Laser micro-nano machining control method and system for coping with complex structure design

Abstract

The invention provides a laser micro-nano machining control method and system for coping with complex structure design, and relates to the technical field of laser micro-nano machining, and the method comprises the steps: firstly obtaining a discrete point cloud cluster of a target complex micro-nano structure and a set texture feature constant, expanding discrete point cloud, carrying out topological distance filtering analysis, and generating a vein persistence graph; analyzing the venation nodes based on a preset processing strategy to obtain a venation affiliation result, and calculating the complex saturation to judge the processing difficulty; if the point is the source structure point, planning a smooth benchmarking guide map according to the surface element size and the normal vector of the one-class discrete point cloud, and controlling processing; and if the structural points are derivative structural points, evaluating second-class discrete point clouds to obtain difficulty coefficient freeze-frame, disassembling and searching a global common track, and controlling processing. According to the method, smooth processing of the source complex pattern and rapid communication processing of the derivative complex pattern can be correspondingly executed according to different complex saturation conditions, so that the stability of the complex micro-nano structure is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of laser micro-nano processing technology, and in particular to a laser micro-nano processing control method and system for complex structure design. BACKGROUND

[0002] Laser micro-nano processing technology is an important means in the field of advanced manufacturing, and has been widely used in many high-precision processing fields such as electronics, optics, biomedicine and micro-electro-mechanical systems. This technology realizes high-precision microstructure processing on the surface of materials through focusing ultra-short pulse laser beams, and has the advantages of non-contact, small heat-affected zone and high processing precision.

[0003] With the increasing demand for functional and customized micro-nano structures, the complexity of the processing objects has also increased rapidly, involving multi-level, multi-scale, spatial curved surfaces and irregular geometric features. However, as the complexity of the structure increases, the traditional laser micro-nano processing equipment cannot effectively handle non-uniform curvature (original structure) or multi-scale coupled structure (derived structure), which can easily cause the processing trajectory to deviate from the design model, thereby affecting the forming precision.

[0004] In addition, traditional laser micro-nano processing equipment generally uses static laser parameter setting, which cannot analyze the vein direction, complexity and derived attribution of micro-nano structure points, lines and surfaces, which can easily lead to control drift and precision loss problems.

[0005] Therefore, it is necessary to develop a laser micro-nano processing control method to meet the high-precision and high-efficiency processing requirements of different complex structure designs and solve the above problems. SUMMARY

[0006] Therefore, the present application provides a laser micro-nano processing control method and system for complex structure design, which can solve the problem that the traditional processing control method cannot handle complex structures, causing the processing trajectory to deviate, and the static parameter setting cannot adapt to the complexity of the structure, resulting in control drift and precision loss.

[0007] To solve the above problems, the present application provides a laser micro-nano processing control method for complex structure design, comprising:

[0008] Obtain the discrete point cloud cluster of the target complex micro-nano structure and the given texture feature constant, dilate each discrete point cloud in the discrete point cloud cluster according to the given texture feature constant, and perform topological distance filtering analysis to generate a vein persistence diagram of the target complex micro-nano structure;

[0009] The context node of the context persistent graph is obtained based on the preset processing strategy community of the laser micro-nano processing facility for processing the target complex micro-nano structure, a context attribution result is obtained, and the complexity saturation of the target complex micro-nano structure is covered based on the context attribution result, so as to obtain a processing difficulty judgment result.

[0010] If the processing difficulty judgment result shows that the original structure point, the surface element size and the normal vector of a type of discrete point cloud on the target complex micro-nano structure on the original structure point are planned to program a first reference structure model, a smooth mapping control graph is obtained based on the smooth mapping control graph, and the laser micro-nano processing facility is used to process the target complex micro-nano structure of the original structure point.

[0011] If the processing difficulty judgment result shows that the derived structure point, the difficulty coefficient of a type of discrete point cloud on the target complex micro-nano structure on the derived structure point is evaluated, a global common track of a second reference structure model is searched and decomposed based on the difficulty coefficient, and the laser micro-nano processing facility is used to process the target complex micro-nano structure of the derived structure point based on the global common track.

[0012] Preferably, the process of obtaining the discrete point cloud cluster of the target complex micro-nano structure and the given texture feature constant, expanding each discrete point cloud in the discrete point cloud cluster according to the given texture feature constant, and performing topological distance filtering analysis to generate the context persistent graph of the target complex micro-nano structure includes:

[0013] A design drawing of the target complex micro-nano structure is obtained, a model reconstruction is performed on the design drawing by using PROE modeling software, and a point cloud is extracted to obtain a discrete point cloud cluster of the target complex micro-nano structure.

[0014] A Manhattan distance algorithm is introduced to calculate the distance between each discrete point cloud in the discrete point cloud cluster, to obtain the Manhattan distance of each discrete point cloud pair, and a topological distance filtering matrix is constructed based on the Manhattan distance and the topological structure layout of each discrete point cloud pair.

[0015] The functional design requirement and the full-system structure knowledge graph of the target complex micro-nano structure are obtained, one or more functional inspiration prototype information of the target complex micro-nano structure and the key functional parameters satisfying the functional design requirement of each functional inspiration prototype information are extracted based on the functional design requirement, the functional inspiration prototype information and the functional design parameter are identified through the full-system structure knowledge graph, and the given texture feature constant of the target complex micro-nano structure is output.

[0016] A preset expansion radius interval is set according to the given texture feature constant, a topological layout of the complex micro-nano structure is constructed based on the functional inspiration prototype information, each discrete point cloud is expanded on the topological layout, and an expanded ball set of each discrete point cloud is formed.

[0017] If the topological distance filtering matrix records that the topological filtering distance is located in the non-empty intersection between the two expanded sphere sets of the discrete point cloud pair corresponding to the expanded radius interval, then at this time, the simple filtering complex of each discrete point cloud is constructed according to the volume value of the non-empty intersection;

[0018] The homology group of each simple filtering complex is calculated based on the expanded radius constant value of each expanded radius interval, the origin scale-annihilation scale pair of each simple filtering complex is obtained, the scatter plot is drawn around the origin scale as the horizontal and vertical axes and the annihilation scale as the vertical axis, and the vascular persistence graph of the target complex micro-nano structure is generated.

[0019] Preferably, the preset machining strategy community belonging mobile analysis of the vascular nodes of the vascular persistence graph by the laser micro-nano machining facility responding to the machining of the target complex micro-nano structure obtains the vascular belonging result, and the process of covering the complex saturation degree of the target complex micro-nano structure based on the vascular belonging result to obtain the machining difficulty judgment result includes:

[0020] The parameter control network decision of the laser micro-nano machining facility responds to the preset machining strategy of the target complex micro-nano structure, and extracts the machining base station point, the initial interference working condition parameter and the machining range of each machining base station point through the preset machining strategy;

[0021] The neighbor immigration belonging calculation of the cross-zone module fluctuation of each vascular node in the vascular persistence graph is performed with the machining base station point as the original community tribe, the vascular belonging result of the target complex micro-nano structure is obtained, and the vascular persistence graph is divided into several sub-vascular persistence graphs according to the vascular belonging result;

[0022] Based on the preset termination constraint threshold value of the machining range, the coverage area of the machining base station point of the target complex micro-nano structure is continuously expanded on the vascular persistence graph, and the planning operation is stopped after the termination constraint threshold value is touched, and the coverage edge length value of each machining base station point is generated;

[0023] Based on the coverage edge length value, a coverage box grid is constructed, and the sub-vascular persistence graph is globally covered through the coverage box grid. During the global covering process, the number of coverage box grids containing at least one vascular persistence component is counted to obtain the number of non-empty grid boxes of the sub-vascular persistence graph;

[0024] The coverage edge length-non-empty grid box logarithmic graph of the laser micro-nano machining facility responding to the machining of the sub-vascular persistence graph is established in combination with the logarithmic relationship between the coverage edge length value and the number of non-empty grid boxes, the least square method is introduced to calculate the fractal error of each logarithmic point in the coverage edge length-non-empty grid box logarithmic graph, and a plurality of fractal error squares are obtained.

[0025] linearly fitting each logarithmic point based on a fractal error square sum, generating a complex fractal dimension line of the sub-archival graph, obtaining a vector slope gradient value of the complex fractal dimension line, and determining a complex saturation degree of a micro-nano structure fractal contained in each sub-archival graph according to the vector slope gradient value;

[0026] If the complex saturation degree is less than a preset complex saturation degree, a processing base station point corresponding to the sub-archival graph of the complex saturation degree is marked as an original structure point; if the complex saturation degree is greater than the preset complex saturation degree, the processing base station point corresponding to the sub-archival graph of the complex saturation degree is marked as a derived structure point, and a processing difficulty determination result of a target complex micro-nano structure is obtained.

[0027] Preferably, the neighbor migration attribution calculation of each fractal node in the archival graph based on the processing base station point as the original community tribe and the cross-zone modularity fluctuation obtains a fractal attribution result of the target complex micro-nano structure, and the process of dividing the archival graph into a plurality of sub-archival graphs according to the fractal attribution result comprises:

[0028] The processing base station point is defined as the original community tribe, a cross-zone modularity based on an initial interference working condition parameter is preset, each fractal node in the archival graph is attempted to be moved into the original community tribe of the neighbor fractal node, and a cross-zone modularity fluctuation after the fractal node is moved from the current original community tribe to the neighbor original community tribe is calculated, and a cross-zone modularity fluctuation value of each fractal node is obtained.

[0029] If the cross-zone modularity fluctuation value is a positive value, the fractal node corresponding to the cross-zone modularity fluctuation value is moved to the original community tribe that can make the cross-zone modularity fluctuation value increase to the maximum positive value; if the cross-zone modularity fluctuation value is a negative value, the fractal node corresponding to the cross-zone modularity fluctuation value is stored in the original community tribe without change, and a fractal persistent transition community of each fractal node is obtained.

[0030] The fractal persistent transition community is regarded as a new community tribe, the boundary weight between the original community tribes where each fractal node in the archival graph is located is calculated by addition, an original community boundary total weight is obtained, the boundary of the new community tribe is merged based on the original community boundary total weight, and a fractal persistent transition community graph is constructed.

[0031] The moving step of the fractal node in the archival graph and the graph merging step of the new community tribe are repeated, and the iterative fractal persistent transition community graph is constantly planned and a local maximum value is preset.

[0032] If the cross-zone modularity fluctuation value of each fractal node reaches the local maximum value, the iterative planning and processing are terminated, a fractal attribution result of the target complex micro-nano structure is obtained, and the archival graph is divided into a plurality of sub-archival graphs according to the fractal attribution result.

[0033] Preferably, if the processing difficulty determination result indicates an original structure point, then planning a smoothed alignment guide map of a first reference structure model based on surface element sizes and normal vectors of a class of discrete point clouds of the target complex micro-nano structure at the original structure point, and controlling the laser micro-nano processing facility to process the target complex micro-nano structure at the original structure point based on smoothed coordinate mapping of the smoothed alignment guide map includes:

[0034] If the processing difficulty determination result shows that the current processing base point of the target complex micro-nanostructure is the original structure point, a discrete point cloud of the target complex micro-nanostructure on the original structure point is obtained and defined as a type of discrete point cloud. The target complex micro-nanostructure of the original structure point is constructed according to the design drawing to obtain a first reference structure model.

[0035] Performing a graphic construction analysis on the first reference structure model using PROE modeling software to obtain a hierarchical layout of graphic elements of the first reference structure model, which is defined as a first-level layout. The surface element size and corresponding normal vector of each discrete point cloud of the first type are determined through the first-level layout identification.

[0036] Obtaining design quality requirements for a target complex micro-nanostructure, introducing an adaptive subdivision octree architecture with a specified resolution based on the design quality requirements, embedding a class of discrete point clouds into the adaptive subdivision octree architecture, establishing a normal vector field for the class of discrete point clouds based on normal vectors, and determining the gradient coefficient of each surface element size at a divergence position on the structure surface through the normal vector field;

[0037] A divergence operator is introduced to estimate the discrete vector field of surface element sizes following the gradient coefficient from the normal vector field of the point cloud. The discrete vector field is transformed in the divergence operator based on the surface element sizes to obtain a scalar indicator function for each discrete point cloud class. All scalar indicator functions are combined to fit the smoothed alignment guide map of the first reference structure model.

[0038] Create a smooth coordinate domain, map the first reference structure model to the smooth coordinate domain using the Hilbert curve method according to the smooth benchmarking guide map, and output a number of Hilbert coordinate curves that follow the hierarchical layout of graphic elements;

[0039] Each Hilbert coordinate curve is solved to obtain a smooth mapping coordinate solution set, and based on the smooth mapping coordinate solution, the laser micro-nano processing facility is controlled to perform smooth and simplified photolithography processing of the target complex micro-nano structure at the source structure point.

[0040] Preferably, if the processing difficulty determination result shows that the derived structure point, the difficulty coefficient of the two-class discrete point cloud of the target complex micro-nano structure on the derived structure point is evaluated, and the global common track of the second reference structure model is searched based on the difficulty coefficient. The process of controlling the laser micro-nano processing facility to process the target complex micro-nano structure of the derived structure point based on the global common track comprises:

[0041] If the processing difficulty determination result shows that the current processing base point of the target complex micro-nano structure is a derived structure point, the target complex micro-nano structure of the derived structure point is constructed according to the design drawing, and a second reference structure model is obtained.

[0042] The graphic structure of the second reference structure model is analyzed by PROE modeling software, the graphic element hierarchical layout of the second reference structure model is obtained, which is defined as a second hierarchical layout, the processing difficulty coefficient of each graphic element hierarchy in the second hierarchical layout is evaluated by using the laser processing evaluation system, and a difficulty coefficient scale of the second reference structure model is generated.

[0043] The discrete point cloud of the target complex micro-nano structure on the derived structure point is obtained, which is defined as a two-class discrete point cloud, a residual capacity graph of the second reference structure model is constructed based on the two-class discrete point cloud, and a residual capacity threshold is preset according to the design quality requirement of the target complex micro-nano structure.

[0044] The BFS algorithm is introduced, a connected path from the source point cloud to the sink point cloud is found in the residual capacity graph by using the BFS algorithm, the residual capacity value of the connected path on the difficulty coefficient scale is obtained in the finding process, if the residual capacity value is greater than the residual capacity threshold, the connected path is marked as a candidate difficulty connected path, and the minimum residual capacity value of all connected edges on the candidate difficulty connected path is obtained.

[0045] The flow of the forward connected edge on the candidate difficulty connected path is increased and the flow of the reverse connected edge is reduced until the candidate difficulty connected path cannot be found, and a plurality of connected split points of the second reference structure model are obtained.

[0046] A minimum backtracking spanning tree is constructed, the plurality of connected split points are traversed in common through the minimum backtracking spanning tree, a global common track of the second reference structure model is obtained, and the laser micro-nano processing facility is controlled based on the global common track to perform continuous laser processing of the target complex micro-nano structure on the derived structure point.

[0047] The embodiment of the application also provides a laser micro-nano processing control system for complex structure design, which is used to realize the laser micro-nano processing control method for complex structure design.

[0048] The vein persistent graph generation module is configured to obtain a discrete point cloud cluster of a target complex micro-nano structure and a given texture feature constant, expand each discrete point cloud in the discrete point cloud cluster according to the given texture feature constant, and perform filtering analysis on a topological distance to generate a vein persistent graph of the target complex micro-nano structure.

[0049] The processing difficulty determination module is configured to determine a vein node of the vein persistent graph based on a preset processing strategy community belonging movement analysis of the laser micro-nano processing facility for processing the target complex micro-nano structure, obtain a vein belonging result, and cover and calculate a complexity saturation of the target complex micro-nano structure based on the vein belonging result to obtain a processing difficulty determination result.

[0050] The original structure point processing module is configured to, if the processing difficulty determination result indicates an original structure point, plan a smooth reference structure model based on a surface element size and a normal vector of a first type of discrete point cloud of the target complex micro-nano structure on the original structure point, and control the laser micro-nano processing facility to process the target complex micro-nano structure of the original structure point based on a smooth coordinate mapping of the smooth reference structure model.

[0051] The derived structure point processing module is configured to, if the processing difficulty determination result indicates a derived structure point, evaluate a difficulty coefficient threshold of a second type of discrete point cloud of the target complex micro-nano structure on the derived structure point, search a global common track of a second reference structure model based on the difficulty coefficient threshold, and control the laser micro-nano processing facility to process the target complex micro-nano structure of the derived structure point based on the global common track.

[0052] The embodiment of the present application also provides an electronic device, which comprises a processor, a memory and a bus system, the processor and the memory are connected through the bus system, the memory is used for storing instructions, and the processor is used for executing the instructions stored in the memory to realize the laser micro-nano processing control method for complex structure design.

[0053] The embodiment of the present application also provides a computer storage medium, which stores a computer software product, the computer software product comprises a plurality of instructions, and is used to make a computer device execute the laser micro-nano processing control method for complex structure design.

[0054] From the above technical solutions, the present application has the following beneficial effects:

[0055] The application provides a laser micro-nano machining control method and system for complex structure design. BRIEF DESCRIPTION OF DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly described below. The features and advantages of the present application can be more clearly understood by referring to the drawings. The drawings are schematic and should not be construed as any limitation on the present application. Those skilled in the art can obtain other drawings according to these drawings without creative effort. Among them:

[0057] Figure 1 A flowchart of a laser micro-nano machining control method for complex structure design provided by the present application is provided.

[0058] Figure 2 A flowchart of generating a vascular persistent graph of a target complex micro-nano structure of the present application is provided.

[0059] Figure 3 A flowchart of obtaining a machining difficulty determination result of the present application is provided.

[0060] Figure 4 A flowchart of dividing the vascular persistent graph into several sub-vascular persistent graphs of the present application is provided.

[0061] Figure 5 A flowchart of machining the original structure points of the present application is provided.

[0062] Figure 6 A flowchart of machining the derived structure points of the present application is provided.

[0063] Figure 7 A block diagram of a laser micro-nano machining control system for complex structure design provided by the present application is provided. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below by combining the drawings in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort fall within the scope of the present application.

[0065] Embodiment one:

[0066] In order to solve the problem that the conventional machining control method in the prior art is difficult to handle complex structure, resulting in machining track deviation, static parameter setting cannot adapt to structural complexity, causing control drift and precision loss. As shown in Figure 1 The present application proposes a laser micro-nano machining control method for complex structure design, which comprises:

[0067] S1: Obtain the discrete point cloud cluster of the target complex micro-nano structure and the given texture feature constant, expand each discrete point cloud in the discrete point cloud cluster according to the given texture feature constant, and perform topological distance filtering analysis to generate the vascular persistent graph of the target complex micro-nano structure;

[0068] S2: Based on the preset machining strategy community of the laser micro-nano machining facility for processing the target complex micro-nano structure, analyze the vascular nodes of the vascular persistent graph, obtain the vascular attribution result, and cover the complex saturation of the target complex micro-nano structure based on the vascular attribution result, to obtain the machining difficulty judgment result;

[0069] S3: If the machining difficulty judgment result shows the original structure point, then according to the surface element size and normal vector of a type of discrete point cloud of the target complex micro-nano structure on the original structure point, a smooth reference structure model is planned, a smooth coordinate mapping is controlled based on the smooth reference structure model, and the laser micro-nano machining facility is used to process the target complex micro-nano structure of the original structure point;

[0070] S4: If the machining difficulty judgment result shows the derived structure point, then the difficulty coefficient of the second type of discrete point cloud of the target complex micro-nano structure on the derived structure point is evaluated, the global common track of the second reference structure model is searched and decomposed based on the difficulty coefficient, and the laser micro-nano machining facility is controlled based on the global common track to process the target complex micro-nano structure of the derived structure point.

[0071] From the above technical scheme, the application proposes a laser micro-nano machining control method for complex structure design, which obtains discrete point cloud clusters and given texture feature constants, performs inflation and topological distance filtering analysis on the discrete point cloud to generate a persistent graph, can effectively process point cloud data and extract the topological features of complex structures, and establishes an accurate structure model basis for subsequent machining analysis. Based on the preset machining strategy, the community attribution movement analysis of the graph node is performed, and the complex saturation is calculated combined with the graph attribution result to determine the machining difficulty, which can scientifically quantify the complexity of the structure and provide data support for differentiated machining strategies. For the original structure point, a smoothing guide map is planned according to the surface element size and normal vector of a kind of discrete point cloud, and the machining is controlled through smooth coordinate mapping, which can accurately adapt to the geometric characteristics of the original structure, realize high-precision machining and guarantee the surface quality. For the derived structure point, the difficulty coefficient of the second kind of discrete point cloud is evaluated and the global common trajectory is searched and decomposed to control the machining, which can optimize the machining path of the multi-scale coupled structure and improve the machining efficiency and process consistency of the complex structure. The application generates a persistent graph, determines the machining difficulty, and plans the machining path for the original and derived structure points respectively, realizes high-precision machining of complex micro-nano structures, and solves the problems of machining trajectory deviation, control drift and precision loss in traditional methods, and improves the machining efficiency and reliability.

[0072] Further, in step S1, as shown in Figure 2 Specifically, the following steps are included:

[0073] S11: Obtain the design drawing of the target complex micro-nano structure, reconstruct the model of the design drawing by PROE modeling software and extract the point cloud to obtain the discrete point cloud cluster of the target complex micro-nano structure;

[0074] S12: Introduce Manhattan distance algorithm to calculate the distance between each discrete point cloud in the discrete point cloud cluster, obtain the Manhattan distance of each discrete point cloud pair, and construct a topological distance filtering matrix based on the Manhattan distance and the topological structure layout of each discrete point cloud pair;

[0075] S13: Obtain the functional design requirements and the whole system structure knowledge graph of the target complex micro-nano structure, extract one or more functional inspiration prototype information of the target complex micro-nano structure and the key functional parameters meeting the functional design requirements of each functional inspiration prototype information based on the functional design requirements, identify the functional inspiration prototype information and the functional design parameters through the whole system structure knowledge graph, and output the given texture feature constant of the target complex micro-nano structure;

[0076] S14: According to the preset inflation radius interval of the given texture feature constant, construct the topological layout of the complex micro-nano structure based on the functional inspiration prototype information, inflate each discrete point cloud on the topological layout, and form the inflation ball set of each discrete point cloud;

[0077] S15: If the topological distance filtering matrix records that the topological filtering distance is located in the non-empty intersection between the two expanded sphere sets of the discrete point cloud pair corresponding to the expansion radius interval, then at this time, the simple filtering complex of each discrete point cloud is constructed according to the volume value of the non-empty intersection;

[0078] S16: Based on the expansion radius interval, the homology group of each simple filtering complex is calculated by tracking the expansion radius constant value of each expansion radius, and the origin scale-extinction scale pair of each simple filtering complex is obtained. Around the origin scale as horizontal and vertical, and the extinction scale as the vertical axis, a scatter plot is drawn, and a target complex micro-nano structure is generated.

[0079] It should be noted that the complex micro-nano structure is usually composed of a plurality of points, line segments and face quantitative direction splicing, and some structures will design hierarchical and non-periodic arrangement of complex structure components according to functional requirements. For example, the complex micro-nano structure designed according to the structure of some bird feathers has certain texture level and arrangement trend and other vein law characteristics. If these vein law characteristics are not accurately captured, it may cause errors, omissions and unclear patterns in laser processing of complex micro-nano structures. However, the existing method still cannot realize or has certain limitations in exploring and combing the veins of complex micro-nano structures.

[0080] In view of this, the method first obtains a discrete point cloud cluster of a target complex micro-nano structure, and constructs a topological distance filtering matrix according to the Manhattan distance topology between each discrete point cloud in the cluster. The matrix is mainly used to control the scale of the topological target complex micro-nano structure, is a clue basis for exploring the topological space, and can determine which point clouds are close to each other and may form a connection, thereby providing accurate distance filtering usage basis for subsequent vein combing.

[0081] Then, the given texture feature constant of the target complex micro-nano structure is identified and obtained according to the functional design requirements. The constant is the ordinary definition value of the texture feature attribute of the functional inspiration prototype, for example, the length constant of the shark skin scale texture is 0.2 mm, and the density constant is 1.3 units per square meter. It is a key clue for revealing the texture vein of the target complex micro-nano structure, can make the vein analysis follow the direction of the design prototype common sense, and maximize the accuracy of the vein of the pattern elements such as points, lines and surfaces on the complex micro-nano structure. The functional inspiration prototype information includes shark skin, lotus leaf and moth eye.

[0082] Subsequently, each discrete point cloud of the topological layout of the complex micro-nano structure is inflated with the functional inspiration prototype information, that is, a series of nested topological spaces are constructed by gradually adding point, edge and face elements to form a complex topological structure. The expanded sphere set expresses the geometric change of each discrete point cloud from local to global, reflects the multi-scale geometric structure trend of the target complex micro-nano structure, and makes the vein topology trend of the pattern elements at different levels clear and visualized.

[0083] If the topological distance filtering matrix records that the topological filtering distance is located in the inflation radius interval, and there is a non-empty intersection between the two inflation sphere sets of the corresponding discrete point cloud pair, it indicates that there may be complex connected branches, rings and cavities between the two discrete point clouds of the discrete point cloud pair, and then a simple filtering complex is displayed according to the volume value of the non-empty intersection. According to the given texture feature constant, the homology group is tracked and calculated, and the complex topological context features of different dimensions in the simple filtering complex that follow the design prototype common sense direction guide are extracted, such as 0 dimension representing connected branches, 1 dimension representing rings, and 2 dimension representing cavities, to help understand the shape trend of the context.

[0084] Through the method, the trend of the context composed of pattern elements such as points, lines and surfaces in the target complex micro-nano structure can be accurately explored and captured, which provides a reliable analysis basis for accurate laser processing control of the complex micro-nano structure, and improves the processing accuracy and performance stability of the target complex micro-nano structure.

[0085] Further, in step S2, as shown in Figure 3 , specifically comprising the following steps:

[0086] S21: Obtain the preset processing strategy of the parameter control network decision of the laser micro-nano processing facility to cope with the target complex micro-nano structure, and extract the processing base station points, the initial interference working condition parameters and the processing range of each processing base station point through the preset processing strategy;

[0087] S22: Calculate the neighbor migration and ownership of the cross-zone modularity fluctuation of each context node in the context persistent graph with the processing base station point as the original community tribe, obtain the context ownership result of the target complex micro-nano structure, and divide the context persistent graph into several sub-context persistent graphs according to the context ownership result;

[0088] S23: Based on the preset termination constraint threshold of the processing range, continuously expand the coverage area of the processing base station point of the target complex micro-nano structure on the context persistent graph until the termination constraint threshold is touched and the planning operation is stopped, and generate the coverage edge length value of each processing base station point;

[0089] S24: Based on the coverage edge length value, construct a coverage box grid, and perform global coverage on the sub-context persistent graph through the coverage box grid. In the global coverage process, the number of coverage box grids containing at least one context persistent component is counted, and the non-empty grid box number of the sub-context persistent graph is obtained;

[0090] S25: Establish a coverage edge length-non-empty grid box logarithmic graph of the laser micro-nano processing facility coping with the sub-context persistent graph processing by combining the logarithmic relationship between the coverage edge length value and the non-empty grid box number, introduce the least square method to calculate the fractal error of each logarithmic point in the coverage edge length-non-empty grid box logarithmic graph, and obtain a plurality of fractal error squares;

[0091] S26: Linear fitting is performed on each logarithmic point based on the fractal error sum of squares to generate a complex fractal dimension line of the sub-vascular persistent graph, a vector slope gradient value of the complex fractal dimension line is obtained, and the complex saturation of the micro-nano structure vascular contained in each sub-vascular persistent graph is determined according to the vector slope gradient value;

[0092] S27: If the complex saturation is less than a preset complex saturation, the processing base station point to which the sub-vascular persistent graph of the complex saturation corresponds is marked as an original structure point; if the complex saturation is greater than the preset complex saturation, the processing base station point to which the sub-vascular persistent graph of the complex saturation corresponds is marked as a derived structure point, and a processing difficulty determination result of the target complex micro-nano structure is obtained.

[0093] It should be noted that different complex micro-nano structures may be formed by interweaving of points, lines and surfaces of a single complex pattern, or may be composed of superimposed and crossed multiple complex patterns, which makes part of the complex micro-nano structures appear a structure layout of derived branches. For example, part of the texture structure on a lotus leaf is a growth structure in which a zigzag stem node derives several stem branch connections to other stem nodes. This stem node increases the overall complexity due to the derivation and protrusion of multiple stem branches, resulting in more complex and difficult processing of the micro-nano structure simulating this region under the premise of ensuring clarity and performance stability, and increasing the processing difficulty coefficient. If the control performance of laser processing is poor, it is easy to cause unclear, missing and deviation of the processed complex micro-nano structure, and therefore it is necessary to decide the corresponding laser processing control according to the complexity of different local complex micro-nano structures. However, the existing method cannot accurately analyze whether the micro-nano structure has structural derivation and calculate the complexity accordingly, resulting in improper laser micro-nano processing control.

[0094] To this end, the community department tribe cross-module degree fluctuation neighbor movement is used to perform attribution calculation on the vascular persistent graph of the target complex micro-nano structure, so as to analyze whether the structure vascular around the main processing region on the target complex micro-nano structure belongs to the region by the laser micro-nano processing facility, to realize preliminary accurate division of the structure morphology, to make the laser micro-nano processing facility more accurate in processing control for different local structure complexity differences on the target complex micro-nano structure, and to improve the control subdivision and instantaneous accuracy. Subsequently, the structure vascular attribution of each processing base station determined according to the vascular attribution result is subjected to vascular coverage fractal calculation, so as to analyze the complexity degree of the vascular composition of each main processing region, and further judge whether there is a structural derivation phenomenon in each local processing region on the target complex micro-nano structure according to the complexity degree of the vascular composition. This method can efficiently identify the derived region and distinguish different complexity degrees of the structure morphology components on the target complex micro-nano structure, so as to make the processing control target of the laser micro-nano processing facility more clear and improve the processing control accuracy of different complex structure morphologies.

[0095] It should be noted that the processing base station is the main processing area of the target complex micro-nano structure, and the processing range is the interference processing size range of the laser micro-nano processing facility for the main processing area of the target complex micro-nano structure. The coverage edge length value of each processing base station is constrained by the processing range, which is a key tool and mechanism for multi-scale observation of structure vein nodes. By globally covering the sub-vein persistent graph with the covering box grid, the attribution details of the structure vein are viewed with a fine covering box grid, and the self-similarity of the vein is revealed by continuously reducing the observation scale based on the coverage edge length value, and then the complexity of the micro-nano structure in the main processing area at different scales is explored. The number of covering box grids containing at least one vein persistent component (i.e. calculating how many box grids are needed to "cover" the entire processing base station, where "non-empty" means that the covering box grid contains a part of the vein node) is counted, so as to describe the density and complexity of the structure vein at this scale, and to make the micro-nano structure complexity calculation in the main processing area more accurate.

[0096] Then, the logarithmic graph of the covering edge length-non-empty grid box is linearly fitted by using the least square method, which combines the logarithmic relationship between the covering edge length value and the non-empty grid box number, and converts the geometric growth rate into the dimension, reflects the complexity growth law of the structure vein of the processing base station with the scale change, and thus reveals the complexity saturation of the processing base station. If the complexity saturation is less than the preset complexity saturation, it means that the vein attribution of the micro-nano structure in the main processing area is less, and does not form a high complexity structure pattern, and there is no structure derivation, so the sub-vein persistent graph with the complexity saturation corresponding to the attribution of the processing base station is the original structure point; otherwise, the vein attribution is more, and the main processing area appears multiple structure pattern derivation branches, and the processing complexity is high, so the sub-vein persistent graph with the complexity saturation corresponding to the attribution of the processing base station is the derived structure point.

[0097] Further, in step S22, as shown in Figure 4 , specifically comprising the following steps:

[0098] S221: defining the processing base station as an original community tribe, presetting the cross-zone modularity based on the initial interference working condition parameters, trying to move each vein node in the vein persistent graph into the original community tribe where the neighbor vein node is located, and calculating the cross-zone modularity fluctuation after moving the vein node from the current original community tribe to the neighbor original community tribe, to obtain the cross-zone modularity fluctuation value of each vein node;

[0099] S222: If the cross-community modularity fluctuation value is positive, the cross-community modularity fluctuation value corresponding to the context node is moved to the original community tribe that can make the cross-community modularity fluctuation value increase to the maximum positive value; if the cross-community modularity fluctuation value is negative, the context node corresponding to the cross-community modularity fluctuation value is stored in the original community tribe where it is located unchanged, to obtain the context persistent transition community of each context node;

[0100] S223: The context persistent transition community is regarded as a new community tribe, the boundary weight between the original community tribes where each context node in the context persistent graph is located is calculated by addition, to obtain the total weight of the original community boundary, the boundary of the new community tribe is merged based on the total weight of the original community boundary, and a context persistent transition community graph is constructed;

[0101] S224: The moving step of the context node in the context persistent graph and the graph merging step of the new community tribe are repeated, and the iterative context persistent transition community graph is planned and a local maximum value is preset;

[0102] S225: If the cross-community modularity fluctuation value of each context node reaches the local maximum value, the planning iteration process is terminated, the context attribution result of the target complex micro-nano structure is obtained, and the context persistent graph is divided into several sub-context persistent graphs according to the context attribution result.

[0103] It should be noted that when the neighbor moving attribution calculation of the cross-community modularity fluctuation of the context persistent graph of the target complex micro-nano structure is performed, the method first presets a cross-community modularity according to the initial interference working condition parameters. The cross-community modularity is a key index for measuring the quality of community division, and is used to evaluate the rationality of the division of the context node into different community tribes.

[0104] Specifically, by traversing each context node in the context persistent graph, the context node is tried to be added to the community tribe of the adjacent context node, and the fluctuation change of the cross-community modularity is calculated. This process enables the context node to continuously adjust the community attribution according to the connection tightness with the neighbor node, and gradually merge into a more reasonable community tribe. The nodes gradually gather in the community tribe that can make the modularity increase, so as to promote the community tribe division of the context attribution to gradually tend to be reasonable, and thus improve the attribution accuracy of each structure context.

[0105] During the process, the modularity is gradually improved. If the cross-zone modularity fluctuation value is positive, it indicates that the context node corresponding to the fluctuation value belongs to the original community tribe of the neighbor context node, which meets the rationality standard, but is not the most reasonable attribution, so the attribution division needs to be continued until the cross-zone modularity fluctuation value increases to the maximum and is positive, and then the context node is moved to the original community tribe that can make the cross-zone modularity fluctuation value increase to the maximum positive value; if the cross-zone modularity fluctuation value is negative, it indicates that the context node does not belong to the original community tribe of the neighbor context node, which is unreasonable, and there is no need to continue to move in, and the context node is stored in the original community tribe where it is located, and the context persistent transition community of each context node after migration is obtained.

[0106] When the community allocation of all nodes no longer changes, the modularity reaches a local maximum value, and the community structure begins to appear obviously, at this time, the connection within the community tribe is more close, and the connection between the community tribes is relatively sparse.

[0107] Through the method, the attribution calculation of the micro-nano structure context around the main processing area corresponding to the processing base station can be performed, which provides a strong basis for judging whether each main processing area on the target complex micro-nano structure is connected to other pattern structure branches, and guarantees the reliability of the subsequent complex positioning accuracy of the processing base station and the structure derivation phenomenon judgment response.

[0108] Further, in step S3, as shown in Figure 5 , specifically includes the following steps:

[0109] S31: If the processing difficulty judgment result shows that the current processing base station of the target complex micro-nano structure is the original structure point, the discrete point cloud of the target complex micro-nano structure on the original structure point is obtained, which is defined as a type of discrete point cloud, and the target complex micro-nano structure of the original structure point is constructed according to the design drawing, to obtain a first reference structure model;

[0110] S32: The first reference structure model is analyzed by using the PROE modeling software to obtain the graphic element hierarchical layout of the first reference structure model, which is defined as a first hierarchical layout, and the surface element size and the corresponding normal vector of each type of discrete point cloud are determined through the first hierarchical layout identification;

[0111] S33: The design quality requirement of the target complex micro-nano structure is obtained, the adaptive subdivision octree architecture with a specified resolution is introduced according to the design quality requirement, the type of discrete point cloud is embedded into the adaptive subdivision octree architecture, the normal vector field of the type of discrete point cloud is established according to the normal vector, and the gradient coefficient of each surface element size located at the divergence position of the structure surface is determined through the normal vector field;

[0112] S34: Introducing a divergence operator, estimating the surface element size of the normal vector field of the point cloud to follow the discrete vector field of the gradient coefficient, converting the discrete vector field in the divergence operator based on the surface element size, obtaining the scalar indicator function of each one-class discrete point cloud, and fitting the smooth counter indicator map of the first reference structure model combined with all the scalar indicator functions;

[0113] S35: Create a smooth coordinate field, map the first reference structure model to the smooth coordinate field using the Hilbert curve method according to the smooth counter indicator map as a reference, and output several Hilbert coordinate curves following the hierarchical layout of the graphical elements;

[0114] S36: Solve each Hilbert coordinate curve to obtain a smooth mapping coordinate solution set, and control the laser micro-nano machining facility to perform smooth and simplified photolithography processing of the original structure point on the target complex micro-nano structure based on the smooth mapping coordinate solution.

[0115] It should be noted that if the processing difficulty determination result shows that the current processing base point of the target complex micro-nano structure is the original structure point, it means that the micro-nano pattern structure corresponding to the main processing area is mostly a single pattern and a coherent closed or open loop structure, i.e., there is no additional structure branch, and the pattern superposition, bending, mutation and inflection point are less, and the non-uniform curvature is high, which can be attributed to low complexity micro-nano structure composition, such as single skin tissue block pattern on shark skin. When traditional laser micro-nano machining facilities process such complex micro-nano structures, they usually encounter laser interference pauses, smoothness, exposure shifts, and omissions due to the complexity of the structure, resulting in unclear, missing, and deformation problems in the pattern processing of such complex micro-nano structures, which further affects the drag reduction, ice and dirt prevention performance of the complex micro-nano structure.

[0116] To this end, the method first identifies the graphical structure of the first reference structure model of the target complex micro-nano structure on the original structure point, and determines the surface element size and corresponding normal vector of each one-class discrete point cloud. The hierarchical layout of the graphical elements is the hierarchical layout of the point, line, and surface graphical elements on the first reference structure model, and the normal vector is the direction and length of the normal, which provides the directionality of the surface of the target complex micro-nano structure on the original structure point and is the key to deriving the complete surface and constructing the gradient field. Then an adaptive subdivision octree architecture with a performance resolution that meets the design quality requirements is used, and the leaf nodes of the octree architecture cover the structure area of the one-class discrete point cloud. Since the divergence position of the surface corresponding indicator function gradient is the normal vector field, the normal vector field of the one-class discrete point cloud is established according to the normal vector, and the gradient coefficient of each surface element size located at the divergence position of the structure surface is determined, thereby avoiding direct fitting of the one-class discrete point cloud, better handling of noise and voids, and improving the smooth copying effect of the target complex micro-nano structure on the original structure point.

[0117] It should be noted that a gradient field (i.e. a discrete vector field) is constructed from the input normal vector using the divergence operator, which is theoretically the gradient of the indicator function, represents the normal direction of the surface, is the bridge between a class of discrete point clouds and the implicit surface (indicator function) representation, can inversely deduce the guide scalar, and the divergence operator can naturally complement the small gap between the point clouds, further improving the smoothness of the first reference structure model. Finally, a smooth alignment guide map is formed, which is a smooth version of the first reference structure model, can best depict the smooth connection and smooth transition of points, lines and surfaces on the first reference structure model, and can make the laser micro-nano machining facility more smooth, smooth and stable when dealing with the target complex micro-nano structure of the original structure point according to the map. Finally, according to the smooth alignment guide map, the first reference structure model is mapped to the smooth coordinate field using the Hilbert curve method, and the control positioning coordinates of the target complex micro-nano structure on the original structure point are obtained. Through this method, the laser micro-nano machining facility can deal with the target complex micro-nano structure of the original structure point more smoothly, improve the processing control fluency, avoid processing pause, incoordination and exposure offset phenomenon due to structural complexity, and ensure the integrity and performance of the complex micro-nano structure.

[0118] Further, in step S4, as shown in Figure 6 , specifically comprising the following steps:

[0119] S41: If the processing difficulty determination result shows that the current processing base point of the target complex micro-nano structure is a derived structure point, construct the target complex micro-nano structure of the derived structure point according to the design drawing, and obtain a second reference structure model;

[0120] S42: Analyze the graphic structure of the second reference structure model by PROE modeling software, obtain the graphic element hierarchical layout of the second reference structure model, define it as a second hierarchical layout, evaluate the processing difficulty coefficient of each graphic element level in the second hierarchical layout using the laser processing evaluation system, and generate a difficulty coefficient specification of the second reference structure model;

[0121] S43: Obtain the discrete point cloud of the target complex micro-nano structure on the derived structure point, define it as a second type of discrete point cloud, construct a residual capacity map of the second reference structure model based on the second type of discrete point cloud, and preset a residual capacity threshold according to the design quality requirement of the target complex micro-nano structure;

[0122] S44: Introducing the BFS algorithm, finding a connected path from the source cloud to the sink cloud in the residual capacity graph using the BFS algorithm, obtaining the residual capacity value of the connected path on the difficulty coefficient scale during the search process, if the residual capacity value is greater than the residual capacity threshold, then the connected path is marked as a candidate difficulty connected path, and the minimum residual capacity value of all connected edges on the candidate difficulty connected path is obtained;

[0123] S45: Increase the flow of the forward connected edge on the candidate difficulty connected path and reduce the flow of the reverse connected edge until the candidate difficulty connected path cannot be found, obtaining multiple connected split points of the second reference structure model;

[0124] S46: Constructing a minimum backtracking spanning tree, traversing multiple connected split points through the minimum backtracking spanning tree, obtaining a global common trajectory of the second reference structure model, and controlling the laser micro-nano machining facility to perform continuous laser machining of the target complex micro-nano structure on the derived structure point based on the global common trajectory.

[0125] It should be noted that if the processing difficulty determination result shows that the current processing base point of the target complex micro-nano structure is a derived structure point, it means that the micro-nano pattern structure corresponding to the processing base point in the main processing area has multiple structure branches attached, and there is a pattern extension phenomenon. This may be formed by the superposition, interweaving or splicing of multiple complex micro-nano structure patterns, such as the complex texture pattern formed by some stem nodes of a lotus leaf connecting other stem nodes through multiple stems, etc., there are multi-scale coupled structures. Such micro-nano structures are usually complex and have a high processing difficulty coefficient. When traditional laser micro-nano machining facilities process such structures, they usually process one structure texture or one area structure pattern first, then reset the control to process the adjacent texture and pattern. This repeated processing control greatly reduces the efficiency of laser micro-nano machining, and the continuity of processing is difficult to guarantee, resulting in path drift, lack of engraving and transition processing, etc., causing defects in the pattern processing of such complex micro-nano structures.

[0126] To address this, this method uses a laser processing evaluation system to first evaluate the processing difficulty coefficient of the second-level layout of the target complex micro-nanostructure on the derived structural point, and obtain the processing difficulty coefficient distribution of the second reference structural model, that is, the difficulty coefficient freeze. This difficulty coefficient freeze is a hierarchical standard for processing continuity, which clarifies the division order for the subsequent disassembly of the target complex micro-nanostructure on the derived structural point. Then, based on the two-class discrete point cloud, a residual capacity diagram of the second reference structural model is constructed. The residual capacity diagram expresses the structural path display network of whether all the lines and patterns to be processed on the second reference structural model are connected or not. Among them, the source point cloud represents the continuously connected point cloud, and the sink point cloud represents the point cloud with interrupted connectivity. In the residual capacity diagram, a connected path from the source point cloud to the sink point cloud is found, which is to use the BFS algorithm to continuously explore the structural path that is continuously connected until the connectivity is disconnected on the second reference structural model. The residual capacity value of the connected path at the fixed difficulty coefficient reflects the exploration weight of the connected path from the perspective of the difficulty coefficient of processing. It aims to reasonably explore and split the connected path according to the difficulty coefficient, so that the second reference structure model can better follow the reasonable splitting of processing difficulty, and effectively improve the processing control accuracy and efficiency of the target complex micro-nano structure at the derived structure point.

[0127] It should be noted that if the residual capacity value is greater than the residual capacity threshold, the processing difficulty of the connected path meets the working condition standards of the current laser micro-nano processing facility for the design quality requirements of the target complex micro-nano structure. Therefore, the connected path is included and marked as a candidate difficult connected path. By increasing the flow of the forward connected edges and reducing the flow of the reverse connected edges on the candidate difficult connected path, the erroneous paths when the residual graph changes are regressed, ensuring that the connectivity split of the second reference structure model remains globally optimized. This makes the positioning of multiple connected split points of the second reference structure model more accurate and significantly improves the rationality of the split. The common path of these connected split points is the optimal connectivity path for processing the target complex micro-nano structure at the derived structure point. Therefore, this method further traverses multiple connected split points through the minimum backtracking spanning tree to generate a global common trajectory of the second reference structure model. This method can significantly improve the processing consistency of laser micro-nano processing facilities for complex micro-nano structures derived from structures, reduce processing control reciprocity, optimize processing control efficiency, avoid the occurrence of processing defects and unclear patterns, and ensure that the overall performance of complex micro-nano structure processing meets the application requirements.

[0128] Example 2:

[0129] like Figure 7 As shown, the present invention provides a laser micro-nano processing control system for complex structural designs. The system is used to implement the laser micro-nano processing control method for complex structural designs of the first embodiment, specifically comprising:

[0130] The vein persistent graph generation module 100 is used for acquiring a discrete point cloud cluster of a target complex micro-nano structure and a given texture feature constant, expanding each discrete point cloud in the discrete point cloud cluster according to the given texture feature constant, and performing filtering analysis on a topological distance to generate a vein persistent graph of the target complex micro-nano structure.

[0131] The processing difficulty determination module 200 is used for moving analysis of a vein node of the vein persistent graph based on a preset processing strategy community of a laser micro-nano processing facility for processing the target complex micro-nano structure, obtaining a vein attribution result, and covering and calculating a complex saturation degree of the target complex micro-nano structure based on the vein attribution result to obtain a processing difficulty determination result.

[0132] The original structure point processing module 300 is used for, if the processing difficulty determination result shows an original structure point, planning a smooth reference structure model according to a surface element size and a normal vector of a first type of discrete point cloud of the target complex micro-nano structure on the original structure point, controlling the laser micro-nano processing facility to process the target complex micro-nano structure of the original structure point based on a smooth coordinate mapping of the smooth reference structure model.

[0133] The derived structure point processing module 400 is used for, if the processing difficulty determination result shows a derived structure point, evaluating a difficulty coefficient threshold of a second type of discrete point cloud of the target complex micro-nano structure on the derived structure point, searching a global common track of a second reference structure model based on the difficulty coefficient threshold, and controlling the laser micro-nano processing facility to process the target complex micro-nano structure of the derived structure point based on the global common track.

[0134] The laser micro-nano processing control system for complex structure design of the embodiment is used for implementing the laser micro-nano processing control method for complex structure design, and thus the specific embodiments of the laser micro-nano processing control system for complex structure design can be seen from the foregoing embodiment part of the laser micro-nano processing control method for complex structure design, for example, the vein persistent graph generation module 100, the processing difficulty determination module 200, the original structure point processing module 300, and the derived structure point processing module 400 are respectively used for implementing steps S1, S2, S3, and S4 of the foregoing laser micro-nano processing control method for complex structure design, and thus the specific embodiments can be referred to the description of the corresponding respective embodiment part, and details are not described herein again to avoid redundancy.

[0135] Embodiment three:

[0136] The embodiment of the present application provides an electronic device, which comprises a processor, a memory and a bus system, the processor and the memory are connected through the bus system, the memory is used for storing instructions, and the processor is used for executing the instructions stored in the memory to realize the laser micro-nano processing control method for complex structure design.

[0137] Example 4

[0138] The embodiment of the present application provides a computer storage medium, the computer storage medium stores a computer software product, the computer software product includes a plurality of instructions, so as to make a computer device execute the above-mentioned laser micro-nano machining control method for coping with complex structure design.

[0139] Obviously, the above embodiment is only an example for clearly illustrating, not limited to the implementation. For those skilled in the art, on the basis of the above description, other different forms of changes or variations can also be made. Here, it is not necessary and impossible to enumerate all the implementation. The obvious changes or variations derived therefrom are still within the scope of the present application.

Claims

1. A laser micro-nano processing control method for complex structure design, characterized in that: include: Obtain a discrete point cloud cluster of the target complex micro-nanostructure and a predetermined texture feature constant, expand each discrete point cloud in the discrete point cloud cluster according to the predetermined texture feature constant, and perform filtering analysis of the topological distance to generate a vein persistence map of the target complex micro-nanostructure; Analyzing the network nodes of the network persistence graph based on the preset processing strategy of the laser micro-nano processing facility for processing the target complex micro-nano structure through community affiliation movement to obtain a network affiliation result, and calculating the complexity saturation of the target complex micro-nano structure based on the coverage of the network affiliation result to obtain a processing difficulty determination result; If the processing difficulty determination result indicates that the target complex micro-nano structure is an original structure point, a smoothed alignment guide map of the first reference structure model is planned based on the surface element size and normal vector of a discrete point cloud of the target complex micro-nano structure at the original structure point, and a laser micro-nano processing facility is controlled to process the target complex micro-nano structure at the original structure point based on the smoothed coordinate mapping of the smoothed alignment guide map; If the processing difficulty determination result shows a derived structure point, the difficulty coefficient of obtaining the second type of discrete point cloud of the target complex micro-nano structure on the derived structure point is evaluated, and the global common trajectory of searching for the second reference structure model is disassembled based on the difficulty coefficient freeze, and the laser micro-nano processing facility is controlled based on the global common trajectory to process the target complex micro-nano structure of the derived structure point.

2. The laser micro-nano processing control method for complex structure design according to claim 1 is characterized in that: The process of obtaining a discrete point cloud cluster of a target complex micro-nanostructure and a predetermined texture feature constant, expanding each discrete point cloud in the discrete point cloud cluster according to the predetermined texture feature constant, and performing a topological distance filtering analysis to generate a vein persistence map of the target complex micro-nanostructure includes: Obtaining a design drawing of a target complex micro-nanostructure, reconstructing a model of the design drawing using PROE modeling software and extracting a point cloud to obtain a discrete point cloud cluster of the target complex micro-nanostructure; A Manhattan distance algorithm is introduced to calculate the distance between each discrete point cloud in the discrete point cloud cluster to obtain the Manhattan distance of each discrete point cloud pair, and a topological distance filter matrix is ​​constructed based on the Manhattan distance and the topological structure layout of each discrete point cloud pair; Obtaining the functional design requirements of the target complex micro-nanostructure and the full-system structural knowledge graph, extracting one or more functional inspiration prototype information of the target complex micro-nanostructure and key functional parameters of each functional inspiration prototype information that meet the functional design requirements based on the functional design requirements, identifying the functional inspiration prototype information and functional design parameters through the full-system structural knowledge graph, and outputting the established texture feature constants of the target complex micro-nanostructure; According to the predetermined texture feature constant, the expansion radius interval is preset, and the topological layout of the complex micro-nano structure is constructed based on the functional inspiration prototype information. Each discrete point cloud is expanded on the topological layout to form an expansion sphere set for each discrete point cloud. If the topological distance filter matrix records that there is a non-empty intersection between two expansion ball sets of discrete point cloud pairs corresponding to the expansion radius interval, then a simple filter complex of each discrete point cloud is constructed according to the volume value of the non-empty intersection; Based on the tracking of the permanent values ​​of each expansion radius in the expansion radius interval, the homology group of each simple filter complex is calculated, and the origin scale-destruction scale pair of each simple filter complex is obtained. A scatter plot is drawn with the origin scale as the horizontal axis and the destruction scale as the vertical axis to generate a venation persistence diagram of the target complex micro-nano structure.

3. The laser micro-nano processing control method for complex structure design according to claim 1, characterized in that: The process of analyzing the network nodes of the network persistence graph based on the preset processing strategy community affiliation movement of the laser micro-nano processing facility for processing the target complex micro-nano structure to obtain the network affiliation result, and calculating the complexity saturation of the target complex micro-nano structure based on the network affiliation result to obtain the processing difficulty determination result includes: Obtain the parameter control network decision-making of the laser micro-nano processing facility to deal with the preset processing strategy of the target complex micro-nano structure, and extract the processing base station of the target complex micro-nano structure, the initial interference working condition parameters and processing range of each processing base station through the preset processing strategy; Taking the processing base site as the original community tribe, a neighbor migration and attribution calculation of cross-region modularity fluctuation is performed on each vein node in the vein persistence graph to obtain the vein attribution result of the target complex micro-nanostructure, and the vein persistence graph is divided into a plurality of sub-vein persistence graphs according to the vein attribution result; Based on the preset termination constraint threshold of the processing range, the coverage area of ​​the processing base points of the target complex micro-nano structure is continuously expanded on the vein persistence graph until the termination constraint threshold is touched, and the planning operation is stopped. The coverage side length value of each processing base point is generated; Constructing a cover box grid based on the cover side length value, and globally covering the sub-vein persistence graph through the cover box grid. During the global covering process, counting the number of cover box grids containing at least one vein persistence component to obtain the number of non-empty grid boxes of the sub-vein persistence graph; Combining the logarithmic relationship between the coverage edge length and the number of non-empty grid boxes, a logarithmic graph of coverage edge length and non-empty grid boxes is established for laser micro-nano processing facilities to process sub-vein persistence graphs. The least squares method is introduced to calculate the fractal error of each logarithmic point in the logarithmic graph of coverage edge length and non-empty grid boxes, and the sum of squares of multiple fractal errors is obtained. Performing linear fitting on each logarithmic point based on the sum of squared fractal errors to generate a complex venation dimension line of the sub-venation persistence map, obtaining a vector slope gradient value of the complex venation dimension line, and determining the complex saturation of the micro-nanostructure venation contained in each sub-venation persistence map according to the vector slope gradient value; If the complexity saturation is less than the preset complexity saturation, the processing base site corresponding to the sub-vein persistence graph of the complexity saturation is calibrated as the original structure point; if the complexity saturation is greater than the preset complexity saturation, the processing base site corresponding to the sub-vein persistence graph of the complexity saturation is calibrated as the derived structure point, and the processing difficulty judgment result of the target complex micro-nano structure is obtained.

4. The laser micro-nano processing control method for complex structure design according to claim 3 is characterized in that: The process of performing neighbor migration and attribution calculation of cross-region modularity fluctuation on each ventricle node in the ventricle persistence graph using the processing base site as the original community tribe to obtain the ventricle attribution result of the target complex micro-nanostructure, and dividing the ventricle persistence graph into a plurality of sub-ventricle persistence graphs according to the ventricle attribution result includes: The processing base station is defined as the original community tribe. The cross-region modularity is preset based on the initial interference working condition parameters. Each vein node in the vein persistence graph is attempted to be moved to the original community tribe where the neighboring vein node is located. The cross-region modularity fluctuation after the vein node is moved from the current original community tribe to the neighboring original community tribe is calculated, and the cross-region modularity fluctuation value of each vein node is obtained. If the cross-region modularity fluctuation value is positive, the context node corresponding to the cross-region modularity fluctuation value is moved to the original community tribe that can increase the cross-region modularity fluctuation value to the maximum positive value; if the cross-region modularity fluctuation value is negative, the context node corresponding to the cross-region modularity fluctuation value is stored in the original community tribe unchanged, and the context persistent change community of each context node is obtained; The persistently changing community is regarded as a new community tribe, and the boundary weights between the original community tribes where each node in the persistent graph is located are added to obtain the total weight of the original community boundaries. The boundaries of the new community tribes are merged based on the total weight of the original community boundaries to construct a persistently changing community graph. Repeat the above steps of moving the vein nodes in the vein persistence graph and merging the graphs of the new community tribes, continuously planning and iterating the vein persistence change community graph and presetting the local maximum; If the cross-region modularity fluctuation value of each context node reaches a local maximum, the planning iteration process is terminated to obtain the context attribution result of the target complex micro-nanostructure, and the context persistence graph is divided into several sub-context persistence graphs according to the context attribution result.

5. The laser micro-nano processing control method for complex structure design according to claim 1, characterized in that: If the processing difficulty determination result indicates that the target complex micro-nano structure is an original structure point, a smooth alignment guide map of a first reference structure model is planned based on surface element sizes and normal vectors of a discrete point cloud of the target complex micro-nano structure at the original structure point, and a process of controlling the laser micro-nano processing facility to process the target complex micro-nano structure at the original structure point based on smooth coordinate mapping of the smooth alignment guide map includes: If the processing difficulty determination result shows that the current processing base point of the target complex micro-nanostructure is the original structure point, a discrete point cloud of the target complex micro-nanostructure on the original structure point is obtained and defined as a type of discrete point cloud. The target complex micro-nanostructure of the original structure point is constructed according to the design drawing to obtain a first reference structure model. Performing a graphic construction analysis on the first reference structure model using PROE modeling software to obtain a hierarchical layout of graphic elements of the first reference structure model, which is defined as a first-level layout. The surface element size and corresponding normal vector of each discrete point cloud of the first type are determined through the first-level layout identification. Obtaining design quality requirements for a target complex micro-nanostructure, introducing an adaptive subdivision octree architecture with a specified resolution based on the design quality requirements, embedding a class of discrete point clouds into the adaptive subdivision octree architecture, establishing a normal vector field for the class of discrete point clouds based on normal vectors, and determining the gradient coefficient of each surface element size at a divergence position on the structure surface through the normal vector field; A divergence operator is introduced to estimate the discrete vector field of surface element sizes following the gradient coefficient from the normal vector field of the point cloud. The discrete vector field is transformed in the divergence operator based on the surface element sizes to obtain a scalar indicator function for each discrete point cloud class. All scalar indicator functions are combined to fit the smoothed alignment guide map of the first reference structure model. Create a smooth coordinate domain, map the first reference structure model to the smooth coordinate domain using the Hilbert curve method according to the smooth benchmarking guide map, and output a number of Hilbert coordinate curves that follow the hierarchical layout of graphic elements; Each Hilbert coordinate curve is solved to obtain a smooth mapping coordinate solution set, and based on the smooth mapping coordinate solution, the laser micro-nano processing facility is controlled to perform smooth and simplified photolithography processing of the target complex micro-nano structure at the source structure point.

6. The laser micro-nano processing control method for complex structure design according to claim 1, characterized in that: If the processing difficulty determination result indicates a derived structure point, evaluating the second discrete point cloud of the target complex micro-nano structure on the derived structure point to obtain a difficulty coefficient freeze, searching for a global common trajectory of the second reference structure model based on the difficulty coefficient freeze, and controlling the laser micro-nano processing facility to process the target complex micro-nano structure at the derived structure point based on the global common trajectory. The process includes: If the processing difficulty determination result shows that the current processing base point of the target complex micro-nanostructure is a derived structure point, the target complex micro-nanostructure of the derived structure point is constructed according to the design drawing to obtain a second reference structure model; Analyzing the graphic structure of the second reference structure model using PROE modeling software to obtain a hierarchical layout of graphic elements of the second reference structure model, which is defined as a second-level layout; and evaluating the processing difficulty coefficient of each graphic element level in the second-level layout using a laser processing evaluation system to generate a difficulty coefficient freeze frame for the second reference structure model; Obtain a discrete point cloud of the target complex micro-nanostructure at the derived structural point, which is defined as a second-class discrete point cloud. Construct a residual capacity map of the second reference structure model based on the second-class discrete point cloud, and preset a residual capacity threshold based on the design quality requirements of the target complex micro-nanostructure. The BFS algorithm is introduced. Starting from the source point cloud, a connected path to the sink point cloud is found using the BFS algorithm in the residual capacity graph. During the search process, the residual capacity value of the connected path at the difficulty coefficient grid is obtained. If the residual capacity value is greater than the residual capacity threshold, the connected path is marked as a candidate difficulty connected path, and the minimum residual capacity value of all connected edges on the candidate difficulty connected path is obtained. Increasing the flow of the forward connected edges on the candidate difficulty connected path and reducing the flow of the reverse connected edges until no candidate difficulty connected path can be found, thereby obtaining multiple connected split points of the second reference structure model; A minimum backtracking spanning tree is constructed, and multiple connected split points are commonly traversed through the minimum backtracking spanning tree to obtain a global common trajectory of the second reference structure model. Based on the global common trajectory, the laser micro-nano processing facility is controlled to perform coherent laser processing of the target complex micro-nano structure on the derived structure point.

7. A laser micro-nano processing control system for complex structural design, characterized by: The system is used to implement the laser micro-nano processing control method for complex structure design according to any one of claims 1 to 6, specifically comprising: A vein persistence map generation module is used to obtain a discrete point cloud cluster of the target complex micro-nanostructure and a predetermined texture feature constant, expand each discrete point cloud in the discrete point cloud cluster according to the predetermined texture feature constant, and perform a topological distance filtering analysis to generate a vein persistence map of the target complex micro-nanostructure; a processing difficulty determination module for analyzing the network nodes of the network persistence graph based on a preset processing strategy community affiliation movement of the laser micro-nano processing facility for processing the target complex micro-nano structure, obtaining a network affiliation result, and calculating the complexity saturation of the target complex micro-nano structure based on the coverage of the network affiliation result to obtain a processing difficulty determination result; an original structure point processing module for, if the processing difficulty determination result indicates an original structure point, planning a smoothed alignment guide map of a first reference structure model based on surface element dimensions and normal vectors of a discrete point cloud of a target complex micro-nano structure at the original structure point, and controlling a laser micro-nano processing facility to process the target complex micro-nano structure at the original structure point based on smoothed coordinate mapping of the smoothed alignment guide map; The derived structure point processing module is used to evaluate the difficulty coefficient of the second discrete point cloud of the target complex micro-nano structure on the derived structure point if the processing difficulty judgment result shows that it is a derived structure point, and to decompose and search the global common trajectory of the second reference structure model based on the difficulty coefficient freeze, and control the laser micro-nano processing facility to process the target complex micro-nano structure of the derived structure point based on the global common trajectory.

8. An electronic device, characterized in that: The electronic device includes a processor, a memory and a bus system, the processor and the memory are connected through the bus system, the memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the laser micro-nano processing control method for complex structure design as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that The computer storage medium stores a computer software product, which includes several instructions for enabling a computer device to execute the laser micro-nano processing control method for complex structure design according to any one of claims 1 to 6.

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