the final report of the processing quality is output.

9. The highly integrated variable focus laser engraving control method according to claim 1, characterized in that: Generating a complete log file of the machining process according to the final machining quality assessment result, and determining iterative reference information through key data points in the complete log file, including: Obtaining at least one key data point from the processing log according to a pre-established repository, and comparing and collating the key data point with the process record to obtain a complete data set of the processing process; Using the complete data set, the monitoring indicators are matched item by item with the actual values ​​in the processing log according to the requirements of real-time monitoring; If the matching result deviates from the preset matching threshold, relevant parameters are adjusted to determine the deviation range of the monitoring indicator; Based on the deviation range and in combination with the dynamic adjustment target, correlation analysis is performed on the adjustment parameter and the control performance, at least one mismatch point is obtained, and the specific impact degree of the mismatch point is determined; According to the determination result of the specific impact degree, the mismatch points and the results of the performance analysis are comprehensively processed to obtain a retrospective data report.

10. A highly integrated variable focus laser engraving control system, characterized in that: include: An acquisition module is used to obtain an initial surface state data set of workpiece surface changes and obtain a dynamic trend distribution diagram; a judgment module, configured to obtain a current focus position deviation value and an adjusted focus position coordinate value based on the dynamic trend distribution diagram, so as to drive an actuator of the processing equipment to perform real-time position correction and determine whether the correction meets a preset accuracy standard; A generation module is used to evaluate the fluctuation of the processing effect if the correction is successful, and generate parameter optimization instructions if the fluctuation value exceeds a preset fluctuation threshold; An output module is used to fine-tune the operating parameters of the processing equipment according to the parameter optimization instructions, obtain a record of the optimized processing effect, combine historical processing data and current surface conditions, determine whether the requirements of high-end manufacturing standards are met, and output a final processing quality evaluation result; The determination module is used to generate a complete log file of the processing process according to the final processing quality evaluation result, and determine iterative reference information through key data points in the complete log file.

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